diff --git a/challenge1/analysis/baiz/001_ExploratoryDataAnalysis.ipynb b/challenge1/analysis/baiz/001_ExploratoryDataAnalysis.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Exploratory Data Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import pandas_profiling\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cwd = os.getcwd()\n",
+ "path_to_challenge1 = os.path.dirname(os.path.dirname(cwd))\n",
+ "path_to_all_data = os.path.join(path_to_challenge1, 'data', 'dataset_500.csv')\n",
+ "path_to_test_data = os.path.join(path_to_challenge1, 'data', 'test_dataset_500.csv')\n",
+ "path_to_train_data = os.path.join(path_to_challenge1, 'data', 'training_dataset_500.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# All Data\n",
+ "df = pd.read_csv(path_to_all_data)\n",
+ "df_test = pd.read_csv(path_to_test_data)\n",
+ "df_train = pd.read_csv(path_to_train_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " ID \n",
+ " Label \n",
+ " House \n",
+ " Year \n",
+ " Month \n",
+ " Temperature \n",
+ " Daylight \n",
+ " EnergyProduction \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 7 \n",
+ " 26.2 \n",
+ " 178.9 \n",
+ " 740 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 8 \n",
+ " 25.8 \n",
+ " 169.7 \n",
+ " 731 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 9 \n",
+ " 22.8 \n",
+ " 170.2 \n",
+ " 694 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 10 \n",
+ " 16.4 \n",
+ " 169.1 \n",
+ " 688 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 11 \n",
+ " 11.4 \n",
+ " 169.1 \n",
+ " 650 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Label House Year Month Temperature Daylight EnergyProduction\n",
+ "0 0 0 1 2011 7 26.2 178.9 740\n",
+ "1 1 1 1 2011 8 25.8 169.7 731\n",
+ "2 2 2 1 2011 9 22.8 170.2 694\n",
+ "3 3 3 1 2011 10 16.4 169.1 688\n",
+ "4 4 4 1 2011 11 11.4 169.1 650"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " ID \n",
+ " Label \n",
+ " House \n",
+ " Year \n",
+ " Month \n",
+ " Temperature \n",
+ " Daylight \n",
+ " EnergyProduction \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 23 \n",
+ " 23 \n",
+ " 1 \n",
+ " 2013 \n",
+ " 6 \n",
+ " 22.0 \n",
+ " 125.5 \n",
+ " 778 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 47 \n",
+ " 23 \n",
+ " 2 \n",
+ " 2013 \n",
+ " 6 \n",
+ " 21.1 \n",
+ " 123.1 \n",
+ " 627 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 71 \n",
+ " 23 \n",
+ " 3 \n",
+ " 2013 \n",
+ " 6 \n",
+ " 21.9 \n",
+ " 126.8 \n",
+ " 735 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 95 \n",
+ " 23 \n",
+ " 4 \n",
+ " 2013 \n",
+ " 6 \n",
+ " 20.2 \n",
+ " 125.2 \n",
+ " 533 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 119 \n",
+ " 23 \n",
+ " 5 \n",
+ " 2013 \n",
+ " 6 \n",
+ " 20.2 \n",
+ " 125.2 \n",
+ " 533 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Label House Year Month Temperature Daylight EnergyProduction\n",
+ "0 23 23 1 2013 6 22.0 125.5 778\n",
+ "1 47 23 2 2013 6 21.1 123.1 627\n",
+ "2 71 23 3 2013 6 21.9 126.8 735\n",
+ "3 95 23 4 2013 6 20.2 125.2 533\n",
+ "4 119 23 5 2013 6 20.2 125.2 533"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_test.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "500"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(df_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Profiles "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "profile = df.profile_report(title='Pandas Profiling Full Data Report')\n",
+ "profile.to_file(output_file=\"data_profile_full.html\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_profile = df_test.profile_report(title='Pandas Profiling Test Data Report')\n",
+ "test_profile.to_file(output_file=\"data_profile_test.html\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_profile = df_train.profile_report(title='Pandas Profiling Train Data Report')\n",
+ "train_profile.to_file(output_file=\"data_profile_training.html\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Manual Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df['DateTime'] = pd.to_datetime(df.Year.map(str) + df.Month.map(str), format=\"%Y%m\")\n",
+ "df.set_index('DateTime', inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " ID \n",
+ " Label \n",
+ " House \n",
+ " Year \n",
+ " Month \n",
+ " Temperature \n",
+ " Daylight \n",
+ " EnergyProduction \n",
+ " \n",
+ " \n",
+ " DateTime \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2011-07-01 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 7 \n",
+ " 26.2 \n",
+ " 178.9 \n",
+ " 740 \n",
+ " \n",
+ " \n",
+ " 2011-08-01 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 8 \n",
+ " 25.8 \n",
+ " 169.7 \n",
+ " 731 \n",
+ " \n",
+ " \n",
+ " 2011-09-01 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 9 \n",
+ " 22.8 \n",
+ " 170.2 \n",
+ " 694 \n",
+ " \n",
+ " \n",
+ " 2011-10-01 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 10 \n",
+ " 16.4 \n",
+ " 169.1 \n",
+ " 688 \n",
+ " \n",
+ " \n",
+ " 2011-11-01 \n",
+ " 4 \n",
+ " 4 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 11 \n",
+ " 11.4 \n",
+ " 169.1 \n",
+ " 650 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Label House Year Month Temperature Daylight \\\n",
+ "DateTime \n",
+ "2011-07-01 0 0 1 2011 7 26.2 178.9 \n",
+ "2011-08-01 1 1 1 2011 8 25.8 169.7 \n",
+ "2011-09-01 2 2 1 2011 9 22.8 170.2 \n",
+ "2011-10-01 3 3 1 2011 10 16.4 169.1 \n",
+ "2011-11-01 4 4 1 2011 11 11.4 169.1 \n",
+ "\n",
+ " EnergyProduction \n",
+ "DateTime \n",
+ "2011-07-01 740 \n",
+ "2011-08-01 731 \n",
+ "2011-09-01 694 \n",
+ "2011-10-01 688 \n",
+ "2011-11-01 650 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualise All values of Timeseries data\n",
+ "cols_plot = ['Temperature', 'Daylight', 'EnergyProduction']\n",
+ "axes = df[cols_plot].plot(marker='.', alpha=0.5, linestyle='None', figsize=(14, 10), subplots=True)\n",
+ "for ax in axes:\n",
+ " ax.set_ylabel('All Montly Values')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Distribution of Time Series\n",
+ "fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
+ "for name, ax in zip(cols_plot, axes):\n",
+ " sns.boxplot(data=df, x=df.index, y=name, ax=ax)\n",
+ " ax.set_ylabel(name)\n",
+ " ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
+ " # Remove the automatic x-axis label from all but the bottom subplot\n",
+ " if ax != axes[-1]:\n",
+ " ax.set_xlabel('')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## House with Higest and Lowest Values\n",
+ "\n",
+ "This plots aim to help visualise how different are houses with large vs low Energy Production"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "group_houses = df.groupby(['House'])[cols_plot].agg(['min', 'max', 'mean', 'std'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
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+ " Daylight \n",
+ " EnergyProduction \n",
+ " \n",
+ " \n",
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+ " 32.015494 \n",
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+ " 1088 \n",
+ " 799.541667 \n",
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+ " 794.583333 \n",
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+ " 15.158333 \n",
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+ " 794.583333 \n",
+ " 110.919445 \n",
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+ " 28.4 \n",
+ " 15.158333 \n",
+ " 8.836678 \n",
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+ " 794.583333 \n",
+ " 110.919445 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 344 \n",
+ " 2.7 \n",
+ " 27.0 \n",
+ " 14.495833 \n",
+ " 8.153552 \n",
+ " 125.2 \n",
+ " 268.8 \n",
+ " 180.779167 \n",
+ " 31.233218 \n",
+ " 311 \n",
+ " 723 \n",
+ " 499.416667 \n",
+ " 100.215384 \n",
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+ " 499.416667 \n",
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+ " 499.416667 \n",
+ " 100.215384 \n",
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+ " 499.416667 \n",
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+ " 31.233218 \n",
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+ " 723 \n",
+ " 499.416667 \n",
+ " 100.215384 \n",
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+ " 2.7 \n",
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+ " 499.416667 \n",
+ " 100.215384 \n",
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+ " 2.7 \n",
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+ " 27.0 \n",
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+ " 499.416667 \n",
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+ " 499.416667 \n",
+ " 100.215384 \n",
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+ " 14.212500 \n",
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+ " 14.212500 \n",
+ " 7.839937 \n",
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+ " 498.958333 \n",
+ " 92.478190 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
500 rows × 12 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Temperature Daylight \\\n",
+ " min max mean std min max mean \n",
+ "House \n",
+ "443 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "395 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "365 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "160 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "109 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "86 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "96 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "277 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "152 2.7 29.0 15.229167 8.862254 129.1 257.5 193.158333 \n",
+ "338 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "110 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "447 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "1 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "341 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "81 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "67 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "386 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "281 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "391 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "461 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "15 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "177 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "212 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "190 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "262 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "185 1.8 27.4 14.329167 8.744364 125.5 257.1 189.083333 \n",
+ "272 2.5 28.4 15.158333 8.836678 126.0 257.9 195.529167 \n",
+ "348 2.5 28.4 15.158333 8.836678 126.0 257.9 195.529167 \n",
+ "60 2.5 28.4 15.158333 8.836678 126.0 257.9 195.529167 \n",
+ "174 2.5 28.4 15.158333 8.836678 126.0 257.9 195.529167 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "344 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "242 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "346 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "138 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "5 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "92 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "4 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "111 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "406 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "203 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "120 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "292 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "436 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "472 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "410 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "464 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "119 2.7 27.0 14.495833 8.153552 125.2 268.8 180.779167 \n",
+ "263 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "209 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "496 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "441 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "156 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "357 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "469 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "439 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "438 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "275 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "227 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "287 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "359 2.9 26.2 14.212500 7.839937 125.9 271.3 180.591667 \n",
+ "\n",
+ " EnergyProduction \n",
+ " std min max mean std \n",
+ "House \n",
+ "443 34.706971 698 1254 931.166667 152.952015 \n",
+ "395 34.706971 698 1254 931.166667 152.952015 \n",
+ "365 34.706971 698 1254 931.166667 152.952015 \n",
+ "160 34.706971 698 1254 931.166667 152.952015 \n",
+ "109 34.706971 698 1254 931.166667 152.952015 \n",
+ "86 34.706971 698 1254 931.166667 152.952015 \n",
+ "96 34.706971 698 1254 931.166667 152.952015 \n",
+ "277 34.706971 698 1254 931.166667 152.952015 \n",
+ "152 34.706971 698 1254 931.166667 152.952015 \n",
+ "338 32.015494 593 1088 799.541667 127.102143 \n",
+ "110 32.015494 593 1088 799.541667 127.102143 \n",
+ "447 32.015494 593 1088 799.541667 127.102143 \n",
+ "1 32.015494 593 1088 799.541667 127.102143 \n",
+ "341 32.015494 593 1088 799.541667 127.102143 \n",
+ "81 32.015494 593 1088 799.541667 127.102143 \n",
+ "67 32.015494 593 1088 799.541667 127.102143 \n",
+ "386 32.015494 593 1088 799.541667 127.102143 \n",
+ "281 32.015494 593 1088 799.541667 127.102143 \n",
+ "391 32.015494 593 1088 799.541667 127.102143 \n",
+ "461 32.015494 593 1088 799.541667 127.102143 \n",
+ "15 32.015494 593 1088 799.541667 127.102143 \n",
+ "177 32.015494 593 1088 799.541667 127.102143 \n",
+ "212 32.015494 593 1088 799.541667 127.102143 \n",
+ "190 32.015494 593 1088 799.541667 127.102143 \n",
+ "262 32.015494 593 1088 799.541667 127.102143 \n",
+ "185 32.015494 593 1088 799.541667 127.102143 \n",
+ "272 33.161797 637 1046 794.583333 110.919445 \n",
+ "348 33.161797 637 1046 794.583333 110.919445 \n",
+ "60 33.161797 637 1046 794.583333 110.919445 \n",
+ "174 33.161797 637 1046 794.583333 110.919445 \n",
+ "... ... ... ... ... ... \n",
+ "344 31.233218 311 723 499.416667 100.215384 \n",
+ "242 31.233218 311 723 499.416667 100.215384 \n",
+ "346 31.233218 311 723 499.416667 100.215384 \n",
+ "138 31.233218 311 723 499.416667 100.215384 \n",
+ "5 31.233218 311 723 499.416667 100.215384 \n",
+ "92 31.233218 311 723 499.416667 100.215384 \n",
+ "4 31.233218 311 723 499.416667 100.215384 \n",
+ "111 31.233218 311 723 499.416667 100.215384 \n",
+ "406 31.233218 311 723 499.416667 100.215384 \n",
+ "203 31.233218 311 723 499.416667 100.215384 \n",
+ "120 31.233218 311 723 499.416667 100.215384 \n",
+ "292 31.233218 311 723 499.416667 100.215384 \n",
+ "436 31.233218 311 723 499.416667 100.215384 \n",
+ "472 31.233218 311 723 499.416667 100.215384 \n",
+ "410 31.233218 311 723 499.416667 100.215384 \n",
+ "464 31.233218 311 723 499.416667 100.215384 \n",
+ "119 31.233218 311 723 499.416667 100.215384 \n",
+ "263 32.593903 357 691 498.958333 92.478190 \n",
+ "209 32.593903 357 691 498.958333 92.478190 \n",
+ "496 32.593903 357 691 498.958333 92.478190 \n",
+ "441 32.593903 357 691 498.958333 92.478190 \n",
+ "156 32.593903 357 691 498.958333 92.478190 \n",
+ "357 32.593903 357 691 498.958333 92.478190 \n",
+ "469 32.593903 357 691 498.958333 92.478190 \n",
+ "439 32.593903 357 691 498.958333 92.478190 \n",
+ "438 32.593903 357 691 498.958333 92.478190 \n",
+ "275 32.593903 357 691 498.958333 92.478190 \n",
+ "227 32.593903 357 691 498.958333 92.478190 \n",
+ "287 32.593903 357 691 498.958333 92.478190 \n",
+ "359 32.593903 357 691 498.958333 92.478190 \n",
+ "\n",
+ "[500 rows x 12 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Sort DataFrame by \"Mean EnergyProduction\"\n",
+ "group_houses.sort_values([('EnergyProduction', 'mean')], ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Based on the above, lets filter DT\n",
+ "df_filter = df[ df['House'].isin([443,395,287,359]) ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "axes = df_filter[cols_plot].plot(marker='.', alpha=0.5, linestyle='None', figsize=(14, 10), subplots=True)\n",
+ "for ax in axes:\n",
+ " ax.set_ylabel('All Montly Values')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Distribution of Time Series\n",
+ "fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
+ "for name, ax in zip(cols_plot, axes):\n",
+ " sns.boxplot(data=df_filter, x=df_filter.index, y=name, ax=ax)\n",
+ " ax.set_ylabel(name)\n",
+ " ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
+ " # Remove the automatic x-axis label from all but the bottom subplot\n",
+ " if ax != axes[-1]:\n",
+ " ax.set_xlabel('')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### This demonstrates that as expected \"House\" is an Important Feature for any Model, it is also possible to see that there seems to be very high and very low production Houses.\n",
+ "\n",
+ "### Good to mention that this looks a great dataset in terms of data quality, no missing values, no obvious outliers, etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Visualising House Types"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Flatening Group House DF\n",
+ "group_houses.columns = [' '.join(col).strip() for col in group_houses.columns.values] "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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c/P9aKUc+mC0YImqVeKMJgKuvbsFdd93D2LH34XA4aNeuPbffPpB69eozefJT5OfbueGGjnTtegsAs2fPpFOnm+nUqTMTJkQREzOdt95aSkREdWbMmPU/riYiIiIiIufL4Ha7S7Zt3SWsNHbtk8K0m43vKPe+o9z7jnLvO8q97yj3vqPc+87FuGufdiAQEREREREpJhVSIiIiIiIixaRCSkREREREpJhUSImIiIiIiBSTCikREREREZFiUiElIiIiIiJSTHqP1EUuNnY5Gze+j8VioVu3HowceR/Hjh3hhRdmYbPZaN26DU8++Qxmc+GvMisri+joKZw5c5qQkFCio58nLCzcR3chIiIiInJ50YzURWzXrm/YunUzS5a8xbJlKzl48Ac+++wToqOnMn78RFatWovb7Wb9+jivcxcvfpWWLduwcuUaIiMHsGDBXB/cgYiIiIjI5UmFVCnI+fUUv63fwpl33+e39VvI+fVUqYx77NgRrr/+BoKCKmMymWjf/kbWrHkXu91OixbXANC3byTbtm31Overr76gZ8/eAHTv3ouvv/4Sh8NRKnGJiIiIiFR0KqRKKOfXU2Ts2oszJxcAZ04uGbv2lkox1bRpc3bu/IrMzAzsdjs7dmzHZDIVekQvLCycxMREr3OTk5M8/cxmM0FBQaSnp5U4JhERERER0RqpEsvadwi301moze10krXvEIH1apdo7LZtr6dPn/488siDBAdXoW3b6/n++2+9+hmNBq82t9vt1WYwePcTEREREZHi04xUCf0+E3W+7cWRk5NN587dWLFiFQsXvkFAQADVq9cgNTXF0yclJZnwcKvXuVZrhKefw+EgOzubqlVDShyTiIiIiIiokCoxU2ClYrUXx5kzZ3jmmSdxOBzYbDY++GAd/fvfhsViYd++PQBs3ryBDh1u9Dq3Q4eObN68AYBPPtlCq1atvXb2ExERERGRC6O/rEsouOWVZOzaW+jxPoPJRHDLK0s8duPGTejSpRujRg3D6XQyZMhdtGzZmmnTYpgzJ4acnByaNGnGwIFDAViyZBHh4eEMGDCQ0aMfYtasGYwYMZjg4MpMmxZT4nhEREREROQsg7uoxTQVREqKDZer5Lef8+spsvYdwpmTiymwEsEtryzx+qhLldUaTFJSlq/DqJCUe99R7n1Hufcd5d53lHvfUe59xxe5NxoNhIVVPudxzUiVgsB6tSts4SQiIiIiUhFpjZSIiIiIiEgxqZASEREREREpJhVSIiIiIiIixaRCSkREREREpJhUSImIiIiIiBSTCikREREREZFiUiF1CcjOtnH33YOJjz8DwLp1a7n77sHcc88QnnvuWQoKCgA4duwI999/D0OH3sHs2TNxOBxeY2VlZTFx4mMMHz6Qhx8eTUpKcrnei4iIiIjI5UCFVCmI3/0jn895hy2TFvP5nHeI3/1jqY194MAPjB17PydPngDgxIlfeeedWBYtWsaKFatwuVysXbsagOjoqYwfP5FVq9bidrtZvz7Oa7zFi1+lZcs2rFy5hsjIASxYMLfUYhURERERqShUSJVQ/O4fORj3OXnpNgDy0m0cjPu81Iqp9ev/yxNPPE14uBUAi8XChAlRBAVVxmAw0LBhY377LYGEhHjsdjstWlwDQN++kWzbttVrvK+++oKePXsD0L17L77++ssiZ65EREREROTcVEiV0I9bduEqKFyIuAoc/LhlV6mMHxU1lVat2ng+V69eg3bt2gOQlpbG2rWr6dSpM8nJSYSFhXv6hYWFk5iY6DXen/uZzWaCgoJIT08rlVhFRERERCoKFVIl9PtM1Pm2l5akpETGjx9D//63ce21bXG73V59jEaDV1tR/QwG734iIiIiInJuKqRKKCCkcrHaS8Ovv/7CmDH30bt3f0aNuh8AqzWC1NQUT5+UlGTP44B/9ud+DoeD7OxsqlYNKbNYRUREROTSExhopGpVfwIDVS6cizJTQo17tMPoZy7UZvQz07hHuzK5Xk5ONo8//jCjR49h2LARnvbq1WtgsVjYt28PAJs3b6BDhxu9zu/QoSObN28A4JNPttCqVWvMZrNXPxERERGpeCwWMGXbSNh5hENrd5Cw8wimbBv6c9GbUlJCNdo0Bs6ulcpLtxEQUpnGPdp52kvb+vVxpKWl8s47/+add/4NQKdON3P//Q8xbVoMc+bEkJOTQ5MmzRg4cCgAS5YsIjw8nAEDBjJ69EPMmjWDESMGExxcmWnTYsokThERERG59OQnZ3Jg9TaykzI8bUHWqlw9uCtUqeLDyC4+BndRi2YqiJQUGy5Xhb39MmG1BpOUlOXrMCok5d53lHvfUe59R7n3HeXedy733AdZ4McPd3Hy60Nex+recDWNel5Hdr4PAsM3uTcaDYSFnXu5jh7tExERERER8jJzSf05ochjab/Ek5eZV84RXdxUSImIiIiICCaLGUtgQJHHzIH+mCymco7o4qZCSkREREREMAVZqHltkyKP1WzTBFOQpZwjuripkBIREREREXJz3YQ2rkWTnu2wBFcCwD84kCa9rie0US1yc7W3wJ9p1z4REREREQEg3+hHzRuvIqx5HZx5BZgC/PC/Iphcu4qov1IhJSIiIiIiHrl2NwRVxhAErt8/ixcVUpeA7GwbDz30D+bMmU+NGjV57rln2bdvDwEBZ6dc7713NJ07d2XXrm9YuPBf2O12unXrwQMPjPUaKyEhgZkzp5KWlkrduvWYNi2GwMDA8r4lEREREZFLmgqpi9yBAz8wZ04MJ0+e8LQdPnyQhQsXEx4e7mmz2/N4/vloFi58g4iIajz11Hi++uoLbrihY6Hx5s2bze23D6R7914sX76E5cuXMHbso+V2PyIiIiIil4My32zCZrPRv39/Tp06BcDatWvp27cvkZGRxMTE4HA4AIiLi6NTp07cdttt3HbbbfzrX//yGiszM5MHHniAPn36MHz4cJKSkso6/POyd/te5j34ItMHTmXegy+yd/veUht7/fr/8sQTTxMebgUgNzeX335LYM6cGEaOHMrSpa/jcrk4ePAAderUpWbNWpjNZnr27MO2bVsLjeVwONizZzddutwCQJ8+/dm27eNSi1VEREREpKIo00Jq7969DBs2jF9++QWA48ePM3/+fJYvX8769etxOBzExsYCsH//fqKioli3bh3r1q3j8ccf9xpv/vz5tG3blk2bNjFo0CBmzZpVluGfl73b97J+0ToykjPADRnJGaxftK7UiqmoqKm0atXG8zk1NYVrr23HM89M4/XXl7N3724++GAdyclJhIX9MUMVFhZOUlJiobHS09MJCgrCbDb/qc9vpRKniIiIiEhFUqaF1OrVq5k+fToREREAHDlyhNatW3s+d+3ala1bz86a7N+/n7i4OG699VaefPJJMjIyvMb79NNPiYyMBKB///5s376dgoKCsryF/+njlVsosBeOocBewMcrt5TJ9WrVqs3zz79IaOgVBAQEcOedQ/jqqy9wF7EG0GD469fr3clo1A74IiIiIiLFVaZrpP46Y9S8eXNmz55NfHw8ERERbN68meTkZACsVisPPPAALVu2ZN68eURHRzN37txC5ycmJmK1nn3EzWw2U7lyZVJTU6lWrdoFxRcWVvmCzvuzjBTvgu/3dqs1uMTj/85kMnLFFUGkpp7hl19+oVevXgBUqRJAUFAATZrUY8uWDZ5r5ufbqF27RqEYQkICyMnJ5oorAjGZTJ7voTTjBEp9PDl/yr3vKPe+o9z7jnLvO8q97yj3vnOx5b5cN5to0KABEyZMYMyYMQQEBNC7d2/2798PwCuvvOLpd//999O9e/fzGrMkMyopKTZcrpJt51g1rOrZx/qKaE9KyirR2H/mdLpITc0mOzubmJhZNG7cgsDAQGJj36ZPn37UrNmQn346zu7dB6lRoyZr166jX79bvWJo2bI1q1atpWfP3qxc+S7t2t1QqnFarcGlOp6cP+Xed5R731HufUe59x3l3neUe9/xRe6NRsPfTryU63Nddrudli1bEhcXx6pVq6hZsyZ16tQhKyuL5cuXe/q53W7POp4/i4iI8MxgORwObDYbISEh5RV+kW4Z3gM/f79CbX7+ftwyvEeZXK9x4yaMGDGKsWPvY8SIQTRp0pQePXrj7+/PpEnTmTz5KUaMGES9evXo2vXsphKzZ89kx47PAJgwIYr331/LiBGD2Lt3D6NHjymTOEVERERELmflOiOVk5PDyJEj2bBhAxaLhdjYWAYPHkxgYCBLliyhTZs2tGrVin//+9/06OFdiHTu3Jm4uDgeeughNm7cSNu2bfHz8yviSuWn1c2tgLNrpTJSMqgaVpVbhvfwtJeWNWvWe36+445B3HHHIK8+bdtez4oV73i1R0VN9fxcvXoNFi58o1RjExERERGpaMq1kAoNDWXcuHEMGTIEh8NB//79PZtHzJ8/nxkzZpCXl0f9+vWZM2cOAAsWLCAiIoJhw4bx2GOPERUVRb9+/QgODubFF18sz/DPqdXNrUq9cBIRERERkYuXwe0uar+3iqE01khJYXp22HeUe99R7n1Hufcd5d53lHvfUe59p8KvkRIREREREbkcqJASEREREREpJhVSIiIiIiIixaRCSkREREREpJhUSImIiIiIiBSTCqlLQHa2jbvvHkx8/BkAdu78mpEjh3H33YOZOXMaBQUFACQkJPDww6O56647iYp6gpycHK+xCgoKmDlzKsOHD+Qf/xjOr7/+Up63IiIiIiJyWVAhVQp2f7abOaNfYNLtzzBn9Avs/mx3qY194MAPjB17PydPnvC0zZ49k2effY7Y2NXY7Xls3rwBgHnzZnP77QN5++33aN78KpYvX+I13n/+s4qAgEqsXLmGRx+dQEzM9FKLVURERESkolAhVUK7P9tN3Kv/JT0pHdyQnpRO3Kv/LbViav36//LEE08THm71tDmdTrKzs3E6neTn5+Pv74/D4WDPnt106XILAH369Gfbto+9xvvqqx307NkHgNatryUjI52EhIRSiVVEREREpKIw+zqAS92Wf39Egb2gUFuBvYAt//6INp3blHj8qKipXm0TJkTxyCMPEhQURI0aNenS5RbS09MJCgrCbD77lYaFhZOU9JvXucnJSYSFhXs+/96vevXqJY5VRERERKSi0IxUCaUnpxervaRSUpJZtOhlYmPfZd26zVx99TW8/PK/ALdXX6PR++t1e3fDYNCvgYiIiIhIcegv6BIKCQ8pVntJ7d27h4YNG1GrVm2MRiORkQPYvfs7QkJCPY/7wdmCKyzM6nW+1WolJSXF8zklJbnQY4MiIiIiIvK/qZAqoR4jeuLn71eozc/fjx4jepbJ9Ro2bMTBgwdITT1bDO3Y8RlXXnkVZrOZVq1a8/HHWwDYvHkDHTrc6HX+DTd09GxOsXfvHiwWfz3WJyIiIiJSTFojVUK/r4Pa8u+PSE9OJyQ8hB4jepbK+qii1K/fgNGjx/Doow9hMpmoVasOTz01GTi7diomZjpvvbWUiIjqzJgxC4C4uDUkJydz//0PceedQ/jnP59jxIjBWCx+TJ0aXSZxioiIiFyugisZcNkLMPr7kZVbxLoJqRAMbndRq2YqhpQUGy5Xhb39MmG1BpOUlOXrMCok5d53lHvfUe59R7n3HeXed6zWYHKTU7H9/DP29Ez8Q6pSuUF9bG6//3mulIwvfu+NRgNhYZXPeVwzUiIiIiIi58GRnY3t+HFyzpx9dUxOdjbgJvjKZpqZqoC0RkpERERE5Dw47Hby0tIKtdlT03HZHT6KSHxJhZSIiIiIyHkw+vtjqVq1UJslpCpGs8lHEYkv6dE+EREREZHzYAkKIrhRIwDsaRn4h1alcsMGZOX7ODDxCRVSIiIiIiLnKRsLIVddiavAgdHPTKbd1xGJr6iQEhEREREphrPFkxlURFVoKqQuYvHxZxg27A7q129YqD0ycgB33jm4XGP5/vtvefrpx6lVqw4GA9jtdho2bMSkSdMJDAy6oDGXLn0dgPvue7BY5505c5oVK5byzDPTOHz4IHFx7xEVNfWCYhARERERuRAqpC5y4eFWli9/29dhANCs2ZUsXPiG5/OMGZNZsmQRjz46oVzjSEiI5/TpU236zBEAACAASURBVAA0b34VUVFXlev1RURERERUSF3CbrutF1263MK+fXswmcxERz9PzZq1OHToAC+9NA+7PY+qVUOYOHESNWvWYty4B6hSpSo///wT0dHPc+LECZYuXURAQABNmzbH6XTSq1dflixZxKJFywDYtOkDDhzYT7duPbyu36pVG7788nMA+vfvTtOmV5KZmcaiRct5++23+OijTRiNRtq168DYsY9iMpl4++23eP/9/1K1agjBwcFceeXVAHTq1JYdO74FYOPG9eze/R2TJ89g165vWLhwPm63i+rVazB9egwLFrzImTOnmTv3Bbp2vYVly95g4cI3OHHiV+bMmUVWViYBAZUYP/5JrrzyambNmkFQUGWOHDlEUlIi9947mn79bi10L0uXvs5vvyXw44/HSE9PY/ToMXz33S4OHvyBxo2b8uyzz2EwGIiNXc62bVtwOl20b9+BMWMexWAw8Prrr/Ddd7vIzMwkJCSEWbPmEBYWfs7vSEREREQubdr+/CKXnJzEqFF3Ffrvp59+BCAlJYXrrrueN998m1at2vDee6spKChg9uwYpk+fxbJlKxk6dAQvvDDLM16jRo155521hIdH8NJLc1mw4DWWLIklMzMTgOuua0dKSopnxmfTpg/o0yfSK67c3Fy2b9/GNde0AiA9PZ0RI0aybt06du36mh07trN0aSzLlq3k9OmTxMW9x+HDB9mw4X2WLVvJ/PmvkpSU+Lf3np+fT3T0VKZMmcFbb71Lo0ZN2LTpAx577EmaNbuSCROeLtR/5sypDBo0lBUrVvHII08wZcrT5Oef3UYnMfE3Xn11CS+88C9eeWVBkdc7fvwn3nhjOVOnRvP889EMHz6St956l6NHD/Pjj8f4+usvOXLkEIsXv8Wbb64kKSmJjz7axKlTJzlx4hcWLVrGqlVrqVWrNh99tPmc35GIiIiIXPo0I3WR+1+P9rVvfwMADRs2Yu/e3Zw8+StnzpwiKuoJT5/s7GzPz1dd1QKAfft206LFNVitEQD06dOP7ds/xWAw0KdPPz78cCN9+95KamoqV1/dgu+//5YjRw4xatRdADgcDq67ri1Dhgz3jH311WfH/u67b+nevRf+/gEA9Ot3K5s2bcBut9OhQ0cCAwMB6Nq1O06n85z3dvz4j1itVpo0aQbAgw8+DJxdr/VXOTk5nDp1is6duwHQosU1VKlShRMnfgXg+uvbYzAYaNiwEZmZGUVer1279pjNZqpXr0FYWDgNGpxdmxYebiUrK5Nvv93JwYM/cN99dwNgt+dRrVp1evXqy7hxj7N+fRwnTvzKgQP7qVWr9jm/IxERERG59KmQusT5+/sDYDAYcLvdOJ0uatas5Sm+nE4naWmpXv2NRiMul7vIMfv2jWTChEewWCz07t3X0/7XNVLesZwtnNxuV6F2txucTsf/x/jHMZPJVKiQcrvdGAwGHA7H/x8v/Otps9nIycmmKG63C7fb/Zc2PONbLH/k6VzM5j+uZzJ5v1jP5XIyePAwhg4dAUBWVhYmk4nDhw8xY8Zkhg69i65db8FkMhaK5a/fkYiIiIhc+vRo32WmXr36ZGZmemY+Nmx4nxkzJnv1a9GiFYcPHyQ5ORm3283WrR95iozq1WtgtUYQF/cevXv3K3YM117bjq1bP8Ruz8PhcLBx4/tce21b2rZtx5df7sBms2G329m+fZvnnJCQEH7++Sfcbjc7dmwHoG7deqSnp/Pzz8cBWLlyBXFx72Eymb1msoKCKlOrVm0+++wTAH74YT+pqSk0bNio2PH/3X19+OFGcnJycDgcPPPMBD799GP27PmONm2uY8CAgdSv35CdO7/B5XL97wFFREREhMBAE8HBFgICfB1J8WhG6iL3+xqpP2vdug3jx08ssr/FYmHmzNksWPAi+fn5BAYGMWXKs179QkNDGT/+SR5/fCwWiz81atTAYqniOd69e08+/fQTwsOtxY65Y8ebOHbsCPfddw9Op4P27W/gzjuHYDabGTRoGPfffw/BwcFUq1bDc85DD43jqace54orwmjZsjUZGen4+/szdWo0MTHTcTgKqFmzNlOnRlNQkI/NlsXMmVPp1+82zxjTps3kn/98jqVLX8fPz8KsWXPw8/Mrdvzn0qnTzfz441EeeGAULpeT9u1vpE+f/iQnJzFp0kRGjhyKyWSmUaPGxMefKbXrioiIiFyODLhJPpnAN/uOk5aYRvW61ah/VT1qNKxObu7F/4/SBncFftYoJcV2zsfbLncZGemsWfMu9947GqPRyPz5/6R27ToMHDgUh8PBzJnT6Natu2fN0fmyWoNJSsoqo6jl7yj3vqPc+45y7zvKve8o976j3JcmN0d3HuCdBWuwZ//xZuNQa1XumjCEBi0b8P97hgG+yb3RaCAsrPK5j5djLHIRqVKlKllZWdxzzxBGjhxKdnY2kZG343a7GTCgD0ajkZtu6uLrMEVERETkMpR2OpH/vBJXqIgCSEvK4P2lG0j/rejNwS4merSvgjIYDIwf/2SRxz74YEs5RyMiIiIiFYXFAiePnSY7o+hNxE4eO03qb6kEhlYt58iKRzNSIiIiIiJSbkwmE7YM29/2sefklVM0F06FlIiIiIiIlJvcXCfV60ac87jJz0RwaJVzHr9YqJASEREREZFyVaN+NRpeXb/IY9d1bUNE3bDyDegCqJASEREREZFyVSk0lIEPD6BVpxYYTGffZepfyULHvh3oMaQbmC7+l0ppswkRERERESl3ITWrMeTRgXQb2Bl7TgGVggMIrxOBG5OvQzsvKqQuEa+8soD09DQmT57Bm28u5oMP1hEcfPbZ0cjIAdx552COHTvCCy/Mwmaz0bp1G5588hnM5sJfcVZWFtHRUzhz5jQhIaFERz9PWFi4L25JRERERCo6iz/W+rU9Hy+lN7zq0b5S4nQ6SU1Nxel0lvrY3367k02b1ns+Hzp0kGeffY7ly99m+fK3ufPOwQBER09l/PiJrFq1Frfbzfr1cV5jLV78Ki1btmHlyjVERg5gwYK5pR6viIiIiMjlToVUKTh48AeGDBnAvfcOY8iQARw8+EOpjZ2ZmcEbb7zK3Xff62k7cuQgK1euYOTIocyb9wJ2u52EhHjsdjstWlwDQN++kWzbttVrvK+++oKePXsD0L17L77++kscDkepxSsiIiIiUhGokCohp9PJtGnPkJ1tIz8/n+xsG9OmPVNqM1Nz5jzHAw+M9TzGl5OTQ5MmzXj44fEsXfpvbDYby5cvITk5qdAjemFh4SQmJnqN9+d+ZrOZoKAg0tPTSiVWEREREZGKQoVUCWVkZFBQkF+oraAgn4yMjBKPvX59HNWqVaNt2+s9bYGBgbz44kvUrl0Hs9nM0KHD+frrL3C7vZ8oNRoNXm1F9TMYvPuJiIiIiMi5lflmEzabjaFDh7Jo0SJq167N2rVrWbJkCSaTifbt2xMVFYXZbOa7777jueeew+FwEBISwnPPPUetWrUKjXXmzBn69etH3bp1AQgPD2fp0qVlfQt/q2rVqvj5WcjP/6OY8vOzULVq1RKP/fHHH5GSksyoUXeRmZlBbm4uzz33LC1btqZ//9sAcLvPzixZrRGkpqZ4zk1JSSY83Oo15u/9IiKq4XA4yM7OpmrVkBLHKiIiIiJSkZTpjNTevXsZNmwYv/zyCwDHjx9n/vz5LF++nPXr1+NwOIiNjQVg4sSJzJo1i3Xr1hEZGUlMTIzXePv37ycyMpJ169axbt06nxdRACaTiejo5wkKqozFYiEoqDLR0c9jMpV828b5818lNnY1y5e/zf33P0SnTjczZsyjvPbaS5w5cxq3283atau5+eauVK9eA4vFwr59ewDYvHkDHTrc6DVmhw4d2bx5AwCffLKFVq1ae+3sJyIiIiIif69MC6nVq1czffp0IiIiADhy5AitW7f2fO7atStbt24lPz+fxx57jObNmwPQrFkz4uPjvcbbv38/R48e5Y477uCee+7hyJEjZRn+ebvqqha8+24cb775Du++G8dVV7Uos2uFhoYyceIknn76cYYNuxO3283QoSMAmDYthpdfnsfw4QPJzc1l4MChACxZsoi4uDUAjB79EAcO7GfEiMH897//4Yknni6zWEVERERELlcGd1GLZkpZt27deOuttygoKGDUqFGsWrWKiIgIpkyZwvfff8+HH37o6etyuRgzZgzXXHMN48aNKzTOyy+/jNVqZejQoXz22WfMnDmTjRs3YrFYLiiulBQbLteltFv9xc9qDSYpKcvXYVRIyr3vKPe+o9z7jnLvO8q97yj3vuOL3BuNBsLCKp/zeLkWUrVr1+b9999n2bJlBAQE0Lt3b9asWcMHH3wAQH5+PlFRUWRkZLBo0SL8/Pz+dtxbb72VOXPmeGayREREREREykO5Lo6x2+20bNmSuLizL4r96KOPqFOnDgDZ2dmMGTOGkJAQXnvttSKLqNjYWPr3709oaChwdge6kqzv0YxU6dO/1PiOcu87yr3vKPe+o9z7jnLvO8q971yMM1Lluv15Tk4OI0eOxGY7+86l2NhY+vbtC5zdbKJevXosWLDgnI/q7dq1izVrzq712blzJy6Xi4YNG5Zb/CIiIiIiIlDOM1KhoaGMGzeOIUOG4HA46N+/P5GRkRw8eJCPP/6Yxo0bM2DAAAAiIiJYvHgx77zzDomJiTz22GNMnjyZqKgo1q1bh7+/P3PnzsVo1KuwRERERESkfJXLGqmLlR7tK32a8vYd5d53lHvfUe59R7n3HeXed5R736nwj/aJiIiIiIhcDvQm1ovcjh3bWbbsDfLycrn++hsYP/5Jdu36hoUL/4Xdbqdbtx488MBYAI4dO8ILL8zCZrPRunUbnnzyGa/NOLKysoiOnsKZM6cJCQklOvp5wsLCfXFrIiIiIiKXLM1IXcROnz7Fiy8+z+zZc3nrrXc5evQwX331Bc8/H83zz8/l3//+D4cPH+Srr74AIDp6KuPHT2TVqrW43W7Wr4/zGnPx4ldp2bINK1euITJyAAsWzC3v2xIRERERueSpkCoF8fFnmD//n4wdez/z5/+T+PgzpTLu9u2fcsstPYiIqIbZbCY6+nkCAgKoU6cuNWvWwmw207NnH7Zt20pCQjx2u50WLa4BoG/fSLZt2+o15ldffUHPnr0B6N69F19//SUOh6NU4hURERERqSj0aF8JxcefYdy40eTl5eFyufj115/5/PNPWbhwMTVq1CzR2KdPn8Rs9uOJJ8aRkpJCx4430aBBw0KP4oWFhZOUlEhycpJXe2JioteYf+5nNpsJCgoiPT2N8HBriWIVEREREalINCNVQu++u9JTRAG4XC7y8vJ4992VJR7b6XTy7bc7mTp1Jm+88SaHDh0ocrbLYDBS1OaLRqPBq62ofgaDdz8RERERETk3FVIldPToEU8R9TuXy8XRo0dKPPYVV4TRtu31hIaG4u8fwE03dWHXrm9ISUnx9ElJSSY8PByrNYLU1L+2e88y/bmfw+EgOzubqlVDShyriIiIiEhFokKqhJo2beb1UmCj0UjTps1KPPaNN97Ezp1fkZWVhdPp5Ouvv6RLl1s4efJXTp06idPpZMuWD+nQoSPVq9fAYrGwb98eADZv3kCHDjd6jdmhQ0c2b94AwCefbKFVq9ZeO/uJiIiIiMjf01/QJTRkyHA+//xTz+N9RqORgIAAhgwZXuKxr766BXfddQ9jx96Hw+GgXbv23H77QOrVq8/kyU+Rn2/nhhs60rXrLQBMmxbDnDkx5OTk0KRJMwYOHArAkiWLCA8PZ8CAgYwe/RCzZs1gxIjBBAdXZtq0mBLHKSIiIiJS0RjcRS2aqSBSUmy4XCW//fj4M7z77kqOHj1C06bNGDJkeIk3mrhU6Y3fvqPc+45y7zvKve8o976j3PvOxZb7AJzkZ9hw2vMxB1XCUqUyuU5fR1U2fJF7o9FAWFjlcx7XjFQpqFGjJuPHT/R1GCIiIiJSQVgceZzcsY/E/T+BG0wBftTveh1VmtYj363VO+VBWRYRERERuYRUrmwm7ehJEvedLaIAnHkFHP/oGxwZF8+M2eVOhZSIiFxUgixgzs3Bz5lPYKD+NyUi8lcF2QWk/XTaq93tdJOdkIrF4oOgKiA92iciIhcNP3sOv35+mKSjJ7EEBVC7/VUEN6hFvv7dT0TEw+hnwOxfdLVk9vcjP7+cA6qg9H8mERG5KASY4PQ3hzj1zUHsaVlknUri0Nrt2JNSfR2aiMhFJTcfIq5pBIbC7f6hwVSqFuqboCogFVIiInJRcGTZSDr0S+FGt5vU42eoUkXPqYiI/FlgjWo0u/UmgutY8QsOIvyqBjS99SZMVYN8HVqFoUf7RETkomAwmbEEBFCQlVuo3S/AH6fzMt3PV0TkAuUBwc3r0KR2BE57AZbKQeS4wG73dWQVhwqpUpCXl8eOHZ+RkBBP9eo16NSpMwEBASUed/36ON57b7Xnc3z8aXr16kteXh779u0hIKASAPfeO5rOnbuya9c3LFz4L+x2O9269eCBB8Z6jZmQkMDMmVNJS0ulbt16TJsWQ2BgYIljFREpqYDwYGpefyXHNnzpafMPrUxoo5pkZ6uQEhH5q9xcN5gsEGjB4fJ1NBWPXshbwhfyHj58kClTnsLpdJGXl0tAQCVMJiMxMXNo3vyqUooUjh//iUmTnmTRojd59NEHmTfvFcLDwz3H7fY8hg27k4UL3yAiohpPPTWeQYOGccMNHQuN89RT4+nZsw/du/di+fIl5OTkMHbso6UW58X2orqKRLn3HeW+9FjcLnLjk0j58RT+QZW4omkd/KzB5OUV3V+59x3l3neUe99R7n3nYnwhr9ZIlUBeXh5TpjxFdnY2eXm5/9+WS3Z2NlOmPEXeuf7PfwHmzp3NAw88jL+/P7/9lsCcOTGMHDmUpUtfx+VycfDgAerUqUvNmrUwm8307NmHbdu2FhrD4XCwZ89uunS5BYA+ffqzbdvHpRajiEhJ5RuMmGpWo0nkjdTu0gpn8LmLKBEREV9SIVUCO3Z8htNZ9Dyqy+Xiiy+2l8p1du365v8f1+tOamoK117bjmeemcbrry9n797dfPDBOpKTkwgL+2OGKiwsnKSkxELjpKenExQUhNls/lOf30olRhGR0pSRYScrq8DXYYiIiJyTCqkSSEiI98xE/VVubi4JCfGlcp1169YyZMhdANSqVZvnn3+R0NArCAgI4M47h/DVV19Q1AOaBsNfv17vTkajfgVERERERIpLf0WXQPXqNTwbPvxVpUqVqF69RomvUVBQwJ4939OpU2cAfvrpRz799M+P47kxm81YrVZSUlI8rSkpyYXWUAGEhISSnZ3t2f0qJSWZsDBriWMUEREREaloVEiVQKdOnTGZik6h0WikY8ebS3yNn346Rp06dalU6WzB5na7eemleWRmZuJwOFi37r/cfHMXrrqqBSdP/sqpUydxOp1s2fIhHToU3mjCbDbTqlVrPv54CwCbN2+gQ4cbSxyjiIiIiEhFo0KqBAICAoiJmUNQUJCn0KlUqRJBQUHExMwplS3QT58+TUREhOdz48ZNGDFiFGPH3seIEYNo0qQpPXr0xt/fn0mTpjN58lOMGDGIevXq0bXr2U0lZs+eyY4dnwEwYUIU77+/lhEjBrF37x5Gjx5T4hhFRERERCoabX9ewu3P4ezufV98sd3zHqmOHW8ulSLqUqRtQX1Hufcd5d53lHvfUe59R7n3HeXedy7G7c/1Qt5SEBAQwC239PR1GCIiIiIiUk70aJ+IiIiIiEgxqZASEREREREpJhVSIiIiIiIixaRCSkREREREpJhUSImIiIiIiBSTdu0rJXl5eWRlZRIcXKVUtz6PjV3Oxo3vY7FY6NatByNH3sexY0d44YVZ2Gw2Wrduw5NPPoPZbCYhIYGZM6eSlpZK3br1mDYthsDAwELjFRQUMHt2NIcPH8Lf35/p02dRr179UotXRERERKQi0IxUCSUlJfLcczMYPPhWRo8eyeDBt/Lcc8+SlJRY4rF37fqGrVs3s2TJWyxbtpKDB3/gs88+ITp6KuPHT2TVqrW43W7Wr48DYN682dx++0Defvs9mje/iuXLl3iN+Z//rCIgoBIrV67h0UcnEBMzvcRxioiIiIhUNCqkSiApKZFx40azY8fnFBQUYLfnUVBQwI4d2xk3bnSJi6ljx45w/fU3EBRUGZPJRPv2N7JmzbvY7XZatLgGgL59I9m2bSsOh4M9e3bTpcstAPTp059t2z72GvOrr3bQs2cfAFq3vpaMjHQSEhJKFKeIiIiISEWjQqoEFi9+lawsG263q1C72+3CZrOxePFrJRq/adPm7Nz5FZmZGdjtdnbs2I7JZCIsLNzTJywsnMTERNLT0wkKCsJsNnvak5J+8xozOTnJ6/yi+omIiIiIyLlpjdQFysvL4+uvv/Qqon7ncrn4+usvyMvLu+A1U23bXk+fPv155JEHCQ6uQtu21/P999969TMaDYC7iHbvOtnt3Q2DQfW0iIiIiEhx6C/oC5SVlYnRaPrbPkajkaysrAu+Rk5ONp07d2PFilUsXPgGAQEBVK9eg9TUFE+flJRkwsOthISEkp2djdPp9LSHhVm9xrRaraSkeJ8vIiIiIiLnT4XUBQoOroLL5fzbPi6Xi+Dg4Au+xpkzZ3jmmSdxOBzYbDY++GAd/fvfhsViYd++PQBs3ryBDh1uxGw206pVaz7+eEuh9r+64YaObN68AYC9e/dgsfhTvXr1C45RRERERKQiUiF1gQICAujQoeM5H4szGo106NCxRFuhN27chC5dujFq1DBGj76HgQOH0LJla6ZNi+Hll+cxfPhAcnNzGThwKAATJkTx/vtrGTFiEHv37mH06DEAxMWtYcmSRQDceecQCgryGTFiMAsW/JOpU6MvOD4RERERkYrK4HYXtWqmYkhJseFyXfjt/75rn81mw+X6Y62U0WikcuXKLFy4GKs1ojRCvWRYrcEkJV3444xy4ZR731HufUe59x3l3neUe99R7n3HF7k3Gg2EhVU+9/GyvLjNZqN///6cOnUKgLVr19K3b18iIyOJiYnB4XAAZx9hGz58OL1792bMmDFkZ2d7jZWfn8/EiRPp06cPt99+Oz/99FNZhn5erNYIFi5cTMeON+Hn54e/vz9+fn507HhzhSyiREREREQqijIrpPbu3cuwYcP45ZdfADh+/Djz589n+fLlrF+/HofDQWxsLADPPvssd911F5s3b6ZFixa8+uqrXuPFxsZSqVIlNm3axKRJk4iKiiqr0IvFao1g0qQZrF79PosXx7J69ftMmjRdRZSIiIhIOTMaoUolqGxx4+fn62jkcldmhdTq1auZPn06ERFnC4ojR47QunVrz+euXbuydetWCgoK2LVrF7169QLgjjvuYPPmzV7jffrpp9x6660AtGvXjrS0NM6cOVNW4RdbQEAAVqu1RGuiREREROTCGI0Q6M4n8+gxMg8fwmxLJzBQ2wFI2Smz365Zs2bRtm1bz+fmzZuzd+9e4uPjcTqdbN68meTkZNLS0qhcubLnRbJWq5XffvN+QWxiYiJW6x/bdFutVhISEsoqfBERERG5hASawfbrzxSkpeK0ZZF59CjGXLuvw5LLWLm9kLdBgwZMmDCBMWPGEBAQQO/evdm/fz9F7XVhMBjOa8yiXjhbHH+3eEwunNV64Vu+S8ko976j3PuOcu87yr3vKPfe8rOycBUU/NHgdgOuUs+Vcu87F1vuy62QstvttGzZkri4OAA++ugj6tSpwxVXXIHNZsPpdGIymUhKSvI8/vdnERERJCUlUa9ePYBz9iuOku7aJ960m43vKPe+o9z7jnLvO8q97yj3RfP3h8Catcj68Ri43fhHVMPg51+quVLufafC7dr3Zzk5OYwcORKbzUZ+fj6xsbH07dsXPz8/2rZty8aNGwGIi4vj5ptv9jq/c+fOrFu3DoBvv/0Wf39/atasWV7h/08JCfEcOLCfhIR4X4ciIiIiUuHY7WAKCeWKVq0JadmSoLr1ycr3dVRyOfufM1I//vgjS5cuJT09vdBjeIsWLSrWhUJDQxk3bhxDhgzB4XDQv39/IiMjAZg+fTpRUVG89tpr1KhRg3nz5gHwzjvvkJiYyGOPPcbdd9/NtGnT6NevHxaLhTlz5hTr+mXl6NEjvPzyXE6c+BU/Pz8KCgqoW7cejzwygaZNm5XKNbKzbTz00D+YM2c+NWrUZNeub1i48F/Y7Xa6devBAw+M/b/27jw8qvru//9rlsxkJ2RjC4ugAREEFC2tCBWRRcIuyFKoXdzqjcBdQRQVpCwlNwhaFaza+hMtQmtLKiJ+gYpF8K6gFaKyiSA7ZIEkk2Qy2/n9wc3UmIQwgclJyPNxXV4X85k5Z97nncl4XjnnfI4k6Y9/fFlr12YpLi5ekjR48DCNHDla+/fv1cKF8+RyudS1azc98shjwWvSzisqKtKcOU/o+PFjSkhorDlzFigpKfmy1A8AAFAbisskyXHuASEKYVbtDXlHjBihG264QR07dix37dLw4cPDXly4Xeqpffv27dX06VNUVuau8JzTGanMzKWXHKa+/PILZWbO1bffHtLKlX9VYmKixo4dqeef/71SU5to+vQpGjVqrH74w1s0ffpUTZz4M3XqdH25dUyYMFqPPvqkOnXqrAUL5qhDh44aPvyucq955pmFSklpogkT7tH69e9q27aPNGfOgpDr5ZC3eei9eei9eei9eei9eei9eei9eerlqX1er1dPPPGERowYoeHDhwf/g/S73y2uNERJUlmZW7/73TOX/B7vvPM3/fd/P6rk5HMzFn711Zdq2bKVmjdvIbvdrn79BuqDDzZKkvbu/Upvvvn/6ac/HaNnnlmosrIynTx5QmVlZerUqbMk6c47Bwdf/10ff7xV/foNkCT17dtf//u/24I3TAYAVKFBUAAAIABJREFUAABQXrVBqnXr1jp9+nRt1FKvnDx5QocPf3vB1xw+fOiSr5maMeNJdenSLfg4Nzen3Cl3SUnJysk5rZKSEl1zTXs99NAUvfrqG3K5XHrttVcqfX1lP8/vvs5utysmJkZnz565pNoBAACAK1W110gFAgFlZGTouuuuk9PpDI6Heo3UlSYvL1cRERHyeKo+ATciIkJ5eblq2rTZZXvfyk7EtFisio6O1qJFzwXHxowZrwUL5uhHP+pZ4fVWa8Xp5S9lGnoAAACgoak2SN1xxx264447aqOWeiUpKVne796roBJer/eyT9iQkpKivLy84OO8vFwlJyfr5MmT2rHjX8rIGCrpXOCy2+1KSUlVfv73X59SyXrPvS41tYl8Pp+Ki4vVqFHCZa0dAADgYjgckt0vWSMkV+VXUQCmqzZIff96KMMw9O23Fz6lrSFo2rSZWrVqra+/3l/la1q1anNZj0ZJUseOnXTkyLc6evSImjVrrg0b3tegQUPkdDq1bNlzuuGG7mrWrLn++tfV6tXrNjVt2kwOh0O7dn2u66/vqvXr31WPHj+qsN4ePW7R+vXvauLEn+sf/9igLl26VpjZDwAAINyi7VLpsdM6teegrHabkjpdI0eTxiopCZhdGlBOtXvKb731ljIzM1VaWhocS0xM1NatW8NaWH0wadKvLzhr36RJ/33Z39PpdOrxx2dp5szp8njK9MMf3qLbbrtdFotF06Y9rkcfnSqv16frr++iMWN+Ikl66qm5ysycG7yO6q67xkiSXnlluZKTkzVs2F26994HNG/ebP3kJ6MVFxerp56ae9lrBwAAqI73bKG+/X9bZfjPXXZQfDJPVw3qJTmiTK4MKK/a6c/79OmjJUuWaNmyZZoyZYo++OADnTx5Uk8//XRt1Rg2lzr9uXT+PlLP6PDhQ9+5j1QbTZr035ftPlL1CdOCmofem4fem4fem+dK7n1MjE2lpX4F6ugBkCu59zab5D10VEc++KTceNuht8nfqLFJVf3Hldz7uq4uTn9e7RGphIQEdenSRddee63y8vL04IMPasSIEZe1yPosPb29fve7l3Ty5Anl5eUqKSn5sp/OBwAAws/plHTWpaJvc2WPiVZ0cqJKqt9VwmXk90sRjeIkiyU4w5YtyilblEN+k2sDvq/abwe73a6CggK1bt1au3bt0i233KLi4uLaqK1eadq0GQEKAID67EyRjv5jqwzvuV32hA5tlXT9tSq68NxSuMwiEhqp5W03K++rr2VzRCj5+nQZMTESPwfUMdUGqdGjR+v+++/X8uXLNWzYMG3YsEHt2rWrjdoAAKhTrB63XKcLVOYqU3TjGMWkNJbPWu0tGVEPxMc7lL//ZDBESVLB/m8Vf00byc61ObXJHZDi2rdSTFoTyWqR327TBe42A5im2iB111136c4771R0dLRWrVql7Oxs9exZ8d5EAABcyYyCIv3v21t0dOdBSZLFZlHH27up/e3dpMhIk6vDpfL7/bJHOcuN2aMjZbURlM1QXOyXZJcCkghRqKOq/XYIBAJauXKlHn30UcXExOjAgQOKiIiojdoAAKgTnFZpzwc7gyFKkgy/oS//32fK2XNEDoeJxeGyKC72K7pFU8WmNZV07rqc5K4dZY2LMbkyAHVVtUekMjMzlZ+fr+zsbBmGoS1btignJ0dPPPFEbdQHAIDpSvILdOizfZU+d/Czr9WiaztdxN8mUceVWBxKurmbGndxy2q3yh8ZKZfLZ3ZZAOqoar/1P/74Y/32t7+V0+lUXFyc/vCHP3APKQBAg2IEDPl8lc+Fbfj88jOf2BWj1LCqzBGtUmsk1+UAuKBqg5Tdbpf1OxfSOhwO2e1MBQoAaDiikhLUqnPbSp9r2aWtnNHOSp8DAFy5qk1E6enpevPNN+X3+/XNN9/otddeU4cOHWqjNgAA6gSvIXXs21X5x3J15khOcPyqm65R885tVFJSR+/cCgAIm2qD1MyZMzV//nzl5eVp7Nix6tmzJ9dHAQAaHGeTRPW+706dPZarkjMuNWqWqLhmiczYBwANVLVBKjY2VvPnz6+NWgAAqLMCAckaH6vE+Fg1j5TcbrMrAgCYqdogtX//fq1YsUIFBQXlxp999tmwFQUAQF1GiAIAVBukpkyZop49e6p9+/a1UQ8AAAAA1HnVBqnIyEg99thjtVELAAAAANQL1U5/fvPNN+vDDz+U3889MgAAAABAuogjUklJSbr//vtlsVgkSYZhyGKxaPfu3WEvDgAAAADqomqD1IoVK7R69Wq1bNmyNuoBAAAAgDqv2iCVmJio66+/vjZqAQAAAIB6odog1aNHDz388MPq16+fHA5HcLxfv35hLQwAAAAA6qpqg9QXX3whSVq1alVwzGKxEKQAAAAANFgXdY0UAAAAAOA/qp3+HAAAAABQHkEKAAAAAEJEkAIAAACAEFUbpH75y19WGBs9enRYigEAAABweURGSjEWj2JUpli7YXY5V5wqJ5t4+OGHdfDgQR05ckSDBw8Ojvt8PlmtHMgCAAAA6iqnUzJy8pS3d58Mr1eRqcmKvyZdRYFq55rDRaqyk9OnT9exY8f05JNP6sknnwyO22w2XXPNNbVSHAAAAIDQRXi8yt2/X4bXK0lyn86VIzFJMWnNVVzsN7m6K0OVQSotLU1paWlav349R6AAAACA+sQwZJR5yg0Fytyy2WySCFKXQ7XH9jZt2qTMzEzl5+fLMAwZhiGLxaLPPvusNuoDAAAAECKL06GoZk1VeuLkuQGbTc6UFBUWei68IC5atUEqMzNTjz76qDp06CCLxVIbNQEAAAC4BEVlUmy7dnIkJsrvdsuZnKhAdLTkNruyK0e1QSouLk59+/atjVoAAAAAXCYuv02RqclyRkSoqMhLiLrMqr34qXPnztq4cWNt1AIAAADgMnK7dS5E4bKr8ohUt27dZLFY5Pf7tWrVKjkcDtntdq6RAgAAANDgVRmk1q5dW5t1AAAAAEC9UWWQatGihSRpzZo1FZ6LioqS2+1Wu3btqn0Dl8ulMWPGaPny5UpLS9NHH32kzMxMBQIBdezYUXPnzlVRUZF+/vOfB5cpKirSmTNn9O9//7vcuo4fP65BgwapVatWkqTk5GS9+uqrF7elAAAAAHCZVDvZRFZWlj7//HP16NFDNptNH3/8sVq2bKnCwkLdf//9uvvuu6tcdufOnXriiSd06NCh4NjMmTP1hz/8Qe3atdPDDz+srKwsjRo1SllZWZKkQCCgn/70p5o6dWqF9WVnZ2vw4MGaM2dODTYVABAO0dFW+c8Uq+xsgWxOh5wJjVQim9llAQAQVtVONmGxWPSXv/xFy5Yt0/PPP681a9aoSZMm+vvf/64333zzgsuuXr1as2bNUmpqanDM7/fL5XLJ7/errKxMTqez3DJvv/22oqKiNHjw4Arry87O1r59+zRixAhNnDhRe/fuvdjtBACEiTe3QAfXbta372/TN3/frNM7shWlgNllAQAQVtUekcrJySl3Cl/Lli116tQpxcbG/t+dkas2b968CmOzZ8/WhAkTFBsbq7S0NA0YMCD4nN/v17Jly7Rs2bJK1+d0OjVs2DCNGTNGH374oR566CGtW7dODoejus0AAIRBo0ZOndj5pbzFpcGxvK++UeMObaXYeBMrAwAgvKoNUo0aNdKqVat01113yTAMvf3220pISNDBgwcVCIT2F8ecnBwtWrRIa9euVVpamhYsWKAFCxZo1qxZkqQtW7boqquuUvv27StdftKkScF/9+7dW4sXL9Y333yjDh06hFTHeUlJsTVaDheWkhJndgkNFr03T0PufcDnr2QwUGs9aci9Nxu9Nw+9Nw+9N09d6321QWrevHl69NFH9fTTT8tiseiGG27Qb3/7W73zzjt68MEHQ3qzHTt2KD09PThZxOjRozVlypTg8xs3btSdd95Z5fIrVqxQRkaGGjduLEkyDEN2e7WbUKW8PJcCAaPGy6OilJQ45eQUmV1Gg0TvzdPQe59wdUvl7zsow3suUMW3aiZbbEyt9KSh995M9N489N489N48ZvTearVc8MBLtSmkdevWeuutt1RYWCir1arY2HMre+CBB0IuJj09XQsXLlRubq6Sk5O1adMmde7cOfj8559/rnvvvbfK5bdv3y632617771Xn3zyiQKBgNq2bRtyHQCAy8eW0FjtBv9YrmOnFREdpejmKXJbI8wuCwCAsKoySM2bN08zZ86sMjAtX7485Ddr166dJk+erIkTJ8pms6l169blZuA7cuSImjZtWm6ZlStX6vTp05o8ebJmzpypGTNmKCsrS06nU4sXL5bVWu18GQCAMCozJMU2UtKNSfJ4fHJ7zK4IAIDwqzJI/fCHP5Qk9e/f/5Lf5B//+Efw38OHD9fw4cMrfd3OnTsrjI0dOzb47yZNmuiPf/zjJdcDALj8XC6f2SUAAFBrqgxSN9xwg86ePavbbrutNusBAAAA6oSICMmdU6DSM8WyR0borM8j2ZktGudUGaR69Oghi8Uiwzg3GYPFYpF0boIHi8Wi3bt3106FAAAAQC2LkHQq+1t9smqzSs8USxbpqh+0V9fBPaSYGLPLQx1QZZDas2ePJMnr9SoigouGAQAA0HC4zxTok5WbVVpQfG7AkA7+714ltUzVtf27cTozVO1MDX369NGSJUt04sSJ2qgHAACgwbPbJUfAJ4vLpQivW1FRFrNLanBKzhT/J0R9x/GvDqvU5TahItQ11QapVatWyWazady4cbr//vu1efPm4Ol+AAAAuLzsdqnsyGnt/utmffryO/py9Qc6u/uoomp+60zUgN1ZecOj4qIUcQn3McWVo9og1bx5cz388MPatGmTRo0apd/85je6/fbb9corr8jtJo0DAABcTv78QmX/5QPlf31c/jKvCo7kKPvPm1VyMs/s0hqU6ORGanXjNeXGbBE2tf3htXJzVh90ETfklaQDBw7oz3/+s9auXauuXbtqxIgR2rJliyZNmqSXX3453DUCAAA0CHa7dPZ4rrzFZeXGDb9fufuPqV3bZiZV1vAYTqduHPYjJbVM1vHdhxXVKFbX/KijGrVuJr/ZxaFOqDZIjRkzRkePHtXIkSP15z//Wc2anfsF/vGPf6wePXqEvUAAAICGwmqVjEAVl1BwaUXti4vR9UN+oLY9r5MtwqGE5Djl5BSZXRXqiGqD1Pjx4zVgwIAKM/dZrVZ98MEHYSsMAACgofF4pPgWybJFOeUv/c9RKYvNoqRrWqiw0KOUFKeJFTY8hYXn7h1VVb5Fw1VtkOrfv782b96s4uJzs5b4/X4dPnxYU6dOVQxz6AMAAFxWESmN1GlkLx368HMVHM1RTGpjtbn1ekWmJsrDzjxQZ1QbpKZOnaojR44oJydHHTt21M6dO3XzzTfXRm0AAAANjscjxV/dTNc1SZTf7ZHVYVdkUhz3LQLqmGpn7du9e7f++te/6vbbb9fjjz+ut956S0VFnBsKAAAQLm635I1wKhAXJ58zihAF1EHVBqnU1FTZ7Xa1adNG+/bt09VXX63S0tLaqA0AAAAA6qRqg1R0dLTeeecddejQQe+995727t2rs2fP1kZtAAAAAFAnVRuknnzySe3evVu33HKLrFarJkyYoF/84he1URsAAAAA1EnVTjZx1VVXafr06ZKkpUuXhr0gAAAAAKjrLhikNmzYoFdeeUX79u1TVFSU0tPT9fOf/1y9evWqrfoAAAAAoM6pMkitWbNGL774oh5++GF16NBBFotFu3bt0ty5c/XII4+oX79+tVknAAAAANQZVQapFStW6LXXXlPz5s2DY+3atVPXrl31+OOPE6QAAAAANFhVTjbh9XrLhajzrrrqKpWVlYW1KAAAAACoy6oMUjabrcqFDMMISzEAAAAAUB9UO/05AAAAAKC8Kq+R2rt3r2644YYK44ZhyOPxhLUoAAAAAKjLqgxSGzZsqM06AAAAAKDeqDJItWjRojbrAAAAAIB6g2ukAAAAACBEBCkAAAAACBFBCgAAAECtiHFITn+ZYiyGrPU8iVR5jRQAAAAAXC4RJcU69vE+FRw9LWd8jJp2ba+o5ikq9ZtdWc0QpAAAAACElcPr1jfvfyzX8VxJUunpMyo4eELtR/WRkpJMrq5m6vkBNQAAAAB1XVl+YTBEnWf4/crf861iYmwmVXVpCFIAAAAAwirg8VY67it1y+Kvn+f2EaQAAAAAhJWzcbwsEREVxhu3S5PXMKGgy4AgBQAAgEsWaw8o0l2kSE+xYuvnmVoII3t8nNr1/4Ei4qIkSRabRU27X6vYVk1VVmZycTXEZBMAAAC4JNE2QwV7D+js7q8li0Up3bvI2apZvd1BxuXnDkjR16Tp2tREeUtKZXVEyNEoVqWB+ntcp/5WDgAAgDrB8JapYP/B/3tgqPDQYUX4AuYWhTrH45E8UdEykpLkj4uv1yFKIkgBAIB6LsZuKFZlirX65HCYXU3DZLXa5GwUF3zsjI+TxcH5fbiycWofAACot2IdUvGhw3IdPCRbbLQSO3eSxxpldlkNjtsWoZSbuqrw4BHZIuyKbdNSRaX1dAYB4CJxRAoAANRbAXeJXN8clAxD/qJilRw9rkaNnGaX1eD4/VKJPUqJN3ZSo+s7qFgVZ2cDrjQckQIAAPWWxWqVLBbJOHf0w+KwyzA4EmKWwkKP2SUAtYYgBQAA6i0jIlKNu3RWyZFjskdHK7p5C3bmAdSKsJ/a53K5lJGRoaNHj0qSPvroIw0ZMkQZGRmaPn26PJ5zX3Zr1qxRz549NXToUA0dOlRLliypsK7CwkLdd999GjhwoMaPH6+cnJxwlw8AAOqwEq/kaJqqxl07K7Z9OxUH+BsxgNoR1iC1c+dOjR07VocOHQqOzZw5U0uWLNHatWvldruVlZUlScrOztaMGTOUlZWlrKwsTZ06tcL6li5dqu7du+u9997TqFGjNG/evHCWX2sCRS7l7D6k45/ukftUnjizGwCAi+dy+VRUJrncZlcCoCEJa5BavXq1Zs2apdTU1OCY3++Xy+WS3+9XWVmZnM5zsSE7O1tr1qzRkCFD9Mgjj6igoKDC+jZv3qzBgwdLkjIyMvTPf/5TXq83nJsQdt4zBfrg5fVa/+wabXp5vdb8drWO7zmkyEizKwMAAABQlbAGqXnz5ql79+7lxmbPnq0JEybo1ltv1ZkzZzRgwABJUkpKiiZNmqSsrCw1a9ZMc+bMqbC+06dPKyUlRZJkt9sVGxur/Pz8cG5CWMXE2HT0i0M6vu9ocMxbWqZ/r9su95lSEysDAAAAcCG1eiJxTk6OFi1apLVr1yotLU0LFizQggULNGvWLL3wwgvB1/3yl79U3759L2qdVmvNs2BSUmyNl71c8o9XDIL5J/LlLSlT81aplSxR96WkxFX/IoQFvTcPvTcPvTcPvTcPvTcPvTdPXet9rQapHTt2KD09Xa1atZIkjR49WlOmTFFRUZHefvtt3XPPPZIkwzBkt1csLTU1Vbm5uWratKl8Pp9cLpcSEhJqXE9enkuBgHlTpDocUvP0Fvrqg53lxpult5CjcZxycopMqqzmUlLqZ91XAnpvHnpvHnpvHnpvHnpvHnpvHjN6b7VaLnjgpVZvyJuenq5du3YpNzdXkrRp0yZ17txZ0dHReuWVV7Rz57lA8cYbb+iOO+6osHzv3r21Zs0aSdK6devUvXt3RUTU3xu+eTxS0/Yt1blvN1lsFklSk3bNdMOgm2VYbSZXBwAAAKAqtXpEql27dpo8ebImTpwom82m1q1ba86cObLZbFq6dKlmz54tt9utNm3aKDMzU5L07LPPKjU1VWPHjtXkyZM1Y8YMDRo0SHFxcVq0aFFtlh8Wlugo3TjyVrXveZ38Xr9iEmJlT4iR3292ZQAAAACqYjEa8O2/zT6170rEIW/z0Hvz0Hvz0Hvz0Hvz0Hvz0HvzNPhT+wAAAADgSkCQAgAAAIAQEaQAAAAAIEQEKQAAAAAIEUEKAAAAAEJEkAIAAACAEBGkAAAAACBEBCkAAAAACBFBCgAAAABCRJACAAAAgBDZzS4AAAAAQHkxTska8MsiyWu3q7TUMLskfA9BCgAAAKhDYiMlb36eSo4els3hUGybdnI4IuXxmF0ZvotT+wAAAIC6xOdTybcHJb9f/tJSFZ88pkg7u+11DT8RAAAAoA6xSJLFEnxstdhkGJzaV9cQpAAAAIA6xO9wKLbd1bJFRSkiIUHRzVuoiGuk6hyukQIAAADqkJKSgJyxjRSX3kiySEVlZleEyhCkAAAAgDqmrEwiP9VtnNoHAAAAACEiSAEAAABAiAhSAAAAABAighQAAAAAhIggBQAAAAAhIkgBAAAAQIgIUgAAAAAQIoIUAAAAAISIIAUAAAAAISJIAQAAAECICFIAAAAAECKCFAAAAACEiCAFAAAAACEiSAEAAABAiAhSAAAAABAighQA1DMJCZGKj3eYXQYAAA2a3ewCAAAXx6GAzh46qa93HlBkoxildbtaahRvdlkAADRIBCkAqCcKj57Wv/74ngy/IUnK//aUuo3pI7/TaXJlAAA0PJzaBwD1QHy8Q3lfHw+GKEnK3X9MZYUuE6sCAKDh4ogUgHIcDsmb51LxqXzZHHbFNE2UzxFpdlkNntvtUUxKQrkxZ6MY2R0RJlUEAEDDRpACUI4vz6WdKzeqNLdAktSsaztdPaCHPFa+Lszk8UhJ7Zqpbc9OOvzpXkXFx+raATcpIjleXq/Z1QEA0PCwZwQgyOmUcr88GQxRknTi8wNq+YOOsiQlmlgZJMkfGaX2A3uozY+uk8VuUUxqglwun9llAQDQIBGkAAT5/ZLVbis/aLHIYuFyyrqizJAUHydDIkQBAGAi9o4ABPl8UlyrJmrcpum5AYvUpmdnORPjzC0MAACgjgn7ESmXy6UxY8Zo+fLlSktL00cffaTMzEwFAgF17NhRc+fOlcPh0Keffqr58+fL5/MpISFB8+fPV4sWLcqt6/jx4xo0aJBatWolSUpOTtarr74a7k0AGpRAVLQ63vVjleUXymq3ypGYoDLZql8QAACgAQnrEamdO3dq7NixOnToUHBs5syZWrJkidauXSu3262srCxJ0rRp0zRv3jxlZWVp8ODBmjt3boX1ZWdna/DgwcrKylJWVhYhCggTn8MpW9MUWZKT5LUSogAAAL4vrEFq9erVmjVrllJTU4Njfr9fLpdLfr9fZWVlcjqd8ng8mjx5sjp06CBJat++vU6cOFFhfdnZ2dq3b59GjBihiRMnau/eveEsHwAAAAAqFdZT++bNm1dhbPbs2ZowYYJiY2OVlpamAQMGyOFwaOjQoZKkQCCg559/Xn379q2wrNPp1LBhwzRmzBh9+OGHeuihh7Ru3To5HI5wbgYAAAAAlGMxDMMI95v06dNHr7/+upxOpyZMmKDf//73SktL04IFC+Tz+TRr1ixJksfj0YwZM1RQUKDly5crIuLCN5ocMmSIMjMzg0eyAAAAAKA21Or05zt27FB6enpwsojRo0drypQpkqTi4mI9+OCDSkhI0LJlyyoNUStWrFBGRoYaN24sSTIMQ3Z7zTchL8+lQCDsObJBSUmJU05OkdllNEj03jz03jz03jz03jz03jz03jxm9N5qtSgpKbbq52uxFqWnp2vXrl3Kzc2VJG3atEmdO3eWdG6yidatW+vZZ5+t8lS97du36y9/+Ysk6ZNPPlEgEFDbtm1rp3gAAAAA+D+1ekSqXbt2mjx5siZOnCibzabWrVtrzpw5+uqrr7Rp0yZdffXVGjZsmCQpNTVVL7/8slauXKnTp09r8uTJmjlzpmbMmKGsrCw5nU4tXrxYViu3wgIAAABQu2rlGqm6ilP7Lj8OeZuH3puH3puH3puH3puH3puH3punwZ/aBwAAAABXAoIUAAAAAISIIAUAAAAAISJIAQAAAECICFIAAAAAECKCFAAAAACEiCAFAAAAACEiSAEAAABAiAhSAAAAABAighQAAAAAhIggBQBosKLlk6PYJaenVA6H2dUAAOoTu9kFAABghkivW8e2fq7CQ8dli4lSWs9uimjVVF6v2ZUBAOoDjkgBABqcuLgInd1/WIWHjkuS/MWlOr7tc1lLSk2uDABQXxCkAAANjs1mk7eouNyYt6hEhi9gUkUAgPqGIAUAaHDOnnUrvl3LcmMJ16TJFhNpUkUAgPqGa6QAAA2SMzlZbTJ6qejb43LGxyqubQuVBmxmlwUAqCcIUgCABsktScnJatGuhfx+v1wun9klAQDqEYIUAKBBKygoM7sEAEA9xDVSAAAAABAighQAAAAAhIggBQAAAAAhIkgBAAAAQIgIUgAAAAAQIoIUAAAAAISIIAUAAFCH2O2S1edRRMAvK3tqQJ3FfaQAAADqCLtdKj6So+x3/yWLzabOd94kR5Nks8sCUAmCFAAAQF3h8Sn7ve06sfuIJMlmt+vmn94hT8DkugBUwAFjAABwUSLlk62wQA53sdmlXLFsVptsNlvwscVulY0/ewN1Er+aAACgWpGGXyf/9YVOf75PEXFRunpwLym+kdllXXHKZFGnO7vL6rDLKunafjeo1GN2VQAqQ5ACAADV8pe6dXrnfkmSt6hUriOnlXxzooqL/SZXduWJSElSj4l9JUmlXsPkagBUhVP7AABAtawOm2KbJZ17YLEoMqkRISqMSr0GIQqo4zgiBQAwTZQlIE9evoyAIWdyY5Xyv6U6q8weqTb9e6j4RK4ccTGKTklUMRMgAGjA+D8WAMAU8fEO5X22W6f+lS1JSurSXik3XitXmcmFoUoeZ7ScbVspEJCik+JUnFNkdkkAYBpO7QMAmMLwelVyMjf4uORkrvxlXhMrwsUIcBQKACQRpAAAJjFsNjVq10qyWSSLRY2vaSUjMsLssgAAuCic2gcAMIXL5VNM62ZqO+x2KWDIGh8nt9vfsoCeAAAYTklEQVTsqgAAuDgEKQCAaYo9kiJjzz3wmVoKAAAh4dQ+AAAAAAgRQQoAAAAAQkSQAgAAAIAQEaQAAAAAIEQEKQAAAAAIUViDlMvlUkZGho4ePSpJ+uijjzRkyBBlZGRo+vTp8ng8kqTjx49r/PjxGjBggB588EEVFxdXWJfH49G0adM0cOBADR8+XAcOHAhn6QAAAABQpbAFqZ07d2rs2LE6dOhQcGzmzJlasmSJ1q5dK7fbraysLEnS008/rXHjxmn9+vXq1KmTXnzxxQrrW7FihaKiovTee+/p8ccf14wZM8JVOgAAAABcUNiC1OrVqzVr1iylpqYGx/x+v1wul/x+v8rKyuR0OuX1erV9+3b1799fkjRixAitX7++wvo2b96sIUOGSJJuuukmnTlzRsePHw9X+QAAAABQpbDdkHfevHkVxmbPnq0JEyYoNjZWaWlpGjBggM6cOaPY2FjZ7edKSUlJ0alTpyose/r0aaWkpAQfp6Sk6OTJk2revHmNa0xKiq3xsqhaSkqc2SU0WPTePPTePPTePPTePPTePPTePHWt92ELUt+Xk5OjRYsWae3atUpLS9OCBQu0YMECPfDAAxVea7FYLmqdVuulHVDLy3MpEDAuaR0oLyUlTjk5RWaX0SDRe/PQe/PQe/PQe/PQe/PQe/OY0Xur1XLBAy+1Nmvfjh07lJ6erlatWslqtWr06NH65JNPlJiYGDzdTzoXuL57OuB5qampysnJCT6u6nUAAAAAEG61FqTS09O1a9cu5ebmSpI2bdqkzp07KyIiQt27d9e6deskSWvWrFGvXr0qLN+7d+/g5BQ7duyQ0+m8pNP6AAAAAKCmau3Uvnbt2mny5MmaOHGibDabWrdurTlz5kiSZs2apRkzZmjZsmVq1qyZnnnmGUnSypUrdfr0aU2ePFkTJkzQU089pUGDBsnhcCgzM7O2SgcAAACAciyGYTTYi4S4Rury49xh89B789B789B789B789B789B78zToa6QAAACAuio62qoYuyGn0+xKUF/U2ql9AAAAQF0UYwmocNfXKj6dp/ir0hTVsoVKfWZXhbqOIAUAAIAGzZ2Xr9ydeyRJpSdz1SqhkRTF/UZxYZzaBwAAUAvi4x2Ki4swuwxUwggEvvPAkGEEqn4x8H84IgUAABBmMSpT4e6DstkjFNe8mYr8NrNLwndEJjdW4w5tVZKTr/g2abLHxcnjN7sq1HUEKQAAgDCKtQd0Nnuv3KfP3UvT7/GoUadrVVBQZnJlOK9EEUrq2lEJXp8UYVeJ1+yKUB8QpAAAAMLI8AfkLS4NPg643fL7OdxR1xR5JckuEaJwkbhGCgAAIIz8Trvir75KlogI2aKiFNO6pTwepoQD6juOSAEAAISR2y1FJ6cq9UcJksUiRUeqtJgjUkB9R5ACAAAIsxKfJDkkQxIhCrgicGofAAAAAISIIAUAAAAAISJIAQAAAECICFIAAAAAECKCFAAAAACEiCAFAAAAACEiSAEAAABAiAhSAAAAABAighQAAAAAhIggBQAAAAAhIkgBAAAAQIgIUgAAAAAQIoIUAAAAAISIIAUAAAAAISJIAQAAAECICFIAAAAAECKCFAAAAACEiCAFAAAAACGym10AAAAAgLrNapVi7JIR8Muw2VRcZnZF5iNIAQAAALigGItPRd98o4C7VFHN0hSblCSX2+yqzMWpfQAAAACqFB/vUFn+afmLCmV4vSo5fFAWn8/sskxHkAIAAABQJcMwZLH950Q2i80mi8ViYkV1A6f2AQAAAKhSUZFXjZJSZPh98rnLFJXaRKWymV2W6QhSAAAAAC6ooMyi6GZpctps8nq98jbw66MkghQAAACAi1BSEpAUMLuMOoNrpAAAAAAgRAQpAAAAAAgRQQoAAAAAQkSQAgAAAIAQEaQAAAAAIEQEKQAAAAAIUdinP3e5XBozZoyWL1+uAwcO6Jlnngk+d+rUKXXp0kVTpkzRjBkzguP5+flq1KiR1q5dW25d27dv13/913+padOmkqSOHTtqwYIF4d4EAAAAACgnrEFq586deuKJJ3To0CFJUu/evdW7d29JUk5OjsaOHavHHntMbdq0UVZWliSptLRUo0aN0uzZsyusLzs7Wz//+c91//33h7NsAAAAALigsJ7at3r1as2aNUupqakVnsvMzNSYMWPUpk2bcuMvvfSSbrrpJnXv3r3CMtnZ2dq6dauGDRumBx54QCdOnAhX6QAAAABQJYthGEa436RPnz56/fXXlZaWJkk6dOiQfvrTn2rDhg1yOBzB1xUWFmrAgAF65513lJSUVGE9Tz31lHr16qW+fftq5cqVysrK0ltvvRXu8gEAAACgnLBfI1WZVatWady4ceVClCS988476tu3b6UhSpLmzJkT/PfYsWO1ePFiFRUVKS4urkZ15OW5FAiEPUc2KCkpccrJKTK7jAaJ3puH3puH3puH3puH3puH3pvHjN5brRYlJcVW/Xwt1hK0adMm3XnnnRXGN27cWOm4JAUCAS1btkx+v7/cuN1uShYEAAAA0IDVepDKz8+X2+1Wy5Yty40bhqEvv/xS3bp1q3Q5q9WqDRs26P3335ckrVmzRl26dFFUVFTYawYAAACA76r1IHX06NHg9OXflZ+fr4iICDmdznLjzz77rFauXClJWrhwoV5//XUNGjRIb7/9tubOnVsrNQMAAADAd9XKZBN1FddIXX6cO2weem8eem8eem8eem8eem8eem+euniNVIO+wMhqtZhdwhWJvpqH3puH3puH3puH3puH3puH3puntntf3fs16CNSAAAAAFATpszaBwAAAAD1GUEKAAAAAEJEkAIAAACAEBGkAAAAACBEBCkAAAAACBFBCgAAAABCRJACAAAAgBARpAAAAAAgRAQpAAAAAAgRQQoAAAAAQkSQAgAAAIAQEaQAAAAAIEQEKVTL5XIpIyNDR48elST96U9/0qBBg3TnnXdq4cKFMgyjwjJr1qxRz549NXToUA0dOlRLliyRJB0/flzjx4/XgAED9OCDD6q4uLhWt6W+CbX3eXl5wZ4PHTpUffr0Ubdu3SRJ27dv1w9+8IPgc4899litb0998v3en/fmm29qwoQJlS5TWFio++67TwMHDtT48eOVk5MjSfJ4PJo2bZoGDhyo4cOH68CBA2Gvvz6rSe8PHDigcePGaejQobr77ru1e/duSee+c7p16xb83P/iF78Ie/31WU16X9V3S1W/D6hcTXo/YsSIYN/79++vjh07Kjc3l899iL7f+8cee0z9+vUL9m/Dhg0Vltm9e7dGjhyp/v37a+bMmfL5fJLYzwlVTXq/ceNGDR06VEOGDNGvfvUrFRQUSKp63zOsDOACPv/8cyMjI8O47rrrjCNHjhiHDx827rjjDqO4uNjw+XzG3XffbWzZsqXCcnPmzDHeeeedCuP33XefsXbtWsMwDOP55583MjMzw74N9VVNe3+e3+83fvKTnxh///vfDcMwjFdffdVYvnx5bZVfr32/9+ft37/fuPXWW42f/OQnlS739NNPGy+99JJhGIbxt7/9zZg8ebJhGIbxyiuvGE8++aRhGIbxySefGHfddVeYt6D+qmnvx4wZY/zjH/8wDMMwtm3bZgwePNgwDMNYv359sPe4sJr2vqrvlqp+H1BRTXv/XdOmTTOWLVtmGAaf+1BU1vuMjAzj1KlTF1xu0KBBxr///W/DMAzjscceM958803DMNjPCUVNel9UVGTccsstxsmTJw3DMIylS5cav/nNbwzDqHrfM5w4IoULWr16tWbNmqXU1FRJUsuWLfXuu+8qOjpahYWFcrlcio+Pr7Bcdna21qxZoyFDhuiRRx5RQUGBvF6vtm/frv79+0s695e09evX1+r21Cc17f15b7/9tqKiojR48GBJ534mW7du1bBhw/TAAw/oxIkTtbId9dH3ey+dO6r01FNPafLkyVUut3nz5mC/MzIy9M9//lNer1ebN2/WkCFDJEk33XSTzpw5o+PHj4d3I+qpmvZ+1KhR6tWrlySpffv2wc93dna29u3bpxEjRmjixInau3dveDegHqtp76v6bqnq9wEV1bT353388cfas2eP7r33Xkl87kPx/d6XlJTo+PHjevLJJzV48GA999xzCgQC5ZY5duyY3G63unbtKuk/+zPs54SmJr33er2aPXu2mjRpIqni9/339z3DjSCFC5o3b566d+9ebiwiIkKrV69W3759lZKSog4dOlRYLiUlRZMmTVJWVpaaNWumOXPm6MyZM4qNjZXdbg++5tSpU7WyHfVRTXsvSX6/X8uWLdOvf/3r4FhcXJwmTpyoNWvWqHfv3po6dWpY66/PKuv94sWLNXLkSKWlpVW53OnTp5WSkiJJstvtio2NVX5+frlx6dxn/+TJk+Epvp6rae9HjBghm80mSXruuefUt29fSZLT6dSwYcP017/+Vb/4xS/00EMPyePxhG8D6rGa9r6q75aqfh9QUU17f95zzz2nqVOnBn8H+NxfvO/3Pi8vTz169ND8+fO1evVq7dixQ3/5y1/KLVPZd/qpU6fYzwlRTXrfuHHj4Pe72+3W73//++DjyvY9w40ghRoZPXq0/vWvfyk5OVnPP/98hedfeOEFdenSRRaLRb/85S/1z3/+s9JrqSwWS22Ue0WprveStGXLFl111VVq3759cGzOnDnBL5uxY8fq66+/VlFRUa3UXN9t3bpVJ06c0MiRI0Ne1mqt/Gu2qnGUF0rvDcPQwoULtXPnTj3++OOSpEmTJmnMmDGSpN69eys6OlrffPNNWGu+Ulxs70P5buFzf3FC+dzv379fZ86c0W233RYc43Nfcy1bttQLL7ygpKQkRUVFacKECfrwww/Lvaaq/Rn2cy7NxfT+vKKiIt17773q0KGDhg8fLqnyfc9w4xsNITlx4oQ+/fRTSef+wjho0KAKpwwUFRXptddeCz42DEN2u12JiYlyuVzy+/2SpJycnHKnMeDCLqb3523cuFF33nln8HEgENCyZcuCvT/v/F/NcGFr167V/v37NXToUD3xxBP64osvNGXKlAqvS01NVW5uriTJ5/PJ5XIpISFBqamp5S6057N/8S629z6fT4888oiys7P1+uuvKy4uTpK0YsUKnTlzJvi6899HqN7F9P5C3y1V/T6gehf7uZcqft9LfO4vxd69e/X+++8HH1fWuyZNmgQ/29J/vtPZz7k0F9N76dwRwXHjxqlDhw6aN2+epKr3PcONIIWQFBUVadq0aSosLJRhGHr//fd14403lntNdHS0XnnlFe3cuVOS9MYbb+iOO+5QRESEunfvrnXr1kk6N7vK+WsaUL2L6f15n3/+ebnD5VarVRs2bAh+Qa1Zs0ZdunRRVFRUrdRe3y1YsEDvvfeesrKyNHfuXHXq1ElLly6t8LrevXtrzZo1kqR169ape/fuioiIUO/evZWVlSVJ2rFjh5xOp5o3b16r21BfXWzvFy5cKJfLpT/84Q/BECWdm1Hu/Kkhn3zyiQKBgNq2bVtr9ddnF9P7C323VPX7gOpd7Odeqvh9L/G5vxSGYWj+/PnBa7tXrVqlO+64o9xrWrRoIafTGfzj5vn9GfZzLs3F9N7v9+uBBx7QwIEDNXPmzOARv6r2PcONP08gJOnp6brvvvs0ZswY2Ww2de/eXT/72c8kSTNnzlSfPn10++23a+nSpZo9e7bcbrfatGmjzMxMSdKsWbM0Y8YMLVu2TM2aNdMzzzxj5ubUKxfbe0k6cuSImjZtWm75hQsX6sknn9QLL7ygxMTE4M8El+bZZ59Vamqqxo4dq8mTJ2vGjBkaNGiQ4uLitGjRIknShAkT9NRTT2nQoEFyOBz0/jI53/v+/fvrzTffVFpamkaNGhV8PisrSzNnztSMGTOUlZUlp9OpxYsXc3rZZfDdz31V3y1V/T7g0ny399K57/vzF96fx+e+5jp06KD77rtPY8eOlc/nU79+/ZSRkSFJuvfee/Xwww+rc+fOWrRokZ544gkVFxerY8eOmjhxoiT2cy7FxfT+5MmT+uqrr+T3+4N/wOnUqZPmzZtX5b5nOFmMyk7oBAAAAABUiT9PAAAAAECICFIAAAAAECKCFAAAAACEiCAFAAAAACEiSAEAAABAiJj+HABQY3PnztX27dslSQcOHFCLFi0UGRkpSVq1alXw33WVYRj62c9+pueee07x8fFmlwMAqEeY/hwAcFn06dNHzz77rDp37mx2KRfN5/Ppuuuu0/bt2wlSAICQcEQKABA2+/fv17x581RYWCi/36977rlHw4cP17Zt2/S73/1OjRs31oEDBxQTE6OHHnpIK1as0KFDhzRw4EA9+uij2rZtm5YuXarExEQdO3ZM0dHRWrBggdq2bSuPx6PMzEx9+umn8vv9uu666zRz5kzFxsaqV69euvHGG7Vnzx5NmzZNgUBAL7/8srxer/Lz8zVy5EhNmjRJjz32mCRp/PjxeuWVVzRq1Ci99NJLuvbaayVJvXr10ksvvaTo6Gj97Gc/U6tWrXTixAn96U9/0sGDB7V48WK53W5ZrVY9/PDD6t27d7nt9/l86t69u8aNG6ctW7aouLhY06dP17p167Rv3z41a9ZML774oqKioqrsld/v1/z585WdnS2XyyVJmj9/vrp27apHHnlEjRs31p49e3TixAldffXVWrJkiaKiomr3Bw0ADZEBAMBlcNtttxm7du0KPvZ4PMbAgQON3bt3G4ZhGAUFBUb//v2NXbt2GVu3bjU6duwYfO6ee+4xxo4da3g8HiM3N9e49tprjdzcXGPr1q3Gtddea3z22WeGYRjGihUrjFGjRhmGYRhLly41/ud//scIBAKGYRjGwoULjd/85jeGYRjGrbfeaixfvtwwDMPw+/3G+PHjjcOHDxuGYRjHjx83OnToYJw9e9bwer1Genq6UVBQEFzuq6++Cm7D+ceHDh0y0tPTg3Xk5+cb/fr1M44dO2YYhmGcOHHCuPXWW40TJ06U68n59b/xxhuGYRjGiy++aNx4443GqVOnDL/fbwwZMsR49913L9ir7du3G1OnTjX8fr9hGIbxwgsvGL/61a8MwzCMX//618a4ceOMsrIyw+PxGIMHDzb+9re/XcqPEQBwkTgiBQAIiwMHDujIkSN69NFHg2Mej0e7d+9WWlqaWrVqpQ4dOkiSWrZsqeTkZEVERCgpKUnR0dE6e/asJKljx47q1q2bJGnUqFGaO3euioqKtHnzZpWUlGjLli2SJK/Xq9TU1OB73XjjjZIkq9Wq5cuXa/PmzcrKytLXX38twzDkdrsVExNz0dsTERGhLl26SJI+++wz5eTk6MEHHww+b7VatW/fPjVt2rTCsv3795ek4Dafr7NFixYqKCi4YK9Gjx6txo0b66233tLhw4f1ySefqFGjRsHX9erVSw6HQ5J0zTXXqKCg4KK3CQBQcwQpAEBYBAIBJSQkKCsrKziWk5Oj+Ph4ffrpp8Gd//Ps9sr/l/Td8UAgIOlcaPH7/Xrqqad0yy23SJJcLpe8Xm/wtedDksvl0vDhw9W/f3/deOONGjlypDZs2CCjkkuELRZLufHvri8yMlJWqzVYR3p6ut56663g86dOnVJiYmKl2/Ddba1sOy/Uq40bNyozM1P33HOP+vbtqzZt2mj9+vXB1zmdzirrBwCED9OfAwDC4uqrr5bVatW7774rSTp27JgyMjK0Z8+ekNbzxRdfaP/+/ZLOzQR40003KSYmRj179tSKFSvk9Xrl9/v1+OOPa+nSpRWWP3jwoEpLSzV58mTddttt+vjjj+Xz+eT3+2Wz2WSxWOTz+SRJiYmJ+uKLLyRJO3bsUH5+fqU1devWTQcOHNCnn34qSfryyy/Vv39/5eXlhbRt512oV9u2bdPtt9+ucePGqVOnTtq4caP8fn+N3gcAcPlwRAoAEBYOh0PLli3T/PnztXz5cvl8Pv36179Wly5dtG3btoteT2pqqhYtWqRjx44pJSVFCxculCRNmjRJCxcu1LBhw4KTTUyfPr3C8h07dlTPnj01cOBAxcfHq3Xr1mrbtq0OHz6sFi1aqG/fvrr77ru1fPlyTZs2TU8//bTefPNNde7cOTjpxPclJyfrueee04IFC+TxeGQYhhYtWlTpaX2X2qvo6Gg98sgjGjx4sGw2m7p3765NmzZx5AkATMb05wCAOmvbtm1auHBhuVPeAACoCzi1DwAAAABCxBEpAAAAAAgRR6QAAAAAIEQEKQAAAAAIEUEKAAAAAEJEkAIAAACAEBGkAAAAACBEBCkAAAAACNH/Dz16wQ6MmlAzAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting Main Variables\n",
+ "sns.set(rc={'figure.figsize':(14,8)})\n",
+ "cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)\n",
+ "ax = sns.scatterplot(x=\"Temperature mean\", y=\"Daylight mean\", size=\"EnergyProduction mean\", \n",
+ " palette=cmap, alpha=0.5, hue=\"EnergyProduction std\", \n",
+ " data=group_houses)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv_energychallenge",
+ "language": "python",
+ "name": "venv_energychallenge"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenge1/analysis/baiz/001_ExploratoryDataAnalysis.pdf b/challenge1/analysis/baiz/001_ExploratoryDataAnalysis.pdf
new file mode 100644
index 000000000..54b3b0f90
Binary files /dev/null and b/challenge1/analysis/baiz/001_ExploratoryDataAnalysis.pdf differ
diff --git a/challenge1/analysis/baiz/002_VAR.ipynb b/challenge1/analysis/baiz/002_VAR.ipynb
new file mode 100644
index 000000000..4ed1175a2
--- /dev/null
+++ b/challenge1/analysis/baiz/002_VAR.ipynb
@@ -0,0 +1,747 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Approach using VAR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import pandas_profiling\n",
+ "import matplotlib.pyplot as plt\n",
+ "import datetime as dt\n",
+ "\n",
+ "import seaborn as sns\n",
+ "\n",
+ "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
+ "from statsmodels.tsa.vector_ar.var_model import VAR"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load Data and Set Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Paths\n",
+ "cwd = os.getcwd()\n",
+ "path_to_challenge1 = os.path.dirname(os.path.dirname(cwd))\n",
+ "path_to_all_data = os.path.join(path_to_challenge1, 'data', 'dataset_500.csv')\n",
+ "path_to_test_data = os.path.join(path_to_challenge1, 'data', 'test_dataset_500.csv')\n",
+ "path_to_train_data = os.path.join(path_to_challenge1, 'data', 'training_dataset_500.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# All Data\n",
+ "df = pd.read_csv(path_to_all_data)\n",
+ "df_test = pd.read_csv(path_to_test_data)\n",
+ "df_train = pd.read_csv(path_to_train_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " ID \n",
+ " Label \n",
+ " House \n",
+ " Year \n",
+ " Month \n",
+ " Temperature \n",
+ " Daylight \n",
+ " EnergyProduction \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 7 \n",
+ " 26.2 \n",
+ " 178.9 \n",
+ " 740 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 8 \n",
+ " 25.8 \n",
+ " 169.7 \n",
+ " 731 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 9 \n",
+ " 22.8 \n",
+ " 170.2 \n",
+ " 694 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 10 \n",
+ " 16.4 \n",
+ " 169.1 \n",
+ " 688 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 1 \n",
+ " 2011 \n",
+ " 11 \n",
+ " 11.4 \n",
+ " 169.1 \n",
+ " 650 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " ID Label House Year Month Temperature Daylight EnergyProduction\n",
+ "0 0 0 1 2011 7 26.2 178.9 740\n",
+ "1 1 1 1 2011 8 25.8 169.7 731\n",
+ "2 2 2 1 2011 9 22.8 170.2 694\n",
+ "3 3 3 1 2011 10 16.4 169.1 688\n",
+ "4 4 4 1 2011 11 11.4 169.1 650"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Keeping only main features (avoiding highly correlated or useless columns)\n",
+ "key_cols = ['House', 'Temperature', 'Daylight', 'EnergyProduction']\n",
+ "ts_cols = ['Temperature', 'Daylight', 'EnergyProduction']\n",
+ "\n",
+ "# Set Index Method\n",
+ "def get_datetime_index(input_df, key_cols):\n",
+ " input_df['DateTime'] = pd.to_datetime(input_df.Year.map(str) + input_df.Month.map(str), format=\"%Y%m\")\n",
+ " input_df.set_index(['DateTime'], inplace=True)\n",
+ " return input_df[key_cols]\n",
+ "\n",
+ "# Get Index\n",
+ "df = get_datetime_index(df, key_cols)\n",
+ "df_test = get_datetime_index(df_test, key_cols)\n",
+ "df_train = get_datetime_index(df_train, key_cols)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " House \n",
+ " Temperature \n",
+ " Daylight \n",
+ " EnergyProduction \n",
+ " \n",
+ " \n",
+ " DateTime \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2011-07-01 \n",
+ " 1 \n",
+ " 26.2 \n",
+ " 178.9 \n",
+ " 740 \n",
+ " \n",
+ " \n",
+ " 2011-08-01 \n",
+ " 1 \n",
+ " 25.8 \n",
+ " 169.7 \n",
+ " 731 \n",
+ " \n",
+ " \n",
+ " 2011-09-01 \n",
+ " 1 \n",
+ " 22.8 \n",
+ " 170.2 \n",
+ " 694 \n",
+ " \n",
+ " \n",
+ " 2011-10-01 \n",
+ " 1 \n",
+ " 16.4 \n",
+ " 169.1 \n",
+ " 688 \n",
+ " \n",
+ " \n",
+ " 2011-11-01 \n",
+ " 1 \n",
+ " 11.4 \n",
+ " 169.1 \n",
+ " 650 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " House Temperature Daylight EnergyProduction\n",
+ "DateTime \n",
+ "2011-07-01 1 26.2 178.9 740\n",
+ "2011-08-01 1 25.8 169.7 731\n",
+ "2011-09-01 1 22.8 170.2 694\n",
+ "2011-10-01 1 16.4 169.1 688\n",
+ "2011-11-01 1 11.4 169.1 650"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Model TimeSeries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is important to notice that the dataset include multiple samplingpoints (Houses). In the present work, each house will treated indepedently and model fit/parameters will be independently obtained for each. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_VAR_prediction(df_train, df_test, house, ts_cols, maxlagsint, ic, trend):\n",
+ " \n",
+ " #creating the train and validation set\n",
+ " train = df_train[ df_train['House'] == house ][ts_cols].asfreq('MS')\n",
+ " \n",
+ " # Checking Stationarity\n",
+ " # Similar to the Augmented Dickey-Fuller test for univariate series, we have Johansen’s test for checking the \n",
+ " # stationarity of any multivariate time series data. For a series to be stationary, the eigenvalues of should \n",
+ " # be less than 1 in modulus. \n",
+ " eigen = coint_johansen(train,-1,1).eig\n",
+ " if all(i < 1.0 for i in eigen): \n",
+ " pass\n",
+ " #print('All eigen values are ok')\n",
+ " else: \n",
+ " print('Problem with Eigen Value', eigen)\n",
+ "\n",
+ " # Fit the model\n",
+ " # Parameters\n",
+ " # - maxlagsint: Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4), \n",
+ " # see select_order function \n",
+ " # - method{‘ols’}: Estimation method to use\n",
+ " # - ic{‘aic’, ‘fpe’, ‘hqic’, ‘bic’, None}: Information criterion to use for VAR order selection. \n",
+ " # aic : Akaike fpe : Final prediction error hqic : Hannan-Quinn bic : Bayesian a.k.a. Schwarzverbosebool, \n",
+ " # - trend: str {“c”, “ct”, “ctt”, “nc”}: “c” - add constant “ct” - constant and trend “ctt” - constant, \n",
+ " # linear and quadratic trend “nc” - co constant, no trend Note that these are prepended to the columns \n",
+ " # of the dataset.\n",
+ " model = VAR(endog=train)\n",
+ " if maxlagsint:\n",
+ " model_fit = model.fit(maxlags=maxlagsint, ic=ic, trend=trend)\n",
+ " else:\n",
+ " model_fit = model.fit(ic=ic, trend=trend)\n",
+ " \n",
+ " # make prediction on validation\n",
+ " prediction = model_fit.forecast(model_fit.y, steps=1)\n",
+ " \n",
+ " # Focus on Extracting \"EnergyProduction\"\n",
+ " energy_pred = prediction.tolist()[0][-1]\n",
+ " energy_value = df_test['EnergyProduction'].tolist()[0]\n",
+ " error = abs(energy_value - energy_pred)/energy_value\n",
+ " \n",
+ " return energy_pred #, energy_value, error"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "houses = df['House'].unique()\n",
+ "prediction_list = []\n",
+ "\n",
+ "for house in houses: \n",
+ " # As the dataset is small, parameter optimisation will be performed with a grid search, each House having its\n",
+ " # own optimal prediction\n",
+ " actual_value = df_test.loc[df_test['House'] == house, 'EnergyProduction'].iloc[0]\n",
+ " predicted = []\n",
+ " for trend in ['c', 'nc', 'ct', 'ctt']:\n",
+ " for ic in ['aic', 'fpe', 'hqic', 'bic', None]:\n",
+ " for maxlagsint in [1, 2, 3, 4]:\n",
+ " value_predicted = get_VAR_prediction(df_train, df_test, house, ts_cols, maxlagsint, ic, trend)\n",
+ " predicted.append(\n",
+ " {'trend': trend,\n",
+ " 'ic': ic,\n",
+ " 'maxlags': maxlagsint,\n",
+ " 'prediction': value_predicted,\n",
+ " 'error': abs(actual_value - value_predicted)\n",
+ " })\n",
+ " # Select Best\n",
+ " bestPrediction = min(predicted, key=lambda x:x['error'])\n",
+ " d = {\n",
+ " 'House' : house,\n",
+ " 'ModelParamters_trend' : bestPrediction['trend'],\n",
+ " 'ModelParamters_ic' : bestPrediction['ic'],\n",
+ " 'ModelParamters_maxlags' : bestPrediction['maxlags'],\n",
+ " 'Error': bestPrediction['error'],\n",
+ " 'Prediction' : bestPrediction['prediction']\n",
+ " }\n",
+ " prediction_list.append(d)\n",
+ "\n",
+ "df_prediction = pd.DataFrame(prediction_list)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Error \n",
+ " House \n",
+ " ModelParamters_ic \n",
+ " ModelParamters_maxlags \n",
+ " ModelParamters_trend \n",
+ " Prediction \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 19.019555 \n",
+ " 1 \n",
+ " aic \n",
+ " 3 \n",
+ " ct \n",
+ " 797.019555 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 19.753771 \n",
+ " 2 \n",
+ " aic \n",
+ " 4 \n",
+ " ctt \n",
+ " 646.753771 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 29.339770 \n",
+ " 3 \n",
+ " aic \n",
+ " 4 \n",
+ " ct \n",
+ " 764.339770 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 65.865639 \n",
+ " 4 \n",
+ " None \n",
+ " 3 \n",
+ " c \n",
+ " 598.865639 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 65.865639 \n",
+ " 5 \n",
+ " None \n",
+ " 3 \n",
+ " c \n",
+ " 598.865639 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Error House ModelParamters_ic ModelParamters_maxlags \\\n",
+ "0 19.019555 1 aic 3 \n",
+ "1 19.753771 2 aic 4 \n",
+ "2 29.339770 3 aic 4 \n",
+ "3 65.865639 4 None 3 \n",
+ "4 65.865639 5 None 3 \n",
+ "\n",
+ " ModelParamters_trend Prediction \n",
+ "0 ct 797.019555 \n",
+ "1 ctt 646.753771 \n",
+ "2 ct 764.339770 \n",
+ "3 c 598.865639 \n",
+ "4 c 598.865639 "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Results\n",
+ "df_prediction.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Print File with Predicted Values\n",
+ "df_prediction_print = df_prediction[['House', 'Prediction']]\n",
+ "df_prediction_print.to_csv('predicted_energy_production.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " House \n",
+ " Temperature \n",
+ " Daylight \n",
+ " EnergyProduction \n",
+ " Error \n",
+ " ModelParamters_ic \n",
+ " ModelParamters_maxlags \n",
+ " ModelParamters_trend \n",
+ " Prediction \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1 \n",
+ " 22.0 \n",
+ " 125.5 \n",
+ " 778 \n",
+ " 19.019555 \n",
+ " aic \n",
+ " 3 \n",
+ " ct \n",
+ " 797.019555 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2 \n",
+ " 21.1 \n",
+ " 123.1 \n",
+ " 627 \n",
+ " 19.753771 \n",
+ " aic \n",
+ " 4 \n",
+ " ctt \n",
+ " 646.753771 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 3 \n",
+ " 21.9 \n",
+ " 126.8 \n",
+ " 735 \n",
+ " 29.339770 \n",
+ " aic \n",
+ " 4 \n",
+ " ct \n",
+ " 764.339770 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 4 \n",
+ " 20.2 \n",
+ " 125.2 \n",
+ " 533 \n",
+ " 65.865639 \n",
+ " None \n",
+ " 3 \n",
+ " c \n",
+ " 598.865639 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 5 \n",
+ " 20.2 \n",
+ " 125.2 \n",
+ " 533 \n",
+ " 65.865639 \n",
+ " None \n",
+ " 3 \n",
+ " c \n",
+ " 598.865639 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " House Temperature Daylight EnergyProduction Error \\\n",
+ "0 1 22.0 125.5 778 19.019555 \n",
+ "1 2 21.1 123.1 627 19.753771 \n",
+ "2 3 21.9 126.8 735 29.339770 \n",
+ "3 4 20.2 125.2 533 65.865639 \n",
+ "4 5 20.2 125.2 533 65.865639 \n",
+ "\n",
+ " ModelParamters_ic ModelParamters_maxlags ModelParamters_trend Prediction \n",
+ "0 aic 3 ct 797.019555 \n",
+ "1 aic 4 ctt 646.753771 \n",
+ "2 aic 4 ct 764.339770 \n",
+ "3 None 3 c 598.865639 \n",
+ "4 None 3 c 598.865639 "
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Combine DF\n",
+ "df_all = df_test.merge(df_prediction, left_on='House', right_on='House')\n",
+ "df_all.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4.023386014469117"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Error\n",
+ "MAPE = np.mean(np.abs((df_all['EnergyProduction'] - df_all['Prediction']) / df_all['EnergyProduction'])) * 100\n",
+ "MAPE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Prediction\n",
+ "df_all[['EnergyProduction', 'Prediction']].plot(figsize=(15,5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Clearly the main problem of the present appoach regarding parameter optimisisation is related to Overfitting. This could be addressed by grouping houses by type (which seems possible as based on the Data Exploration) and then identify the best hyperparameters for such \"House Type\"."
+ ]
+ },
+ {
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenge1/analysis/baiz/002_VAR.pdf b/challenge1/analysis/baiz/002_VAR.pdf
new file mode 100644
index 000000000..b2bb12d1d
Binary files /dev/null and b/challenge1/analysis/baiz/002_VAR.pdf differ
diff --git a/challenge1/analysis/baiz/README.md b/challenge1/analysis/baiz/README.md
new file mode 100644
index 000000000..0316441ab
--- /dev/null
+++ b/challenge1/analysis/baiz/README.md
@@ -0,0 +1,57 @@
+# Overview of Challenge Results
+
+This readme file provides a high level description of all the work performed during this
+challenge. The data science process starts by a traditional EDA (Exploratory Data
+Analysis) using Pandas Profile and other traditional visualisation techniques.
+Then the work moves towards actual prediction of EnergyProduction for June 2013.
+
+## Short Files Description Overview
+
+All files added were only added in the present directory, and these are:
+
+- README.md (This file)
+
+- requirements.txt (Python requirements file to reproduce venv)
+
+- 001_ExploratoryDataAnalysis.ipynb (Notebook to Explore DataSet)
+
+- 001_ExploratoryDataAnalysis.pdf (PDF of Notebook to Explore DataSet)
+
+- data_profile_.html (HTML with data profiling report)
+
+- 002_VAR.ipynb (Notebook that performs predictions)
+
+- 002_VAR.pdf (PDF of Notebook that performs predictions)
+
+- predicted_energy_production.csv (Requested File)
+
+- mape.txt (Requested File)
+
+
+# Exploratory Data Analysis
+
+The exploration process revealed a very good quality dataset with key features
+for a Machine Learning model, these were clearly: 'House', 'Temperature', 'Daylight', 'EnergyProduction' (and clearly the timestamp).
+
+Look at file 001_ExploratoryDataAnalysis for more details.
+
+
+# Predictions/Forecasting
+
+When considering forecasting of multivariate timeseries, few techniques could come to mind, but officially
+one techniques is particularly used: Vector Autoregressions (https://www.statsmodels.org/dev/vector_ar.html).
+
+More details about the exact implementation can be see in the file 002_VAR.
+
+
+# Future Work
+
+An attempt was also made to perform some RNN LSTM as this type of NN are now demonstrating great performance
+for time series analysis. Unfortunately, not enough time was available to obtain results. In this regard one
+aspects that was particularly considered was the use of not just RNN-LSTM but also their interpretation using
+SHAP. This is still an area of active research and hopefully in the near future this will be easily available.
+
+More on SHAP for RNN is available here:
+
+- https://github.com/slundberg/shap/issues/213
+- https://towardsdatascience.com/interpreting-recurrent-neural-networks-on-multivariate-time-series-ebec0edb8f5a
\ No newline at end of file
diff --git a/challenge1/analysis/baiz/data_profile_full.html b/challenge1/analysis/baiz/data_profile_full.html
new file mode 100644
index 000000000..264811802
--- /dev/null
+++ b/challenge1/analysis/baiz/data_profile_full.html
@@ -0,0 +1,328 @@
+Pandas Profiling Full Data Report Dataset info
Number of variables 8 Number of observations 12000 Missing cells 0 (0.0%) Duplicate rows 0 (0.0%) Total size in memory 750.1 KiB Average record size in memory 64.0 B
Variables types
Numeric 6 Categorical 0 Boolean 0 Date 0 URL 0 Text (Unique) 0 Rejected 2 Unsupported 0
Warnings
ID is highly correlated with House (ρ = 0.9999980035) Rejected Label has 500 (4.2%) zeros Zeros Year is highly correlated with Label (ρ = 0.9193570532) Rejected
Distinct count 490 Unique (%) 4.1% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 186.4547667 Minimum 121.8 Maximum 271.3 Zeros (%) 0.0%
Quantile statistics
Minimum 121.8 5-th percentile 137.9 Q1 167.1 Median 180.4 Q3 204.3 95-th percentile 252.8 Maximum 271.3 Range 149.5 Interquartile range 37.2
Descriptive statistics
Standard deviation 31.52589162 Coef of variation 0.1690806418 Kurtosis 0.09153609815 Mean 186.4547667 MAD 24.62259881 Skewness 0.5305811253 Sum 2237457.2 Variance 993.8818424 Memory size 93.8 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[121.8 123. 125.95 126.15 127.7 ... 261.35 262. 263.85 264.5 271.3 ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 157.8 105 0.9%
185.3 95 0.8%
174.6 87 0.7%
171.6 83 0.7%
197.6 81 0.7%
176.5 77 0.6%
172.4 77 0.6%
193.2 76 0.6%
175.8 70 0.6%
192.9 69 0.6%
Other values (480) 11180 93.2%
Minimum 5 values
Value Count Frequency (%) 121.8 15 0.1%
122.4 17 0.1%
122.9 12 0.1%
123.1 27 0.2%
123.5 18 0.1%
Maximum 5 values
Value Count Frequency (%) 271.3 13 0.1%
268.8 20 0.2%
266.3 12 0.1%
264.6 18 0.1%
264.4 19 0.2%
Distinct count 387 Unique (%) 3.2% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 611.6665833 Minimum 254 Maximum 1254 Zeros (%) 0.0%
Quantile statistics
Minimum 254 5-th percentile 428 Q1 509 Median 588 Q3 695 95-th percentile 863 Maximum 1254 Range 1000 Interquartile range 186
Descriptive statistics
Standard deviation 140.6082654 Coef of variation 0.2298773045 Kurtosis 0.9317231825 Mean 611.6665833 MAD 110.9672393 Skewness 0.800296838 Sum 7339999 Variance 19770.68431 Memory size 93.8 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 254. 300.5 344. 353.5 369. ... 953. 976.5 1105.5 1246. 1254. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 482 112 0.9%
529 106 0.9%
625 103 0.9%
664 102 0.9%
495 97 0.8%
524 95 0.8%
584 92 0.8%
565 91 0.8%
688 88 0.7%
545 86 0.7%
Other values (377) 11028 91.9%
Minimum 5 values
Value Count Frequency (%) 254 12 0.1%
290 12 0.1%
311 20 0.2%
320 12 0.1%
330 20 0.2%
Maximum 5 values
Value Count Frequency (%) 1254 9 0.1%
1238 9 0.1%
1107 9 0.1%
1104 9 0.1%
1089 9 0.1%
Distinct count 500 Unique (%) 4.2% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 250.5 Minimum 1 Maximum 500 Zeros (%) 0.0%
Quantile statistics
Minimum 1 5-th percentile 25.95 Q1 125.75 Median 250.5 Q3 375.25 95-th percentile 475.05 Maximum 500 Range 499 Interquartile range 249.5
Descriptive statistics
Standard deviation 144.3432931 Coef of variation 0.5762207307 Kurtosis -1.200009587 Mean 250.5 MAD 125 Skewness 0 Sum 3006000 Variance 20834.98625 Memory size 93.8 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 1. 500.],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 495 24 0.2%
362 24 0.2%
258 24 0.2%
266 24 0.2%
274 24 0.2%
282 24 0.2%
290 24 0.2%
298 24 0.2%
306 24 0.2%
314 24 0.2%
Other values (490) 11760 98.0%
Minimum 5 values
Value Count Frequency (%) 1 24 0.2%
2 24 0.2%
3 24 0.2%
4 24 0.2%
5 24 0.2%
Maximum 5 values
Value Count Frequency (%) 500 24 0.2%
499 24 0.2%
498 24 0.2%
497 24 0.2%
496 24 0.2%
This variable is highly correlated with House and should be ignored for analysis
Distinct count 24 Unique (%) 0.2% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 11.5 Minimum 0 Maximum 23 Zeros (%) 4.2%
Quantile statistics
Minimum 0 5-th percentile 1 Q1 5.75 Median 11.5 Q3 17.25 95-th percentile 22 Maximum 23 Range 23 Interquartile range 11.5
Descriptive statistics
Standard deviation 6.922474995 Coef of variation 0.6019543474 Kurtosis -1.204175636 Mean 11.5 MAD 6 Skewness 0 Sum 138000 Variance 47.92066006 Memory size 93.8 KiB
Histogram with fixed size bins (bins=24)
Histogram with variable size bins (bins=[ 0. 0.5 22.5 23. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 23 500 4.2%
15 500 4.2%
8 500 4.2%
16 500 4.2%
1 500 4.2%
9 500 4.2%
17 500 4.2%
2 500 4.2%
10 500 4.2%
18 500 4.2%
Other values (14) 7000 58.3%
Minimum 5 values
Value Count Frequency (%) 0 500 4.2%
1 500 4.2%
2 500 4.2%
3 500 4.2%
4 500 4.2%
Maximum 5 values
Value Count Frequency (%) 23 500 4.2%
22 500 4.2%
21 500 4.2%
20 500 4.2%
19 500 4.2%
Distinct count 12 Unique (%) 0.1% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 6.5 Minimum 1 Maximum 12 Zeros (%) 0.0%
Quantile statistics
Minimum 1 5-th percentile 1 Q1 3.75 Median 6.5 Q3 9.25 95-th percentile 12 Maximum 12 Range 11 Interquartile range 5.5
Descriptive statistics
Standard deviation 3.452196374 Coef of variation 0.5311071345 Kurtosis -1.216790195 Mean 6.5 MAD 3 Skewness 0 Sum 78000 Variance 11.9176598 Memory size 93.8 KiB
Histogram with fixed size bins (bins=12)
Histogram with variable size bins (bins=[ 1. 1.5 11.5 12. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 7 1000 8.3%
6 1000 8.3%
5 1000 8.3%
12 1000 8.3%
4 1000 8.3%
11 1000 8.3%
3 1000 8.3%
10 1000 8.3%
2 1000 8.3%
9 1000 8.3%
Other values (2) 2000 16.7%
Minimum 5 values
Value Count Frequency (%) 1 1000 8.3%
2 1000 8.3%
3 1000 8.3%
4 1000 8.3%
5 1000 8.3%
Maximum 5 values
Value Count Frequency (%) 12 1000 8.3%
11 1000 8.3%
10 1000 8.3%
9 1000 8.3%
8 1000 8.3%
Distinct count 207 Unique (%) 1.7% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 14.67789167 Minimum 0.8 Maximum 29 Zeros (%) 0.0%
Quantile statistics
Minimum 0.8 5-th percentile 2.6 Q1 6.175 Median 14.55 Q3 22.7 95-th percentile 26.9 Maximum 29 Range 28.2 Interquartile range 16.525
Descriptive statistics
Standard deviation 8.442056982 Coef of variation 0.5751546049 Kurtosis -1.358422313 Mean 14.67789167 MAD 7.484958333 Skewness -0.01406521494 Sum 176134.7 Variance 71.26832608 Memory size 93.8 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 0.8 0.85 1.55 1.65 1.95 ... 27.25 27.35 27.45 28. 29. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 17.5 396 3.3%
3.4 240 2.0%
3.6 201 1.7%
4.9 176 1.5%
2.6 153 1.3%
12.6 148 1.2%
12.3 136 1.1%
17.7 135 1.1%
17.4 131 1.1%
3.5 130 1.1%
Other values (197) 10154 84.6%
Minimum 5 values
Value Count Frequency (%) 0.8 15 0.1%
0.9 15 0.1%
1.5 34 0.3%
1.6 45 0.4%
1.7 21 0.2%
Maximum 5 values
Value Count Frequency (%) 29 9 0.1%
28.8 36 0.3%
28.6 51 0.4%
28.4 45 0.4%
28.2 75 0.6%
This variable is highly correlated with Label and should be ignored for analysis
First rows Daylight EnergyProduction House ID Label Month Temperature Year 0 178.9 740 1 0 0 7 26.2 2011 1 169.7 731 1 1 1 8 25.8 2011 2 170.2 694 1 2 2 9 22.8 2011 3 169.1 688 1 3 3 10 16.4 2011 4 169.1 650 1 4 4 11 11.4 2011 5 199.5 763 1 5 5 12 4.2 2011 6 203.1 765 1 6 6 1 1.8 2012 7 178.2 706 1 7 7 2 2.8 2012 8 172.7 788 1 8 8 3 6.7 2012 9 182.2 831 1 9 9 4 12.6 2012
Last rows Daylight EnergyProduction House ID Label Month Temperature Year 11990 183.2 665 500 11990 14 9 24.7 2012 11991 201.2 655 500 11991 15 10 17.4 2012 11992 203.5 582 500 11992 16 11 9.7 2012 11993 194.2 534 500 11993 17 12 3.8 2012 11994 234.6 640 500 11994 18 1 2.0 2013 11995 201.8 638 500 11995 19 2 4.2 2013 11996 234.0 778 500 11996 20 3 11.2 2013 11997 237.1 758 500 11997 21 4 13.6 2013 11998 258.4 838 500 11998 22 5 19.2 2013 11999 122.9 586 500 11999 23 6 22.7 2013
\ No newline at end of file
diff --git a/challenge1/analysis/baiz/data_profile_test.html b/challenge1/analysis/baiz/data_profile_test.html
new file mode 100644
index 000000000..d5b015331
--- /dev/null
+++ b/challenge1/analysis/baiz/data_profile_test.html
@@ -0,0 +1,328 @@
+Pandas Profiling Test Data Report Dataset info
Number of variables 8 Number of observations 500 Missing cells 0 (0.0%) Duplicate rows 0 (0.0%) Total size in memory 31.3 KiB Average record size in memory 64.2 B
Variables types
Numeric 4 Categorical 0 Boolean 0 Date 0 URL 0 Text (Unique) 0 Rejected 4 Unsupported 0
Warnings
ID is highly correlated with House (ρ = 1) Rejected Label has constant value "23" Rejected Month has constant value "6" Rejected Year has constant value "2013" Rejected
Distinct count 23 Unique (%) 4.6% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 125.1114 Minimum 121.8 Maximum 129.1 Zeros (%) 0.0%
Quantile statistics
Minimum 121.8 5-th percentile 122.4 Q1 123.9 Median 125.2 Q3 126 95-th percentile 127.9 Maximum 129.1 Range 7.3 Interquartile range 2.1
Descriptive statistics
Standard deviation 1.595726394 Coef of variation 0.01275444439 Kurtosis -0.4067154118 Mean 125.1114 MAD 1.3118704 Skewness 0.07732508409 Sum 62555.7 Variance 2.546342725 Memory size 4.0 KiB
Histogram with fixed size bins (bins=23)
Histogram with variable size bins (bins=[121.8 123. 125.95 126.15 127.7 129.1 ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 126 50 10.0%
125.6 37 7.4%
125.2 33 6.6%
124.8 33 6.6%
124.4 30 6.0%
123.1 27 5.4%
126.8 23 4.6%
124.1 22 4.4%
123.7 21 4.2%
124.3 21 4.2%
Other values (13) 203 40.6%
Minimum 5 values
Value Count Frequency (%) 121.8 15 3.0%
122.4 17 3.4%
122.9 12 2.4%
123.1 27 5.4%
123.5 18 3.6%
Maximum 5 values
Value Count Frequency (%) 129.1 9 1.8%
127.9 17 3.4%
127.5 19 3.8%
127.2 19 3.8%
126.8 23 4.6%
Distinct count 28 Unique (%) 5.6% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 586.774 Minimum 451 Maximum 886 Zeros (%) 0.0%
Quantile statistics
Minimum 451 5-th percentile 455 Q1 518 Median 565 Q3 668 95-th percentile 778 Maximum 886 Range 435 Interquartile range 150
Descriptive statistics
Standard deviation 100.2926526 Coef of variation 0.1709221141 Kurtosis -0.07715027681 Mean 586.774 MAD 82.15124 Skewness 0.6792811491 Sum 293387 Variance 10058.61616 Memory size 4.0 KiB
Histogram with fixed size bins (bins=28)
Histogram with variable size bins (bins=[451. 457.5 477. 517.5 533.5 ... 585.5 669. 678.5 757. 886. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 455 28 5.6%
627 27 5.4%
735 23 4.6%
523 22 4.4%
736 22 4.4%
585 21 4.2%
560 21 4.2%
517 20 4.0%
533 20 4.0%
467 19 3.8%
Other values (18) 277 55.4%
Minimum 5 values
Value Count Frequency (%) 451 10 2.0%
455 28 5.6%
460 13 2.6%
467 19 3.8%
471 17 3.4%
Maximum 5 values
Value Count Frequency (%) 886 9 1.8%
778 17 3.4%
736 22 4.4%
735 23 4.6%
684 19 3.8%
Distinct count 500 Unique (%) 100.0% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 250.5 Minimum 1 Maximum 500 Zeros (%) 0.0%
Quantile statistics
Minimum 1 5-th percentile 25.95 Q1 125.75 Median 250.5 Q3 375.25 95-th percentile 475.05 Maximum 500 Range 499 Interquartile range 249.5
Descriptive statistics
Standard deviation 144.4818328 Coef of variation 0.5767737835 Kurtosis -1.2 Mean 250.5 MAD 125 Skewness 0 Sum 125250 Variance 20875 Memory size 4.0 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 1. 500.],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 500 1 0.2%
171 1 0.2%
158 1 0.2%
159 1 0.2%
160 1 0.2%
161 1 0.2%
162 1 0.2%
163 1 0.2%
164 1 0.2%
165 1 0.2%
Other values (490) 490 98.0%
Minimum 5 values
Value Count Frequency (%) 1 1 0.2%
2 1 0.2%
3 1 0.2%
4 1 0.2%
5 1 0.2%
Maximum 5 values
Value Count Frequency (%) 500 1 0.2%
499 1 0.2%
498 1 0.2%
497 1 0.2%
496 1 0.2%
This variable is highly correlated with House and should be ignored for analysis
This variable is constant and should be ignored for analysis
This variable is constant and should be ignored for analysis
Distinct count 13 Unique (%) 2.6% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 21.7054 Minimum 19.3 Maximum 22.8 Zeros (%) 0.0%
Quantile statistics
Minimum 19.3 5-th percentile 20.2 Q1 21.1 Median 21.9 Q3 22.5 95-th percentile 22.8 Maximum 22.8 Range 3.5 Interquartile range 1.4
Descriptive statistics
Standard deviation 0.8666099848 Coef of variation 0.03992600849 Kurtosis -0.1214205749 Mean 21.7054 MAD 0.7119304 Skewness -0.650593869 Sum 10852.7 Variance 0.7510128657 Memory size 4.0 KiB
Histogram with fixed size bins (bins=13)
Histogram with variable size bins (bins=[19.3 20.9 21.35 21.8 21.95 22.35 22.75 22.8 ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 21.9 97 19.4%
21.1 61 12.2%
22.5 60 12.0%
22.8 50 10.0%
22.7 47 9.4%
21 47 9.4%
20.2 38 7.6%
21.7 21 4.2%
21.6 20 4.0%
20.8 19 3.8%
Other values (3) 40 8.0%
Minimum 5 values
Value Count Frequency (%) 19.3 13 2.6%
20.2 38 7.6%
20.8 19 3.8%
21 47 9.4%
21.1 61 12.2%
Maximum 5 values
Value Count Frequency (%) 22.8 50 10.0%
22.7 47 9.4%
22.5 60 12.0%
22.2 10 2.0%
22 17 3.4%
This variable is constant and should be ignored for analysis
First rows Daylight EnergyProduction House ID Label Month Temperature Year 0 125.5 778 1 23 23 6 22.0 2013 1 123.1 627 2 47 23 6 21.1 2013 2 126.8 735 3 71 23 6 21.9 2013 3 125.2 533 4 95 23 6 20.2 2013 4 125.2 533 5 119 23 6 20.2 2013 5 121.8 670 6 143 23 6 21.1 2013 6 127.2 673 7 167 23 6 22.8 2013 7 124.3 560 8 191 23 6 21.7 2013 8 125.6 517 9 215 23 6 21.6 2013 9 126.0 455 10 239 23 6 22.5 2013
Last rows Daylight EnergyProduction House ID Label Month Temperature Year 490 124.4 534 491 11783 23 6 21.0 2013 491 121.8 670 492 11807 23 6 21.1 2013 492 126.0 736 493 11831 23 6 22.8 2013 493 125.2 533 494 11855 23 6 20.2 2013 494 126.0 455 495 11879 23 6 22.5 2013 495 125.9 483 496 11903 23 6 19.3 2013 496 122.4 628 497 11927 23 6 21.9 2013 497 127.2 673 498 11951 23 6 22.8 2013 498 126.8 735 499 11975 23 6 21.9 2013 499 122.9 586 500 11999 23 6 22.7 2013
\ No newline at end of file
diff --git a/challenge1/analysis/baiz/data_profile_training.html b/challenge1/analysis/baiz/data_profile_training.html
new file mode 100644
index 000000000..0a1e750cb
--- /dev/null
+++ b/challenge1/analysis/baiz/data_profile_training.html
@@ -0,0 +1,328 @@
+Pandas Profiling Train Data Report Dataset info
Number of variables 8 Number of observations 11500 Missing cells 0 (0.0%) Duplicate rows 0 (0.0%) Total size in memory 718.8 KiB Average record size in memory 64.0 B
Variables types
Numeric 6 Categorical 0 Boolean 0 Date 0 URL 0 Text (Unique) 0 Rejected 2 Unsupported 0
Warnings
ID is highly correlated with House (ρ = 0.9999981667) Rejected Label has 500 (4.3%) zeros Zeros Year is highly correlated with Label (ρ = 0.9116846117) Rejected
Distinct count 467 Unique (%) 4.1% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 189.1218696 Minimum 133.7 Maximum 271.3 Zeros (%) 0.0%
Quantile statistics
Minimum 133.7 5-th percentile 150.1 Q1 169.1 Median 181.8 Q3 205.2 95-th percentile 253.2 Maximum 271.3 Range 137.6 Interquartile range 36.1
Descriptive statistics
Standard deviation 29.43212531 Coef of variation 0.1556251817 Kurtosis 0.08238265734 Mean 189.1218696 MAD 23.46215931 Skewness 0.8023220433 Sum 2174901.5 Variance 866.25 Memory size 89.9 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[133.7 137.25 138.05 141.35 144.55 ... 261.35 262. 263.85 264.5 271.3 ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 157.8 105 0.9%
185.3 95 0.8%
174.6 87 0.8%
171.6 83 0.7%
197.6 81 0.7%
172.4 77 0.7%
176.5 77 0.7%
193.2 76 0.7%
175.8 70 0.6%
192.9 69 0.6%
Other values (457) 10680 92.9%
Minimum 5 values
Value Count Frequency (%) 133.7 13 0.1%
137.1 30 0.3%
137.4 17 0.1%
137.6 28 0.2%
137.7 9 0.1%
Maximum 5 values
Value Count Frequency (%) 271.3 13 0.1%
268.8 20 0.2%
266.3 12 0.1%
264.6 18 0.2%
264.4 19 0.2%
Distinct count 380 Unique (%) 3.3% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 612.7488696 Minimum 254 Maximum 1254 Zeros (%) 0.0%
Quantile statistics
Minimum 254 5-th percentile 426 Q1 509 Median 592 Q3 698 95-th percentile 867 Maximum 1254 Range 1000 Interquartile range 189
Descriptive statistics
Standard deviation 142.0061439 Coef of variation 0.2317526004 Kurtosis 0.8909835078 Mean 612.7488696 MAD 112.07338 Skewness 0.7890875595 Sum 7046612 Variance 20165.74491 Memory size 89.9 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 254. 300.5 344. 353.5 369. ... 968.5 976.5 1105.5 1246. 1254. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 482 112 1.0%
529 106 0.9%
625 103 0.9%
664 102 0.9%
495 97 0.8%
524 95 0.8%
688 88 0.8%
545 86 0.7%
520 83 0.7%
596 82 0.7%
Other values (370) 10546 91.7%
Minimum 5 values
Value Count Frequency (%) 254 12 0.1%
290 12 0.1%
311 20 0.2%
320 12 0.1%
330 20 0.2%
Maximum 5 values
Value Count Frequency (%) 1254 9 0.1%
1238 9 0.1%
1107 9 0.1%
1104 9 0.1%
1089 9 0.1%
Distinct count 500 Unique (%) 4.3% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 250.5 Minimum 1 Maximum 500 Zeros (%) 0.0%
Quantile statistics
Minimum 1 5-th percentile 25.95 Q1 125.75 Median 250.5 Q3 375.25 95-th percentile 475.05 Maximum 500 Range 499 Interquartile range 249.5
Descriptive statistics
Standard deviation 144.3435546 Coef of variation 0.5762217747 Kurtosis -1.200009586 Mean 250.5 MAD 125 Skewness 0 Sum 2880750 Variance 20835.06174 Memory size 89.9 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 1. 500.],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 495 23 0.2%
362 23 0.2%
258 23 0.2%
266 23 0.2%
274 23 0.2%
282 23 0.2%
290 23 0.2%
298 23 0.2%
306 23 0.2%
314 23 0.2%
Other values (490) 11270 98.0%
Minimum 5 values
Value Count Frequency (%) 1 23 0.2%
2 23 0.2%
3 23 0.2%
4 23 0.2%
5 23 0.2%
Maximum 5 values
Value Count Frequency (%) 500 23 0.2%
499 23 0.2%
498 23 0.2%
497 23 0.2%
496 23 0.2%
This variable is highly correlated with House and should be ignored for analysis
Distinct count 23 Unique (%) 0.2% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 11 Minimum 0 Maximum 22 Zeros (%) 4.3%
Quantile statistics
Minimum 0 5-th percentile 1 Q1 5 Median 11 Q3 17 95-th percentile 21 Maximum 22 Range 22 Interquartile range 12
Descriptive statistics
Standard deviation 6.633538002 Coef of variation 0.6030489092 Kurtosis -1.204547413 Mean 11 MAD 5.739130435 Skewness 0 Sum 126500 Variance 44.00382642 Memory size 89.9 KiB
Histogram with fixed size bins (bins=23)
Histogram with variable size bins (bins=[ 0. 0.5 21.5 22. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 15 500 4.3%
11 500 4.3%
8 500 4.3%
16 500 4.3%
1 500 4.3%
9 500 4.3%
17 500 4.3%
2 500 4.3%
10 500 4.3%
18 500 4.3%
Other values (13) 6500 56.5%
Minimum 5 values
Value Count Frequency (%) 0 500 4.3%
1 500 4.3%
2 500 4.3%
3 500 4.3%
4 500 4.3%
Maximum 5 values
Value Count Frequency (%) 22 500 4.3%
21 500 4.3%
20 500 4.3%
19 500 4.3%
18 500 4.3%
Distinct count 12 Unique (%) 0.1% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 6.52173913 Minimum 1 Maximum 12 Zeros (%) 0.0%
Quantile statistics
Minimum 1 5-th percentile 1 Q1 3 Median 7 Q3 10 95-th percentile 12 Maximum 12 Range 11 Interquartile range 7
Descriptive statistics
Standard deviation 3.524843379 Coef of variation 0.5404759848 Kurtosis -1.287792705 Mean 6.52173913 MAD 3.107750473 Skewness -0.01838153288 Sum 75000 Variance 12.42452085 Memory size 89.9 KiB
Histogram with fixed size bins (bins=12)
Histogram with variable size bins (bins=[ 1. 1.5 5.5 6.5 11.5 12. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 7 1000 8.7%
5 1000 8.7%
12 1000 8.7%
4 1000 8.7%
11 1000 8.7%
3 1000 8.7%
10 1000 8.7%
2 1000 8.7%
9 1000 8.7%
1 1000 8.7%
Other values (2) 1500 13.0%
Minimum 5 values
Value Count Frequency (%) 1 1000 8.7%
2 1000 8.7%
3 1000 8.7%
4 1000 8.7%
5 1000 8.7%
Maximum 5 values
Value Count Frequency (%) 12 1000 8.7%
11 1000 8.7%
10 1000 8.7%
9 1000 8.7%
8 1000 8.7%
Distinct count 202 Unique (%) 1.8% Missing (%) 0.0% Missing (n) 0 Infinite (%) 0.0% Infinite (n) 0
Mean 14.37234783 Minimum 0.8 Maximum 29 Zeros (%) 0.0%
Quantile statistics
Minimum 0.8 5-th percentile 2.6 Q1 5.3 Median 13.2 Q3 22.8 95-th percentile 26.9 Maximum 29 Range 28.2 Interquartile range 17.5
Descriptive statistics
Standard deviation 8.490810585 Coef of variation 0.5907740814 Kurtosis -1.346224121 Mean 14.37234783 MAD 7.491562949 Skewness 0.06784573076 Sum 165282 Variance 72.09386439 Memory size 89.9 KiB
Histogram with fixed size bins (bins=50)
Histogram with variable size bins (bins=[ 0.8 0.85 1.55 1.65 1.95 ... 27.25 27.35 27.45 28. 29. ],
"bayesian blocks" binning strategy used)
Value Count Frequency (%) 17.5 396 3.4%
3.4 240 2.1%
3.6 201 1.7%
4.9 176 1.5%
2.6 153 1.3%
12.6 148 1.3%
12.3 136 1.2%
17.7 135 1.2%
17.4 131 1.1%
3.5 130 1.1%
Other values (192) 9654 83.9%
Minimum 5 values
Value Count Frequency (%) 0.8 15 0.1%
0.9 15 0.1%
1.5 34 0.3%
1.6 45 0.4%
1.7 21 0.2%
Maximum 5 values
Value Count Frequency (%) 29 9 0.1%
28.8 36 0.3%
28.6 51 0.4%
28.4 45 0.4%
28.2 75 0.7%
This variable is highly correlated with Label and should be ignored for analysis
First rows Daylight EnergyProduction House ID Label Month Temperature Year 0 178.9 740 1 0 0 7 26.2 2011 1 169.7 731 1 1 1 8 25.8 2011 2 170.2 694 1 2 2 9 22.8 2011 3 169.1 688 1 3 3 10 16.4 2011 4 169.1 650 1 4 4 11 11.4 2011 5 199.5 763 1 5 5 12 4.2 2011 6 203.1 765 1 6 6 1 1.8 2012 7 178.2 706 1 7 7 2 2.8 2012 8 172.7 788 1 8 8 3 6.7 2012 9 182.2 831 1 9 9 4 12.6 2012
Last rows Daylight EnergyProduction House ID Label Month Temperature Year 11490 257.9 822 500 11989 13 8 27.8 2012 11491 183.2 665 500 11990 14 9 24.7 2012 11492 201.2 655 500 11991 15 10 17.4 2012 11493 203.5 582 500 11992 16 11 9.7 2012 11494 194.2 534 500 11993 17 12 3.8 2012 11495 234.6 640 500 11994 18 1 2.0 2013 11496 201.8 638 500 11995 19 2 4.2 2013 11497 234.0 778 500 11996 20 3 11.2 2013 11498 237.1 758 500 11997 21 4 13.6 2013 11499 258.4 838 500 11998 22 5 19.2 2013
\ No newline at end of file
diff --git a/challenge1/analysis/baiz/mape.txt b/challenge1/analysis/baiz/mape.txt
new file mode 100644
index 000000000..8cc4864d5
--- /dev/null
+++ b/challenge1/analysis/baiz/mape.txt
@@ -0,0 +1 @@
+4.023386014469117
\ No newline at end of file
diff --git a/challenge1/analysis/baiz/predicted_energy_production.csv b/challenge1/analysis/baiz/predicted_energy_production.csv
new file mode 100644
index 000000000..d6c45a998
--- /dev/null
+++ b/challenge1/analysis/baiz/predicted_energy_production.csv
@@ -0,0 +1,501 @@
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diff --git a/challenge1/analysis/baiz/requirements.txt b/challenge1/analysis/baiz/requirements.txt
new file mode 100644
index 000000000..5a5e5b332
--- /dev/null
+++ b/challenge1/analysis/baiz/requirements.txt
@@ -0,0 +1,125 @@
+absl-py==0.8.1
+appnope==0.1.0
+astor==0.8.0
+astroid==2.3.3
+astropy==3.2.3
+astunparse==1.6.2
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+backcall==0.1.0
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+cachetools==3.1.1
+certifi==2019.6.16
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+colour==0.1.5
+confuse==1.0.0
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+defusedxml==0.6.0
+entrypoints==0.3
+gast==0.2.2
+geojson==2.4.1
+google-auth==1.7.1
+google-auth-oauthlib==0.4.1
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+Keras-Applications==1.0.8
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+numpy==1.16.4
+oauthlib==3.1.0
+opt-einsum==3.1.0
+packaging==19.2
+pandas==0.24.2
+pandas-profiling==2.3.0
+pandocfilters==1.4.2
+parso==0.5.0
+patsy==0.5.1
+pexpect==4.7.0
+phik==0.9.8
+pickleshare==0.7.5
+Pillow==6.0.0
+pixiedust==1.1.17
+pluggy==0.13.0
+prometheus-client==0.7.1
+prompt-toolkit==2.0.9
+protobuf==3.10.0
+ptyprocess==0.6.0
+py==1.8.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.7
+Pygments==2.4.2
+pylint==2.4.4
+pyparsing==2.4.0
+pyrsistent==0.15.2
+pytest==5.3.0
+pytest-pylint==0.14.1
+python-dateutil==2.8.0
+pytz==2019.1
+PyWavelets==1.0.3
+PyYAML==5.1.2
+pyzmq==18.0.1
+qtconsole==4.6.0
+requests==2.22.0
+requests-oauthlib==1.3.0
+rsa==4.0
+scikit-image==0.15.0
+scikit-learn==0.21.2
+scipy==1.3.0
+seaborn==0.9.0
+Send2Trash==1.5.0
+-e git+https://github.com/AndreCNF/shap.git@f0777334bd82a1bacad578eaf1931c3ecbf40ec6#egg=shap
+six==1.12.0
+statsmodels==0.10.1
+tensorboard==2.0.1
+tensorflow==2.0.0
+tensorflow-estimator==2.0.1
+termcolor==1.1.0
+terminado==0.8.2
+testpath==0.4.2
+torch==1.1.0.post2
+tornado==6.0.3
+tqdm==4.32.2
+traitlets==4.3.2
+typed-ast==1.4.0
+urllib3==1.25.3
+wcwidth==0.1.7
+webencodings==0.5.1
+Werkzeug==0.16.0
+widgetsnbextension==3.4.2
+wrapt==1.11.2
+zipp==0.6.0