sharingBicycle_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "__file__ = \"sharingBicycle_solution.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import pytest\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor\n",
    "import ipytest.magics\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BicycleSharingAnalysis(object):\n",
    "        \n",
    "    def barplot(self, x, y, df, figname):\n",
    "        # plot average feature y based on feature x\n",
    "        # x: feature on x axis\n",
    "        # y: feature on y axis\n",
    "        # figname: figure name\n",
    "        sns.barplot(x=x, y=y, data=df, ci=None)\n",
    "        plt.ylabel(\"Count of Total Rental Bikes\")\n",
    "        plt.savefig(figname+\".pdf\", bbox_inches='tight')\n",
    "    \n",
    "    def diff_day_type_hour_plot(self, df_hour, season_name, season_num):\n",
    "        # plot average hourly count curve for working day weekend and holiday for a given season\n",
    "        # df_hour: dataframe\n",
    "        # season_name: string, season name\n",
    "        # season_num: int, the number stands for the season\n",
    "        df_hour_spring = df_hour[df_hour[\"season\"] == season_num]\n",
    "        sns.set_style(\"whitegrid\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour_spring[df_hour_spring[\"workingday\"]==1], ci = None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour_spring[(df_hour_spring[\"weekday\"]==0) | (df_hour_spring[\"weekday\"]==6)], ci = None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour_spring[df_hour_spring[\"holiday\"]==1], ci = None, marker=\"o\")\n",
    "        # plt.title(\"Average of Bicycle Hourly Utilization\")\n",
    "        plt.xlabel(\"Hour (0-23)\")\n",
    "        plt.legend([\"working day\", \"weekend\", \"holiday\"])\n",
    "        plt.ylabel(\"Count of Total Rental Bikes\")\n",
    "        plt.title(season_name)\n",
    "        plt.xticks(np.arange(24, step=1));\n",
    "        plt.savefig(season_name+\"_curve.pdf\", bbox_inches='tight')\n",
    "    \n",
    "    def season_curve_plot(self, df_hour):\n",
    "        # plot average hourly count curve for each season\n",
    "        # df_hour: dataframe\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"season\"]==1], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"season\"]==2], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"season\"]==3], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"season\"]==4], ci=None, marker=\"o\")\n",
    "        plt.xlabel(\"Hour (0-23)\")\n",
    "        plt.ylabel(\"Count of Total Rental Bikes\")\n",
    "        plt.legend([\"Spring\", \"Summer\", \"Fall\", \"Winter\"])\n",
    "        plt.xticks(np.arange(24, step=1));\n",
    "        plt.savefig(\"season_curve.pdf\", bbox_inches='tight')\n",
    "    \n",
    "    def weekday_hour_curve_plot(self, df_hour):\n",
    "        # plot average hourly count curves for each weekday\n",
    "        # df_hour: dataframe contains hourly data\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==0], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==1], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==2], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==3], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==4], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==5], ci=None, marker=\"o\")\n",
    "        sns.lineplot(x=\"hr\", y=\"cnt\", data=df_hour[df_hour[\"weekday\"]==6], ci=None, marker=\"o\")\n",
    "        plt.xlabel(\"Hour (0-23)\")\n",
    "        plt.ylabel(\"Count of Total Rental Bikes\")\n",
    "        plt.legend([\"Sunday\", \"Monday\", \"Tuesday\", \"Wensday\", \"Thursday\", \"Friday\", \"Saturday\"])\n",
    "        plt.xticks(np.arange(24, step=1));\n",
    "        plt.savefig(\"weekday_curve.pdf\", bbox_inches='tight')\n",
    "    \n",
    "    def correlation_map(self, df, features):\n",
    "        # plot a correlation map\n",
    "        # df: dataframe\n",
    "        # features: list of features\n",
    "        corrmat = df[features].corr()\n",
    "        mask = np.array(corrmat)\n",
    "        mask[np.triu_indices_from(mask)] = False\n",
    "        ax = sns.heatmap(data=corrmat, mask = mask, annot=True, square=True)\n",
    "        plt.yticks(rotation=0)\n",
    "        ax.set_xlim(0,np.size(features))\n",
    "        ax.set_ylim(0,np.size(features))\n",
    "        plt.savefig(\"correlation_map.pdf\", bbox_inches='tight')\n",
    "        \n",
    "    def feature_drop(self, df, name, axis):\n",
    "        # drop fatures\n",
    "        # name: name of features\n",
    "        # axis: axis, 0, 1\n",
    "        df = df.drop(name, axis = axis)\n",
    "        return df\n",
    "        \n",
    "    def feature_engineering(self, df):\n",
    "        # split season feature to binary features and add weekend features\n",
    "        \n",
    "        n = np.shape(df)[0]\n",
    "        season_1 = np.zeros(n)\n",
    "        season_2 = np.zeros(n)\n",
    "        season_3 = np.zeros(n)\n",
    "        season_4 = np.zeros(n)\n",
    "        season_1[df[\"season\"] == 1] = 1\n",
    "        season_2[df[\"season\"] == 2] = 1\n",
    "        season_3[df[\"season\"] == 3] = 1\n",
    "        season_4[df[\"season\"] == 4] = 1\n",
    "        \n",
    "        df = df.drop([\"season\"], axis=1)\n",
    "        \n",
    "        df.insert(loc=1, column=\"season_1\", value=season_1)\n",
    "        df.insert(loc=1, column=\"season_2\", value=season_2)\n",
    "        df.insert(loc=1, column=\"season_3\", value=season_3)\n",
    "        df.insert(loc=1, column=\"season_4\", value=season_4)\n",
    "        \n",
    "        n = np.shape(df)[0]\n",
    "        weekend = np.zeros(n)\n",
    "        weekend[(df[\"holiday\"]==0) & (df[\"workingday\"]==0)]=1\n",
    "        \n",
    "        df.insert(loc=1, column=\"weekend\", value=weekend)\n",
    "        \n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_hour = pd.read_csv(\"Bike-Sharing-Dataset/hour.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      "instant       17379 non-null int64\n",
      "dteday        17379 non-null object\n",
      "season        17379 non-null int64\n",
      "yr            17379 non-null int64\n",
      "mnth          17379 non-null int64\n",
      "hr            17379 non-null int64\n",
      "holiday       17379 non-null int64\n",
      "weekday       17379 non-null int64\n",
      "workingday    17379 non-null int64\n",
      "weathersit    17379 non-null int64\n",
      "temp          17379 non-null float64\n",
      "atemp         17379 non-null float64\n",
      "hum           17379 non-null float64\n",
      "windspeed     17379 non-null float64\n",
      "casual        17379 non-null int64\n",
      "registered    17379 non-null int64\n",
      "cnt           17379 non-null int64\n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_hour.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = BicycleSharingAnalysis().feature_drop(df_hour, [\"instant\", \"casual\", \"registered\", \"dteday\"], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  hr  holiday  weekday  workingday  weathersit  temp  \\\n",
       "0       1   0     1   0        0        6           0           1  0.24   \n",
       "1       1   0     1   1        0        6           0           1  0.22   \n",
       "2       1   0     1   2        0        6           0           1  0.22   \n",
       "3       1   0     1   3        0        6           0           1  0.24   \n",
       "4       1   0     1   4        0        6           0           1  0.24   \n",
       "\n",
       "    atemp   hum  windspeed  cnt  \n",
       "0  0.2879  0.81        0.0   16  \n",
       "1  0.2727  0.80        0.0   40  \n",
       "2  0.2727  0.80        0.0   32  \n",
       "3  0.2879  0.75        0.0   13  \n",
       "4  0.2879  0.75        0.0    1  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to binary features and add features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = BicycleSharingAnalysis().feature_engineering(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>weekend</th>\n",
       "      <th>season_4</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_1</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
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       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
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       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  weekend  season_4  season_3  season_2  season_1  mnth  hr  holiday  \\\n",
       "0   0      1.0       0.0       0.0       0.0       1.0     1   0        0   \n",
       "1   0      1.0       0.0       0.0       0.0       1.0     1   1        0   \n",
       "2   0      1.0       0.0       0.0       0.0       1.0     1   2        0   \n",
       "3   0      1.0       0.0       0.0       0.0       1.0     1   3        0   \n",
       "4   0      1.0       0.0       0.0       0.0       1.0     1   4        0   \n",
       "\n",
       "   weekday  workingday  weathersit  temp   atemp   hum  windspeed  cnt  \n",
       "0        6           0           1  0.24  0.2879  0.81        0.0   16  \n",
       "1        6           0           1  0.22  0.2727  0.80        0.0   40  \n",
       "2        6           0           1  0.22  0.2727  0.80        0.0   32  \n",
       "3        6           0           1  0.24  0.2879  0.75        0.0   13  \n",
       "4        6           0           1  0.24  0.2879  0.75        0.0    1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mae:  25.47349002685079\n"
     ]
    }
   ],
   "source": [
    "n = np.shape(df)[1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, 0:n-1], df.iloc[:, n-1], random_state=42)\n",
    "reg = RandomForestRegressor(max_depth=50, random_state=0, n_estimators=300)\n",
    "reg.fit(X_train, y_train)\n",
    "predict = reg.predict(X_test)\n",
    "mae = mean_absolute_error(y_test, predict)\n",
    "print(\"mae: \", mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================= test session starts ==============================\n",
      "platform linux -- Python 3.7.3, pytest-5.0.1, py-1.8.0, pluggy-0.12.0\n",
      "rootdir: /home/kai/Projects/Sharing_Bicycle\n",
      "collected 1 item\n",
      "\n",
      "sharingBicycle_solution.py .                                             [100%]\n",
      "\n",
      "=============================== warnings summary ===============================\n",
      "sharingBicycle_solution.py::test_analysis\n",
      "sharingBicycle_solution.py::test_analysis\n",
      "  /home/kai/miniconda3/envs/sharing_bicycle/lib/python3.7/site-packages/seaborn/matrix.py:268: PendingDeprecationWarning: \n",
      "  The label function will be deprecated in a future version. Use Tick.label1 instead.\n",
      "    fontsize = tick.label.get_size()\n",
      "\n",
      "-- Docs: https://docs.pytest.org/en/latest/warnings.html\n",
      "===================== 1 passed, 2 warnings in 2.56 seconds =====================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%run_pytest\n",
    "\n",
    "@pytest.fixture\n",
    "def config():\n",
    "    df = pd.read_csv(\"Bike-Sharing-Dataset/hour.csv\")\n",
    "    return df\n",
    "\n",
    "def test_analysis(config, a = BicycleSharingAnalysis()):\n",
    "    plt.figure()\n",
    "    a.barplot(\"season\", \"cnt\", config, \"a\")\n",
    "    plt.figure()\n",
    "    a.diff_day_type_hour_plot(config, \"summer\", 3)\n",
    "    plt.figure()\n",
    "    a.season_curve_plot(config)\n",
    "    plt.figure()\n",
    "    a.weekday_hour_curve_plot(config)\n",
    "    plt.figure()\n",
    "    a.correlation_map(config, [\"hum\", \"temp\", \"cnt\"])\n",
    "    \n",
    "    ls = glob.glob(\"*.pdf\")\n",
    "    num = np.size(ls)\n",
    "    \n",
    "    n = np.shape(config)[1]\n",
    "    res1 = a.feature_drop(config, \"instant\",1)\n",
    "    n1 = np.shape(res1)[1]\n",
    "    res2 = a.feature_engineering(config)\n",
    "    n2 = np.shape(res2)[1]\n",
    "    \n",
    "    assert num == 5, \"Plots do not work as expected\"\n",
    "    assert n1 == n-1, \"Feature drop do not work as expected\"\n",
    "    assert n2 == n+5, \"Feature engineering do not work as expected\"    "
   ]
  }
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