diff --git a/Kelly/Kaggle_house_price/housing price data manipulation.ipynb b/Kelly/Kaggle_house_price/housing price data manipulation.ipynb index 7a6d232..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 \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 442\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\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;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 785\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 786\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 788\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 789\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\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\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1012\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1013\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1014\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1015\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1016\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\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;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1706\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1708\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\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 1709\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1710\u001b[0m \u001b[0mpassed_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \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", 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/Data_Preparation.ipynb b/Sam/Kaggle/Data_Preparation.ipynb index 7316fc3..b104af7 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 +1820,51 @@ }, { "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()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## check if double column" ] }, { @@ -2916,7 +1876,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -2926,7 +1886,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -2935,15 +1895,15 @@ }, { "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 +1921,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -2975,13 +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/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 new file mode 100644 index 0000000..9e67c61 --- /dev/null +++ b/Sam/Kaggle/GB_Group_data.ipynb @@ -0,0 +1,333 @@ +{ + "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=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": 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": {}, + "source": [ + "## PARAMETER Tuning label encoding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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/Group_Data_BoxCox.ipynb b/Sam/Kaggle/Group_Data_BoxCox.ipynb new file mode 100644 index 0000000..d5eeec3 --- /dev/null +++ b/Sam/Kaggle/Group_Data_BoxCox.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_onehot[forbx]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "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": null, + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for i in skewed.index:\n", + " all_data_onehot[i]=boxcox1p(all_data_onehot[i],0.001)\n", + " print(train_onehot[i])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "sns.distplot(train_onehot[\"SalePrice\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_data_onehot.to_csv(\"bx_all_data_onehot.csv\",index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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, + "metadata": {}, + "outputs": [], + "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": null, + "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)" + ] + }, + { + "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_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/Modeling.ipynb b/Sam/Kaggle/Modeling.ipynb index d74fb80..1a6be00 100644 --- a/Sam/Kaggle/Modeling.ipynb +++ b/Sam/Kaggle/Modeling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 303, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,40 +62,18 @@ }, { "cell_type": "code", - "execution_count": 304, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8934991498492062" - ] - }, - "execution_count": 304, - "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": [ @@ -145,33 +123,11 @@ }, { "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", @@ -193,123 +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_" ] @@ -323,19 +185,19 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "MSE: 697736138.9349\n" + "MSE: 881652069.8311\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -346,6 +208,7 @@ ], "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", @@ -356,8 +219,8 @@ "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", - " '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", @@ -400,17 +263,17 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 17, "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([6.85279418e+09, 6.84526835e+09, 6.83806614e+09, ...,\n", + " 6.97095686e+08, 6.97095721e+08, 6.97095143e+08])" ] }, - "execution_count": 76, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -421,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -430,7 +293,7 @@ "(1459,)" ] }, - "execution_count": 108, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -443,28 +306,16 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([125402.34533708, 159250.18466496, 191188.66316833, ...,\n", - " 176534.46140884, 119239.69567213, 215389.35250534])" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "res" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -483,113 +334,135 @@ }, { "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": [], + "source": [ + "ols.coef_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 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": [ - "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, 5.87890625e-01, 1.01901087e+01, 3.06966899e+07,\n", - " -3.02744802e+12, 4.89160156e+00, -5.91355576e+02, 5.74134709e+03,\n", - " 6.86211711e+03, -1.99837189e+01, 2.12585777e+04, 2.91762245e+03,\n", - " 7.61932636e+12, 1.01615601e+01, 2.25159771e+02, 3.43666840e+00,\n", - " 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"execution_count": 110, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ols.coef_" + "combined=pd.concat([train,test])\n", + "combined.shape #2919\n", + "#combined.iloc[:1460:,:] #train\n", + "#combined.iloc[1460:,:] #test" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": {}, + "outputs": [], "source": [ - "### RR train-test-split" + "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" ] }, { @@ -617,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -636,37 +509,135 @@ }, { "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", + " 1029123678.8138592,\n", + " 1038255550.5794978,\n", + " 1048512063.8489825,\n", + " 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\n", + "image/png": 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Yj09EYleL332UkaKkMCwZGRnU1tbG7I63934KGRkZQYciInGopaObrLRkkpIif0JsQhx9VFZWRmVlJbF6XSTYd+c1EZGRaunsjkrXESRIUkhNTdUdzUQkYbW0d0XlHAVIkO4jEZFE1tTeTXZadH7DKymIiMS45vYucqJwiQtQUhARiXnNHV3kZCgpiIgI0NTWFZWL4YGSgohIzGtq7yJHYwoiIgL+mIK6j0REpKfH0dzRre4jERHZdyvOnChcDA+UFEREYlpzu3cxPLUURESEpvZOAJ2nICIi3tnMoKQgIiLsu+uauo9ERISm9t6BZiUFEZExr7HNSwp5GalRWZ+SgohIDGto9Qaac3XymoiINLQpKYiIiK+xrYvstGRSkqOzu1ZSEBGJYQ2tneRGaTwBlBRERGJaY1sXeZnRu3OykoKISAxraOuM2pFHoKQgIhLTGtu6ojbIDBFMCmZ2j5lVm9mqMNPzzewpM1thZqvN7KpIxSIiEq8a2jrJy0yMlsJi4JxBpt8IrHHOzQcWAf9pZmkRjEdEJO54A80J0FJwzr0K7BmsCJBrZgbk+GW7IhWPiEi8cc55A81RHFOIXvo50J3An4AdQC5wiXOuJ8B4RERiSktHN109LmG6j4ZyNvAOMBk4BrjTzPIGKmhm15vZMjNbVlNTE80YRUQCs7elA4DCrLGRFK4CHneeDcBmYO5ABZ1zdzvnFjrnFpaUlEQ1SBGRoNS1eJe4KMiK3nBrkElhG/AhADObAMwBNgUYj4hITOlLClHsPorYmIKZPYh3VFGxmVUCtwGpAM65u4D/ABab2buAAbc653ZHKh4RkXhT1+p3H2VHr6UQsaTgnLt0iOk7gLMitX4RkXi3N4CWgs5oFhGJUfX+QHP+GBloFhGRQext6SQrLZn0lOSorVNJQUQkRtW1dEa16wiUFEREYlZdSwf5UTwcFZQURERiVm1zB8U5SgoiIgLUNrczLoqHo4KSgohIzNrT1EFRdnpU16mkICISg1o7umnu6Gacuo9ERKS2uR1AYwoiIgJ7mr0T19R9JCIi1DZ5SUHdRyIiwu4mr/tIRx+JiAg1flIoyVX3kYjImFfd0E5uRgpZadG9a7KSgohIDKpqaGN8lFsJoKQgIhKTqhramJCXEfX1KimIiMSgqoZ2JQUREQHnHDWN7YzPU/eRiMiYV9fSSUd3D+Nz1VIQERnztte1AjA5X0lBRGTM600KpYWZUV+3koKISIzZ0ZsUCpQURETGvO17W8lITaIoype4ACUFEZGYs72ulckFmZhZ1NetpCAiEmO217UG0nUESgoiIjFna20LU4uyAlm3koKISAzZ29xBfWsn04uzA1m/koKISAzZXNsMQPk4JQURkTFvy24/KailICIiW3Y3k2QwpUgDzSIiY9771U1MG5dNekpyIOuPWFIws3vMrNrMVg1SZpGZvWNmq83sr5GKRUQkXqzf1cjsCTmBrT+SLYXFwDnhJppZAfAr4GPOuXnAJyMYi4hIzGvr7GZLbTNzJuYFFkPEkoJz7lVgzyBFPg087pzb5pevjlQsIiLxYEN1Ez0O5kzIDSyGIMcUZgOFZvaKmS03s8+EK2hm15vZMjNbVlNTE8UQRUSiZ9X2egAOnzQ2k0IKcBzwEeBs4JtmNnuggs65u51zC51zC0tKSqIZo4hI1KyorCMvIyWwE9fA2zEHpRLY7ZxrBprN7FVgPvBegDGJiARmRUU986cUBHIhvF5BthSeBD5gZilmlgWcCKwNMB4RkcC0dHSxvqqR+WUFgcYRsZaCmT0ILAKKzawSuA1IBXDO3eWcW2tmzwErgR7gd865sIeviogksn9s2Ut3j+PEGUWBxhGxpOCcu3QYZe4A7ohUDCIi8WLJxt2kJhsLpwWbFHRGs4hIDHjt/d0cM6WAzLRgzmTupaQgIhKw7XWtrN7RwAfnTgg6FCUFEZGgvbimCoCz5ikpiIiMeX98eztzJuQysyS4ax71UlIQEQnQhupG3qmo4+LjyoIOBRhBUjCz08zsKv9xiZlNj1xYIiJjw+IlW0hLTuLCBaVBhwIMMymY2W3ArcDX/JdSgfsjFZSIyFhQ09jOo8sruXDBZEpy04MOBxh+S+Ei4GNAM4BzbgcQ3BWbREQSwH//ZQOd3Y4bzpgZdCh9hpsUOpxzDnAAZhbc1ZpERBLAmh0N3Ld0K586fgozYmCAuddwz2h+2Mx+AxSY2XXA1cBvIxeWiEhw6ls6WbJxN+t2NVLf2kl6ShKTCzKZNzmPo8sKSEs5tGN02jq7ufmRFRRkpvKVs+eMUtSjY1hJwTn3EzP7MNAAzAG+5Zx7IaKRiYhE2aaaJn7x0vs88+5OOrsdALnpKbR399DR1QNAdloyi+aO5+MLSjljdgkpySNLEM45vvnEKtbubOCeKxdSkJU26u/jUAwrKfjdRS87514wsznAHDNLdc51RjY8EZHI6+lx/ObVTfzshfdITjIuP2kaHz16MkeW5pGekoxzjurGdt6pqOOV9TU8v3oXf165k8n5GVx+8jQ+fcLUYe3cnXP84Nl1PLK8kn/94KyYOIO5P/OGCoYoZLYc+ABQCCwFlgEtzrnLIhvegRYuXOiWLVsW7dWKSIJq6+zm5odX8Od3d3LeURP59seOHPJIoM7uHl5aW8V9S7fy+oZaMlKTuPi4Mj57cjmHhbmVZn1rJ//+x3d5euVOrjhpGt+5YF5U75tgZsudcwuHLDfMpPCWc+5YM7sJyHTO/djM3nbOLRiNYEdCSUFERkt7VzfX3bucv71fw9fPPZxrPzB9xDvqdbsa+J+/bebJFTvo6OphwdQCzp43kQVTCijOTWdvcwevvr+b+/6+hYa2Lm4+azafP2Nm1G+kM9ykMNyBZjOzk4HLgGtGOK+ISMxxzvHlh1fw6ns1/OgTR3HJ8VMPajlzJ+Zxxyfn89Vz5/LYW5U88fYOfvjsuv3KmMEZs0u45aw5HFmaPxrhR8xwd+xfBL4KPO6cW+2fzfxy5MISEYmsO1/ewJ9X7uSr58496IQQalxOOtefPpPrT59JdUMba3Y2UNfSSX5mKkeW5sfMyWlDGW5SaMG7O9qlZnY5YPjnLIiIxJs3NtXy0xff44JjJvO502eM+vLH52UwPi9j1JcbDcNNCr8HbgFW4SUHEZG41NDWyZcfXsG0oiy+f9FRUe/bj3XDTQo1zrmnIhqJiEgU3PHcenbWt/LY508hO11Do/0N9xO5zcx+B7wEtPe+6Jx7PCJRiYhEwFvb9nL/G1u58pRyFkwtDDqcmDTcpHAVMBfv6qi93UcOUFIQkbjQ0+O47cnVTMjN4OazYuvSErFkuElhvnPuqIhGIiISQX98ezvvbq/nZ5fMJ0fdRmEN96IdS83siIhGIiISIW2d3dzx/Hrml+VzwfzYuJlNrBpuujwN+KyZbcYbUzDAOeeOjlhkIiKj5P6lW9nV0MZPL5lPUpKONhrMcJPCORGNQkQkQprbu/j1Kxs5ddY4TplZHHQ4MW+4l87eGulAREQi4b6lW6lt7uDLH9bg8nAc2p0iRERiWFtnN7/722ZOm1XMcdN0COpwKCmISMJ6eFkFu5vaufGfZgUdStxQUhCRhNTV3cPdr27iuGmFnDSjKOhw4oaSgogkpOdW76JybyvXnz5D1zcagYglBTO7x8yqzWzVEOWON7NuM7s4UrGIyNjinOO3r25ienE2Zx4ee7e8jGWRbCksZohDWc0sGfgR8HwE4xCRMWbZ1r2sqKzn6tOmk6zzEkYkYknBOfcqsGeIYjcBjwHVkYpDRMaexa9vIT8zlU8cq7OXRyqwMQUzKwUuAu4aRtnrzWyZmS2rqamJfHAiErd21LXy3OpdfOr4KWSl6RpHIxXkQPPPgVudc91DFXTO3e2cW+icW1hSUhKF0EQkXt23dCvOOa44eVrQocSlINPoQuAh/6iAYuA8M+tyzj0RYEwiEsfaOrv5wz8q+PAREygrzAo6nLgUWFJwzk3vfWxmi4GnlRBE5FA88+5O9jR38JmTy4MOJW5FLCmY2YPAIqDYzCqB2/Bu0oNzbshxBBGRkbr371uZUZLNKTPHBR1K3IpYUnDOXTqCsldGKg4RGRtWba/nnYo6bjv/CJ2sdgh0RrOIJITfv7GVzNRkPnFcWdChxDUlBRGJew1tnTz5zg7Onz+JvIzUoMOJa0oKIhL3nnx7Oy0d3Vx2og5DPVRKCiIS15xz/P6NbRxZmsfRZflBhxP3lBREJK69ta2Odbsa+fQJ0zTAPAqUFEQkrj3wxjZy0lP42DGTgw4lISgpiEjcqm/p5OmVO7jgmMnkpOs6R6NBSUFE4tbjb1fS3tXDp0+cGnQoCUNJQUTiknOOB97YxvwpBcybrAHm0aKkICJxadnWvbxf3cSnT5gSdCgJRUlBROLSA29sIzc9hfPna4B5NCkpiEjc2dvcwZ/f3cmFC0p1I51RpqQgInHnsbcq6dAAc0QoKYhIXOkdYD5uWiGHT8oLOpyEo6QgInHl75tq2bS7mcvUSogIJQURiSu/f2Mb+ZmpnHfUpKBDSUhKCiISN6ob2nh+1S7+eWEZGanJQYeTkJQURCRuPPSPCrp6nC6RHUFKCiISF7q6e3jgjW2cPruE8uLsoMNJWEoKIhIXXlxbxa6GNq44Sa2ESFJSEJG4sHjJFkoLMvmnOSVBh5LQlBREJOat29XA0k17uOLkaaQka7cVSfp0RSTm/d+SraSnJHHJQl38LtKUFEQkptW3dPLHtyu58JhSCrPTgg4n4SkpiEhMe+DNbbR19nDlqeVBhzImKCmISMzq6Oph8ZLNnDarWNc5ihIlBRGJWc+8u5OqhnauOW160KGMGUoKIhKTnHP87rVNzCzJ5ozZOgw1WpQURCQmLdlYy6rtDVz7gRkkJVnQ4YwZSgoiEpN+/cpGSnLTuWhBadChjCkRSwpmdo+ZVZvZqjDTLzOzlf7fEjObH6lYRCS+vFtZz2sbdnPNadN1NdQoi2RLYTFwziDTNwNnOOeOBv4DuDuCsYhIHLnrrxvJzUjRjXQCELGk4Jx7FdgzyPQlzrm9/tOlQFmkYhGR+PF+VSPPrNrJFSdNIzcjNehwxpxYGVO4Bng23EQzu97MlpnZspqamiiGJSLR9l8vbyAzNZlrPzAj6FDGpMCTgpn9E15SuDVcGefc3c65hc65hSUlOjRNJFFtqG7iqZU7uOLkaRTpkhaBSAly5WZ2NPA74FznXG2QsYhI8O58+X0yUpK5Tq2EwATWUjCzqcDjwBXOufeCikNEYsO6XQ08uWIHnzl5GsU56UGHM2ZFrKVgZg8Ci4BiM6sEbgNSAZxzdwHfAsYBvzIzgC7n3MJIxSMise0nz79HTnoKn180M+hQxrSIJQXn3KVDTL8WuDZS6xeR+LF8615eXFvFLWfNpiBLYwlBCnygWUTGNuccP3p2HcU56Vx1qi58FzQlBREJ1HOrdvHmlj3824cPIzs90GNfBCUFEQlQW2c33392LXMn5upWmzFCSUFEArN4yRYq9rTyjY8cQUqydkexQFtBRAKxo66VX770PmcePp7TDisOOhzxKSmISCC+/dRqepzjtvPnBR2KhFBSEJGoe3FNFc+vruKLH5rNlKKsoMOREEoKIhJVjW2d3Pan1cyekMO1H9AhqLFGx3+JSFR9/5m17Kxv5ZEbTiFVg8sxR1tERKLmr+/V8OCbFVx3+gyOm1YYdDgyACUFEYmKupYObn10JYeNz+HfzpwddDgShrqPRCTinHPc8shKapvb+d1nF+q+yzFMLQURibjFS7bw4toqvnbu4RxZmh90ODIIJQURiah3Kur4/jNrOfPwCVx1annQ4cgQlBREJGKqGtr43H3LmJCXwR0XH41/7xSJYRpTEJGIaOvs5vr7ltPY1sXjXziFQt1zOS4oKYjIqOvpcXzl0ZWsqKjjrsuPY+7EvKBDkmFS95GIjCrnHN95eg1PrdjBrefM5ZwjJwYdkoxA3LUU1u5s4PjvvXjQ86clJ5GWkkRachKpKeb9919LT9n3ODWkXOj/fa+b97/3tXDlBpw3idRkU/+qJKRfvbKRxUu2cM1p07nhjBlBhyMjFHdJIS8zlTMPn3CQczs6uhwd3T10dvXQ0d1Dh/+RD5utAAALd0lEQVS/sa2LPSHPe6e3d/XQ6b/e40b1rYQkCxs0EaWnJJGVnkJuegrZ/l9OejI56alkpyeTk55CTt/r3v/cjBTSU5KUeCSqfv3KRu54fj0XLSjl3887XPUvDsVdUigtyOQHHz8qkHV39zgvafQmFD9ZdIYkk97pB7x2QCJyB5Tt7O6hvf8yu7yE1VzbQlN7F83tXTR3dA8r3uQkIzstmdwML3n0Jo3QBJITkmSy01PIy0ilMDuNcdlpFGankZeRoi+2DMk5xy9f2sDPXnyPj82fzB0XH01SkupNPIq7pBCk5CQjMy2ZzLRgz8bs6XE0d3TR3N5NU3tXX7II/d/3uK2LpvZuP5l00djWxa76tv3KDdYCSkkyCrPTKMpKozA7lXHZ6RRmp1KUnU5RVm8CSd9vWnqKzlYdS3p6HD94di2//dtmPnFsGT+++GiSlRDilpJCHEpKMnIzUsnNSD3kZTnnaOvs6Usi9a2d7GnpYG9zB3v8v70tHdQ2ef/X7mpgb3MHda2duDDJJDstmaIcL1lMyEtnUn4mE/IymJifzoS8DCblZzIxLyPw5CqHrrWjmy/94W2eX13FZ06exu3nz1MLIc4pKYxxZvtaPyW56cOer7vHUdeyf8KobfaSSe//3U0dbKxpZsmGWhrbuw5YRl5GChPzM5iYn8nEvHQm5mUwIT+DSfle4phSlEVOuqporNpe18rn71/Ou9vr+dZHj+CqU8vV1ZgA9I2Tg5KcZIzLSWdcTjqzxg9dvqnd67aqamhjV30buxr2f7xuZwO7m9oP6MoqzEplSlEWUwqzKCvKZEphlv88k9LCTHVVBeSFNVXc8sgKunscv7n8OM6ap8NOE4WSgkRFTnoKs8bnMGt8TtgyXd091DS1s6u+je11rVTsaaVibwsVe1pYs7OBF9ZU0dHd01feDCbkZjClKJOpRdlML86ivDib6cXZlI/LJlutjFHX3N7FHc+vZ/GSLRxZmsedlx5LeXF20GHJKNK3RmJGSnISk/IzmZSfyYKpB96ApafHUdXY5iWLPS1U7G1h254WKve08tqGGh57q32/8hPy0ikfl82MEi9JTC/2Hk8tyiYtRedtjtRf1lXzjSdWsb2ulatOLeer585VSy0BKSlI3EhKsr6kccL0ogOmN7d3saW2mS27W9i8u4nNu1vYUtvM86ur2NPc0VcuOcmYVpTFjBKv5TKzJJuZ43OYWZJDfuahD94nmg3Vjdzx/HqeX13FYeNzePSGk1lYfuDnL4lBSUESRnZ6CvMm5zNv8oHX669v6WRzbTObaprYVNPMxpomNtY08df3quns3jeQUZKbzsySbK+rqySHWeNzmTU+hwl56WNuELViTwv/9fL7PLq8kqy0FG45azbXnT5DrYMEp6QgY0J+VirHZBVwzJSC/V7v6u6hYm8rG6ub+hLFhuom/vTODhra9h0xlZuewozxORw2vrd14bUwphZlkZJAN593zvHG5j387+ubeWFNFSlJSVx16nS+sGgm43KGf3SaxC9z4Q42P9QFm90DfBSods4dOcB0A34BnAe0AFc6594aarkLFy50y5YtG+1wRfbjnKOmqZ0NVU1s8BNF7191476xi9Rko3xctpckxnv/y4uzmVGcTUFW/FwqemNNE0+t2MFTK3awsaaZwqxULj1hKlecPI1J+ZlBhyejwMyWO+cWDlUuki2FxcCdwL1hpp8LHOb/nQj82v8vEjgzY3xuBuNzMzhlVvF+0+pbO9lU08TGmmY2+C2M96obeWFtFd0hx9QWZqV6R0IVZzN9XDZTx2VRVpjFlKJMSnKC7Y5qbOvkzc17eH1DLa9v2M36qkbM4MTpRVx/+gwuOKZU91EeoyKWFJxzr5pZ+SBFLgDudV5TZamZFZjZJOfczkjFJDIa8jNTWTC18IAjpDq6eqjY28LmmmY2725mc20zm2ua+fvGWh5/a/t+ZTNTkynzz7WYlJ/hn+nt/Z+Ql0FRdhoFWYd2yRDnHA1tXeyoa2X73lber25i7c4G1uxsYFNNEz0O0lOSWFheyCcXHs5Hj57MxPyMg16fJIYgxxRKgYqQ55X+a0oKEpfSUpL8sYYDz8Vo6+ym0j+EtveQ2m17WthR38qq7d6JewPJTkumICuN/MxUsvwzzzNSk8lMTSbJwAE9zksA7V09NLZ10tTuXeOqtqmDpn5nkpcWZHL4pDzOO2oSJ80o4tiphWoRyH6CTAoDtZ0HHOAws+uB6wGmTp0ayZhEIiIjNdk/kil3wOkdXT1UN3pneVc1tLO37/pTndS1dFDf2klrp3cBxJrGdto6u+lxkGReV5fhJaXcjBTG52YwsySFwqw0SgsymVyQyaSCDGYW55CfpUNuZXBBJoVKYErI8zJgx0AFnXN3A3eDN9Ac+dBEoistJYmyQm/MQSRIQR5L9yfgM+Y5CajXeIKISLAi1lIwsweBRUCxmVUCtwGpAM65u4Bn8A5H3YB3SOpVkYpFRESGJ5JHH106xHQH3Bip9YuIyMglzqmYIiJyyJQURESkj5KCiIj0UVIQEZE+SgoiItInYldJjRQzqwHqgPowRfIHmVYM7I5EXBE22HuK5XUdyrJGOu9wyw+n3FBlVMdiZ12JWMciVb+mOedKhizlnIu7P+Dug5y2LOjYR/v9xvK6DmVZI513uOWHU26oMqpjsbOuRKxjQdeveO0+euogp8WraL6n0VzXoSxrpPMOt/xwyg1VRnUsdtaViHUs0PoVd91Hh8LMlrlh3GRC5GCpjkkkRaN+xWtL4WDdHXQAkvBUxySSIl6/xlRLQUREBjfWWgoiIjIIJQUREemjpCAiIn3GdFIws2wz+z8z+62ZXRZ0PJJ4zGyGmf2PmT0adCySeMzsQn//9aSZnTUay0y4pGBm95hZtZmt6vf6OWa23sw2mNlX/Zc/DjzqnLsO+FjUg5W4NJI65pzb5Jy7JphIJR6NsH494e+/rgQuGY31J1xSABYD54S+YGbJwH8D5wJHAJea2RF494Wu8It1RzFGiW+LGX4dExmpxYy8fn3Dn37IEi4pOOdeBfb0e/kEYIP/q60DeAi4AKjESwyQgJ+FRMYI65jIiIykfvn3uP8R8Kxz7q3RWP9Y2RGWsq9FAF4yKAUeBz5hZr8mMS9dINEzYB0zs3FmdhewwMy+FkxokgDC7cNuAs4ELjazG0ZjRRG7R3OMsQFec865ZuCqaAcjCSlcHasFRuXLKmNauPr1S+CXo7misdJSqASmhDwvA3YEFIskJtUxiaSo1a+xkhT+ARxmZtPNLA34FPCngGOSxKI6JpEUtfqVcEnBzB4E/g7MMbNKM7vGOdcF/AvwPLAWeNg5tzrIOCV+qY5JJAVdv3RBPBER6ZNwLQURETl4SgoiItJHSUFERPooKYiISB8lBRER6aOkICIifZQUREbAzLaYWfGhlhGJVUoKIiLSR0lBJAwze8LMlpvZajO7vt+0cjNb59+5b6WZPWpmWSFFbjKzt8zsXTOb689zgpktMbO3/f9zovqGRIZBSUEkvKudc8cBC4F/NbNx/abPAe52zh0NNABfCJm22zl3LPBr4Bb/tXXA6c65BcC3gO9HNHqRg6CkIBLev5rZCmAp3hUqD+s3vcI597r/+H7gtJBpj/v/lwPl/uN84BH/Nos/A+ZFImiRQ6GkIDIAM1uEd/OSk51z84G3gYx+xfpfOCz0ebv/v5t99y35D+AvzrkjgfMHWJ5I4JQURAaWD+x1zrX4YwInDVBmqpmd7D++FHhtGMvc7j++clSiFBllSgoiA3sOSDGzlXi/8JcOUGYt8Fm/TBHe+MFgfgz8wMxeB5JHM1iR0aJLZ4scBDMrB572u4JEEoZaCiIi0kctBRER6aOWgoiI9FFSEBGRPkoKIiLSR0lBRET6KCmIiEgfJQUREenz/wHeR77r1A91bAAAAABJRU5ErkJggg==\n", 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" ] @@ -685,6 +656,27 @@ "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": {}, @@ -694,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -725,7 +717,7 @@ " svd_solver='auto', tol=0.0, whiten=False)" ] }, - "execution_count": 137, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -736,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -745,7 +737,7 @@ "1" ] }, - "execution_count": 133, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -756,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -766,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -776,7 +768,7 @@ " normalize=False, random_state=None, solver='auto', tol=0.001)" ] }, - "execution_count": 140, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -788,16 +780,16 @@ }, { "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" } @@ -806,6 +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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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(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=2)\n", + "\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", + " 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( title=title)\n", + "axes.set_xlabel('alpha')\n", + "axes.set_ylabel('mse')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([227577.21702408, 194251.52422304, 105297.32506833, 80375.91532951,\n", + " 135937.78069232, 301607.50014446, 289155.87279029, 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208740.36056491,\n", + " 106006.29493841, 106283.20697876, 144313.66750026, 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": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridge.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RR: onehot" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "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(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=0.2, random_state=4)\n", + "\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", + " 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": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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 + 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))) + + 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 + + # 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))) + 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: + + 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))) + 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,test,number_of_trees=1000, min_leaf=4,min_split=4,rs=1, save=False): + """ + 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))) + 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,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 + 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]}] + + grid_search_xg = model_selection.GridSearchCV(xg, grid_para_xg, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1) + + 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-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=",") + diff --git a/Sam/Kaggle/preprocess.py b/Sam/Kaggle/preprocess.py new file mode 100644 index 0000000..8d58f94 --- /dev/null +++ b/Sam/Kaggle/preprocess.py @@ -0,0 +1,214 @@ +class LabelCountEncoder(object): + def __init__(self): + self.count_dict = {} + self.rev_count_dict = {} + def fit(self, column): + # We want to rank the key by its value and use the rank as the new value + count = column.value_counts() + self.count_dict = dict( list( zip (count.index, reversed(range(len(count)+1 ) ) ) ) ) + self.rev_count_dict = dict( list( zip ( reversed(range(len(count)+1 ) ) , count.index ) ) ) + def transform(self, column): + # If a category only appears in the test set, we will assign the value to zero. + missing = 0 + return column.map(lambda x: self.count_dict.get(x, missing)) + def fit_transform(self, column): + self.fit(column) + return self.transform(column) + + + +import numpy as np +import pandas as pd +import math +import re + +def impute( inputDF, onehot = False): + + #input: pd.dataframe + #one-hot or label encoding for the categorical field + #return: + # Imputed pd.DataFrame + # label-encoded dictionary + + + encodedDic = {} + + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Brk Cmn", "BrkComm") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("CmentBd", "CemntBd") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("CmentBd", "CemntBd") + inputDF.Exterior1st = inputDF.Exterior1st.str.replace("Wd Shng", "WdShing") + inputDF.Exterior2nd = inputDF.Exterior2nd.str.replace("Wd Shng", "WdShing") + + ############################### purposelyEncodeData ######################################## + ## Start to purposely encode the information based on our best understanding. + ## Combine Exterior1st and Exterior2nd to Exterior + ## Add TotalFlrSF = 1stFlrSF + 2ndFlrSF + TotalBsmtSF + ## BsmtFinType1 and BsmtFinType2 to Bsmt -Replace each type to it's actually square feet BsmtFinSF1, BsmtFinSF2 -For Unf in type 1 and type2, replace it with the BsmtUnfSF + ## Combine BsmtFullBath, BsmtHalfBath to BsmtBath + ## Add all different PorchSF to TotalProchSF + ## Dummy MasVnrType to MasVnr and replace the value with MasVnrArea + ############################################################################################### + + preProcessCatField = ["MasVnrType", "Exterior1st", "Exterior2nd", "BsmtFinType1", "BsmtFinType2"] + preProcessNumFiled = ["1stFlrSF", "2ndFlrSF", "MasVnrArea", "BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF", "BsmtFullBath", "BsmtHalfBath"] + inputDF[preProcessCatField] = inputDF[preProcessCatField].fillna("Unknown") + inputDF[preProcessNumFiled] = inputDF[preProcessNumFiled].fillna(-1) + + # Exterior1st, Exterior2nd (Exterior covering on house) + var1_dummy_columns = pd.get_dummies(inputDF['Exterior1st'], prefix= "Exterior") + var2_dummy_columns = pd.get_dummies(inputDF['Exterior2nd'], prefix= "Exterior") + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns], join='outer', sort=True).groupby(level=0).sum() + var_dummy_columns = var_dummy_columns.replace(2, 1) + + inputDF = pd.concat([inputDF, var_dummy_columns], join='outer', sort=True, axis=1) + inputDF = inputDF.drop( columns=['Exterior1st', 'Exterior2nd'] ) + + #TotalBsmtFinSF = "BsmtFinSF1"+ "BsmtFinSF2" ( + "TotalBsmtSF" ) + inputDF["TotalFlrSF"] = inputDF["1stFlrSF"].fillna(0) + inputDF["2ndFlrSF"].fillna(0) + inputDF["TotalBsmtSF"].fillna(0) + inputDF["TotalFlrSF"].replace(0, math.nan) + # BsmtFinType1, BsmtFinType2, BsmtFinSF1 (Type 1 finished square feet), BsmtFinSF2 (Type 1 finished square feet), BsmtUnfSF: Unfinished square feet of basement area + var1_dummy_columns = pd.get_dummies(inputDF['BsmtFinType1'], prefix= "Bsmt") + var1_dummy_columns = var1_dummy_columns.mul( inputDF['BsmtFinSF1'] , axis=0) + tmp = var1_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType1'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var1_dummy_columns['Bsmt_Unf'] = tmp + + var2_dummy_columns = pd.get_dummies(inputDF['BsmtFinType2'], prefix= "Bsmt") + var2_dummy_columns = var2_dummy_columns.mul( inputDF['BsmtFinSF2'] , axis=0) + tmp = var2_dummy_columns['Bsmt_Unf'] + tmp [ inputDF.loc[inputDF['BsmtFinType2'] == "Unf"].index ] = 1 + tmp = tmp.mul( inputDF['BsmtUnfSF'] , axis=0) + var2_dummy_columns['Bsmt_Unf'] = tmp + + var_dummy_columns = pd.concat([var1_dummy_columns,var2_dummy_columns], join='outer', sort=True).groupby(level=0).sum() + inputDF = pd.concat([inputDF, var_dummy_columns], join='outer', sort=True, axis=1) + inputDF = inputDF.drop( columns=['BsmtFinType1', 'BsmtFinType1', 'BsmtFinType2', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF'] ) + + #BsmtFullBath, BsmtHalfBath (number of type of bathroom in the basement) + inputDF['BsmtBath'] = inputDF["BsmtFullBath"] + 0.5* inputDF["BsmtHalfBath"] + inputDF = inputDF.drop( columns=['BsmtFullBath', 'BsmtHalfBath'] ) + + inputDF["TotalProchSF"] = inputDF["OpenPorchSF"] + inputDF["EnclosedPorch"] + inputDF["3SsnPorch"] + inputDF["ScreenPorch"] + + #MasVnrType, MasVnrArea + var_dummy_columns = pd.get_dummies(inputDF['MasVnrType'], prefix= "MasVnr") + var_dummy_columns = var_dummy_columns.mul( inputDF['MasVnrArea'] , axis=0) + inputDF = pd.concat([inputDF, var_dummy_columns], join='outer', sort=True, axis=1) + inputDF = inputDF.drop( columns=['MasVnrType', 'MasVnrArea'] ) + + ############################### Ordinal Features Label encoding ####################### + + ord_cols = ['ExterQual', 'ExterCond','BsmtCond','HeatingQC', 'KitchenQual', + 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC', 'BsmtQual'] + ord_dic = {'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa':2, 'Po':1} + + for col in ord_cols: + inputDF[col] = inputDF[col].map(lambda x: ord_dic.get(x, 0)) + + + ############################### Median Impute ####################### + impute_cols = ['GarageArea', 'GarageCars','GarageYrBlt','LotFrontage', 'TotalBsmtSF', 'TotalFlrSF'] + for i, c in enumerate ( impute_cols ): + if inputDF[c].isnull().any(): + inputDF[c] = inputDF[c].fillna( inputDF[c].median() ) + + ############################### Label frequency encoding ####################### + inputDF.MSSubClass = inputDF.MSSubClass.astype('object') + if onehot: + + object_feats = inputDF.dtypes[inputDF.dtypes == "object"].index + numeric_feats = inputDF.dtypes[inputDF.dtypes != "object"].index + objEnc = pd.get_dummies(inputDF[object_feats], drop_first=True, dummy_na=True) + numEnc = inputDF[numeric_feats] + inputDF = pd.concat( [objEnc,numEnc] , axis=1, join='outer', sort=True) + + else: + for i, c in enumerate ( inputDF.columns ): + if inputDF[c].dtype == 'object': + lce = LabelCountEncoder() + inputDF[c] = lce.fit_transform(inputDF[c]) + encodedDic[inputDF.columns[i]] = lce.rev_count_dict #add reversed dic back to the global variable + + return inputDF, encodedDic + + +##################################This is from Zeyu's original file. ######################## +###########???? Should we also include those ???????????????????????????????################## +# # LotFrontage : Since the area of each street connected to the house property most likely have a similar area to other houses in its neighborhood , we can fill in missing values by the median LotFrontage of the neighborhood. +# # Group by neighborhood and fill in missing value by the median LotFrontage of all the neighborhood +# all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(lambda x: x.fillna(x.median())) +# +# # GarageYrBlt, GarageArea and GarageCars : Replacing missing data with 0 (Since No garage = no cars in such garage.) +# for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'): +# all_data[col] = all_data[col].fillna(0) +# +# # Functional : data description says NA means typical +# all_data["Functional"] = all_data["Functional"].fillna("Typ") +# +# # Electrical : It has one NA value. Since this feature has mostly 'SBrkr', we can set that for the missing value. +# all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0]) +# +# # KitchenQual: Only one NA value, and same as Electrical, we set 'TA' (which is the most frequent) for the missing value in KitchenQual. +# all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0]) +# +# # SaleType : Fill in again with most frequent which is "WD" +# all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0]) +# +# # Transforming some numerical variables that are really categorical +# #Year and month sold are transformed into categorical features. +# all_data['YrSold'] = all_data['YrSold'].astype(str) +# all_data['MoSold'] = all_data['MoSold'].astype(str) +# +################################################################################################ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +