From 78459bd655226a47e823a8edac8de21b2aa7f2e5 Mon Sep 17 00:00:00 2001 From: samuelmao415 Date: Wed, 22 Aug 2018 15:45:12 -0400 Subject: [PATCH 1/8] after code review --- .../housing price data manipulation.ipynb | 25 +- Sam/Kaggle/Data_Preparation.ipynb | 1899 ++++------------- Sam/Kaggle/Modeling.ipynb | 71 +- 3 files changed, 472 insertions(+), 1523 deletions(-) diff --git a/Kelly/Kaggle_house_price/housing price data manipulation.ipynb b/Kelly/Kaggle_house_price/housing price data manipulation.ipynb index 2aca66e..d133ce6 100644 --- a/Kelly/Kaggle_house_price/housing price data manipulation.ipynb +++ b/Kelly/Kaggle_house_price/housing price data manipulation.ipynb @@ -11,9 +11,28 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "File b'train.csv' does not exist", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtrain\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"train.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"test.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 676\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 680\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 439\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 441\u001b[0m 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\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: File b'train.csv' does not exist" + ] + } + ], "source": [ "import pandas as pd\n", "train = pd.read_csv(\"train.csv\")\n", @@ -7846,7 +7865,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/Sam/Kaggle/Data_Preparation.ipynb b/Sam/Kaggle/Data_Preparation.ipynb index 7316fc3..f33333a 100644 --- a/Sam/Kaggle/Data_Preparation.ipynb +++ b/Sam/Kaggle/Data_Preparation.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -66,7 +66,7 @@ "(2919, 81)" ] }, - "execution_count": 71, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -324,7 +324,7 @@ "[6 rows x 82 columns]" ] }, - "execution_count": 73, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -460,7 +460,7 @@ "Length: 82, dtype: object" ] }, - "execution_count": 76, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -478,11 +478,11 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "part=pd.get_dummies(combined[[\"Exterior1st\",\"Exterior2nd\"]],drop_first=True,dummy_na=True) #dummify both the exterior column\n", + "part=pd.get_dummies(combined[[\"Exterior1st\",\"Exterior2nd\"]],dummy_na=True) #dummify both the exterior column\n", "part.columns=part.columns.str.replace(\"1st\",\"\") #change name of the column\n", "part.columns=part.columns.str.replace(\"2nd\",\"\")\n", "part=part.groupby(part.columns, axis=1).sum() #group by the same column and thus summing duplicate as two\n", @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -503,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -511,7 +511,7 @@ "output_type": "stream", "text": [ "(2919, 80)\n", - "(2919, 19)\n" + "(2919, 20)\n" ] } ], @@ -522,16 +522,16 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2919, 99)" + "(2919, 100)" ] }, - "execution_count": 80, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -551,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -622,7 +622,7 @@ "3 ALQ 216.0 Unf 0.0" ] }, - "execution_count": 81, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -635,90 +635,7 @@ }, { "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"execution_count": 84, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -753,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -762,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -771,7 +688,16 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "## basement unfinished need to be added" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -789,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -801,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -821,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -838,7 +764,7 @@ }, { "cell_type": "code", - 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"execution_count": 91, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1440,14 +1366,14 @@ "source": [ "#Masonry veneer area\n", "combined[[\"MasVnrType\", \"MasVnrArea\"]]\n", - "tmp=combined.pivot(columns=\"MasVnrType\", values=\"MasVnrArea\").fillna(0)\n", + "tmp=combined.pivot(columns=\"MasVnrType\", values=\"MasVnrArea\").fillna(-1)\n", "#sum(tmp[np.nan])\n", "tmp" ] }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1457,7 +1383,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1475,1020 +1401,9 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[4,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 5,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 5,\n", - " 3,\n", - " 4,\n", - " 3,\n", - 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" 4,\n", - " 4,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 5,\n", - " 4,\n", - " 4,\n", - " 2,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 5,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 4,\n", - " 0,\n", - " 3,\n", - " 3,\n", - " 5,\n", - " 3,\n", - " 4,\n", - " 4,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " 5,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 3,\n", - " 4,\n", - " ...]" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tmp_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', \n", " 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC',\"BsmtQual\"]\n", @@ -2502,7 +1417,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 27, "metadata": { "scrolled": true }, @@ -2519,7 +1434,7 @@ " dtype='object')" ] }, - "execution_count": 95, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2530,16 +1445,16 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2919, 102)" + "(2919, 103)" ] }, - "execution_count": 96, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2549,18 +1464,25 @@ "tmp.shape #101" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### one-hot" + ] + }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2919, 238)" + "(2919, 268)" ] }, - "execution_count": 97, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2571,7 +1493,7 @@ " 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'MasVnrType',\n", " 'Foundation', 'BsmtExposure', 'Heating', 'CentralAir',\n", " 'Electrical', 'Functional', 'GarageType', 'GarageFinish', 'PavedDrive',\n", - " 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']], drop_first=True, dummy_na=True)\n", + " 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']], dummy_na=True)\n", "\n", "tmp=tmp.drop(columns=['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n", " 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n", @@ -2585,7 +1507,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -2594,16 +1516,16 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2919, 238)" + "(2919, 268)" ] }, - "execution_count": 99, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2621,7 +1543,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -2630,22 +1552,22 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2919, 253)" + "(2919, 284)" ] }, - "execution_count": 101, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tmp1=pd.get_dummies(tmp[\"MSSubClass\"], prefix=\"MSSub\" ,drop_first=True, dummy_na=True)\n", + "tmp1=pd.get_dummies(tmp[\"MSSubClass\"], prefix=\"MSSub\" , dummy_na=True)\n", "tmp=tmp.drop(columns=\"MSSubClass\")\n", "tmp=tmp.join(tmp1)\n", "tmp.shape" @@ -2653,7 +1575,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -2669,7 +1591,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2678,7 +1600,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -2687,16 +1609,16 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2919, 251)" + "(2919, 282)" ] }, - "execution_count": 107, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2707,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 38, "metadata": { "scrolled": true }, @@ -2831,7 +1753,7 @@ " \n", " \n", "\n", - "

3 rows × 251 columns

\n", + "

3 rows × 282 columns

\n", "" ], "text/plain": [ @@ -2855,10 +1777,10 @@ "1 0 0 \n", "2 0 0 \n", "\n", - "[3 rows x 251 columns]" + "[3 rows x 282 columns]" ] }, - "execution_count": 108, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2876,18 +1798,18 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['BsmtUnfSF', 'GarageArea', 'GarageCars', 'SalePrice', 'TotalBsmtSF',\n", - " 'BsmtBath'],\n", + "Index(['BsmtUnfSF', 'GarageArea', 'GarageCars', 'GarageYrBlt', 'LotFrontage',\n", + " 'MasVnrArea', 'SalePrice', 'TotalBsmtSF', 'BsmtBath'],\n", " dtype='object')" ] }, - "execution_count": 157, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2898,13 +1820,42 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "combined[['BsmtUnfSF', 'GarageArea', 'GarageCars', 'SalePrice', 'TotalBsmtSF',\n", - " 'BsmtBath']]=combined[['BsmtUnfSF', 'GarageArea', 'GarageCars', 'SalePrice', 'TotalBsmtSF',\n", - " 'BsmtBath']].fillna(0)" + " 'BsmtBath','LotFrontage', 'MasVnrArea']]=combined[['BsmtUnfSF', 'GarageArea', 'GarageCars', 'SalePrice', 'TotalBsmtSF',\n", + " 'BsmtBath','LotFrontage', 'MasVnrArea']].fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "combined[\"GarageYrBlt\"]=combined[\"GarageYrBlt\"].fillna((combined[\"GarageYrBlt\"]).median())" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([], dtype='object')" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined.columns[combined.isna().any()]" ] }, { @@ -2916,7 +1867,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -2926,7 +1877,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2935,15 +1886,15 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1460, 251)\n", - "(1459, 250)\n" + "(1460, 282)\n", + "(1459, 281)\n" ] } ], @@ -2961,7 +1912,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ diff --git a/Sam/Kaggle/Modeling.ipynb b/Sam/Kaggle/Modeling.ipynb index d74fb80..b8e7be4 100644 --- a/Sam/Kaggle/Modeling.ipynb +++ b/Sam/Kaggle/Modeling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -62,16 +62,16 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.8934991498492062" + "0.894704854582131" ] }, - "execution_count": 304, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -145,33 +145,11 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise',\n", - " estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n", - " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, presort='auto', random_state=None,\n", - " subsample=1.0, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'n_estimators': [30000], 'max_depth': [4], 'min_samples_split': [6], 'learning_rate': [0.003, 0.002, 0.001], 'loss': ['ls'], 'max_features': [18]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn import model_selection\n", "from sklearn.metrics import mean_squared_error\n", @@ -323,19 +301,19 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MSE: 697736138.9349\n" + "MSE: 724364586.9875\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -346,6 +324,7 @@ ], "source": [ "# from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "from sklearn import datasets\n", "from sklearn import svm\n", "import matplotlib.pyplot as plt\n", @@ -356,7 +335,7 @@ "from sklearn import ensemble\n", "from sklearn.datasets import make_friedman1\n", "from sklearn.ensemble import GradientBoostingRegressor\n", - "params={'n_estimators': 3000, 'max_depth': 4, 'min_samples_split': 6, \n", + "params={'n_estimators': 30000, 'max_depth': 4, 'min_samples_split': 6, \n", " 'learning_rate': 0.01, 'loss':'ls', 'max_features':18}\n", "clf = ensemble.GradientBoostingRegressor(**params)\n", "clf.fit(X_train, y_train)\n", @@ -400,17 +379,17 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([7.16993382e+09, 7.16609741e+09, 7.16163658e+09, ...,\n", - " 7.25731670e+08, 7.25725682e+08, 7.25718148e+08])" + "array([6.78057852e+09, 6.71052313e+09, 6.64815367e+09, ...,\n", + " 7.24559681e+08, 7.24544801e+08, 7.24364587e+08])" ] }, - "execution_count": 76, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -421,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -430,7 +409,7 @@ "(1459,)" ] }, - "execution_count": 108, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -443,17 +422,17 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([125402.34533708, 159250.18466496, 191188.66316833, ...,\n", - " 176534.46140884, 119239.69567213, 215389.35250534])" + "array([126060.56054846, 156983.21628524, 190351.94263919, ...,\n", + " 169133.73139363, 118742.88858703, 217993.256295 ])" ] }, - "execution_count": 109, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -464,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ From ec050cb32dbb3c6c09858794046de1ef566a6ac7 Mon Sep 17 00:00:00 2001 From: samuelmao415 Date: Thu, 23 Aug 2018 12:53:54 -0400 Subject: [PATCH 2/8] get class --- Sam/Kaggle/Data_Preparation.ipynb | 9 + Sam/Kaggle/Modeling.ipynb | 463 ++++++++++++------------------ richie/data_cleaning.ipynb | 190 ++++-------- 3 files changed, 236 insertions(+), 426 deletions(-) diff --git a/Sam/Kaggle/Data_Preparation.ipynb b/Sam/Kaggle/Data_Preparation.ipynb index f33333a..3dcd564 100644 --- a/Sam/Kaggle/Data_Preparation.ipynb +++ b/Sam/Kaggle/Data_Preparation.ipynb @@ -1858,6 +1858,15 @@ "combined.columns[combined.isna().any()]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## check if double column" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/Sam/Kaggle/Modeling.ipynb b/Sam/Kaggle/Modeling.ipynb index b8e7be4..384ed12 100644 --- a/Sam/Kaggle/Modeling.ipynb +++ b/Sam/Kaggle/Modeling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,40 +62,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.894704854582131" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "scores.mean()" ] }, { "cell_type": "code", - "execution_count": 306, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "mean_error = []\n", "std_error = []\n", @@ -125,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -171,123 +149,29 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'mean_fit_time': array([49.92089709, 52.47606937, 46.05956523]),\n", - " 'std_fit_time': array([ 0.653121 , 2.61200623, 11.14731427]),\n", - " 'mean_score_time': array([0.69614077, 0.6492633 , 0.53090175]),\n", - " 'std_score_time': array([0.02045621, 0.010587 , 0.10652993]),\n", - " 'param_learning_rate': masked_array(data=[0.003, 0.002, 0.001],\n", - " mask=[False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_loss': masked_array(data=['ls', 'ls', 'ls'],\n", - " mask=[False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_max_depth': masked_array(data=[4, 4, 4],\n", - " mask=[False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_max_features': masked_array(data=[18, 18, 18],\n", - " mask=[False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_min_samples_split': masked_array(data=[6, 6, 6],\n", - " mask=[False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'param_n_estimators': masked_array(data=[30000, 30000, 30000],\n", - " mask=[False, False, False],\n", - " fill_value='?',\n", - " dtype=object),\n", - " 'params': [{'learning_rate': 0.003,\n", - " 'loss': 'ls',\n", - " 'max_depth': 4,\n", - " 'max_features': 18,\n", - " 'min_samples_split': 6,\n", - " 'n_estimators': 30000},\n", - " {'learning_rate': 0.002,\n", - " 'loss': 'ls',\n", - " 'max_depth': 4,\n", - " 'max_features': 18,\n", - " 'min_samples_split': 6,\n", - " 'n_estimators': 30000},\n", - " {'learning_rate': 0.001,\n", - " 'loss': 'ls',\n", - " 'max_depth': 4,\n", - " 'max_features': 18,\n", - " 'min_samples_split': 6,\n", - " 'n_estimators': 30000}],\n", - " 'split0_test_score': array([-4.52896240e+08, -4.53991959e+08, -4.46488525e+08]),\n", - " 'split1_test_score': array([-8.57680535e+08, -8.32140759e+08, -8.50791982e+08]),\n", - " 'split2_test_score': array([-6.10647276e+08, -5.98954620e+08, -6.04387242e+08]),\n", - " 'mean_test_score': array([-6.40428401e+08, -6.28382588e+08, -6.33909457e+08]),\n", - " 'std_test_score': array([1.66642249e+08, 1.55824227e+08, 1.66424437e+08]),\n", - " 'rank_test_score': array([3, 1, 2]),\n", - " 'split0_train_score': array([ -2549800.2460817 , -8532894.90091377, -34612973.45510744]),\n", - " 'split1_train_score': array([ -2169778.94160865, -7487381.1988952 , -29249067.30843839]),\n", - " 'split2_train_score': array([ -2028403.18831721, -7605268.06947962, -32834996.96060036]),\n", - " 'mean_train_score': array([ -2249327.45866919, -7875181.38976286, -32232345.90804873]),\n", - " 'std_train_score': array([ 220166.14618096, 467557.21619704, 2230883.75700822])}" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "grid_search_xg.cv_results_" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'learning_rate': 0.002,\n", - " 'loss': 'ls',\n", - " 'max_depth': 4,\n", - " 'max_features': 18,\n", - " 'min_samples_split': 6,\n", - " 'n_estimators': 30000}" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "grid_search_xg.best_params_" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-652212483.3011055" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "grid_search_svm.best_score_" ] @@ -301,19 +185,19 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MSE: 724364586.9875\n" + "MSE: 881652069.8311\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -335,8 +219,8 @@ "from sklearn import ensemble\n", "from sklearn.datasets import make_friedman1\n", "from sklearn.ensemble import GradientBoostingRegressor\n", - "params={'n_estimators': 30000, 'max_depth': 4, 'min_samples_split': 6, \n", - " 'learning_rate': 0.01, 'loss':'ls', 'max_features':18}\n", + "params={'n_estimators': 6000, 'max_depth': 4, 'min_samples_split': 6, \n", + " 'learning_rate': 0.001, 'loss':'ls', 'max_features':18}\n", "clf = ensemble.GradientBoostingRegressor(**params)\n", "clf.fit(X_train, y_train)\n", "clf.predict(X_test)\n", @@ -379,17 +263,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([6.78057852e+09, 6.71052313e+09, 6.64815367e+09, ...,\n", - " 7.24559681e+08, 7.24544801e+08, 7.24364587e+08])" + "array([6.85279418e+09, 6.84526835e+09, 6.83806614e+09, ...,\n", + " 6.97095686e+08, 6.97095721e+08, 6.97095143e+08])" ] }, - "execution_count": 12, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -400,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -409,7 +293,7 @@ "(1459,)" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -422,28 +306,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([126060.56054846, 156983.21628524, 190351.94263919, ...,\n", - " 169133.73139363, 118742.88858703, 217993.256295 ])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "res" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -452,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -462,104 +334,20 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ols.fit(X=x_var,y=y_var)" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3.06967150e+07, 3.06967351e+07, -4.56354685e+03, -2.21937026e+03,\n", - " 4.09262525e+03, -7.61932636e+12, -3.30177065e+03, 4.96947203e+03,\n", - " -1.69699258e+03, 6.04843284e+03, 2.68465171e+03, 1.91564941e+01,\n", - " 4.21090719e+03, -7.81812786e+02, 7.25245518e+03, -4.72255859e+01,\n", - " -3.06966706e+07, 3.98699666e+03, 7.76833645e+02, -1.30001412e+04,\n", - " 5.73978449e+03, 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1.01125699e+03, -4.75940424e+02, 9.22535606e+03, 7.03617556e+03,\n", - " -2.67903890e+04, -7.94599036e+03, -4.80614204e+03, 3.60901857e+09,\n", - " -2.05509961e+04, 0.00000000e+00, -1.26927979e+04, -1.84111658e+04,\n", - " -1.85127774e+04, 0.00000000e+00])" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ols.coef_" ] @@ -596,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -615,37 +403,135 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { - "ename": "AttributeError", - "evalue": "'list' object has no attribute 'min'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmsetotal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'min'" - ] + "data": { + "text/plain": [ + "[1079733795.175154,\n", + " 1075095942.8918672,\n", + " 1069031410.6923561,\n", + " 1061924193.6936414,\n", + " 1054173455.5470359,\n", + " 1046156490.6493278,\n", + " 1038202693.2766763,\n", + " 1030580282.1155072,\n", + " 1023494538.6185598,\n", + " 1017094406.8631861,\n", + " 1011483674.0777822,\n", + " 1006733363.1861347,\n", + " 1002892969.4292059,\n", + " 999999315.4356238,\n", + " 998082757.446703,\n", + " 997171074.083405,\n", + " 997291574.4754303,\n", + " 998471866.9855583,\n", + " 1000739515.613392,\n", + " 1004120678.4067101,\n", + " 1008637892.6635238,\n", + " 1014307421.519606,\n", + " 1021136846.4349601,\n", + 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\n", + "image/png": 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\n", 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" ] @@ -664,6 +550,27 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridge.get_params" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -673,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -684,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -704,7 +611,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 137, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -715,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -724,7 +631,7 @@ "1" ] }, - "execution_count": 133, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -735,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -745,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -755,7 +662,7 @@ " normalize=False, random_state=None, solver='auto', tol=0.001)" ] }, - "execution_count": 140, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -767,16 +674,16 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "6578515952.150397" + "6578536428.60889" ] }, - "execution_count": 141, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -798,18 +705,6 @@ "display_name": "Python 3", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" } }, "nbformat": 4, diff --git a/richie/data_cleaning.ipynb b/richie/data_cleaning.ipynb index cb3c6fe..5d620e3 100644 --- a/richie/data_cleaning.ipynb +++ b/richie/data_cleaning.ipynb @@ -33,9 +33,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -231,9 +229,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -429,9 +425,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -450,9 +444,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "train_id = train['Id']\n", @@ -463,9 +455,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "train.drop('Id', axis = 1, inplace = True) " @@ -474,9 +464,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "test.drop('Id', axis = 1, inplace = True)" @@ -485,9 +473,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "train['SalePrice'] = np.log1p(train['SalePrice'])" @@ -496,9 +482,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -536,9 +520,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "mean, sd = norm.fit(train.SalePrice)" @@ -559,9 +541,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "y_train = train['SalePrice'].values" @@ -570,9 +550,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df = pd.concat((train, test)).reset_index(drop=True)" @@ -581,9 +559,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df.drop('SalePrice', axis=1, inplace=True)" @@ -592,9 +568,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -614,9 +588,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -670,9 +642,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['PoolQC'] = df['PoolQC'].fillna('None')" @@ -725,9 +695,7 @@ { "cell_type": "code", "execution_count": 73, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1013,9 +981,7 @@ { "cell_type": "code", "execution_count": 103, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['MSZoning'] = df['MSZoning'].fillna('RL')" @@ -1024,9 +990,7 @@ { "cell_type": "code", "execution_count": 105, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df = df.drop(['Utilities'], axis = 1)" @@ -1035,9 +999,7 @@ { "cell_type": "code", "execution_count": 107, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['Functional'] = df['Functional'].fillna('Typ')" @@ -1046,9 +1008,7 @@ { "cell_type": "code", "execution_count": 109, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['Electrical'] = df['Electrical'].fillna('SBrkr')" @@ -1057,9 +1017,7 @@ { "cell_type": "code", "execution_count": 114, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['Exterior1st'] = df['Exterior1st'].fillna('VinylSd')" @@ -1068,9 +1026,7 @@ { "cell_type": "code", "execution_count": 115, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['Exterior2nd'] = df['Exterior2nd'].fillna('VinylSd')" @@ -1079,9 +1035,7 @@ { "cell_type": "code", "execution_count": 118, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['KitchenQual'] = df['KitchenQual'].fillna('TA')" @@ -1101,9 +1055,7 @@ { "cell_type": "code", "execution_count": 120, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1191,9 +1143,7 @@ { "cell_type": "code", "execution_count": 124, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1296,9 +1246,7 @@ { "cell_type": "code", "execution_count": 129, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['MSSubClass'] = df['MSSubClass'].astype(str)\n", @@ -1310,9 +1258,7 @@ { "cell_type": "code", "execution_count": 133, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df['TotalSF'] = df['1stFlrSF'] + df['2ndFlrSF'] + df['TotalBsmtSF'] \n", @@ -1324,9 +1270,7 @@ { "cell_type": "code", "execution_count": 135, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder" @@ -1364,9 +1308,7 @@ { "cell_type": "code", "execution_count": 146, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "num_data = df.dtypes[df.dtypes != 'object'].index" @@ -1375,9 +1317,7 @@ { "cell_type": "code", "execution_count": 149, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "skewed_data = df[num_data].apply(lambda x: skew(x))" @@ -1397,9 +1337,7 @@ { "cell_type": "code", "execution_count": 166, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "skewed = skewed_data[abs(skewed_data) > 1.0]" @@ -1408,9 +1346,7 @@ { "cell_type": "code", "execution_count": 168, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1438,9 +1374,7 @@ { "cell_type": "code", "execution_count": 169, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "for i in skewed.index:\n", @@ -1450,9 +1384,7 @@ { "cell_type": "code", "execution_count": 170, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1655,9 +1587,7 @@ { "cell_type": "code", "execution_count": 236, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1677,9 +1607,7 @@ { "cell_type": "code", "execution_count": 225, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3522,9 +3450,7 @@ { "cell_type": "code", "execution_count": 181, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n", @@ -3535,9 +3461,7 @@ { "cell_type": "code", "execution_count": 184, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "X = c_train\n", @@ -3558,9 +3482,7 @@ { "cell_type": "code", "execution_count": 202, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "model = XGBRegressor()\n", @@ -3570,9 +3492,7 @@ { "cell_type": "code", "execution_count": 207, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -3610,9 +3530,7 @@ { "cell_type": "code", "execution_count": 189, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3705,9 +3623,7 @@ { "cell_type": "code", "execution_count": 218, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "mse = ((np.log(predict_train) - np.log(train_y))**2/len(train_X))" @@ -3716,9 +3632,7 @@ { "cell_type": "code", "execution_count": 223, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3750,9 +3664,7 @@ { "cell_type": "code", "execution_count": 196, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3772,9 +3684,7 @@ { "cell_type": "code", "execution_count": 197, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3794,9 +3704,7 @@ { "cell_type": "code", "execution_count": 240, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def rmse_cv(model):\n", @@ -3807,9 +3715,7 @@ { "cell_type": "code", "execution_count": 241, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -3856,7 +3762,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, From c8c8d60b4cf2827a08b1539fa50d0b0ad9aa924b Mon Sep 17 00:00:00 2001 From: samuelmao415 Date: Thu, 23 Aug 2018 22:40:33 -0400 Subject: [PATCH 3/8] new model --- Sam/Kaggle/GB_Group_data.ipynb | 454 ++++++++++++++++ Sam/Kaggle/Modeling.ipynb | 118 +++++ Sam/Kaggle/RF_Group_Data.ipynb | 500 ++++++++++++++++++ Sam/Kaggle/RR_Group_Data.ipynb | 368 +++++++++++++ .../__pycache__/preprocess.cpython-36.pyc | Bin 0 -> 3928 bytes Sam/Kaggle/preprocess.py | 214 ++++++++ 6 files changed, 1654 insertions(+) create mode 100644 Sam/Kaggle/GB_Group_data.ipynb create mode 100644 Sam/Kaggle/RF_Group_Data.ipynb create mode 100644 Sam/Kaggle/RR_Group_Data.ipynb create mode 100644 Sam/Kaggle/__pycache__/preprocess.cpython-36.pyc create mode 100644 Sam/Kaggle/preprocess.py diff --git a/Sam/Kaggle/GB_Group_data.ipynb b/Sam/Kaggle/GB_Group_data.ipynb new file mode 100644 index 0000000..1ee7dba --- /dev/null +++ b/Sam/Kaggle/GB_Group_data.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n", + "(1459, 80)\n" + ] + } + ], + "source": [ + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=True'.\n", + "\n", + "To retain the current behavior and silence the warning, pass sort=False\n", + "\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/plain": [ + "(2919, 81)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "combined=combined.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocess import impute\n", + "all_data, encodedDic = impute (combined, False)\n", + "all_data_onehot, encodedDic = impute (combined, True) #one-hot" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "train=all_data.iloc[:1460:,:] #train\n", + "train_y=train[\"SalePrice\"]\n", + "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", + "test=test.iloc[1460:,:]\n", + "\n", + "train_onehot=all_data_onehot.iloc[:1460:,:] #train\n", + "train_y_onehot=train_onehot[\"SalePrice\"]\n", + "train_x_onehot=train_onehot[train_onehot.columns[train_onehot.columns!=\"SalePrice\"]]\n", + "test_onehot=train_onehot.iloc[1460:,:]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PARAMETER Tuning label encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=3, error_score='raise',\n", + " estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n", + " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, presort='auto', random_state=None,\n", + " subsample=1.0, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': [10000], 'max_depth': [4], 'min_samples_split': [6], 'learning_rate': [0.005, 0.002], 'loss': ['ls'], 'max_features': [18]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import model_selection\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn import ensemble\n", + "from sklearn.datasets import make_friedman1\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "xg=ensemble.GradientBoostingRegressor()\n", + "\n", + "grid_para_xg =[{'n_estimators': [10000], 'max_depth': [4], 'min_samples_split': [6], \n", + " 'learning_rate': [0.002], 'loss':['ls'], 'max_features':[18]}]\n", + "\n", + "#max depth 4 #min_sample:6\n", + "#max_features 18\n", + "grid_search_xg = model_selection.GridSearchCV(xg, grid_para_xg, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1)\n", + "#scoring:mse\n", + "grid_search_xg.fit(train_x, train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'learning_rate': 0.002, 'loss': 'ls', 'max_depth': 4, 'max_features': 18, 'min_samples_split': 6, 'n_estimators': 10000}\n", + "-675520831.4785057\n" + ] + } + ], + "source": [ + "print(grid_search_xg.best_params_)\n", + "print(grid_search_xg.best_score_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model testing with label encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 546222446.1916\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n", + "import matplotlib.pyplot as plt\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.2, random_state=20)\n", + "\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn import ensemble\n", + "from sklearn.datasets import make_friedman1\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "params={'n_estimators': 10000, 'max_depth': 4, 'min_samples_split': 6, \n", + " 'learning_rate': 0.002, 'loss':'ls', 'max_features':18}\n", + "clf = ensemble.GradientBoostingRegressor(**params)\n", + "clf.fit(X_train, y_train)\n", + "mse = mean_squared_error(y_test, clf.predict(X_test))\n", + "print(\"MSE: %.4f\" % mse)\n", + "# #############################################################################\n", + "# Plot training deviance\n", + "\n", + "# compute test set deviance\n", + "test_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n", + "\n", + "for i, y_pred in enumerate(clf.staged_predict(X_test)):\n", + " test_score[i] = clf.loss_(y_test, y_pred)\n", + "\n", + "plt.figure(figsize=(50, 40))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title('Deviance')\n", + "plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n", + " label='Training Set Deviance')\n", + "plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n", + " label='Test Set Deviance')\n", + "plt.legend(loc='upper right')\n", + "plt.xlabel('Boosting Iterations')\n", + "plt.ylabel('Deviance')\n", + "\n", + "# # #############################################################################\n", + "# # Plot feature importance\n", + "feature_importance = clf.feature_importances_\n", + "# # make importances relative to max importance\n", + "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n", + "sorted_idx = np.argsort(feature_importance)\n", + "pos = np.arange(sorted_idx.shape[0]) + .5\n", + "plt.subplot(1, 2, 2)\n", + "plt.barh(pos, feature_importance[sorted_idx], align='center')\n", + "plt.yticks(pos, train.columns[sorted_idx])\n", + "plt.xlabel('Relative Importance')\n", + "plt.title('Variable Importance')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PARAMETER Tuning: one hot" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=3, error_score='raise',\n", + " estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n", + " learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n", + " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", + " min_impurity_split=None, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " n_estimators=100, presort='auto', random_state=None,\n", + " subsample=1.0, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': [10000], 'max_depth': [4], 'min_samples_split': [6], 'learning_rate': [0.005, 0.003], 'loss': ['ls'], 'max_features': [18]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import model_selection\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn import ensemble\n", + "from sklearn.datasets import make_friedman1\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "xg=ensemble.GradientBoostingRegressor()\n", + "\n", + "grid_para_xg =[{'n_estimators': [10000], 'max_depth': [4], 'min_samples_split': [6], \n", + " 'learning_rate': [0.005,0.003], 'loss':['ls'], 'max_features':[18]}]\n", + "\n", + "#max depth 4 #min_sample:6\n", + "#max_features 18\n", + "grid_search_xg = model_selection.GridSearchCV(xg, grid_para_xg, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1)\n", + "#scoring:mse\n", + "grid_search_xg.fit(train_x_onehot, train_y_onehot)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'learning_rate': 0.003, 'loss': 'ls', 'max_depth': 4, 'max_features': 18, 'min_samples_split': 6, 'n_estimators': 10000}\n", + "-674568831.5509832\n" + ] + } + ], + "source": [ + "print(grid_search_xg.best_params_)\n", + "print(grid_search_xg.best_score_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model testing: onehot" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 606328800.6819\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import datasets\n", + "from sklearn import svm\n", + "import matplotlib.pyplot as plt\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x_onehot, train_y_onehot, test_size=0.2, random_state=20)\n", + "\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn import ensemble\n", + "from sklearn.datasets import make_friedman1\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "params={'n_estimators': 10000, 'max_depth': 4, 'min_samples_split': 6, \n", + " 'learning_rate': 0.003, 'loss':'ls', 'max_features':18}\n", + "clf = ensemble.GradientBoostingRegressor(**params)\n", + "clf.fit(X_train, y_train)\n", + "mse = mean_squared_error(y_test, clf.predict(X_test))\n", + "print(\"MSE: %.4f\" % mse)\n", + "# #############################################################################\n", + "# Plot training deviance\n", + "\n", + "# compute test set deviance\n", + "test_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n", + "\n", + "for i, y_pred in enumerate(clf.staged_predict(X_test)):\n", + " test_score[i] = clf.loss_(y_test, y_pred)\n", + "\n", + "plt.figure(figsize=(50, 40))\n", + "plt.subplot(1, 2, 1)\n", + "plt.title('Deviance')\n", + "plt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n", + " label='Training Set Deviance')\n", + "plt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n", + " label='Test Set Deviance')\n", + "plt.legend(loc='upper right')\n", + "plt.xlabel('Boosting Iterations')\n", + "plt.ylabel('Deviance')\n", + "\n", + "# # #############################################################################\n", + "# # Plot feature importance\n", + "feature_importance = clf.feature_importances_\n", + "# # make importances relative to max importance\n", + "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n", + "sorted_idx = np.argsort(feature_importance)\n", + "pos = np.arange(sorted_idx.shape[0]) + .5\n", + "plt.subplot(1, 2, 2)\n", + "plt.barh(pos, feature_importance[sorted_idx], align='center')\n", + "plt.yticks(pos, train_onehot.columns[sorted_idx])\n", + "plt.xlabel('Relative Importance')\n", + "plt.title('Variable Importance')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/Modeling.ipynb b/Sam/Kaggle/Modeling.ipynb index 384ed12..70ff007 100644 --- a/Sam/Kaggle/Modeling.ipynb +++ b/Sam/Kaggle/Modeling.ipynb @@ -359,6 +359,112 @@ "### RR train-test-split" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n", + "(1459, 80)\n" + ] + } + ], + "source": [ + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=True'.\n", + "\n", + "To retain the current behavior and silence the warning, pass sort=False\n", + "\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/plain": [ + "(2919, 81)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "combined=combined.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocess import impute\n", + "all_data, encodedDic = impute (combined, False)\n", + "all_data_onehot, encodedDic = impute (combined, True) #one-hot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "train=all_data.iloc[:1460:,:] #train\n", + "train_y=train[\"SalePrice\"]\n", + "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", + "test=test.iloc[1460:,:]\n", + "\n", + "train_onehot=all_data_onehot.iloc[:1460:,:] #train\n", + "train_y_onehot=train_onehot[\"SalePrice\"]\n", + "train_x_onehot=train_onehot[train_onehot.columns[train_onehot.columns!=\"SalePrice\"]]\n", + "test_onehot=train_onehot.iloc[1460:,:]\n" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -705,6 +811,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/Sam/Kaggle/RF_Group_Data.ipynb b/Sam/Kaggle/RF_Group_Data.ipynb new file mode 100644 index 0000000..de0355f --- /dev/null +++ b/Sam/Kaggle/RF_Group_Data.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n", + "(1459, 80)\n" + ] + } + ], + "source": [ + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=True'.\n", + "\n", + "To retain the current behavior and silence the warning, pass sort=False\n", + "\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/plain": [ + "(2919, 81)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "combined=combined.reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from preprocess import impute\n", + "all_data, encodedDic = impute (combined, False)\n", + "all_data_onehot, encodedDic = impute (combined, True) #one-hot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "train=all_data.iloc[:1460:,:] #train\n", + "train_y=train[\"SalePrice\"]\n", + "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", + "test=test.iloc[1460:,:]\n", + "\n", + "train_onehot=all_data_onehot.iloc[:1460:,:] #train\n", + "train_y_onehot=train_onehot[\"SalePrice\"]\n", + "train_x_onehot=train_onehot[train_onehot.columns[train_onehot.columns!=\"SalePrice\"]]\n", + "test_onehot=train_onehot.iloc[1460:,:]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RF: label encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", + " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': [120], 'max_features': ['sqrt'], 'criterion': ['mse'], 'min_samples_leaf': [1], 'min_samples_split': [4], 'oob_score': [True], 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##Grid Search\n", + "from sklearn import ensemble\n", + "from sklearn import model_selection\n", + "randomForest = ensemble.RandomForestRegressor()\n", + "grid_para_forest = [{\n", + " \"n_estimators\": [120],\n", + " \"max_features\": [\"sqrt\"],\n", + " \"criterion\": [\"mse\"],\n", + " \"min_samples_leaf\": [1],\n", + " \"min_samples_split\": [4],\n", + " \"oob_score\": [True],\n", + " \"random_state\": [42]}]\n", + "grid_search_forest = model_selection.GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", + "grid_search_forest.fit(train_x, train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'criterion': 'mse', 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 4, 'n_estimators': 120, 'oob_score': True, 'random_state': 42}\n", + "-848421620.3827785\n" + ] + } + ], + "source": [ + "print(grid_search_forest.best_params_)\n", + "print(grid_search_forest.best_score_)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "931896202.7753068" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Model fitting\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import ensemble\n", + "from sklearn.metrics import mean_squared_error\n", + "import matplotlib.pyplot as plt\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.3, random_state=98)\n", + "params={\"n_estimators\":120, \"max_features\": \"sqrt\", \"criterion\": \"mse\", \"min_samples_leaf\": 1, \"min_samples_split\": 4,\"oob_score\": True,\"random_state\": 42}\n", + "randomForest = ensemble.RandomForestRegressor(**params)\n", + "randomForest.fit(X_train,y_train)\n", + "mse=mean_squared_error(y_test, randomForest.predict(X_test))\n", + "mse\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "feature_importance = list(zip(train_x.columns[:-2], randomForest.feature_importances_))\n", + "dtype = [('feature', 'S10'), ('importance', 'float')]\n", + "feature_importance = np.array(feature_importance, dtype=dtype)\n", + "feature_sort = np.sort(feature_importance, order='importance')[::-1]\n", + "feature_sort" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RF one-hot" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", + " max_features='auto', max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", + " oob_score=False, random_state=None, verbose=0, warm_start=False),\n", + " fit_params=None, iid=True, n_jobs=-1,\n", + " param_grid=[{'n_estimators': [120], 'max_features': ['sqrt'], 'criterion': ['mse'], 'min_samples_leaf': [1], 'min_samples_split': [4], 'oob_score': [True], 'random_state': [42]}],\n", + " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", + " scoring='neg_mean_squared_error', verbose=0)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##Grid Search\n", + "from sklearn import ensemble\n", + "from sklearn import model_selection\n", + "randomForest = ensemble.RandomForestRegressor()\n", + "grid_para_forest = [{\n", + " \"n_estimators\": [120],\n", + " \"max_features\": [\"sqrt\"],\n", + " \"criterion\": [\"mse\"],\n", + " \"min_samples_leaf\": [1],\n", + " \"min_samples_split\": [4],\n", + " \"oob_score\": [True],\n", + " \"random_state\": [42]}]\n", + "grid_search_forest = model_selection.GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n", + "grid_search_forest.fit(train_x_onehot, train_y_onehot)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'criterion': 'mse', 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 4, 'n_estimators': 120, 'oob_score': True, 'random_state': 42}\n", + "-912016494.9534297\n" + ] + } + ], + "source": [ + "print(grid_search_forest.best_params_)\n", + "print(grid_search_forest.best_score_)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "886540227.1488433" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Model fitting\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import ensemble\n", + "from sklearn.metrics import mean_squared_error\n", + "import matplotlib.pyplot as plt\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x_onehot, train_y_onehot, test_size=0.3, random_state=98)\n", + "params={\"n_estimators\":120, \"max_features\": \"sqrt\", \"criterion\": \"mse\", \"min_samples_leaf\": 1, \"min_samples_split\": 4,\"oob_score\": True,\"random_state\": 42}\n", + "randomForest = ensemble.RandomForestRegressor(**params)\n", + "randomForest.fit(X_train,y_train)\n", + "mse=mean_squared_error(y_test, randomForest.predict(X_test))\n", + "mse\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([(b'OverallQua', 8.93884629e-02), (b'TotalFlrSF', 7.60985762e-02),\n", + " (b'GrLivArea', 6.64768126e-02), (b'YearBuilt', 5.08041362e-02),\n", + " (b'GarageCars', 4.61653310e-02), (b'TotalBsmtS', 3.82928249e-02),\n", + " (b'BsmtQual', 3.77060766e-02), (b'KitchenQua', 3.33367002e-02),\n", + " (b'GarageArea', 3.29475059e-02), (b'1stFlrSF', 3.26868442e-02),\n", + " (b'FullBath', 2.75943618e-02), (b'Bsmt_GLQ', 2.63198705e-02),\n", + " (b'GarageYrBl', 2.48776676e-02), (b'YearRemodA', 2.24358899e-02),\n", + " (b'TotRmsAbvG', 2.23495488e-02), (b'ExterQual', 2.19681485e-02),\n", + " (b'LotArea', 2.14525436e-02), (b'FireplaceQ', 2.03310588e-02),\n", + " (b'2ndFlrSF', 1.84905930e-02), (b'Fireplaces', 1.61424316e-02),\n", + " (b'MasVnr_Brk', 1.48874618e-02), (b'OpenPorchS', 1.35584485e-02),\n", + " (b'Foundation', 1.17628528e-02), (b'LotFrontag', 1.10707430e-02),\n", + " (b'HeatingQC', 7.54744374e-03), (b'WoodDeckSF', 7.50954472e-03),\n", + " (b'TotalProch', 7.03355229e-03), (b'MSSubClass', 6.83015880e-03),\n", + " (b'MasVnr_Sto', 6.69955492e-03), (b'BedroomAbv', 6.68561881e-03),\n", + " (b'BsmtExposu', 6.02818887e-03), (b'Neighborho', 5.93466669e-03),\n", + " (b'Id', 5.88261578e-03), (b'Foundation', 5.87113146e-03),\n", + " (b'Bsmt_Unf', 5.80038315e-03), (b'index', 4.92973490e-03),\n", + " (b'SaleType_N', 4.39952739e-03), (b'HalfBath', 4.31423181e-03),\n", + " (b'RoofStyle_', 4.08563640e-03), (b'BsmtExposu', 4.03651287e-03),\n", + " (b'GarageFini', 3.99574003e-03), (b'OverallCon', 3.90864955e-03),\n", + " (b'MSZoning_R', 3.89627602e-03), (b'MoSold', 3.87915443e-03),\n", + " (b'BsmtBath', 3.82018297e-03), (b'PoolQC', 3.76584158e-03),\n", + " (b'GarageType', 3.47796636e-03), (b'GarageCond', 3.41848981e-03),\n", + " (b'Neighborho', 3.10772266e-03), (b'RoofMatl_W', 3.03723560e-03),\n", + " (b'GarageFini', 2.89829274e-03), (b'GarageType', 2.55900585e-03),\n", + " (b'HouseStyle', 2.53915481e-03), (b'PoolArea', 2.32267885e-03),\n", + " (b'SaleCondit', 2.31284954e-03), (b'YrSold', 2.24323662e-03),\n", + " (b'MSZoning_R', 2.16888785e-03), (b'GarageQual', 2.09861939e-03),\n", + " (b'RoofStyle_', 2.08479445e-03), (b'HouseStyle', 2.08410268e-03),\n", + " (b'LotShape_R', 1.98986559e-03), (b'CentralAir', 1.93242372e-03),\n", + " (b'Bsmt_ALQ', 1.89206883e-03), (b'ExterCond', 1.87655974e-03),\n", + " (b'KitchenAbv', 1.77133087e-03), (b'BsmtCond', 1.74151016e-03),\n", + " (b'GarageType', 1.63594350e-03), (b'Exterior_C', 1.57048607e-03),\n", + " (b'Exterior_H', 1.54726740e-03), (b'SaleCondit', 1.51698771e-03),\n", + " (b'EnclosedPo', 1.41232676e-03), (b'ScreenPorc', 1.39722352e-03),\n", + " (b'SaleType_W', 1.36438565e-03), (b'Exterior_I', 1.31040726e-03),\n", + " (b'Neighborho', 1.29557058e-03), (b'Exterior_V', 1.24839069e-03),\n", + " (b'LotConfig_', 1.24729718e-03), (b'GarageFini', 1.24063263e-03),\n", + " (b'PavedDrive', 1.22101950e-03), (b'LandContou', 1.18234250e-03),\n", + " (b'Exterior_B', 1.16205228e-03), (b'GarageType', 1.11201939e-03),\n", + " (b'RoofMatl_C', 1.10674828e-03), (b'Exterior_S', 1.07244497e-03),\n", + " (b'LotConfig_', 1.05590425e-03), (b'Neighborho', 1.04199638e-03),\n", + " (b'LotShape_I', 1.03000991e-03), (b'Electrical', 9.95249281e-04),\n", + " (b'Fence_nan', 9.81336275e-04), (b'Neighborho', 9.13608310e-04),\n", + " (b'Functional', 8.87195154e-04), (b'Neighborho', 8.49417867e-04),\n", + " (b'LotShape_I', 8.49183457e-04), (b'MSSubClass', 7.89863190e-04),\n", + " (b'Bsmt_BLQ', 7.55470521e-04), (b'LandContou', 7.10890744e-04),\n", + " (b'Exterior_W', 6.92680163e-04), (b'BsmtExposu', 6.88486432e-04),\n", + " (b'Bsmt_Rec', 6.82874494e-04), (b'Condition1', 6.46371524e-04),\n", + " (b'Neighborho', 6.26052655e-04), (b'LandContou', 6.03693225e-04),\n", + " (b'Functional', 5.87188396e-04), (b'Exterior_W', 5.77276740e-04),\n", + " (b'BldgType_T', 5.64227674e-04), (b'Neighborho', 5.36120213e-04),\n", + " (b'Alley_nan', 5.32133519e-04), (b'MSSubClass', 5.30605685e-04),\n", + " (b'Neighborho', 5.15432189e-04), (b'Heating_Ga', 5.06963621e-04),\n", + " (b'MSSubClass', 5.00082281e-04), (b'LandSlope_', 4.93072605e-04),\n", + " (b'Neighborho', 4.46267034e-04), (b'Neighborho', 4.43279090e-04),\n", + " (b'Fence_GdWo', 4.37935007e-04), (b'Exterior_M', 4.28576385e-04),\n", + " (b'MSSubClass', 4.28151998e-04), (b'Neighborho', 4.08069538e-04),\n", + " (b'BsmtExposu', 4.03471643e-04), (b'Condition1', 4.02854312e-04),\n", + " (b'LandSlope_', 3.92522870e-04), (b'MSSubClass', 3.78208741e-04),\n", + " (b'Bsmt_LwQ', 3.65074062e-04), (b'Neighborho', 3.62459828e-04),\n", + " (b'Fence_MnPr', 3.58322385e-04), (b'3SsnPorch', 3.32772348e-04),\n", + " (b'Neighborho', 3.28766720e-04), (b'Heating_Ga', 3.24292946e-04),\n", + " (b'Exterior_P', 3.14727786e-04), (b'MSSubClass', 3.06065781e-04),\n", + " (b'Neighborho', 2.84741912e-04), (b'SaleCondit', 2.81848191e-04),\n", + " (b'MSZoning_F', 2.70512340e-04), (b'BldgType_D', 2.68145858e-04),\n", + " (b'Neighborho', 2.63126985e-04), (b'GarageType', 2.42457590e-04),\n", + " (b'Neighborho', 2.40387313e-04), (b'SaleCondit', 2.32537972e-04),\n", + " (b'MiscVal', 2.25140534e-04), (b'MSSubClass', 2.09881174e-04),\n", + " (b'HouseStyle', 1.97740696e-04), (b'Alley_Pave', 1.93651245e-04),\n", + " (b'BldgType_2', 1.79960068e-04), (b'Functional', 1.76519207e-04),\n", + " (b'MSSubClass', 1.66046058e-04), (b'MiscFeatur', 1.65956789e-04),\n", + " (b'Neighborho', 1.60725565e-04), (b'Condition2', 1.59806035e-04),\n", + " (b'Foundation', 1.57927046e-04), (b'Electrical', 1.54909650e-04),\n", + " (b'Condition1', 1.52198523e-04), (b'LowQualFin', 1.50150960e-04),\n", + " (b'MSSubClass', 1.49013760e-04), (b'Neighborho', 1.41832584e-04),\n", + " (b'Condition1', 1.36038709e-04), (b'GarageType', 1.26723721e-04),\n", + " (b'LotConfig_', 1.22367538e-04), (b'Neighborho', 1.17695190e-04),\n", + " (b'HouseStyle', 1.15358078e-04), (b'MasVnr_Brk', 1.14228140e-04),\n", + " (b'Exterior_S', 1.09227940e-04), (b'Street_Pav', 1.07787863e-04),\n", + " (b'HouseStyle', 1.07601479e-04), (b'BldgType_T', 1.03241110e-04),\n", + " (b'Heating_Gr', 1.00315363e-04), (b'MiscFeatur', 9.98805799e-05),\n", + " (b'Neighborho', 9.97590087e-05), (b'RoofStyle_', 9.50231194e-05),\n", + " (b'RoofMatl_T', 9.28491543e-05), (b'RoofMatl_W', 8.30629654e-05),\n", + " (b'PavedDrive', 8.11069571e-05), (b'Functional', 7.95003808e-05),\n", + " (b'Neighborho', 7.48714731e-05), (b'Condition1', 6.93957531e-05),\n", + " (b'Functional', 6.48988214e-05), (b'Condition2', 5.99515569e-05),\n", + " (b'MSSubClass', 5.95976496e-05), (b'Exterior_A', 5.52778694e-05),\n", + " (b'SaleType_C', 4.96305683e-05), (b'HouseStyle', 4.77910189e-05),\n", + " (b'SaleType_C', 4.76814092e-05), (b'HouseStyle', 4.75984452e-05),\n", + " (b'Electrical', 4.70641461e-05), (b'SaleType_C', 4.29952259e-05),\n", + " (b'Foundation', 3.81712567e-05), (b'MSZoning_R', 3.46527673e-05),\n", + " (b'MSSubClass', 3.21673694e-05), (b'Condition1', 3.15787703e-05),\n", + " (b'MSSubClass', 2.60696316e-05), (b'MSSubClass', 2.26014733e-05),\n", + " (b'Condition2', 1.90411907e-05), (b'SaleType_C', 1.84024164e-05),\n", + " (b'MasVnr_Unk', 1.75819920e-05), (b'SaleType_C', 1.73367689e-05),\n", + " (b'RoofMatl_M', 1.67769258e-05), (b'Functional', 1.67712826e-05),\n", + " (b'Fence_MnWw', 1.37178436e-05), (b'SaleType_O', 1.32675817e-05),\n", + " (b'SaleCondit', 1.32286190e-05), (b'Neighborho', 1.14949575e-05),\n", + " (b'Electrical', 1.00999871e-05), (b'Heating_Wa', 8.68107517e-06),\n", + " (b'Bsmt_Unkno', 7.66055954e-06), (b'MasVnr_Non', 7.15495975e-06),\n", + " (b'Exterior_C', 6.33620946e-06), (b'Utilities_', 6.04492184e-06),\n", + " (b'RoofStyle_', 5.26246818e-06), (b'Exterior_B', 5.23739961e-06),\n", + " (b'LotConfig_', 4.23603054e-06), (b'Foundation', 1.65213831e-06),\n", + " (b'Electrical', 1.43183653e-06), (b'MiscFeatur', 1.09681202e-06),\n", + " (b'Neighborho', 9.26080213e-07), (b'Condition2', 7.21130128e-07),\n", + " (b'Exterior_A', 6.96799438e-07), (b'Condition1', 5.25975517e-07),\n", + " (b'RoofMatl_R', 4.32821161e-07), (b'Heating_Ot', 4.17507619e-07),\n", + " (b'Utilities_', 0.00000000e+00), (b'Street_nan', 0.00000000e+00),\n", + " (b'SaleType_n', 0.00000000e+00), (b'SaleCondit', 0.00000000e+00),\n", + " (b'RoofStyle_', 0.00000000e+00), (b'RoofStyle_', 0.00000000e+00),\n", + " (b'RoofMatl_n', 0.00000000e+00), (b'RoofMatl_M', 0.00000000e+00),\n", + " (b'PavedDrive', 0.00000000e+00), (b'Neighborho', 0.00000000e+00),\n", + " (b'MiscFeatur', 0.00000000e+00), (b'MSZoning_n', 0.00000000e+00),\n", + " (b'MSSubClass', 0.00000000e+00), (b'MSSubClass', 0.00000000e+00),\n", + " (b'LotShape_n', 0.00000000e+00), (b'LotConfig_', 0.00000000e+00),\n", + " (b'LandSlope_', 0.00000000e+00), (b'LandContou', 0.00000000e+00),\n", + " (b'HouseStyle', 0.00000000e+00), (b'Heating_na', 0.00000000e+00),\n", + " (b'Functional', 0.00000000e+00), (b'Foundation', 0.00000000e+00),\n", + " (b'Exterior_U', 0.00000000e+00), (b'Exterior_O', 0.00000000e+00),\n", + " (b'Condition2', 0.00000000e+00), (b'Condition2', 0.00000000e+00),\n", + " (b'Condition2', 0.00000000e+00), (b'Condition2', 0.00000000e+00),\n", + " (b'Condition1', 0.00000000e+00), (b'Condition1', 0.00000000e+00),\n", + " (b'CentralAir', 0.00000000e+00), (b'BldgType_n', 0.00000000e+00)],\n", + " dtype=[('feature', 'S10'), ('importance', '" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import linear_model\n", + "ridge = linear_model.Ridge() # create a ridge regression instance\n", + "\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.3, random_state=1)\n", + "\n", + "alpha_100 = np.logspace(0, 8, 100)\n", + "msetotal = []\n", + "for i in alpha_100:\n", + " ridge.set_params(alpha = i)\n", + " ridge.fit(X_train, y_train)\n", + " mse = mean_squared_error(y_test, ridge.predict(X_test))\n", + " msetotal.append(mse)\n", + "\n", + "\n", + "\n", + "df_mse = pd.DataFrame(msetotal, index=list(range(100)))\n", + "import matplotlib.pyplot as plt\n", + "title = 'Ridge mse as a function of the regularization'\n", + "axes = df_mse.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('mse')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([181438.54067535, 181438.53271865, 181438.525794 , 181438.52057415,\n", + " 181438.52906661, 181438.54800174, 181438.54613564, 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181438.52663278,\n", + " 181438.54357796, 181438.5447121 , 181438.54879391, 181438.5382957 ,\n", + " 181438.5317771 , 181438.51852127, 181438.53926298, 181438.52496769,\n", + " 181438.55200749, 181438.54095522, 181438.54073573, 181438.53387187,\n", + " 181438.53791398, 181438.54146411])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridge.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RR: onehot" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import linear_model\n", + "ridge = linear_model.Ridge() # create a ridge regression instance\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x_onehot, train_y_onehot, test_size=0.3, random_state=1)\n", + "\n", + "alpha_100 = np.logspace(0, 8, 100)\n", + "msetotal = []\n", + "for i in alpha_100:\n", + " ridge.set_params(alpha = i)\n", + " ridge.fit(X_train, y_train)\n", + " mse = mean_squared_error(y_test, ridge.predict(X_test))\n", + " msetotal.append(mse)\n", + "\n", + "\n", + "\n", + "df_mse = pd.DataFrame(msetotal, index=list(range(100)))\n", + "import matplotlib.pyplot as plt\n", + "title = 'Ridge mse as a function of the regularization'\n", + "axes = df_mse.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('mse')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/__pycache__/preprocess.cpython-36.pyc b/Sam/Kaggle/__pycache__/preprocess.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..470417692ff4f47d590b0466183a30b656b89ef1 GIT binary patch literal 3928 zcmaJ^ZFAek5yk-^0KQ13-X$xE<-|=)H&$yqNn1yCY|*AO<5aaPyKYf+I1mq{K;wlz zfG(wi`9jaw`JkEnfc}C0lKuyL>P%-kqrcEE?Xw4pmaLAzxx2l^?%wWmd${GTsj1RG z{{8)*{`#?^{7X6U=%DYS#dpv>RJg*~Ycy9GX*T03*L>C1(u%_55ht1atENWcGs7rkXmQ8M9Ztx=Pl(L!coL$$i zNBuB)5Vj)jiWY+vdT1omb+ovO&QnsQnfF)%>TIi~W~CM>H@VkJvT5PIYQCG-SfSvxBgWa1GGh3F9x()L6#`e(9CE>em@Aaf`nFLnRf=g)!JS{ z@WwP#REvGe4i?VB0;vfiv;tYCMdD?2oc$nCd852xZI!a_wKZbcVtwlfua#3tL0WTY z@ie-(z_JUR-YO$?oDwpCOzXG_5`YbmBWB>mu@`n^I44}UHhd@8<<6ZcGKZ!dTtDl1 z0&L=TB!cw~KWaICdDAQS@(e6U^I$9yO+gBYQgLFX1YVv%s?H8p&spyj zUWA#9mh_PgX_;e!fDQre_;t;_&&gc?5xOCQ`Q_@>ClMX}Z z1a7mLm72{U;(ed!O0)U0@A&x}VUi;gZ^SB1DNRmv1dO1Br54i>v6!V<1xuG)L6L4X zCMb|G0#9bm0ML3kLxe<^b>;VFgjtgi(N1x+i449)@5vl<-pSxQ_nyo#N26~SFY*#E zcM3y|SNIg4<`;OC&+u73*Rh6rs-)VA($)Dqo!wO1fH$cwIS(X9-%yf5*GLs^ttfnv zFCD7G!kAN>a13zFw2&Gdl`r!Ze(_N2T6~p%aLBqvd^yb2M8qHRg^@ZmrL2;gW7!4% z(V;#h6iVbRxR(wY&3Q%ou}XgMsbhMH-mIOPT_&&jW$;wKnv}bhk;;y}F2To-bFb4v zcM5VBz**&=P#kH2Us+Lx7GFp$tUrynB>$5aDr{YVRZD8jOf)RWL1%(Vd1Bp24Jlil z$bxgmI?11J9XitbxoDn)V2O4+-{X!kL2?%Bev0VkR+M=~BKR3$2E-{3-XVTZuO1QW_*MHn!dQi+faVgUfzDL@H_k) zewW|lYy5J@7?uZ{$xK=vF=&6aqYf)W_?c23&5o4r+z47?Fy_!Xpf~d)++{>dn}g?s z(L#5TZxXk;kg^|j@ez1S)c9teCqv}TghQw4!tg?VLQ+Cy1;6^CHmt%fa&I^zcdwex z0Eq>8TJBT+hrb&hy zm)FSqRBh8O)7UyNH^qy!dJx(~)T1D<&3fR5$tDNk21Fc~&-vQ+Uf8igZ13Uzu}i;k z;@^kj>3+}6$`1yKE4)bDz^y!)+zh#0*^Gmv;e{l2i|vMOK{ThvR__Vd zu}fp!?Z%B0^i8`+{AXbsin;l|?{7NEUOxNQ@!N7{J_}!j(O*JAUt`-kb`kfSfqJOz z4YESihucqatLojz3sGT3B9WhJ2o-!36#fz2RVW@l!62@n%XDYp#dvS#l|*cppGJw} zlTUD{Bqw<+I`L{ud?weYNfTb%8oCZxc>ei~Z4qDkTmEg&4If9MwFf&>C?Wj_<&0$5 z#ar8P{?0CMw}d+uv@3GC$EfW{$;h|FF3MP+-~t+m_RR6q|lxm%odWhKRxh5oboU+jUzB735q*y=L1JNNbDt zgJ2)U0{uDCL@D$#Y{WM#Gkq zWM)T1{od|Al=}fHTr8UD0Wvz%LMKF7*7s4)MlC_Lome58MK|mRu5c2#>%}2XK&Cn2 zer5zN_nZ)?b9=kLTlbw9%Q$gDr_|sy#NsL$D9A}(oXCPk?%kYidLe+kk7SBMcP~n^ zk}Lnr@cUlNp2fkTWWyhcx`Siw`urqss~OfvEv1!apY)!99%WK*+Go;o$gAHw-Rq@wrmMyB{5j?1|My)8;LCKBD3J3sy*wnCx sZ Date: Sat, 25 Aug 2018 11:46:05 -0400 Subject: [PATCH 4/8] saturday morning --- Sam/Kaggle/EDA.ipynb | 330 ++++++++++------- Sam/Kaggle/EN_Group_Data.ipynb | 115 ++++++ Sam/Kaggle/GB_Group_data.ipynb | 229 +++--------- Sam/Kaggle/Modeling.ipynb | 364 +++++++++++++++++++ Sam/Kaggle/RR_Group_Data.ipynb | 640 +++++++++++++++++++++++++-------- Sam/Kaggle/Untitled.ipynb | 6 + 6 files changed, 1231 insertions(+), 453 deletions(-) create mode 100644 Sam/Kaggle/EN_Group_Data.ipynb create mode 100644 Sam/Kaggle/Untitled.ipynb diff --git a/Sam/Kaggle/EDA.ipynb b/Sam/Kaggle/EDA.ipynb index 395d3b8..342d0b8 100644 --- a/Sam/Kaggle/EDA.ipynb +++ b/Sam/Kaggle/EDA.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -603,39 +603,39 @@ " \n", " \n", " \n", - " 1076\n", - " 1077\n", - " 50\n", + " 560\n", + " 561\n", + " 20\n", " RL\n", - " 60.0\n", - " 10800\n", + " NaN\n", + " 11341\n", " Pave\n", - " Grvl\n", - " Reg\n", + " NaN\n", + " IR1\n", " Lvl\n", " AllPub\n", " ...\n", " 0\n", " NaN\n", " NaN\n", - " Shed\n", - " 500\n", - " 4\n", - " 2006\n", + " NaN\n", + " 0\n", + " 5\n", + " 2010\n", " WD\n", " Normal\n", - " 170000\n", + " 121500\n", " \n", " \n", - " 889\n", - " 890\n", + " 366\n", + " 367\n", " 20\n", " RL\n", - " 128.0\n", - " 12160\n", + " NaN\n", + " 9500\n", " Pave\n", " NaN\n", - " Reg\n", + " IR1\n", " Lvl\n", " AllPub\n", " ...\n", @@ -644,47 +644,47 @@ " NaN\n", " NaN\n", " 0\n", - " 2\n", + " 7\n", " 2009\n", " WD\n", " Normal\n", - " 149500\n", + " 159000\n", " \n", " \n", - " 543\n", - " 544\n", - " 120\n", - " RH\n", - " 34.0\n", - " 4058\n", + " 1404\n", + " 1405\n", + " 50\n", + " RL\n", + " 60.0\n", + " 10410\n", " Pave\n", - " NaN\n", + " Grvl\n", " Reg\n", " Lvl\n", " AllPub\n", " ...\n", " 0\n", " NaN\n", - " NaN\n", + " MnPrv\n", " NaN\n", " 0\n", - " 6\n", - " 2007\n", + " 1\n", + " 2006\n", " WD\n", - " Normal\n", - " 133000\n", + " Family\n", + " 105000\n", " \n", " \n", - " 1376\n", - " 1377\n", - " 30\n", + " 1439\n", + " 1440\n", + " 60\n", " RL\n", - " 52.0\n", - " 6292\n", + " 80.0\n", + " 11584\n", " Pave\n", " NaN\n", " Reg\n", - " Bnk\n", + " Lvl\n", " AllPub\n", " ...\n", " 0\n", @@ -692,19 +692,19 @@ " NaN\n", " NaN\n", " 0\n", - " 4\n", - " 2008\n", + " 11\n", + " 2007\n", " WD\n", " Normal\n", - " 91000\n", + " 197000\n", " \n", " \n", - " 818\n", - " 819\n", - " 80\n", + " 1171\n", + " 1172\n", + " 20\n", " RL\n", - " 80.0\n", - " 8816\n", + " 76.0\n", + " 9120\n", " Pave\n", " NaN\n", " Reg\n", @@ -713,70 +713,70 @@ " ...\n", " 0\n", " NaN\n", - " MnPrv\n", " NaN\n", - " 0\n", - " 6\n", - " 2010\n", + " Shed\n", + " 1400\n", + " 11\n", + " 2008\n", " WD\n", " Normal\n", - " 155000\n", + " 163000\n", " \n", " \n", - " 752\n", - " 753\n", + " 303\n", + " 304\n", " 20\n", " RL\n", - " 79.0\n", - " 9236\n", + " 70.0\n", + " 9800\n", " Pave\n", " NaN\n", - " IR1\n", + " Reg\n", " Lvl\n", " AllPub\n", " ...\n", " 0\n", " NaN\n", - " NaN\n", + " GdWo\n", " NaN\n", " 0\n", " 7\n", " 2006\n", " WD\n", - " Normal\n", - " 217000\n", + " Abnorml\n", + " 149900\n", " \n", " \n", - " 920\n", - " 921\n", - " 60\n", - " RL\n", - " 70.0\n", - " 8462\n", + " 740\n", + " 741\n", + " 70\n", + " RM\n", + " 60.0\n", + " 9600\n", " Pave\n", - " NaN\n", - " IR1\n", + " Grvl\n", + " Reg\n", " Lvl\n", " AllPub\n", " ...\n", " 0\n", " NaN\n", - " NaN\n", + " GdPrv\n", " NaN\n", " 0\n", - " 7\n", + " 5\n", " 2007\n", " WD\n", - " Normal\n", - " 201000\n", + " Abnorml\n", + " 132000\n", " \n", " \n", - " 419\n", - " 420\n", - " 20\n", + " 723\n", + " 724\n", + " 50\n", " RL\n", - " 65.0\n", - " 8450\n", + " 60.0\n", + " 8172\n", " Pave\n", " NaN\n", " Reg\n", @@ -788,19 +788,19 @@ " NaN\n", " NaN\n", " 0\n", - " 7\n", - " 2010\n", + " 5\n", + " 2008\n", " WD\n", " Normal\n", - " 142000\n", + " 135000\n", " \n", " \n", - " 693\n", - " 694\n", - " 30\n", - " RL\n", - " 60.0\n", - " 5400\n", + " 690\n", + " 691\n", + " 120\n", + " RM\n", + " NaN\n", + " 4426\n", " Pave\n", " NaN\n", " Reg\n", @@ -812,19 +812,19 @@ " NaN\n", " NaN\n", " 0\n", - " 12\n", - " 2006\n", + " 5\n", + " 2008\n", " WD\n", - " Abnorml\n", - " 108480\n", + " Normal\n", + " 141000\n", " \n", " \n", - " 5\n", - " 6\n", - " 50\n", + " 1312\n", + " 1313\n", + " 60\n", " RL\n", - " 85.0\n", - " 14115\n", + " NaN\n", + " 9572\n", " Pave\n", " NaN\n", " IR1\n", @@ -833,14 +833,14 @@ " ...\n", " 0\n", " NaN\n", - " MnPrv\n", - " Shed\n", - " 700\n", - " 10\n", - " 2009\n", + " NaN\n", + " NaN\n", + " 0\n", + " 6\n", + " 2007\n", " WD\n", " Normal\n", - " 143000\n", + " 302000\n", " \n", " \n", "\n", @@ -849,45 +849,45 @@ ], "text/plain": [ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "1076 1077 50 RL 60.0 10800 Pave Grvl Reg \n", - "889 890 20 RL 128.0 12160 Pave NaN Reg \n", - "543 544 120 RH 34.0 4058 Pave NaN Reg \n", - "1376 1377 30 RL 52.0 6292 Pave NaN Reg \n", - "818 819 80 RL 80.0 8816 Pave NaN Reg \n", - "752 753 20 RL 79.0 9236 Pave NaN IR1 \n", - "920 921 60 RL 70.0 8462 Pave NaN IR1 \n", - "419 420 20 RL 65.0 8450 Pave NaN Reg \n", - "693 694 30 RL 60.0 5400 Pave NaN Reg \n", - "5 6 50 RL 85.0 14115 Pave NaN IR1 \n", + "560 561 20 RL NaN 11341 Pave NaN IR1 \n", + "366 367 20 RL NaN 9500 Pave NaN IR1 \n", + "1404 1405 50 RL 60.0 10410 Pave Grvl Reg \n", + "1439 1440 60 RL 80.0 11584 Pave NaN Reg \n", + "1171 1172 20 RL 76.0 9120 Pave NaN Reg \n", + "303 304 20 RL 70.0 9800 Pave NaN Reg \n", + "740 741 70 RM 60.0 9600 Pave Grvl Reg \n", + "723 724 50 RL 60.0 8172 Pave NaN Reg \n", + "690 691 120 RM NaN 4426 Pave NaN Reg \n", + "1312 1313 60 RL NaN 9572 Pave NaN IR1 \n", "\n", " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature \\\n", - "1076 Lvl AllPub ... 0 NaN NaN Shed \n", - "889 Lvl AllPub ... 0 NaN NaN NaN \n", - "543 Lvl AllPub ... 0 NaN NaN NaN \n", - "1376 Bnk AllPub ... 0 NaN NaN NaN \n", - "818 Lvl AllPub ... 0 NaN MnPrv NaN \n", - "752 Lvl AllPub ... 0 NaN NaN NaN \n", - "920 Lvl AllPub ... 0 NaN NaN NaN \n", - "419 Lvl AllPub ... 0 NaN NaN NaN \n", - "693 Lvl AllPub ... 0 NaN NaN NaN \n", - "5 Lvl AllPub ... 0 NaN MnPrv Shed \n", + "560 Lvl AllPub ... 0 NaN NaN NaN \n", + "366 Lvl AllPub ... 0 NaN NaN NaN \n", + "1404 Lvl AllPub ... 0 NaN MnPrv NaN \n", + "1439 Lvl AllPub ... 0 NaN NaN NaN \n", + "1171 Lvl AllPub ... 0 NaN NaN Shed \n", + "303 Lvl AllPub ... 0 NaN GdWo NaN \n", + "740 Lvl AllPub ... 0 NaN GdPrv NaN \n", + "723 Lvl AllPub ... 0 NaN NaN NaN \n", + "690 Lvl AllPub ... 0 NaN NaN NaN \n", + "1312 Lvl AllPub ... 0 NaN NaN NaN \n", "\n", " MiscVal MoSold YrSold SaleType SaleCondition SalePrice \n", - "1076 500 4 2006 WD Normal 170000 \n", - "889 0 2 2009 WD Normal 149500 \n", - "543 0 6 2007 WD Normal 133000 \n", - "1376 0 4 2008 WD Normal 91000 \n", - "818 0 6 2010 WD Normal 155000 \n", - "752 0 7 2006 WD Normal 217000 \n", - "920 0 7 2007 WD Normal 201000 \n", - "419 0 7 2010 WD Normal 142000 \n", - "693 0 12 2006 WD Abnorml 108480 \n", - "5 700 10 2009 WD Normal 143000 \n", + "560 0 5 2010 WD Normal 121500 \n", + "366 0 7 2009 WD Normal 159000 \n", + "1404 0 1 2006 WD Family 105000 \n", + "1439 0 11 2007 WD Normal 197000 \n", + "1171 1400 11 2008 WD Normal 163000 \n", + "303 0 7 2006 WD Abnorml 149900 \n", + "740 0 5 2007 WD Abnorml 132000 \n", + "723 0 5 2008 WD Normal 135000 \n", + "690 0 5 2008 WD Normal 141000 \n", + "1312 0 6 2007 WD Normal 302000 \n", "\n", "[10 rows x 81 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -4067,6 +4067,72 @@ "sum(basement_set[\"TotalBsmtSF\"]==0)" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.groupby(['YrSold'])" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.style.use('ggplot')\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "p=train.groupby(['YrSold','Neighborhood']).mean()['SalePrice'].unstack()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p.plot(figsize=(10,10))" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/Sam/Kaggle/EN_Group_Data.ipynb b/Sam/Kaggle/EN_Group_Data.ipynb new file mode 100644 index 0000000..74d6ec0 --- /dev/null +++ b/Sam/Kaggle/EN_Group_Data.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n", + "(1459, 80)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=True'.\n", + "\n", + "To retain the current behavior and silence the warning, pass sort=False\n", + "\n", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + } + ], + "source": [ + "## Data Processing\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport\n", + "\n", + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)\n", + "\n", + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test\n", + "\n", + "combined=combined.reset_index()\n", + "\n", + "from preprocess import impute\n", + "all_data, encodedDic = impute (combined, False)\n", + "all_data_onehot, encodedDic = impute (combined, True) #one-hot\n", + "\n", + "train=all_data.iloc[:1460:,:] #train\n", + "train_y=train[\"SalePrice\"]\n", + "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", + "test=all_data.iloc[1460:,:]\n", + "\n", + "train_onehot=all_data_onehot.iloc[:1460:,:] #train\n", + "train_y_onehot=train_onehot[\"SalePrice\"]\n", + "train_x_onehot=train_onehot[train_onehot.columns[train_onehot.columns!=\"SalePrice\"]]\n", + "test_onehot=all_data_onehot.iloc[1460:,:].drop(columns=\"SalePrice\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##Parameter tuning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import ElasticNet\n", + "from sklearn.datasets import make_regression\n", + "\n", + "X, y = make_regression(n_features=2, random_state=0)\n", + "regr = ElasticNet(random_state=0)\n", + "regr.fit(X, y)\n", + "ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=(0.5,\n", + " max_iter=1000, normalize=False, positive=False, precompute=False,\n", + " random_state=0, selection='cyclic', tol=0.0001, warm_start=False)\n", + "print(regr.coef_) \n", + "print(regr.intercept_) \n", + "print(regr.predict([[0, 0]])) \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/GB_Group_data.ipynb b/Sam/Kaggle/GB_Group_data.ipynb index 1ee7dba..9e67c61 100644 --- a/Sam/Kaggle/GB_Group_data.ipynb +++ b/Sam/Kaggle/GB_Group_data.ipynb @@ -2,18 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as pltimport" - ] - }, - { - "cell_type": "code", - "execution_count": 96, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -23,78 +12,43 @@ "(1460, 81)\n", "(1459, 80)\n" ] - } - ], - "source": [ - "train=pd.read_csv(\"train.csv\")\n", - "test=pd.read_csv(\"test.csv\")\n", - "print(train.shape)\n", - "print(test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ + }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", "of pandas will change to not sort by default.\n", "\n", "To accept the future behavior, pass 'sort=True'.\n", "\n", "To retain the current behavior and silence the warning, pass sort=False\n", "\n", - " \"\"\"Entry point for launching an IPython kernel.\n" + " # Remove the CWD from sys.path while we load stuff.\n" ] - }, - { - "data": { - "text/plain": [ - "(2919, 81)" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport\n", + "\n", + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)\n", + "\n", "combined=pd.concat([train,test])\n", "combined.shape #2919\n", "#combined.iloc[:1460:,:] #train\n", - "#combined.iloc[1460:,:] #test" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "combined=combined.reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ + "#combined.iloc[1460:,:] #test\n", + "\n", + "combined=combined.reset_index()\n", + "\n", "from preprocess import impute\n", "all_data, encodedDic = impute (combined, False)\n", - "all_data_onehot, encodedDic = impute (combined, True) #one-hot" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ + "all_data_onehot, encodedDic = impute (combined, True) #one-hot\n", + "\n", "train=all_data.iloc[:1460:,:] #train\n", "train_y=train[\"SalePrice\"]\n", "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", @@ -106,6 +60,29 @@ "test_onehot=train_onehot.iloc[1460:,:]\n" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'power_transform'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m### Boxcox transformation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpower_transform\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpower_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"box-cox\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mImportError\u001b[0m: cannot import name 'power_transform'" + ] + } + ], + "source": [ + "### Boxcox transformation\n", + "from sklearn.preprocessing import power_transform\n", + "new=power_transform(train_x,method=\"box-cox\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -115,31 +92,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise',\n", - " estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n", - " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, presort='auto', random_state=None,\n", - " subsample=1.0, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'n_estimators': [10000], 'max_depth': [4], 'min_samples_split': [6], 'learning_rate': [0.005, 0.002], 'loss': ['ls'], 'max_features': [18]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn import model_selection\n", "from sklearn.metrics import mean_squared_error\n", @@ -160,18 +115,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'learning_rate': 0.002, 'loss': 'ls', 'max_depth': 4, 'max_features': 18, 'min_samples_split': 6, 'n_estimators': 10000}\n", - "-675520831.4785057\n" - ] - } - ], + "outputs": [], "source": [ "print(grid_search_xg.best_params_)\n", "print(grid_search_xg.best_score_)" @@ -186,27 +132,9 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 546222446.1916\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import train_test_split\n", @@ -269,31 +197,9 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise',\n", - " estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n", - " learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n", - " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", - " min_impurity_split=None, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=100, presort='auto', random_state=None,\n", - " subsample=1.0, verbose=0, warm_start=False),\n", - " fit_params=None, iid=True, n_jobs=-1,\n", - " param_grid=[{'n_estimators': [10000], 'max_depth': [4], 'min_samples_split': [6], 'learning_rate': [0.005, 0.003], 'loss': ['ls'], 'max_features': [18]}],\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", - " scoring='neg_mean_squared_error', verbose=0)" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn import model_selection\n", "from sklearn.metrics import mean_squared_error\n", @@ -314,18 +220,9 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'learning_rate': 0.003, 'loss': 'ls', 'max_depth': 4, 'max_features': 18, 'min_samples_split': 6, 'n_estimators': 10000}\n", - "-674568831.5509832\n" - ] - } - ], + "outputs": [], "source": [ "print(grid_search_xg.best_params_)\n", "print(grid_search_xg.best_score_)" @@ -340,27 +237,9 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE: 606328800.6819\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import train_test_split\n", diff --git a/Sam/Kaggle/Modeling.ipynb b/Sam/Kaggle/Modeling.ipynb index 70ff007..1a6be00 100644 --- a/Sam/Kaggle/Modeling.ipynb +++ b/Sam/Kaggle/Modeling.ipynb @@ -798,6 +798,370 @@ "mean_squared_error(y_test, ridge.predict(test_img))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge Regresion" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import linear_model\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "X_train, X_test, y_train, y_test = train_test_split(x_var, y_var, test_size=0.2, random_state=4)\n", + "\n", + "alpha_100 = np.linspace(0.8,1.2, 50)\n", + "msetotal = []\n", + "for i in alpha_100:\n", + " ridge.set_params(alpha = i)\n", + " ridge.fit(X_train, y_train)\n", + " mse = mean_squared_error(y_test, ridge.predict(X_test))\n", + " msetotal.append(mse)\n", + "\n", + "\n", + "\n", + "df_mse = pd.DataFrame(msetotal, index=alpha_100)\n", + "import matplotlib.pyplot as plt\n", + "title = 'Ridge mse as a function of the regularization'\n", + "axes = df_mse.plot(logx=True, title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('mse')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"1.069388 6.802410e+08\n", + "1.077551 6.815785e+08\n", + "1.085714 6.830023e+08\n", + "1.093878 6.845097e+08\n", + "1.102041 6.860979e+08\n", + "1.110204 6.877644e+08\n", + "1.118367 6.895066e+08\n", + "1.126531 6.913220e+08\n", + "1.134694 6.932084e+08\n", + "1.142857 6.951634e+08\n", + "1.151020 6.971848e+08\n", + "1.159184 6.992705e+08\n", + "1.167347 7.014185e+08\n", + "1.175510 7.036268e+08\n", + "1.183673 7.058933e+08\n", + "1.191837 7.082164e+08\n", + "1.200000 7.105941e+08" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mse" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import linear_model\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "ridge.set_params(alpha = 0.8)\n", + "ridge.fit(x_var,y_var)\n", + "res=ridge.predict(real_test_var)\n", + "\n", + "mean_squared_error(y_var, ridge.predict(x_var))\n", + "\n", + "np.savetxt(\"res.csv\", res, delimiter=\",\")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/Sam/Kaggle/RR_Group_Data.ipynb b/Sam/Kaggle/RR_Group_Data.ipynb index 97b650b..22860bd 100644 --- a/Sam/Kaggle/RR_Group_Data.ipynb +++ b/Sam/Kaggle/RR_Group_Data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -57,7 +57,7 @@ "(2919, 81)" ] }, - "execution_count": 19, + "execution_count": 112, "metadata": {}, "output_type": "execute_result" } @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ @@ -91,19 +91,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ "train=all_data.iloc[:1460:,:] #train\n", "train_y=train[\"SalePrice\"]\n", "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", - "test=test.iloc[1460:,:]\n", + "test=all_data.iloc[1460:,:]\n", "\n", "train_onehot=all_data_onehot.iloc[:1460:,:] #train\n", "train_y_onehot=train_onehot[\"SalePrice\"]\n", "train_x_onehot=train_onehot[train_onehot.columns[train_onehot.columns!=\"SalePrice\"]]\n", - "test_onehot=train_onehot.iloc[1460:,:]\n" + "test_onehot=all_data_onehot.iloc[1460:,:].drop(columns=\"SalePrice\")\n" ] }, { @@ -115,12 +115,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -131,14 +131,14 @@ ], "source": [ "from sklearn import linear_model\n", - "ridge = linear_model.Ridge() # create a ridge regression instance\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", "\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", - "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.3, random_state=1)\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x, train_y, test_size=0.3, random_state=2)\n", "\n", - "alpha_100 = np.logspace(0, 8, 100)\n", + "alpha_100 = np.linspace(0.1,0.3,100)\n", "msetotal = []\n", "for i in alpha_100:\n", " ridge.set_params(alpha = i)\n", @@ -148,10 +148,10 @@ "\n", "\n", "\n", - "df_mse = pd.DataFrame(msetotal, index=list(range(100)))\n", + "df_mse = pd.DataFrame(msetotal, index=alpha_100)\n", "import matplotlib.pyplot as plt\n", "title = 'Ridge mse as a function of the regularization'\n", - "axes = df_mse.plot(logx=True, title=title)\n", + "axes = df_mse.plot( title=title)\n", "axes.set_xlabel('alpha')\n", "axes.set_ylabel('mse')\n", "plt.show()" @@ -159,125 +159,125 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([181438.54067535, 181438.53271865, 181438.525794 , 181438.52057415,\n", - " 181438.52906661, 181438.54800174, 181438.54613564, 181438.53192411,\n", - " 181438.54076496, 181438.54093811, 181438.53507277, 181438.5157751 ,\n", - " 181438.54114678, 181438.54620552, 181438.5423543 , 181438.52399732,\n", - " 181438.52700595, 181438.52867922, 181438.53853897, 181438.52931255,\n", - " 181438.52551876, 181438.52941085, 181438.54181236, 181438.54947199,\n", - " 181438.52069407, 181438.53533096, 181438.528138 , 181438.53587991,\n", - " 181438.55625869, 181438.52938701, 181438.52658651, 181438.52710898,\n", - " 181438.52688347, 181438.51954409, 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360568.58891543,\n", + " 182385.58683262, 39006.59552701, 327020.94507484, 171541.73072872,\n", + " 106484.53114483, 264790.28253652, 183507.69545286, 316522.97854549,\n", + " 138665.5142613 , 87303.82000059, 288648.53088843, 175752.60584928,\n", + " 231209.31517694, 193942.73636479, 198610.07984555, 125153.94644864,\n", + " 84568.95209885, 257676.38267408, 164193.8355468 , 328590.36240425,\n", + " 312508.59533212, 230651.58832268, 242698.72043947, 275103.37204672,\n", + " 208059.06680546, 208444.78996792, 97665.89878886, 138842.65762365,\n", + " 230024.76515225, 191145.87612454, 158904.91603994, 326100.17017211,\n", + " 193151.23580013, 120116.89980327, 55337.17054805, 154649.24125895,\n", + " 126275.86802466, 83652.52544925, 147109.76334075, 270837.64956013,\n", + " 71199.21999106, 100436.40850006, 101450.34396111, 122678.40638679,\n", + " 284599.01091457, 260087.8094705 , 313922.7167625 , 217417.46464086,\n", + " 156733.8733745 , 64842.26566259, 208283.93983992, 103742.50233435,\n", + " 357233.71194824, 233360.782476 , 226978.54430557, 165863.95886363,\n", + " 185741.69655341, 245923.69565522])" ] }, - "execution_count": 15, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -295,12 +295,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 159, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -311,13 +311,13 @@ ], "source": [ "from sklearn import linear_model\n", - "ridge = linear_model.Ridge() # create a ridge regression instance\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", - "X_train, X_test, y_train, y_test = train_test_split(train_x_onehot, train_y_onehot, test_size=0.3, random_state=1)\n", + "X_train, X_test, y_train, y_test = train_test_split(train_x_onehot, train_y_onehot, test_size=0.2, random_state=4)\n", "\n", - "alpha_100 = np.logspace(0, 8, 100)\n", + "alpha_100 = np.linspace(0.22,0.6, 50)\n", "msetotal = []\n", "for i in alpha_100:\n", " ridge.set_params(alpha = i)\n", @@ -327,7 +327,7 @@ "\n", "\n", "\n", - "df_mse = pd.DataFrame(msetotal, index=list(range(100)))\n", + "df_mse = pd.DataFrame(msetotal, index=alpha_100)\n", "import matplotlib.pyplot as plt\n", "title = 'Ridge mse as a function of the regularization'\n", "axes = df_mse.plot(logx=True, title=title)\n", @@ -336,6 +336,366 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=True'.\n", + "\n", + "To retain the current behavior and silence the warning, pass sort=False\n", + "\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport\n", + "\n", + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)\n", + "\n", + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test\n", + "\n", + "combined=combined.reset_index()\n", + "\n", + "from preprocess import impute\n", + "all_data, encodedDic = impute (combined, False)\n", + "all_data_onehot, encodedDic = impute (combined, True) #one-hot\n", + "\n", + "train=all_data.iloc[:1460:,:] #train\n", + "train_y=train[\"SalePrice\"]\n", + "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", + "test=test.iloc[1460:,:]\n", + "\n", + "train_onehot=all_data_onehot.iloc[:1460:,:] #train\n", + "train_y_onehot=train_onehot[\"SalePrice\"]\n", + "train_x_onehot=train_onehot[train_onehot.columns[train_onehot.columns!=\"SalePrice\"]]\n", + "test_onehot=train_onehot.iloc[1460:,:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1stFlrSF2ndFlrSFGarageAreaGrLivAreaLotAreaLotFrontageSalePriceTotalBsmtSFTotalFlrSFBsmt_UnfTotalProchSF
0856854548.01710845065.0208500.0856.02566.0150.061
112620460.01262960080.0181500.01262.02524.0284.00
2920866608.017861125068.0223500.0920.02706.0434.042
3961756642.01717955060.0140000.0756.02473.0540.0307
411451053836.021981426084.0250000.01145.03343.0490.084
5796566480.013621411585.0143000.0796.02158.064.0350
616940636.016941008475.0307000.01686.03380.0317.057
71107983484.020901038268.0200000.01107.03197.00.0432
81022752468.01774612051.0129900.0952.02726.01904.0205
910770205.01077742050.0118000.0991.02068.0140.04
1010400384.010401120070.0129500.01040.02080.0134.00
1111821142736.023241192485.0345000.01175.03499.0177.021
129120352.09121296868.0144000.0912.01824.0175.0176
1314940840.014941065291.0279500.01494.02988.02988.033
1412530352.012531092068.0157000.01253.02506.0520.0389
158540576.0854612051.0132000.0832.01686.01664.0112
1610040480.010041124168.0149000.01004.02008.0426.00
1712960516.012961079172.090000.00.01296.00.00
1811140576.011141369566.0159000.01114.02228.0468.0102
1913390294.01339756070.0139000.01029.02368.0525.00
2011581218853.0237614215101.0325300.01158.03534.02316.0154
2111080280.01108744957.0139400.0637.01745.01274.0205
2217950534.01795974275.0230000.01777.03572.03554.0159
2310600572.01060422444.0129900.01040.02100.0200.0110
2410600270.01060824668.0154000.01060.02120.00.090
2516000890.0160014230110.0256300.01566.03166.03132.056
269000576.0900720060.0134800.0900.01800.00.032
2717040772.017041147898.0306000.01704.03408.0486.050
2816000319.016001632147.0207500.01484.03084.0207.0258
295200240.0520632460.068500.0520.01040.01040.087
....................................
14307341104372.018382193060.0192140.0732.02570.01464.040
14319580440.0958492868.0143750.0958.01916.00.060
14329680216.09681080060.064500.0656.01624.01312.00
1433962830451.017921026193.0186500.0936.02728.01872.00
143411260484.011261740080.0160000.01126.02252.0190.041
143515370462.01537840080.0174000.01319.02856.02638.036
14368640528.0864900060.0120500.0864.01728.0248.00
143719320774.019321244496.0394617.01932.03864.0596.0370
143812360923.01236740790.0149700.0912.02148.0312.0316
14391040685550.017251158480.0197000.0539.02264.00.0304
14401423748672.025551152679.0191000.0588.02759.01176.00
14418480420.0848442668.0149300.0848.01696.0151.00
14421026981812.020071100385.0310000.01017.03024.0252.052
14439520192.0952885468.0121000.0952.01904.01904.0138
144414220626.01422850063.0179600.01422.02844.02844.060
14459130240.0913840070.0129000.0814.01727.00.0252
144611880312.011882614268.0157900.01188.02376.0595.039
14471220870556.020901000080.0240000.01220.03310.0141.065
1448796550384.013461176770.0112000.0560.01906.01120.024
144963000.0630153321.092000.0630.01260.077.00
14508968960.01792900060.0136000.0896.02688.01792.045
145115780840.01578926278.0287090.01573.03151.03146.036
145210720525.01072367535.0145000.0547.01619.00.028
1453114000.011401721790.084500.01140.02280.02280.056
145412210400.01221750062.0185000.01221.02442.0811.0113
1455953694460.01647791762.0175000.0953.02600.01906.040
145620730500.020731317585.0210000.01542.03615.00.00
145711881152252.02340904266.0266500.01152.03492.0877.060
145810780240.01078971768.0142125.01078.02156.00.0112
145912560276.01256993775.0147500.01256.02512.00.068
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1460 rows × 11 columns

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" + ], + "text/plain": [ + " 1stFlrSF 2ndFlrSF GarageArea GrLivArea LotArea LotFrontage \\\n", + "0 856 854 548.0 1710 8450 65.0 \n", + "1 1262 0 460.0 1262 9600 80.0 \n", + "2 920 866 608.0 1786 11250 68.0 \n", + "3 961 756 642.0 1717 9550 60.0 \n", + "4 1145 1053 836.0 2198 14260 84.0 \n", + "5 796 566 480.0 1362 14115 85.0 \n", + "6 1694 0 636.0 1694 10084 75.0 \n", + "7 1107 983 484.0 2090 10382 68.0 \n", + "8 1022 752 468.0 1774 6120 51.0 \n", + "9 1077 0 205.0 1077 7420 50.0 \n", + "10 1040 0 384.0 1040 11200 70.0 \n", + "11 1182 1142 736.0 2324 11924 85.0 \n", + "12 912 0 352.0 912 12968 68.0 \n", + "13 1494 0 840.0 1494 10652 91.0 \n", + "14 1253 0 352.0 1253 10920 68.0 \n", + "15 854 0 576.0 854 6120 51.0 \n", + "16 1004 0 480.0 1004 11241 68.0 \n", + "17 1296 0 516.0 1296 10791 72.0 \n", + "18 1114 0 576.0 1114 13695 66.0 \n", + "19 1339 0 294.0 1339 7560 70.0 \n", + "20 1158 1218 853.0 2376 14215 101.0 \n", + "21 1108 0 280.0 1108 7449 57.0 \n", + "22 1795 0 534.0 1795 9742 75.0 \n", + "23 1060 0 572.0 1060 4224 44.0 \n", + "24 1060 0 270.0 1060 8246 68.0 \n", + "25 1600 0 890.0 1600 14230 110.0 \n", + "26 900 0 576.0 900 7200 60.0 \n", + "27 1704 0 772.0 1704 11478 98.0 \n", + "28 1600 0 319.0 1600 16321 47.0 \n", + "29 520 0 240.0 520 6324 60.0 \n", + "... ... ... ... ... ... ... \n", + "1430 734 1104 372.0 1838 21930 60.0 \n", + "1431 958 0 440.0 958 4928 68.0 \n", + "1432 968 0 216.0 968 10800 60.0 \n", + "1433 962 830 451.0 1792 10261 93.0 \n", + "1434 1126 0 484.0 1126 17400 80.0 \n", + "1435 1537 0 462.0 1537 8400 80.0 \n", + "1436 864 0 528.0 864 9000 60.0 \n", + "1437 1932 0 774.0 1932 12444 96.0 \n", + "1438 1236 0 923.0 1236 7407 90.0 \n", + "1439 1040 685 550.0 1725 11584 80.0 \n", + "1440 1423 748 672.0 2555 11526 79.0 \n", + "1441 848 0 420.0 848 4426 68.0 \n", + "1442 1026 981 812.0 2007 11003 85.0 \n", + "1443 952 0 192.0 952 8854 68.0 \n", + "1444 1422 0 626.0 1422 8500 63.0 \n", + "1445 913 0 240.0 913 8400 70.0 \n", + "1446 1188 0 312.0 1188 26142 68.0 \n", + "1447 1220 870 556.0 2090 10000 80.0 \n", + "1448 796 550 384.0 1346 11767 70.0 \n", + "1449 630 0 0.0 630 1533 21.0 \n", + "1450 896 896 0.0 1792 9000 60.0 \n", + "1451 1578 0 840.0 1578 9262 78.0 \n", + "1452 1072 0 525.0 1072 3675 35.0 \n", + "1453 1140 0 0.0 1140 17217 90.0 \n", + "1454 1221 0 400.0 1221 7500 62.0 \n", + "1455 953 694 460.0 1647 7917 62.0 \n", + "1456 2073 0 500.0 2073 13175 85.0 \n", + "1457 1188 1152 252.0 2340 9042 66.0 \n", + "1458 1078 0 240.0 1078 9717 68.0 \n", + "1459 1256 0 276.0 1256 9937 75.0 \n", + "\n", + " SalePrice TotalBsmtSF TotalFlrSF Bsmt_Unf TotalProchSF \n", + "0 208500.0 856.0 2566.0 150.0 61 \n", + "1 181500.0 1262.0 2524.0 284.0 0 \n", + "2 223500.0 920.0 2706.0 434.0 42 \n", + "3 140000.0 756.0 2473.0 540.0 307 \n", + "4 250000.0 1145.0 3343.0 490.0 84 \n", + "5 143000.0 796.0 2158.0 64.0 350 \n", + "6 307000.0 1686.0 3380.0 317.0 57 \n", + "7 200000.0 1107.0 3197.0 0.0 432 \n", + "8 129900.0 952.0 2726.0 1904.0 205 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1916.0 0.0 60 \n", + "1432 64500.0 656.0 1624.0 1312.0 0 \n", + "1433 186500.0 936.0 2728.0 1872.0 0 \n", + "1434 160000.0 1126.0 2252.0 190.0 41 \n", + "1435 174000.0 1319.0 2856.0 2638.0 36 \n", + "1436 120500.0 864.0 1728.0 248.0 0 \n", + "1437 394617.0 1932.0 3864.0 596.0 370 \n", + "1438 149700.0 912.0 2148.0 312.0 316 \n", + "1439 197000.0 539.0 2264.0 0.0 304 \n", + "1440 191000.0 588.0 2759.0 1176.0 0 \n", + "1441 149300.0 848.0 1696.0 151.0 0 \n", + "1442 310000.0 1017.0 3024.0 252.0 52 \n", + "1443 121000.0 952.0 1904.0 1904.0 138 \n", + "1444 179600.0 1422.0 2844.0 2844.0 60 \n", + "1445 129000.0 814.0 1727.0 0.0 252 \n", + "1446 157900.0 1188.0 2376.0 595.0 39 \n", + "1447 240000.0 1220.0 3310.0 141.0 65 \n", + "1448 112000.0 560.0 1906.0 1120.0 24 \n", + "1449 92000.0 630.0 1260.0 77.0 0 \n", + "1450 136000.0 896.0 2688.0 1792.0 45 \n", + "1451 287090.0 1573.0 3151.0 3146.0 36 \n", + "1452 145000.0 547.0 1619.0 0.0 28 \n", + "1453 84500.0 1140.0 2280.0 2280.0 56 \n", + "1454 185000.0 1221.0 2442.0 811.0 113 \n", + "1455 175000.0 953.0 2600.0 1906.0 40 \n", + "1456 210000.0 1542.0 3615.0 0.0 0 \n", + "1457 266500.0 1152.0 3492.0 877.0 60 \n", + "1458 142125.0 1078.0 2156.0 0.0 112 \n", + "1459 147500.0 1256.0 2512.0 0.0 68 \n", + "\n", + "[1460 rows x 11 columns]" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_onehot[forbx]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n", + " binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n", + " FAC1 = 2*(np.pi*bw/RANGE)**2\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.gridspec as gridspec\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "gs=gridspec.GridSpec(251,1)\n", + "plt.figure(figsize=(16, 340))\n", + "for i in range(train_onehot.shape[1]):\n", + " ax1 = plt.subplot(gs[i, 0])\n", + " ax1.set_title('')\n", + " ax1.set_ylabel(\"\")\n", + " ax1.set_xlabel(\"\")\n", + "# xlim=ax1.get_xlim() \n", + " sns.distplot(train_onehot.iloc[:,i])\n", + " for tick in ax1.xaxis.get_major_ticks():\n", + " tick.label1On = False\n", + " tick.label2On = True\n", + "# for tick in ax1.yaxis.get_major_ticks():\n", + "# tick.label1On=True\n", + "\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# subset numerical variable that look skewed\n", + "forbx=[\"1stFlrSF\",\"2ndFlrSF\",\"GarageArea\",\"GrLivArea\",\"LotArea\",\"LotFrontage\",\"SalePrice\",\"TotalBsmtSF\",\"TotalFlrSF\",\"Bsmt_Unf\",\"TotalProchSF\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1stFlrSF 1.469604\n", + "2ndFlrSF 0.861675\n", + "GrLivArea 1.269358\n", + "LotArea 12.822431\n", + "LotFrontage 1.674852\n", + "TotalBsmtSF 1.162616\n", + "TotalFlrSF 1.511479\n", + "Bsmt_Unf 1.336178\n", + "TotalProchSF 2.237266\n", + "dtype: float64" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the skewness of the selected column\n", + "from scipy.stats import skew\n", + "skewed_data=all_data_onehot[forbx].apply(lambda x: skew(x))\n", + "skewed = skewed_data[abs(skewed_data) > 0.75]\n", + "skewed" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.special import boxcox1p\n", + "import matplotlib.pyplot as plt\n", + "\n", + "para=np.linspace(0.0005,0.00001, 50) #lambda tuning\n", + "\n", + "tunning_result=boxcox1p(skewed[5],para)\n", + "xlen=len(tunning_result)\n", + "df_= pd.DataFrame(tunning_result, index=range(xlen))\n", + "\n", + "\n", + "# title = 'Ridge mse as a function of the regularization'\n", + "axes = df_.plot()\n", + "axes.set_xlabel('lambda')\n", + "axes.set_ylabel('skewness')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1.224998\n", + "1 1.244497\n", + "2 1.228710\n", + "3 1.230934\n", + "4 1.239719\n", + "5 1.221213\n", + "6 1.258535\n", + "7 1.238045\n", + "8 1.234047\n", + "9 1.236676\n", + "10 1.234925\n", + "11 1.241288\n", + "12 1.228262\n", + "13 1.252619\n", + "14 1.244147\n", + "15 1.224877\n", + "16 1.233151\n", + "17 1.245790\n", + "18 1.238358\n", + "19 1.247370\n", + "20 1.240277\n", + "21 1.238090\n", + "22 1.261225\n", + "23 1.235880\n", + "24 1.235880\n", + "25 1.255860\n", + "26 1.227583\n", + "27 1.258809\n", + "28 1.255860\n", + "29 1.198114\n", + " ... \n", + "1430 1.216938\n", + "1431 1.230775\n", + "1432 1.231302\n", + "1433 1.230987\n", + "1434 1.238890\n", + "1435 1.253964\n", + "1436 1.225479\n", + "1437 1.264610\n", + "1438 1.243480\n", + "1439 1.234925\n", + "1440 1.250296\n", + 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"C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(train_onehot[\"SalePrice\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/Untitled.ipynb b/Sam/Kaggle/Untitled.ipynb deleted file mode 100644 index 2fd6442..0000000 --- a/Sam/Kaggle/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 2 -} From a0471b227a1f79d43bed15dad1c659eb6e2dd7e9 Mon Sep 17 00:00:00 2001 From: samuelmao415 Date: Sat, 25 Aug 2018 16:27:00 -0400 Subject: [PATCH 6/8] boxcox RR LR --- Sam/Kaggle/Group_Data_BoxCox.ipynb | 1741 +++++++++++++++++++++++++++- 1 file changed, 1717 insertions(+), 24 deletions(-) diff --git a/Sam/Kaggle/Group_Data_BoxCox.ipynb b/Sam/Kaggle/Group_Data_BoxCox.ipynb index 4230862..936d817 100644 --- a/Sam/Kaggle/Group_Data_BoxCox.ipynb +++ b/Sam/Kaggle/Group_Data_BoxCox.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 200, + "execution_count": 221, "metadata": { "scrolled": false }, @@ -1772,21 +1772,11 @@ }, { "cell_type": "code", - "execution_count": 208, - "metadata": {}, + "execution_count": 212, + "metadata": { + "scrolled": true + }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", - " \n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -2285,13 +2275,7 @@ "1456 0.000000\n", "1457 2.549514\n", "1458 0.000000\n", - "1459 0.000000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "1459 0.000000\n", "Name: Bsmt_Unf, Length: 1460, dtype: float64\n", "0 1.906109\n", "1 0.000000\n", @@ -2360,14 +2344,16 @@ ], "source": [ "for i in skewed.index:\n", - " train_onehot[i]=boxcox1p(train_onehot[i],0.001)\n", + " all_data_onehot[i]=boxcox1p(all_data_onehot[i],0.001)\n", " print(train_onehot[i])" ] }, { "cell_type": "code", "execution_count": 210, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", @@ -2402,6 +2388,1713 @@ "sns.distplot(train_onehot[\"SalePrice\"])" ] }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "all_data_onehot.to_csv(\"bx_all_data_onehot.csv\",index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "all_data_=pd.read_csv(\"bx_all_data_onehot.csv\")\n", + "\n", + "\n", + "bx_train_onehot=all_data_.iloc[:1460:,:] #train\n", + "bx_train_y_onehot=bx_train_onehot[\"SalePrice\"]\n", + "bx_train_x_onehot=bx_train_onehot[bx_train_onehot.columns[bx_train_onehot.columns!=\"SalePrice\"]]\n", + "bx_test_onehot=bx_train_onehot.iloc[1460:,:]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ridge Regression Comparison with BoxCox" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mgrid_param\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'alpha'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mpara_search_ridge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mridge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_param\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mpara_search_ridge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbx_train_x_onehot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbx_train_y_onehot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpara_search_ridge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m 637\u001b[0m error_score=self.error_score)\n\u001b[0;32m 638\u001b[0m for parameters, (train, test) in product(candidate_params,\n\u001b[1;32m--> 639\u001b[1;33m cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m 640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# if one choose to see train score, \"out\" will contain train score info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 777\u001b[0m \u001b[1;31m# was dispatched. 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\u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\ridge.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0man\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mof\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \"\"\"\n\u001b[1;32m--> 665\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRidge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 667\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\ridge.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 489\u001b[0m X, y, X_offset, y_offset, X_scale = self._preprocess_data(\n\u001b[0;32m 490\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_intercept\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy_X\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m 492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[1;31m# temporary fix for fitting the intercept with sparse data using 'sag'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36m_preprocess_data\u001b[1;34m(X, y, fit_intercept, normalize, copy, sample_weight, return_mean)\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 193\u001b[0m \u001b[0mX_offset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 194\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mX_offset\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 195\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 196\u001b[0m X, X_scale = f_normalize(X, axis=0, copy=False,\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "## With BX\n", + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", + "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_ridge.fit(bx_train_x_onehot, bx_train_y_onehot)\n", + "\n", + "print(para_search_ridge.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n", + "\n", + "# fit best ridge equation to all train data\n", + "best_ridge_y = para_search_ridge.best_estimator_.predict(bx_train_x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((train_y_onehot-best_ridge_y)**2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.49770235643321137}\n", + "Lowest RMSE found: 33289.6071074413\n", + "RMSE: 27791.48846091089\n" + ] + } + ], + "source": [ + "# No BX\n", + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4, 2, 100)}]\n", + "para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_ridge.fit(train_x_onehot, train_y_onehot)\n", + "\n", + "print(para_search_ridge.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_ridge.best_score_)))\n", + "\n", + "# fit best ridge equation to all train data\n", + "best_ridge_y = para_search_ridge.best_estimator_.predict(train_x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((train_y_onehot-best_ridge_y)**2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lasso Regression Comparion with BoxCox" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 25.65020905680051}\n", + "Lowest RMSE found: 33809.958651596615\n", + "RMSE: 27539.866355369504\n" + ] + } + ], + "source": [ + "## NO BX\n", + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "lasso= linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_lasso.fit(train_x_onehot, train_y_onehot)\n", + "\n", + "print(para_search_lasso.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))\n", + "\n", + "# fit best ridge equation to all train data\n", + "best_lasso_y = para_search_lasso.best_estimator_.predict(train_x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((train_y_onehot-best_lasso_y)**2)))\n", + "\n", + "# {'alpha': 25.65020905680051}\n", + "# Lowest RMSE found: 33809.958651596615\n", + "# RMSE: 27539.866355369504" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 25.65020905680051}\n", + "Lowest RMSE found: 32559.718946794015\n", + "RMSE: 28119.588145632715\n" + ] + } + ], + "source": [ + "## BX\n", + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "lasso= linear_model.Lasso(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}]\n", + "para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_lasso.fit(bx_train_x_onehot, bx_train_y_onehot)\n", + "\n", + "print(para_search_lasso.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_lasso.best_score_)))\n", + "\n", + "# fit best ridge equation to all train data\n", + "best_lasso_y = para_search_lasso.best_estimator_.predict(bx_train_x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((bx_train_y_onehot-best_lasso_y)**2)))\n", + "\n", + "# {'alpha': 25.65020905680051}\n", + "# Lowest RMSE found: 32559.718946794015\n", + "# RMSE: 28119.588145632715" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elastic Net Comparison with BoxCox" + ] + }, { "cell_type": "code", "execution_count": null, From a532601f86f9daa4e5098e70572af10af60eae72 Mon Sep 17 00:00:00 2001 From: samuelmao415 Date: Sat, 25 Aug 2018 19:12:52 -0400 Subject: [PATCH 7/8] added self-defined functions --- Sam/Kaggle/Data_Preparation.ipynb | 7 - Sam/Kaggle/Group_Data_BoxCox.ipynb | 3864 +---------------- Sam/Kaggle/Untitled.ipynb | 6 + .../__pycache__/myfunction.cpython-36.pyc | Bin 0 -> 3201 bytes Sam/Kaggle/myfunction.py | 108 + 5 files changed, 188 insertions(+), 3797 deletions(-) create mode 100644 Sam/Kaggle/Untitled.ipynb create mode 100644 Sam/Kaggle/__pycache__/myfunction.cpython-36.pyc create mode 100644 Sam/Kaggle/myfunction.py diff --git a/Sam/Kaggle/Data_Preparation.ipynb b/Sam/Kaggle/Data_Preparation.ipynb index 3dcd564..b104af7 100644 --- a/Sam/Kaggle/Data_Preparation.ipynb +++ b/Sam/Kaggle/Data_Preparation.ipynb @@ -1935,13 +1935,6 @@ "source": [ "### Feature engineering 2nd round: box-cox transformation" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/Sam/Kaggle/Group_Data_BoxCox.ipynb b/Sam/Kaggle/Group_Data_BoxCox.ipynb index 936d817..f37b884 100644 --- a/Sam/Kaggle/Group_Data_BoxCox.ipynb +++ b/Sam/Kaggle/Group_Data_BoxCox.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 221, + "execution_count": 1, "metadata": { "scrolled": false }, @@ -64,1608 +64,20 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1460 rows × 11 columns

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" - ], - "text/plain": [ - " 1stFlrSF 2ndFlrSF GarageArea GrLivArea LotArea LotFrontage \\\n", - "0 856 854 548.0 1710 8450 65.0 \n", - "1 1262 0 460.0 1262 9600 80.0 \n", - "2 920 866 608.0 1786 11250 68.0 \n", - "3 961 756 642.0 1717 9550 60.0 \n", - "4 1145 1053 836.0 2198 14260 84.0 \n", - "5 796 566 480.0 1362 14115 85.0 \n", - "6 1694 0 636.0 1694 10084 75.0 \n", - "7 1107 983 484.0 2090 10382 68.0 \n", - "8 1022 752 468.0 1774 6120 51.0 \n", - "9 1077 0 205.0 1077 7420 50.0 \n", - "10 1040 0 384.0 1040 11200 70.0 \n", - "11 1182 1142 736.0 2324 11924 85.0 \n", - "12 912 0 352.0 912 12968 68.0 \n", - "13 1494 0 840.0 1494 10652 91.0 \n", - "14 1253 0 352.0 1253 10920 68.0 \n", - "15 854 0 576.0 854 6120 51.0 \n", - "16 1004 0 480.0 1004 11241 68.0 \n", - "17 1296 0 516.0 1296 10791 72.0 \n", - "18 1114 0 576.0 1114 13695 66.0 \n", - "19 1339 0 294.0 1339 7560 70.0 \n", - "20 1158 1218 853.0 2376 14215 101.0 \n", - "21 1108 0 280.0 1108 7449 57.0 \n", - "22 1795 0 534.0 1795 9742 75.0 \n", - "23 1060 0 572.0 1060 4224 44.0 \n", - "24 1060 0 270.0 1060 8246 68.0 \n", - "25 1600 0 890.0 1600 14230 110.0 \n", - "26 900 0 576.0 900 7200 60.0 \n", - "27 1704 0 772.0 1704 11478 98.0 \n", - "28 1600 0 319.0 1600 16321 47.0 \n", - "29 520 0 240.0 520 6324 60.0 \n", - "... ... ... ... ... ... ... \n", - "1430 734 1104 372.0 1838 21930 60.0 \n", - "1431 958 0 440.0 958 4928 68.0 \n", - "1432 968 0 216.0 968 10800 60.0 \n", - "1433 962 830 451.0 1792 10261 93.0 \n", - "1434 1126 0 484.0 1126 17400 80.0 \n", - "1435 1537 0 462.0 1537 8400 80.0 \n", - "1436 864 0 528.0 864 9000 60.0 \n", - "1437 1932 0 774.0 1932 12444 96.0 \n", - "1438 1236 0 923.0 1236 7407 90.0 \n", - "1439 1040 685 550.0 1725 11584 80.0 \n", - "1440 1423 748 672.0 2555 11526 79.0 \n", - "1441 848 0 420.0 848 4426 68.0 \n", - "1442 1026 981 812.0 2007 11003 85.0 \n", - "1443 952 0 192.0 952 8854 68.0 \n", - "1444 1422 0 626.0 1422 8500 63.0 \n", - "1445 913 0 240.0 913 8400 70.0 \n", - "1446 1188 0 312.0 1188 26142 68.0 \n", - "1447 1220 870 556.0 2090 10000 80.0 \n", - "1448 796 550 384.0 1346 11767 70.0 \n", - "1449 630 0 0.0 630 1533 21.0 \n", - "1450 896 896 0.0 1792 9000 60.0 \n", - "1451 1578 0 840.0 1578 9262 78.0 \n", - "1452 1072 0 525.0 1072 3675 35.0 \n", - "1453 1140 0 0.0 1140 17217 90.0 \n", - "1454 1221 0 400.0 1221 7500 62.0 \n", - "1455 953 694 460.0 1647 7917 62.0 \n", - "1456 2073 0 500.0 2073 13175 85.0 \n", - "1457 1188 1152 252.0 2340 9042 66.0 \n", - "1458 1078 0 240.0 1078 9717 68.0 \n", - "1459 1256 0 276.0 1256 9937 75.0 \n", - "\n", - " SalePrice TotalBsmtSF TotalFlrSF Bsmt_Unf TotalProchSF \n", - "0 208500.0 856.0 2566.0 150.0 61 \n", - "1 181500.0 1262.0 2524.0 284.0 0 \n", - "2 223500.0 920.0 2706.0 434.0 42 \n", - "3 140000.0 756.0 2473.0 540.0 307 \n", - "4 250000.0 1145.0 3343.0 490.0 84 \n", - "5 143000.0 796.0 2158.0 64.0 350 \n", - "6 307000.0 1686.0 3380.0 317.0 57 \n", - "7 200000.0 1107.0 3197.0 0.0 432 \n", - "8 129900.0 952.0 2726.0 1904.0 205 \n", - "9 118000.0 991.0 2068.0 140.0 4 \n", - "10 129500.0 1040.0 2080.0 134.0 0 \n", - "11 345000.0 1175.0 3499.0 177.0 21 \n", - "12 144000.0 912.0 1824.0 175.0 176 \n", - "13 279500.0 1494.0 2988.0 2988.0 33 \n", - "14 157000.0 1253.0 2506.0 520.0 389 \n", - "15 132000.0 832.0 1686.0 1664.0 112 \n", - "16 149000.0 1004.0 2008.0 426.0 0 \n", - "17 90000.0 0.0 1296.0 0.0 0 \n", - "18 159000.0 1114.0 2228.0 468.0 102 \n", - "19 139000.0 1029.0 2368.0 525.0 0 \n", - "20 325300.0 1158.0 3534.0 2316.0 154 \n", - "21 139400.0 637.0 1745.0 1274.0 205 \n", - "22 230000.0 1777.0 3572.0 3554.0 159 \n", - "23 129900.0 1040.0 2100.0 200.0 110 \n", - "24 154000.0 1060.0 2120.0 0.0 90 \n", - "25 256300.0 1566.0 3166.0 3132.0 56 \n", - "26 134800.0 900.0 1800.0 0.0 32 \n", - "27 306000.0 1704.0 3408.0 486.0 50 \n", - "28 207500.0 1484.0 3084.0 207.0 258 \n", - "29 68500.0 520.0 1040.0 1040.0 87 \n", - "... ... ... ... ... ... \n", - "1430 192140.0 732.0 2570.0 1464.0 40 \n", - "1431 143750.0 958.0 1916.0 0.0 60 \n", - "1432 64500.0 656.0 1624.0 1312.0 0 \n", - "1433 186500.0 936.0 2728.0 1872.0 0 \n", - "1434 160000.0 1126.0 2252.0 190.0 41 \n", - "1435 174000.0 1319.0 2856.0 2638.0 36 \n", - "1436 120500.0 864.0 1728.0 248.0 0 \n", - "1437 394617.0 1932.0 3864.0 596.0 370 \n", - "1438 149700.0 912.0 2148.0 312.0 316 \n", - "1439 197000.0 539.0 2264.0 0.0 304 \n", - "1440 191000.0 588.0 2759.0 1176.0 0 \n", - "1441 149300.0 848.0 1696.0 151.0 0 \n", - "1442 310000.0 1017.0 3024.0 252.0 52 \n", - "1443 121000.0 952.0 1904.0 1904.0 138 \n", - "1444 179600.0 1422.0 2844.0 2844.0 60 \n", - "1445 129000.0 814.0 1727.0 0.0 252 \n", - "1446 157900.0 1188.0 2376.0 595.0 39 \n", - "1447 240000.0 1220.0 3310.0 141.0 65 \n", - "1448 112000.0 560.0 1906.0 1120.0 24 \n", - "1449 92000.0 630.0 1260.0 77.0 0 \n", - "1450 136000.0 896.0 2688.0 1792.0 45 \n", - "1451 287090.0 1573.0 3151.0 3146.0 36 \n", - "1452 145000.0 547.0 1619.0 0.0 28 \n", - "1453 84500.0 1140.0 2280.0 2280.0 56 \n", - "1454 185000.0 1221.0 2442.0 811.0 113 \n", - "1455 175000.0 953.0 2600.0 1906.0 40 \n", - "1456 210000.0 1542.0 3615.0 0.0 0 \n", - "1457 266500.0 1152.0 3492.0 877.0 60 \n", - "1458 142125.0 1078.0 2156.0 0.0 112 \n", - "1459 147500.0 1256.0 2512.0 0.0 68 \n", - "\n", - "[1460 rows x 11 columns]" - ] - }, - "execution_count": 201, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train_onehot[forbx]" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:488: RuntimeWarning: invalid value encountered in true_divide\n", - " binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n", - " FAC1 = 2*(np.pi*bw/RANGE)**2\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.gridspec as gridspec\n", "import matplotlib as mpl\n", @@ -1693,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": null, "metadata": { "scrolled": true }, @@ -1705,29 +117,9 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1stFlrSF 1.469604\n", - "2ndFlrSF 0.861675\n", - "GrLivArea 1.269358\n", - "LotArea 12.822431\n", - "LotFrontage 1.674852\n", - "TotalBsmtSF 1.162616\n", - "TotalFlrSF 1.511479\n", - "Bsmt_Unf 1.336178\n", - "TotalProchSF 2.237266\n", - "dtype: float64" - ] - }, - "execution_count": 192, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# check the skewness of the selected column\n", "from scipy.stats import skew\n", @@ -1738,20 +130,9 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from scipy.special import boxcox1p\n", "import matplotlib.pyplot as plt\n", @@ -1772,576 +153,11 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1.224998\n", - "1 1.244497\n", - "2 1.228710\n", - "3 1.230934\n", - "4 1.239719\n", - "5 1.221213\n", - "6 1.258535\n", - "7 1.238045\n", - "8 1.234047\n", - "9 1.236676\n", - "10 1.234925\n", - "11 1.241288\n", - "12 1.228262\n", - "13 1.252619\n", - "14 1.244147\n", - "15 1.224877\n", - "16 1.233151\n", - "17 1.245790\n", - "18 1.238358\n", - "19 1.247370\n", - "20 1.240277\n", - "21 1.238090\n", - "22 1.261225\n", - "23 1.235880\n", - "24 1.235880\n", - "25 1.255860\n", - "26 1.227583\n", - "27 1.258809\n", - "28 1.255860\n", - "29 1.198114\n", - " ... \n", - "1430 1.216938\n", - "1431 1.230775\n", - "1432 1.231302\n", - "1433 1.230987\n", - "1434 1.238890\n", - "1435 1.253964\n", - "1436 1.225479\n", - "1437 1.264610\n", - "1438 1.243480\n", - "1439 1.234925\n", - "1440 1.250296\n", - "1441 1.224511\n", - "1442 1.234244\n", - "1443 1.230455\n", - "1444 1.250263\n", - "1445 1.228318\n", - "1446 1.241538\n", - "1447 1.242842\n", - "1448 1.221213\n", - "1449 1.208727\n", - "1450 1.227354\n", - "1451 1.255208\n", - "1452 1.236443\n", - "1453 1.239502\n", - "1454 1.242883\n", - "1455 1.230508\n", - "1456 1.267818\n", - "1457 1.241538\n", - "1458 1.236722\n", - "1459 1.244264\n", - "Name: 1stFlrSF, Length: 1460, dtype: float64\n", - "0 2.543744\n", - "1 0.000000\n", - "2 2.546775\n", - "3 2.517150\n", - "4 2.588954\n", - "5 2.453110\n", - "6 0.000000\n", - "7 2.574174\n", - "8 2.515988\n", - "9 0.000000\n", - "10 0.000000\n", - "11 2.606306\n", - "12 0.000000\n", - "13 0.000000\n", - "14 0.000000\n", - "15 0.000000\n", - "16 0.000000\n", - "17 0.000000\n", - "18 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1.851300\n", - "28 2.272237\n", - "29 2.001105\n", - " ... \n", - "1430 1.788334\n", - "1431 1.901598\n", - "1432 0.000000\n", - "1433 0.000000\n", - "1434 1.795380\n", - "1435 1.758044\n", - "1436 0.000000\n", - "1437 2.356608\n", - "1438 2.319991\n", - "1439 2.310935\n", - "1440 0.000000\n", - "1441 0.000000\n", - "1442 1.862206\n", - "1443 2.119537\n", - "1444 1.901598\n", - "1445 2.266643\n", - "1446 1.781089\n", - "1447 1.923366\n", - "1448 1.637948\n", - "1449 0.000000\n", - "1450 1.821767\n", - "1451 1.758044\n", - "1452 1.684281\n", - "1453 1.882683\n", - "1454 2.068887\n", - "1455 1.788334\n", - "1456 0.000000\n", - "1457 1.901598\n", - "1458 2.066611\n", - "1459 1.935555\n", - "Name: TotalProchSF, Length: 1460, dtype: float64\n" - ] - } - ], + "outputs": [], "source": [ "for i in skewed.index:\n", " all_data_onehot[i]=boxcox1p(all_data_onehot[i],0.001)\n", @@ -2350,47 +166,18 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", - " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sns.distplot(train_onehot[\"SalePrice\"])" ] }, { "cell_type": "code", - "execution_count": 214, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2399,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 3, "metadata": { "scrolled": false }, @@ -2423,33 +210,9 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mgrid_param\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'alpha'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mpara_search_ridge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mridge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_param\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mpara_search_ridge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbx_train_x_onehot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbx_train_y_onehot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpara_search_ridge\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m 637\u001b[0m error_score=self.error_score)\n\u001b[0;32m 638\u001b[0m for parameters, (train, test) in product(candidate_params,\n\u001b[1;32m--> 639\u001b[1;33m cv.split(X, y, groups)))\n\u001b[0m\u001b[0;32m 640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# if one choose to see train score, \"out\" will contain train score info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 777\u001b[0m \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 778\u001b[0m \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 780\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 781\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m 623\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 624\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 626\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m 586\u001b[0m \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 587\u001b[0m \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 589\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 110\u001b[0m \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 112\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m 330\u001b[0m \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 331\u001b[0m \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 334\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 133\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - 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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)\u001b[0m\n\u001b[0;32m 456\u001b[0m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 457\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\ridge.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 663\u001b[0m \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mreturns\u001b[0m \u001b[0man\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mof\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \"\"\"\n\u001b[1;32m--> 665\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRidge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 666\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 667\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\ridge.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 489\u001b[0m X, y, X_offset, y_offset, X_scale = self._preprocess_data(\n\u001b[0;32m 490\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_intercept\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy_X\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m 492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[1;31m# temporary fix for fitting the intercept with sparse data using 'sag'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36m_preprocess_data\u001b[1;34m(X, y, fit_intercept, normalize, copy, sample_weight, return_mean)\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 193\u001b[0m \u001b[0mX_offset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 194\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mX_offset\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 195\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 196\u001b[0m X, X_scale = f_normalize(X, axis=0, copy=False,\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "## With BX\n", "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", @@ -2472,19 +235,9 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 0.49770235643321137}\n", - "Lowest RMSE found: 33289.6071074413\n", - "RMSE: 27791.48846091089\n" - ] - } - ], + "outputs": [], "source": [ "# No BX\n", "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", @@ -2514,843 +267,9 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 25.65020905680051}\n", - "Lowest RMSE found: 33809.958651596615\n", - "RMSE: 27539.866355369504\n" - ] - } - ], + "outputs": [], "source": [ "## NO BX\n", "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", @@ -3377,693 +296,9 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n", - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", - " ConvergenceWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'alpha': 25.65020905680051}\n", - "Lowest RMSE found: 32559.718946794015\n", - "RMSE: 28119.588145632715\n" - ] - } - ], + "outputs": [], "source": [ "## BX\n", "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", @@ -4100,7 +335,56 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "## with BX\n", + "from sklearn.model_selection import GridSearchCV # search for the best lambda\n", + "from sklearn import linear_model\n", + "\n", + "EN= linear_model.ElasticNet(normalize=True) # create a ridge regression instance\n", + "\n", + "# find the best alpha (lambda) for ridge\n", + "grid_param = [{'alpha': np.logspace(-4.5, 2, 50),'l1_ratio':np.linspace(0.1,0.9,20)}]\n", + "para_search_EN = GridSearchCV(estimator=EN, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True)\n", + "para_search_EN.fit(bx_train_x_onehot, bx_train_y_onehot)\n", + "\n", + "print(para_search_EN.best_params_)\n", + "print(\"Lowest RMSE found: \", np.sqrt(np.abs(para_search_EN.best_score_)))\n", + "\n", + "# fit best ridge equation to all train data\n", + "best_EN_y = para_search_EN.best_estimator_.predict(bx_train_x_onehot)\n", + "print(\"RMSE: \", np.sqrt(np.mean((bx_train_y_onehot-best_EN_y)**2)))\n", + "\n", + "# {'alpha': 0.0004941713361323838, 'l1_ratio': 0.3526315789473684}\n", + "# Lowest RMSE found: 32670.8665945391\n", + "# RMSE: 27932.17975891021" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from myfunction import run_GLM" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from myfunction import run_GB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_GB(bx_train_x_onehot,bx_train_y_onehot,)" + ] } ], "metadata": { diff --git a/Sam/Kaggle/Untitled.ipynb b/Sam/Kaggle/Untitled.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/Sam/Kaggle/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/__pycache__/myfunction.cpython-36.pyc b/Sam/Kaggle/__pycache__/myfunction.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fa802444f24fbfb7c56fb5e74d4e1d7470e8cc9a GIT binary patch literal 3201 zcmb7G&5s;M74Prq?&SCqdgYmWi?ZD{wxBhj-G#>gt-^GeoGO_*H&xX2RLk_aC3Pb;<_)kJsX4bxjhK%$_Q%p1Tf`*R zQ+4i?_P9Z=5t}&YI_NfW@w4YTX+6_ob?%mC>69)2?7)ofQ*+)dn`5U`O7&b*72!g! zd9I>HdNALE)&g7mT!kNafZiE*OXXahd*IVw`t(Y*^mY_tZ^6jYQ%7{aR#^b}`b&H* zvIR35`=FtuNRslA4&y~5PFf~j)_ld;6~Pr$Ub#ME74+PQ+M zyMd`!_BJr}UVy2$hN-v0)W3qMzln($kPbJ_64!_4UK(+c4E>ynoBo4{^Wvs|n2?z6 z`bSjwkJyy@qVT7j`b7?H~m6B*dblu@nR&>7ztc<^6O|sF z@Tw^XFr~!d?S7pYGk=JN_xBj34tTo6<2Ak&L{Ra=BGAjIC5Q^cco@%SMW~-j=t9{keTy;nH)R%P^EZdtFZPUUA@P@@d zU_)64HGJ*R0)dYQ4Kqj9;@^R?s>OBv)uAqH^9IyGYpkCuSc7Az#?Z`IEp=$9TksEk z(s5k1+)wQ74kX6?rp0LLbH1vQKZRktZD z(pvhoq3SlFnzS#gF6rRma&}uX{SeZ%$o7FNTlW1>uA;SveS`eAj2(Pq@#d zD;JTVaw@~HKnbvE&S`d%QqVhBDe?vI3G|H9M}tP?<>4xEdF5r%lW;^MAT8yAGh~UN z3`$hc&5*xQHc2TDfqA2g9R}E`JlskIQJ%sZ-EG2S`XJDjR80>SWTzv=(U6528vfv;Of~IQG5x- zmr=Zo;wvBq9>bde`x=Oe}j1Q!(` zQxh&UK$uc54PqmOi1Q4nH1~jPPT4HI`Z7cMr2W*Ix5^ffZ! zz}8(+s5S15`;tO!(Inla&sM35ZJLUVDaDByI<2Nv5!)j_HdIuWPrJJ1hW@;rhmczV*n zNDI!{w_!wP{{l~!vm*lT+>!8xdY<|1ATvg ze_*h0Kt~=awU7nMU_V${-$D%;2xSY;AjvU?uUNx?rzMk=wcmkqJ5A}k^4!BqjQf_m F^B*BwKTrSw literal 0 HcmV?d00001 diff --git a/Sam/Kaggle/myfunction.py b/Sam/Kaggle/myfunction.py new file mode 100644 index 0000000..c873d96 --- /dev/null +++ b/Sam/Kaggle/myfunction.py @@ -0,0 +1,108 @@ +def run_GLM(x,y,ElasticNet=False,Lasso=False,Ridge=False): + """ + Elastic net, Lasso, Ridge. Set True to use one of the functions; with Pandas and numpy imported + """ + from sklearn.model_selection import GridSearchCV # search for the best lambda + from sklearn import linear_model + import pandas as pd + import numpy as np + + if ElasticNet==True: + EN= linear_model.ElasticNet(normalize=True) # create a ridge regression instance + + # find the best alpha (lambda) for ridge + grid_param = [{'alpha': np.logspace(-4.5, 2, 50),'l1_ratio':np.linspace(0.1,0.9,20)}] + para_search_EN = GridSearchCV(estimator=EN, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True) + para_search_EN.fit(x, y) + + print(para_search_EN.best_params_) + print("Lowest RMSE found: ", np.sqrt(np.abs(para_search_EN.best_score_))) + + # fit best ridge equation to all train data + best_EN_y = para_search_EN.best_estimator_.predict(x) + print("RMSE: ", np.sqrt(np.mean((y-best_EN_y)**2))) + +###### + if Lasso == True: + lasso= linear_model.Lasso(normalize=True) # create a ridge regression instance + + # find the best alpha (lambda) for ridge + grid_param = [{'alpha': np.logspace(-4.5, 2, 100)}] + para_search_lasso = GridSearchCV(estimator=lasso, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True) + para_search_lasso.fit(x, y) + + print(para_search_lasso.best_params_) + print("Lowest RMSE found: ", np.sqrt(np.abs(para_search_lasso.best_score_))) + + # fit best ridge equation to all train data + best_lasso_y = para_search_lasso.best_estimator_.predict(x) + print("RMSE: ", np.sqrt(np.mean((y-best_lasso_y)**2))) + +############## + if Ridge==True: + + ridge = linear_model.Ridge(normalize=True) # create a ridge regression instance + + # find the best alpha (lambda) for ridge + grid_param = [{'alpha': np.logspace(-4, 2, 100)}] + para_search_ridge = GridSearchCV(estimator=ridge, param_grid=grid_param, scoring='neg_mean_squared_error', cv=5, return_train_score=True) + para_search_ridge.fit(x, y) + + print(para_search_ridge.best_params_) + print("Lowest RMSE found: ", np.sqrt(np.abs(para_search_ridge.best_score_))) + + # fit best ridge equation to all train data + best_ridge_y = para_search_ridge.best_estimator_.predict(x) + print("RMSE: ", np.sqrt(np.mean((y-best_ridge_y)**2))) + +def run_RF(x,y,number_of_trees=1000, min_leaf=4,min_split=4,rs=1): + """ + RandomForest. Input number of trees. rs is randomstate + """ + from sklearn import ensemble + from sklearn import model_selection + import pandas as pd + import numpy as np + randomForest = ensemble.RandomForestRegressor() + grid_para_forest = [{ + "n_estimators": [number_of_trees], + "max_features": ["sqrt"], + "criterion": ["mse"], + "min_samples_leaf": [min_leaf], + "min_samples_split": [min_split], + "oob_score": [True], + "random_state": [rs]}] + grid_search_forest = model_selection.GridSearchCV(randomForest, grid_para_forest, scoring='neg_mean_squared_error', cv=5, n_jobs=-1) + grid_search_forest.fit(x, y) + print(grid_search_forest.best_params_) + print("Lowest RMSE found: ", np.sqrt(np.abs(grid_search_forest.best_score_))) + + # fit best ridge equation to all train data + best_rf_y = grid_search_forest.best_estimator_.predict(x) + print("RMSE: ", np.sqrt(np.mean((y-best_rf_y)**2))) + +def run_GB(x,y,number_of_trees=10000, min_split=4,learningrate=0.001, maxfeature=18): + from sklearn import model_selection + from sklearn.metrics import mean_squared_error + from sklearn import ensemble + from sklearn.datasets import make_friedman1 + from sklearn.ensemble import GradientBoostingRegressor + import pandas as pd + import numpy as np + xg=ensemble.GradientBoostingRegressor() + + grid_para_xg =[{'n_estimators': number_of_trees, 'max_depth': [4], 'min_samples_split':min_split, + 'learning_rate': learningrate, 'loss':['ls'], 'max_features':maxfeature}] + + #max depth 4 #min_sample:6 + #max_features 18 + grid_search_xg = model_selection.GridSearchCV(xg, grid_para_xg, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1) + #scoring:mse + grid_search_xg.fit(x,y) + print(grid_search_xg.best_params_) + print("Lowest RMSE found: ", np.sqrt(np.abs(grid_search_xg.best_score_))) + + # fit best ridge equation to all train data + best_gb_y = grid_search_xg.best_estimator_.predict(x) + print("RMSE: ", np.sqrt(np.mean((y-gb_y)**2))) + From 6486c5ef63ab6dbc1e7142c7104a978ad5abe7f5 Mon Sep 17 00:00:00 2001 From: samuelmao415 Date: Sun, 26 Aug 2018 11:12:51 -0400 Subject: [PATCH 8/8] new changes --- Sam/Kaggle/BoxCox-modeling.ipynb | 872 ++++++++++++++++++ Sam/Kaggle/Group_Data_BoxCox.ipynb | 40 +- Sam/Kaggle/Group_data_label_encoding.ipynb | 135 +++ Sam/Kaggle/Untitled.ipynb | 6 - .../__pycache__/myfunction.cpython-36.pyc | Bin 3201 -> 3858 bytes Sam/Kaggle/myfunction.py | 42 +- 6 files changed, 1049 insertions(+), 46 deletions(-) create mode 100644 Sam/Kaggle/BoxCox-modeling.ipynb create mode 100644 Sam/Kaggle/Group_data_label_encoding.ipynb delete mode 100644 Sam/Kaggle/Untitled.ipynb diff --git a/Sam/Kaggle/BoxCox-modeling.ipynb b/Sam/Kaggle/BoxCox-modeling.ipynb new file mode 100644 index 0000000..b41e829 --- /dev/null +++ b/Sam/Kaggle/BoxCox-modeling.ipynb @@ -0,0 +1,872 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from myfunction import *" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "all_data_=pd.read_csv(\"bx_all_data_onehot.csv\")\n", + "\n", + "\n", + "bx_train_onehot=all_data_.iloc[:1460:,:] #train\n", + "bx_train_y_onehot=bx_train_onehot[\"SalePrice\"]\n", + "bx_train_x_onehot=bx_train_onehot[bx_train_onehot.columns[bx_train_onehot.columns!=\"SalePrice\"]]\n", + "bx_test_onehot=all_data_.iloc[1460:,:].drop(columns=\"SalePrice\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'learning_rate': 0.001, 'loss': 'ls', 'max_depth': 4, 'max_features': 18, 'min_samples_split': 4, 'n_estimators': 10000}\n", + "Lowest RMSE found: 25373.847933942496\n", + "RMSE: 12191.477162632229\n" + ] + } + ], + "source": [ + "run_GB(bx_train_x_onehot,bx_train_y_onehot,bx_test_onehot,save=True)\n", + "#kaggle:0.13020" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'criterion': 'mse', 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 4, 'n_estimators': 1000, 'oob_score': True, 'random_state': 1}\n", + "Lowest RMSE found: 31913.436735567364\n", + "RMSE: 23652.021254546475\n" + ] + } + ], + "source": [ + "run_RF(bx_train_x_onehot,bx_train_y_onehot,bx_test_onehot,save=True)\n", + "#kaggle: 0.16197" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.0004941713361323838, 'l1_ratio': 0.3526315789473684}\n", + "Lowest RMSE found: 32670.8665945391\n", + "RMSE: 27932.17975891021\n" + ] + } + ], + "source": [ + "run_GLM(bx_train_x_onehot,bx_train_y_onehot,bx_test_onehot,save=True, ElasticNet=True)\n", + "#kaggle: 0.16834" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n", + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 25.65020905680051}\n", + "Lowest RMSE found: 32559.718946794015\n", + "RMSE: 28119.588145632715\n" + ] + } + ], + "source": [ + "run_GLM(bx_train_x_onehot,bx_train_y_onehot,bx_test_onehot,save=True, Lasso=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'alpha': 0.37649358067924676}\n", + "Lowest RMSE found: 32670.978852037893\n", + "RMSE: 27522.153425513392\n" + ] + } + ], + "source": [ + "run_GLM(bx_train_x_onehot,bx_train_y_onehot,bx_test_onehot,save=True, Ridge=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/Group_Data_BoxCox.ipynb b/Sam/Kaggle/Group_Data_BoxCox.ipynb index f37b884..d5eeec3 100644 --- a/Sam/Kaggle/Group_Data_BoxCox.ipynb +++ b/Sam/Kaggle/Group_Data_BoxCox.ipynb @@ -2,34 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1460, 81)\n", - "(1459, 80)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", - "of pandas will change to not sort by default.\n", - "\n", - "To accept the future behavior, pass 'sort=True'.\n", - "\n", - "To retain the current behavior and silence the warning, pass sort=False\n", - "\n", - " # Remove the CWD from sys.path while we load stuff.\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -186,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "scrolled": false }, @@ -370,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -383,8 +360,15 @@ "metadata": {}, "outputs": [], "source": [ - "run_GB(bx_train_x_onehot,bx_train_y_onehot,)" + "run_GB(bx_train_x_onehot,bx_train_y_onehot)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/Sam/Kaggle/Group_data_label_encoding.ipynb b/Sam/Kaggle/Group_data_label_encoding.ipynb new file mode 100644 index 0000000..6492454 --- /dev/null +++ b/Sam/Kaggle/Group_data_label_encoding.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1460, 81)\n", + "(1459, 80)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\samuelmao\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=True'.\n", + "\n", + "To retain the current behavior and silence the warning, pass sort=False\n", + "\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as pltimport\n", + "\n", + "train=pd.read_csv(\"train.csv\")\n", + "test=pd.read_csv(\"test.csv\")\n", + "print(train.shape)\n", + "print(test.shape)\n", + "\n", + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test\n", + "\n", + "combined=combined.reset_index()\n", + "\n", + "from preprocess import impute\n", + "all_data, encodedDic = impute (combined, False)\n", + "all_data_onehot, encodedDic = impute (combined, True) #one-hot\n", + "\n", + "train=all_data.iloc[:1460:,:] #train\n", + "train_y=train[\"SalePrice\"]\n", + "train_x=train[train.columns[train.columns!=\"SalePrice\"]]\n", + "test=all_data.iloc[1460:,:]\n", + "test=test.drop(columns=\"SalePrice\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from myfunction import *" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'learning_rate': 0.001, 'loss': 'ls', 'max_depth': 4, 'max_features': 18, 'min_samples_split': 4, 'n_estimators': 10000}\n", + "Lowest RMSE found: 25674.565255328434\n", + "RMSE: 11125.834338493634\n" + ] + } + ], + "source": [ + "run_GB(train_x,train_y,test,save=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'criterion': 'mse', 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 4, 'n_estimators': 1000, 'oob_score': True, 'random_state': 1}\n", + "Lowest RMSE found: 30440.954320368855\n", + "RMSE: 21446.313224347952\n" + ] + } + ], + "source": [ + "run_RF(train_x,train_y,test,save=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Sam/Kaggle/Untitled.ipynb b/Sam/Kaggle/Untitled.ipynb deleted file mode 100644 index 2fd6442..0000000 --- a/Sam/Kaggle/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Sam/Kaggle/__pycache__/myfunction.cpython-36.pyc b/Sam/Kaggle/__pycache__/myfunction.cpython-36.pyc index fa802444f24fbfb7c56fb5e74d4e1d7470e8cc9a..092ebd6afb21005e4d947c90019302327fad757c 100644 GIT binary patch literal 3858 zcmcIm%a7bh8E?B^ZjbxjGs(V!dD9RE!Y;35*<;Z3KguTHXE*yPbB+`fr*VRhl_mz8kdS;^)Lc%S7{nh8M%D*bt@AuW4 z^}0EG{+nN3+bR@3F05T;_&0FNFTt<|)0qBS%_*=ZtFS6OjkVYYv*78he!tLf-x^kt zNf(AHkbe`m9O&qd&Oss6=2eaSVxWh`xuN`0P@2~eD+T4bnHE|3p)UTJ8j}huv&vI# zzLA=fDtnC?tae<4Z!nWydt7AoXZl#1TWK}jNG$+tqDSj#d0tQJlUiCxwPRf?BqmxL z#~OL8js6yH%4i&G_#p#+bJ9u+$J)GsoX#?*ooZ?0Vu2Z5JUNr8BeR(TpF(O*I;@*E zS&wZ#En>71#eb)XKVlBuzKubxxfhQAxXh%G`Y~S&uj4#`JJ{$9G`#w+X2V%zU zB(`Ug+i`?@WG4sQ9?ha*;>VG^W^vm}o)FYzS3m->a?LvG6%a-{c&Q;%f%?il&14_-`NZ1hps7vM{wuVvP4 z;j{bP6T^d>zo2&DM`*cW%y_W-0Fhc0i_i=FBR-}+g+k}X0z4}$z6Ue@!|&<(==%7t zf1&GN*B3p6`+8RPg6VR@dQJu2zEoDrJi_$6v0Qi`Qoy`1CD`)ZP zix)5Yo6ZIKFu;06+iJmF$pfywuAn6X`uwRVQmZmmpE@h6@0^*-{jhu38uAW+nN7NV1|gklBWMpq9eTnyRr{NH?oavCjT5$x5{F zfP?LkJi(}Hw`OIvTl-s?_9W8|vl8t$L5eEAOy(;Dx>5eILx1R5|-SlDJ(m??%@(64UR@xEws~KPtQa79F^}LI%T!^XY=wK43$?3}HYU|o?K04+soG_*db%+&ac>}I zttwJuf=Z@pS2HnKW0})L?P@|vn`^Zz&m<*nrBzl%?fN^iTdb9~SetdIb}1e09ovD3YSi8C`cQi&Q2>NiSfc|K;po?QPy6M$Z6mw zPA!fHxehs24$W2AL=A5gCYdV)uqZajw_vhLi!OXYA}rn{L%J(AVVuqN1>NIg%uL22=kP0(^(vXy$k57&H^^Kf^93?CnJwWl=-m&5^SX|Ohf0ou$TvcpV zEEm_1y=Y->|M>L7fB)_7;NJB`2cZ>1X=PUowXowp!%#Y7o-ni!mgc5nYdI)~mHCG9 zD?v3h=ID&R)u1-7tA7(L#5ofzZYmZVYYeU}8EifSkHI7Zd~AY2IFYhC+hWb9l{uZ` ztQCjrXyL4h^Jg7gYfPHBTZpw+9BxlKldj@$2RZF!P7fTWb6jVQ!+9ogxC;)~)j5+i zViPCO?#T(1RdE`+aP(_M#sA}BWyx~2&q46cXHOBJ2JJIEY|vrI9SNVa(2K4h^2%-D zF`q}tdvT02Ml@bgVpqv=rU$ZL5pSWtQzJQGe3~4HA5goliaYwyA%;7pAeK^mA5Rp+ z7gW|C4&^CTBPLihewPIBV6(_Ec2cq06v6xZo3G;61I@@RP~ zl;Xyj@QFBzcd5%tDE?PP=$%sp@nahHD!tM=SueF*CRTH)t+^H`p<3ZqD057w(x5*X zJJzWnpw(Cx1GIA?7!N*A2yVYO031o8l?wqCbXC$J2ao`L3wBx3l^h5Xj@^Fg7Os9- zd>1XXIdVfHPT{v#;h&KAjtUo5c~>f;Mp;uPrBOSCqdgYmWi?ZD{wxBhj-G#>gt-^GeoGO_*H&xX2RLk_aC3Pb;<_)kJsX4bxjhK%$_Q%p1Tf`*R zQ+4i?_P9Z=5t}&YI_NfW@w4YTX+6_ob?%mC>69)2?7)ofQ*+)dn`5U`O7&b*72!g! 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Set True to use one of the functions; with Pandas and numpy imported + Elastic net, Lasso, Ridge. Set True to use one of the functions; with Pandas and numpy imported; test is where you put test dataset, and make sure save is true to out put the data set """ from sklearn.model_selection import GridSearchCV # search for the best lambda from sklearn import linear_model @@ -21,7 +21,10 @@ def run_GLM(x,y,ElasticNet=False,Lasso=False,Ridge=False): # fit best ridge equation to all train data best_EN_y = para_search_EN.best_estimator_.predict(x) print("RMSE: ", np.sqrt(np.mean((y-best_EN_y)**2))) - + + best_test_y = para_search_EN.best_estimator_.predict(test) + if save==True: + np.savetxt("res_EN.csv", best_test_y, delimiter=",") ###### if Lasso == True: lasso= linear_model.Lasso(normalize=True) # create a ridge regression instance @@ -37,6 +40,9 @@ def run_GLM(x,y,ElasticNet=False,Lasso=False,Ridge=False): # fit best ridge equation to all train data best_lasso_y = para_search_lasso.best_estimator_.predict(x) print("RMSE: ", np.sqrt(np.mean((y-best_lasso_y)**2))) + best_test_y = para_search_lasso.best_estimator_.predict(test) + if save==True: + np.savetxt("res_lasso.csv", best_test_y, delimiter=",") ############## if Ridge==True: @@ -54,8 +60,12 @@ def run_GLM(x,y,ElasticNet=False,Lasso=False,Ridge=False): # fit best ridge equation to all train data best_ridge_y = para_search_ridge.best_estimator_.predict(x) print("RMSE: ", np.sqrt(np.mean((y-best_ridge_y)**2))) + best_test_y = para_search_ridge.best_estimator_.predict(test) + if save==True: + np.savetxt("res_ridge.csv", best_test_y, delimiter=",") + -def run_RF(x,y,number_of_trees=1000, min_leaf=4,min_split=4,rs=1): +def run_RF(x,y,test,number_of_trees=1000, min_leaf=4,min_split=4,rs=1, save=False): """ RandomForest. Input number of trees. rs is randomstate """ @@ -80,8 +90,15 @@ def run_RF(x,y,number_of_trees=1000, min_leaf=4,min_split=4,rs=1): # fit best ridge equation to all train data best_rf_y = grid_search_forest.best_estimator_.predict(x) print("RMSE: ", np.sqrt(np.mean((y-best_rf_y)**2))) + best_test_y = grid_search_forest.best_estimator_.predict(test) + if save==True: + np.savetxt("res_rf.csv", best_test_y, delimiter=",") + -def run_GB(x,y,number_of_trees=10000, min_split=4,learningrate=0.001, maxfeature=18): +def run_GB(x,y,test,number_of_trees=10000, min_split=4,learningrate=0.001, maxfeature=18, save=False): + """ + test is where you put test dataset, and make sure save is true to out put the data set + """ from sklearn import model_selection from sklearn.metrics import mean_squared_error from sklearn import ensemble @@ -91,18 +108,19 @@ def run_GB(x,y,number_of_trees=10000, min_split=4,learningrate=0.001, maxfeature import numpy as np xg=ensemble.GradientBoostingRegressor() - grid_para_xg =[{'n_estimators': number_of_trees, 'max_depth': [4], 'min_samples_split':min_split, - 'learning_rate': learningrate, 'loss':['ls'], 'max_features':maxfeature}] - - #max depth 4 #min_sample:6 - #max_features 18 + grid_para_xg =[{'n_estimators': [number_of_trees], 'max_depth': [4], 'min_samples_split':[min_split], + 'learning_rate': [learningrate], 'loss':['ls'], 'max_features':[maxfeature]}] + grid_search_xg = model_selection.GridSearchCV(xg, grid_para_xg, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1) - #scoring:mse + grid_search_xg.fit(x,y) print(grid_search_xg.best_params_) print("Lowest RMSE found: ", np.sqrt(np.abs(grid_search_xg.best_score_))) # fit best ridge equation to all train data best_gb_y = grid_search_xg.best_estimator_.predict(x) - print("RMSE: ", np.sqrt(np.mean((y-gb_y)**2))) + print("RMSE: ", np.sqrt(np.mean((y-best_gb_y)**2))) + best_test_y = grid_search_xg.best_estimator_.predict(test) + if save==True: + np.savetxt("res_gb.csv", best_test_y, delimiter=",")