Uber_Movement_Mdling_v0.4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LINEAR REGRESSION, RF AND ANN MODELING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(16,10)})\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47203 entries, 0 to 47202\n",
      "Data columns (total 34 columns):\n",
      "Origin Movement ID                           47203 non-null int64\n",
      "Destination Movement ID                      47203 non-null int64\n",
      "Mean Travel Time (Seconds)                   47203 non-null int64\n",
      "Range - Lower Bound Travel Time (Seconds)    47203 non-null int64\n",
      "Range - Upper Bound Travel Time (Seconds)    47203 non-null int64\n",
      "Date                                         47203 non-null object\n",
      "AWND                                         47203 non-null float64\n",
      "PRCP                                         47203 non-null float64\n",
      "SNOW                                         47203 non-null float64\n",
      "SNWD                                         47203 non-null float64\n",
      "TAVG                                         47203 non-null int64\n",
      "TMAX                                         47203 non-null int64\n",
      "TMIN                                         47203 non-null int64\n",
      "WSF2                                         47203 non-null float64\n",
      "WSF5                                         46687 non-null float64\n",
      "WT01                                         15667 non-null float64\n",
      "WT02                                         2577 non-null float64\n",
      "WT03                                         535 non-null float64\n",
      "WT04                                         1024 non-null float64\n",
      "WT05                                         0 non-null float64\n",
      "WT06                                         523 non-null float64\n",
      "WT08                                         1044 non-null float64\n",
      "WT09                                         496 non-null float64\n",
      "Month                                        47203 non-null int64\n",
      "DOW                                          47203 non-null int64\n",
      "dst_graph_area_m                             47203 non-null float64\n",
      "dst_clean_intersection_density_km            47203 non-null float64\n",
      "dst_street_density_km                        47203 non-null float64\n",
      "src_mxbc_node                                47203 non-null int64\n",
      "dst_mxbc_node                                47203 non-null int64\n",
      "dst_railsubway_density_km                    47203 non-null float64\n",
      "spath                                        47203 non-null float64\n",
      "epath                                        47203 non-null float64\n",
      "circuit                                      47203 non-null float64\n",
      "dtypes: float64(21), int64(12), object(1)\n",
      "memory usage: 12.2+ MB\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin Movement ID</th>\n",
       "      <th>Destination Movement ID</th>\n",
       "      <th>Mean Travel Time (Seconds)</th>\n",
       "      <th>Range - Lower Bound Travel Time (Seconds)</th>\n",
       "      <th>Range - Upper Bound Travel Time (Seconds)</th>\n",
       "      <th>Date</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNWD</th>\n",
       "      <th>...</th>\n",
       "      <th>DOW</th>\n",
       "      <th>dst_graph_area_m</th>\n",
       "      <th>dst_clean_intersection_density_km</th>\n",
       "      <th>dst_street_density_km</th>\n",
       "      <th>src_mxbc_node</th>\n",
       "      <th>dst_mxbc_node</th>\n",
       "      <th>dst_railsubway_density_km</th>\n",
       "      <th>spath</th>\n",
       "      <th>epath</th>\n",
       "      <th>circuit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2018-01-07</td>\n",
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       "      <td>1292</td>\n",
       "      <td>1931</td>\n",
       "      <td>2018-01-07</td>\n",
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       "      <td>2</td>\n",
       "      <td>186</td>\n",
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       "      <td>947</td>\n",
       "      <td>1452</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>8.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2.583289e+06</td>\n",
       "      <td>30.194068</td>\n",
       "      <td>8810.468252</td>\n",
       "      <td>49762202</td>\n",
       "      <td>49799918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8465.565</td>\n",
       "      <td>7501.845296</td>\n",
       "      <td>1.128464</td>\n",
       "    </tr>\n",
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       "      <td>3</td>\n",
       "      <td>186</td>\n",
       "      <td>4</td>\n",
       "      <td>1634</td>\n",
       "      <td>1403</td>\n",
       "      <td>1902</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>8.95</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>186</td>\n",
       "      <td>5</td>\n",
       "      <td>913</td>\n",
       "      <td>720</td>\n",
       "      <td>1157</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>8.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 34 columns</p>\n",
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      "text/plain": [
       "   Origin Movement ID  Destination Movement ID  Mean Travel Time (Seconds)  \\\n",
       "0                 186                        1                         645   \n",
       "1                 186                        2                        1580   \n",
       "2                 186                        3                        1173   \n",
       "3                 186                        4                        1634   \n",
       "4                 186                        5                         913   \n",
       "\n",
       "   Range - Lower Bound Travel Time (Seconds)  \\\n",
       "0                                        480   \n",
       "1                                       1292   \n",
       "2                                        947   \n",
       "3                                       1403   \n",
       "4                                        720   \n",
       "\n",
       "   Range - Upper Bound Travel Time (Seconds)        Date  AWND  PRCP  SNOW  \\\n",
       "0                                        865  2018-01-07  8.95   0.0   0.0   \n",
       "1                                       1931  2018-01-07  8.95   0.0   0.0   \n",
       "2                                       1452  2018-01-07  8.95   0.0   0.0   \n",
       "3                                       1902  2018-01-07  8.95   0.0   0.0   \n",
       "4                                       1157  2018-01-07  8.95   0.0   0.0   \n",
       "\n",
       "   SNWD  ...  DOW  dst_graph_area_m  dst_clean_intersection_density_km  \\\n",
       "0   0.0  ...    6      5.015114e+06                          46.060764   \n",
       "1   0.0  ...    6      2.910236e+06                          79.718610   \n",
       "2   0.0  ...    6      2.583289e+06                          30.194068   \n",
       "3   0.0  ...    6      5.283216e+06                          24.795505   \n",
       "4   0.0  ...    6      1.779070e+06                          55.084950   \n",
       "\n",
       "   dst_street_density_km  src_mxbc_node  dst_mxbc_node  \\\n",
       "0           10503.313887       49762202       49725080   \n",
       "1           15488.186422       49762202       49745571   \n",
       "2            8810.468252       49762202       49799918   \n",
       "3            7437.739980       49762202       49788157   \n",
       "4           12192.839593       49762202       49743089   \n",
       "\n",
       "   dst_railsubway_density_km      spath        epath   circuit  \n",
       "0                        0.0  10504.058  6549.172599  1.603876  \n",
       "1                        0.0  10226.499  9733.047387  1.050699  \n",
       "2                        0.0   8465.565  7501.845296  1.128464  \n",
       "3                        0.0   9694.971  8490.096313  1.141915  \n",
       "4                        0.0   5718.957  5272.930256  1.084588  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot=pd.read_csv('dc_2018q1_clean_1.csv')\n",
    "df_tot.info()\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"New Year's Day\", \"Washington's Birthday\", None, 'Martin Luther King, Jr. Day'}\n"
     ]
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       "   Origin Movement ID  Destination Movement ID  Mean Travel Time (Seconds)  \\\n",
       "0                 186                        1                         645   \n",
       "1                 186                        2                        1580   \n",
       "2                 186                        3                        1173   \n",
       "3                 186                        4                        1634   \n",
       "4                 186                        5                         913   \n",
       "\n",
       "   Range - Lower Bound Travel Time (Seconds)  \\\n",
       "0                                        480   \n",
       "1                                       1292   \n",
       "2                                        947   \n",
       "3                                       1403   \n",
       "4                                        720   \n",
       "\n",
       "   Range - Upper Bound Travel Time (Seconds)        Date  AWND  PRCP  SNOW  \\\n",
       "0                                        865  2018-01-07  8.95   0.0   0.0   \n",
       "1                                       1931  2018-01-07  8.95   0.0   0.0   \n",
       "2                                       1452  2018-01-07  8.95   0.0   0.0   \n",
       "3                                       1902  2018-01-07  8.95   0.0   0.0   \n",
       "4                                       1157  2018-01-07  8.95   0.0   0.0   \n",
       "\n",
       "   SNWD  ...  DOW_1  DOW_2  DOW_3  DOW_4  DOW_5  DOW_6  MON_1  MON_2  MON_3  \\\n",
       "0   0.0  ...      0      0      0      0      0      1      1      0      0   \n",
       "1   0.0  ...      0      0      0      0      0      1      1      0      0   \n",
       "2   0.0  ...      0      0      0      0      0      1      1      0      0   \n",
       "3   0.0  ...      0      0      0      0      0      1      1      0      0   \n",
       "4   0.0  ...      0      0      0      0      0      1      1      0      0   \n",
       "\n",
       "   Workday  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot=pd.concat([df_tot,pd.get_dummies(data=df_tot['DOW'],prefix='DOW')],axis=1)\n",
    "df_tot=pd.concat([df_tot,pd.get_dummies(data=df_tot['Month'],prefix='MON')],axis=1)\n",
    "import datetime\n",
    "import holidays\n",
    "dc_holidays = holidays.UnitedStates(state='DC')\n",
    "Q1_holiday={dc_holidays.get(i) for i in df_tot.Date}\n",
    "print(Q1_holiday)\n",
    "df_tot['Workday']=df_tot.Date.apply(lambda x: 1 if (not x in dc_holidays) and (pd.to_datetime(x).weekday()<5) else 0)\n",
    "df_tot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47203 entries, 0 to 47202\n",
      "Data columns (total 29 columns):\n",
      "Mean Travel Time (Seconds)    47203 non-null int64\n",
      "Date                          47203 non-null object\n",
      "AWND                          47203 non-null float64\n",
      "PRCP                          47203 non-null float64\n",
      "SNOW                          47203 non-null float64\n",
      "SNWD                          47203 non-null float64\n",
      "TAVG                          47203 non-null int64\n",
      "WT02_HeavyFog                 2577 non-null float64\n",
      "WT03_Thunder                  535 non-null float64\n",
      "WT04_IcePell                  1024 non-null float64\n",
      "WT05_Hail                     0 non-null float64\n",
      "WT06_Glaze/Rime               523 non-null float64\n",
      "WT08_Smoke/Haze               1044 non-null float64\n",
      "Month                         47203 non-null int64\n",
      "Day of Week                   47203 non-null int64\n",
      "DSTStrDen                     47203 non-null float64\n",
      "DSTRailDen                    47203 non-null float64\n",
      "ShorPth                       47203 non-null float64\n",
      "Workday                       47203 non-null int64\n",
      "DoW_Mon                       47203 non-null uint8\n",
      "DoW_Tue                       47203 non-null uint8\n",
      "DoW_Wed                       47203 non-null uint8\n",
      "DoW_Thu                       47203 non-null uint8\n",
      "DoW_Fri                       47203 non-null uint8\n",
      "DoW_Sat                       47203 non-null uint8\n",
      "DoW_Sun                       47203 non-null uint8\n",
      "Month_Jan                     47203 non-null uint8\n",
      "Month_Feb                     47203 non-null uint8\n",
      "Month_Mar                     47203 non-null uint8\n",
      "dtypes: float64(13), int64(5), object(1), uint8(10)\n",
      "memory usage: 7.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_tot=df_tot[['Mean Travel Time (Seconds)', 'Date', 'AWND', 'PRCP', 'SNOW', 'SNWD',\n",
    "       'TAVG', 'WT02', 'WT03', 'WT04', 'WT05', 'WT06',\n",
    "       'WT08', 'Month', 'DOW', 'dst_street_density_km',\n",
    "       'dst_railsubway_density_km', 'spath', 'Workday', 'DOW_0', 'DOW_1',\n",
    "       'DOW_2', 'DOW_3', 'DOW_4', 'DOW_5', 'DOW_6', 'MON_1', 'MON_2', 'MON_3']]\n",
    "\n",
    "df_tot.columns = ['Mean Travel Time (Seconds)', 'Date', 'AWND', 'PRCP', 'SNOW', 'SNWD',\n",
    "       'TAVG', 'WT02_HeavyFog', 'WT03_Thunder', 'WT04_IcePell', 'WT05_Hail', 'WT06_Glaze/Rime',\n",
    "       'WT08_Smoke/Haze', 'Month', 'Day of Week', 'DSTStrDen',\n",
    "       'DSTRailDen', 'ShorPth', 'Workday', 'DoW_Mon', 'DoW_Tue',\n",
    "       'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'DoW_Sat', 'DoW_Sun', 'Month_Jan', 'Month_Feb', 'Month_Mar' ]\n",
    "df_tot.head()\n",
    "df_tot.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Log_Mean Travel Time  Mean Travel Time (Seconds)\n",
      "0              6.469250                         645\n",
      "1              7.365180                        1580\n",
      "2              7.067320                        1173\n",
      "3              7.398786                        1634\n",
      "4              6.816736                         913\n"
     ]
    }
   ],
   "source": [
    "#1.1 log transformation of travel time\n",
    "df_tot['Log_Mean Travel Time']=np.log(df_tot['Mean Travel Time (Seconds)'])\n",
    "print(df_tot[['Log_Mean Travel Time', 'Mean Travel Time (Seconds)']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mean Travel Time (Seconds)', 'Date', 'AWND', 'PRCP', 'SNOW', 'SNWD',\n",
       "       'TAVG', 'WT02_HeavyFog', 'WT03_Thunder', 'WT04_IcePell', 'WT05_Hail',\n",
       "       'WT06_Glaze/Rime', 'WT08_Smoke/Haze', 'Month', 'Day of Week',\n",
       "       'DSTStrDen', 'DSTRailDen', 'ShorPth', 'Workday', 'DoW_Mon', 'DoW_Tue',\n",
       "       'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'DoW_Sat', 'DoW_Sun', 'Month_Jan',\n",
       "       'Month_Feb', 'Month_Mar', 'Log_Mean Travel Time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tot.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT02_HeavyFog\n",
      "WT04_IcePell\n",
      "WT06_Glaze/Rime\n",
      "WT08_Smoke/Haze\n",
      "Workday\n",
      "DoW_Sat\n",
      "DoW_Sun\n",
      "{'WT03_Thunder', 'SNOW', 'WT02_HeavyFog', 'Workday', 'DoW_Sat', 'DoW_Sun', 'WT06_Glaze/Rime', 'AWND', 'WT04_IcePell', 'WT08_Smoke/Haze', 'Date', 'Mean Travel Time (Seconds)', 'WT05_Hail', 'Month', 'SNWD'}\n"
     ]
    },
    {
     "data": {
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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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>PRCP</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.054230</td>\n",
       "      <td>0.146901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TAVG</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>41.789987</td>\n",
       "      <td>10.720233</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Day of Week</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>2.110412</td>\n",
       "      <td>1.376226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSTStrDen</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>15393.293008</td>\n",
       "      <td>5784.275885</td>\n",
       "      <td>2186.370621</td>\n",
       "      <td>11291.640689</td>\n",
       "      <td>14622.614265</td>\n",
       "      <td>18934.416556</td>\n",
       "      <td>32944.964144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSTRailDen</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.626651</td>\n",
       "      <td>4.538108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.605391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ShorPth</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>10830.165126</td>\n",
       "      <td>5285.996754</td>\n",
       "      <td>1138.114000</td>\n",
       "      <td>6911.758000</td>\n",
       "      <td>10530.614000</td>\n",
       "      <td>14259.870000</td>\n",
       "      <td>28964.231000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DoW_Mon</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.158803</td>\n",
       "      <td>0.365498</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",
       "      <td>DoW_Tue</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.208464</td>\n",
       "      <td>0.406216</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",
       "      <td>DoW_Wed</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.209238</td>\n",
       "      <td>0.406771</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",
       "      <td>DoW_Thu</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.210508</td>\n",
       "      <td>0.407676</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",
       "      <td>DoW_Fri</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.212987</td>\n",
       "      <td>0.409425</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",
       "      <td>Month_Jan</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.334676</td>\n",
       "      <td>0.471884</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Month_Feb</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.309427</td>\n",
       "      <td>0.462265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Month_Mar</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>0.355897</td>\n",
       "      <td>0.478792</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Log_Mean Travel Time</td>\n",
       "      <td>32279.0</td>\n",
       "      <td>7.143475</td>\n",
       "      <td>0.408012</td>\n",
       "      <td>5.099866</td>\n",
       "      <td>6.946976</td>\n",
       "      <td>7.221836</td>\n",
       "      <td>7.421776</td>\n",
       "      <td>8.222285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count          mean          std          min  \\\n",
       "PRCP                  32279.0      0.054230     0.146901     0.000000   \n",
       "TAVG                  32279.0     41.789987    10.720233    17.000000   \n",
       "Day of Week           32279.0      2.110412     1.376226     0.000000   \n",
       "DSTStrDen             32279.0  15393.293008  5784.275885  2186.370621   \n",
       "DSTRailDen            32279.0      0.626651     4.538108     0.000000   \n",
       "ShorPth               32279.0  10830.165126  5285.996754  1138.114000   \n",
       "DoW_Mon               32279.0      0.158803     0.365498     0.000000   \n",
       "DoW_Tue               32279.0      0.208464     0.406216     0.000000   \n",
       "DoW_Wed               32279.0      0.209238     0.406771     0.000000   \n",
       "DoW_Thu               32279.0      0.210508     0.407676     0.000000   \n",
       "DoW_Fri               32279.0      0.212987     0.409425     0.000000   \n",
       "Month_Jan             32279.0      0.334676     0.471884     0.000000   \n",
       "Month_Feb             32279.0      0.309427     0.462265     0.000000   \n",
       "Month_Mar             32279.0      0.355897     0.478792     0.000000   \n",
       "Log_Mean Travel Time  32279.0      7.143475     0.408012     5.099866   \n",
       "\n",
       "                               25%           50%           75%           max  \n",
       "PRCP                      0.000000      0.000000      0.030000      0.870000  \n",
       "TAVG                     36.000000     40.000000     47.000000     67.000000  \n",
       "Day of Week               1.000000      2.000000      3.000000      4.000000  \n",
       "DSTStrDen             11291.640689  14622.614265  18934.416556  32944.964144  \n",
       "DSTRailDen                0.000000      0.000000      0.000000     84.605391  \n",
       "ShorPth                6911.758000  10530.614000  14259.870000  28964.231000  \n",
       "DoW_Mon                   0.000000      0.000000      0.000000      1.000000  \n",
       "DoW_Tue                   0.000000      0.000000      0.000000      1.000000  \n",
       "DoW_Wed                   0.000000      0.000000      0.000000      1.000000  \n",
       "DoW_Thu                   0.000000      0.000000      0.000000      1.000000  \n",
       "DoW_Fri                   0.000000      0.000000      0.000000      1.000000  \n",
       "Month_Jan                 0.000000      0.000000      1.000000      1.000000  \n",
       "Month_Feb                 0.000000      0.000000      1.000000      1.000000  \n",
       "Month_Mar                 0.000000      0.000000      1.000000      1.000000  \n",
       "Log_Mean Travel Time      6.946976      7.221836      7.421776      8.222285  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.2 Wkday / Wkend data spliting \n",
    "dropcol=['Mean Travel Time (Seconds)', 'Date', 'AWND', 'SNOW','SNWD',\n",
    "         'WT02_HeavyFog', 'WT03_Thunder', 'WT04_IcePell', 'WT05_Hail','WT06_Glaze/Rime', 'WT08_Smoke/Haze',\n",
    "         'Month']\n",
    "## choose workday data and drop the null columns\n",
    "df_wkday=df_tot[df_tot['Workday']==1]\n",
    "stat_wkday=df_wkday.describe().T\n",
    "wkdaydrop=set(dropcol)\n",
    "for i in stat_wkday.index: \n",
    "    if stat_wkday['std'][i] ==0:\n",
    "        print(i)\n",
    "        wkdaydrop.add(i)\n",
    "print(wkdaydrop)\n",
    "df_wkday=df_wkday.drop(wkdaydrop,axis=1)\n",
    "df_wkday.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNWD\n",
      "WT02_HeavyFog\n",
      "WT03_Thunder\n",
      "Workday\n",
      "DoW_Tue\n",
      "DoW_Wed\n",
      "DoW_Thu\n",
      "DoW_Fri\n",
      "{'WT03_Thunder', 'SNOW', 'WT02_HeavyFog', 'Workday', 'DoW_Tue', 'DoW_Fri', 'WT06_Glaze/Rime', 'AWND', 'DoW_Wed', 'WT04_IcePell', 'DoW_Thu', 'WT08_Smoke/Haze', 'Date', 'Mean Travel Time (Seconds)', 'WT05_Hail', 'Month', 'SNWD'}\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>PRCP</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.153225</td>\n",
       "      <td>0.333339</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>1.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TAVG</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>38.661552</td>\n",
       "      <td>10.708674</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>53.000000</td>\n",
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       "    <tr>\n",
       "      <td>Day of Week</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>4.907397</td>\n",
       "      <td>1.742100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSTStrDen</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>15284.893517</td>\n",
       "      <td>5778.211510</td>\n",
       "      <td>2186.370621</td>\n",
       "      <td>11190.819422</td>\n",
       "      <td>14483.940040</td>\n",
       "      <td>18725.251025</td>\n",
       "      <td>32944.964144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSTRailDen</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.615076</td>\n",
       "      <td>4.487522</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.605391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ShorPth</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>11009.562216</td>\n",
       "      <td>5380.748629</td>\n",
       "      <td>1138.114000</td>\n",
       "      <td>6992.193000</td>\n",
       "      <td>10698.259000</td>\n",
       "      <td>14402.492000</td>\n",
       "      <td>28964.231000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DoW_HDY</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.104530</td>\n",
       "      <td>0.305957</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",
       "      <td>DoW_Sat</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.465425</td>\n",
       "      <td>0.498820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DoW_Sun</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.430046</td>\n",
       "      <td>0.495099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Month_Jan</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.354396</td>\n",
       "      <td>0.478346</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Month_Feb</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.321429</td>\n",
       "      <td>0.467041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Month_Mar</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>0.324176</td>\n",
       "      <td>0.468082</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Log_Mean Travel Time</td>\n",
       "      <td>14924.0</td>\n",
       "      <td>7.022224</td>\n",
       "      <td>0.410193</td>\n",
       "      <td>5.117994</td>\n",
       "      <td>6.816462</td>\n",
       "      <td>7.098376</td>\n",
       "      <td>7.303843</td>\n",
       "      <td>7.868254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count          mean          std          min  \\\n",
       "PRCP                  14924.0      0.153225     0.333339     0.000000   \n",
       "TAVG                  14924.0     38.661552    10.708674    15.000000   \n",
       "Day of Week           14924.0      4.907397     1.742100     0.000000   \n",
       "DSTStrDen             14924.0  15284.893517  5778.211510  2186.370621   \n",
       "DSTRailDen            14924.0      0.615076     4.487522     0.000000   \n",
       "ShorPth               14924.0  11009.562216  5380.748629  1138.114000   \n",
       "DoW_HDY               14924.0      0.104530     0.305957     0.000000   \n",
       "DoW_Sat               14924.0      0.465425     0.498820     0.000000   \n",
       "DoW_Sun               14924.0      0.430046     0.495099     0.000000   \n",
       "Month_Jan             14924.0      0.354396     0.478346     0.000000   \n",
       "Month_Feb             14924.0      0.321429     0.467041     0.000000   \n",
       "Month_Mar             14924.0      0.324176     0.468082     0.000000   \n",
       "Log_Mean Travel Time  14924.0      7.022224     0.410193     5.117994   \n",
       "\n",
       "                               25%           50%           75%           max  \n",
       "PRCP                      0.000000      0.000000      0.160000      1.120000  \n",
       "TAVG                     39.000000     42.000000     45.000000     53.000000  \n",
       "Day of Week               5.000000      5.000000      6.000000      6.000000  \n",
       "DSTStrDen             11190.819422  14483.940040  18725.251025  32944.964144  \n",
       "DSTRailDen                0.000000      0.000000      0.000000     84.605391  \n",
       "ShorPth                6992.193000  10698.259000  14402.492000  28964.231000  \n",
       "DoW_HDY                   0.000000      0.000000      0.000000      1.000000  \n",
       "DoW_Sat                   0.000000      0.000000      1.000000      1.000000  \n",
       "DoW_Sun                   0.000000      0.000000      1.000000      1.000000  \n",
       "Month_Jan                 0.000000      0.000000      1.000000      1.000000  \n",
       "Month_Feb                 0.000000      0.000000      1.000000      1.000000  \n",
       "Month_Mar                 0.000000      0.000000      1.000000      1.000000  \n",
       "Log_Mean Travel Time      6.816462      7.098376      7.303843      7.868254  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## choose weekend data and drop the null columns\n",
    "df_wkend=df_tot[df_tot['Workday']==0]\n",
    "stat_wkend=df_wkend.describe().T\n",
    "wkenddrop=set(dropcol)\n",
    "for i in stat_wkend.index: \n",
    "    if stat_wkend['std'][i] ==0:\n",
    "        print(i)\n",
    "        wkenddrop.add(i)\n",
    "print(wkenddrop)\n",
    "df_wkend=df_wkend.drop(wkenddrop,axis=1)\n",
    "df_wkend=df_wkend.rename(columns = {'DoW_Mon':'DoW_HDY'})\n",
    "df_wkend.describe().T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. TRAIN-TEST SPLITING; X/y setting; Kfold setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22595, 15)\n",
      "(9684, 15)\n",
      "(10446, 13)\n",
      "(4478, 13)\n"
     ]
    }
   ],
   "source": [
    "#2.1 FEATURE STAT DESCRIPTION AND TRANSFORMATION \n",
    "wkday_tr, wkday_ts = train_test_split(df_wkday, test_size=0.3, stratify=df_wkday['Day of Week'],random_state=2019 )\n",
    "print(wkday_tr.shape)\n",
    "print(wkday_ts.shape)\n",
    "wkend_tr, wkend_ts = train_test_split(df_wkend, test_size=0.3, stratify=df_wkend['Day of Week'],random_state=2019 )\n",
    "print(wkend_tr.shape)\n",
    "print(wkend_ts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45565    7.267525\n",
      "41464    7.229839\n",
      "22817    6.836259\n",
      "13029    7.350516\n",
      "23743    7.261927\n",
      "Name: Log_Mean Travel Time, dtype: float64 (22595,)\n",
      "45565    7.267525\n",
      "41464    7.229839\n",
      "22817    6.836259\n",
      "13029    7.350516\n",
      "23743    7.261927\n",
      "Name: Log_Mean Travel Time, dtype: float64 (9684,)\n",
      "Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon',\n",
      "       'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Jan', 'Month_Feb',\n",
      "       'Month_Mar'],\n",
      "      dtype='object') (22595, 13)\n",
      "Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon',\n",
      "       'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Jan', 'Month_Feb',\n",
      "       'Month_Mar'],\n",
      "      dtype='object') (9684, 13)\n"
     ]
    }
   ],
   "source": [
    "#2.2 Weekday X and Y seting \n",
    "Y_wkdaytr=wkday_tr['Log_Mean Travel Time']\n",
    "Y_wkdayts=wkday_ts['Log_Mean Travel Time']\n",
    "print(Y_wkdaytr[0:5],Y_wkdaytr.shape)\n",
    "print(Y_wkdaytr[0:5],Y_wkdayts.shape)\n",
    "X_wkdaytr=wkday_tr.drop(['Day of Week','Log_Mean Travel Time'],axis=1)\n",
    "X_wkdayts=wkday_ts.drop(['Day of Week','Log_Mean Travel Time'],axis=1)\n",
    "print(X_wkdaytr.columns,X_wkdaytr.shape)\n",
    "print(X_wkdayts.columns,X_wkdayts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10703    7.189922\n",
      "37027    7.215240\n",
      "7815     6.860664\n",
      "3599     7.084226\n",
      "32739    7.437206\n",
      "Name: Log_Mean Travel Time, dtype: float64 (10446,)\n",
      "10703    7.189922\n",
      "37027    7.215240\n",
      "7815     6.860664\n",
      "3599     7.084226\n",
      "32739    7.437206\n",
      "Name: Log_Mean Travel Time, dtype: float64 (4478,)\n",
      "Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY',\n",
      "       'DoW_Sat', 'DoW_Sun', 'Month_Jan', 'Month_Feb', 'Month_Mar'],\n",
      "      dtype='object') (10446, 11)\n",
      "Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY',\n",
      "       'DoW_Sat', 'DoW_Sun', 'Month_Jan', 'Month_Feb', 'Month_Mar'],\n",
      "      dtype='object') (4478, 11)\n"
     ]
    }
   ],
   "source": [
    "#2.3 Weekend X and Y seting \n",
    "Y_wkendtr=wkend_tr['Log_Mean Travel Time']\n",
    "Y_wkendts=wkend_ts['Log_Mean Travel Time']\n",
    "print(Y_wkendtr[0:5],Y_wkendtr.shape)\n",
    "print(Y_wkendtr[0:5],Y_wkendts.shape)\n",
    "X_wkendtr=wkend_tr.drop(['Day of Week','Log_Mean Travel Time'],axis=1)\n",
    "X_wkendts=wkend_ts.drop(['Day of Week','Log_Mean Travel Time'],axis=1)\n",
    "print(X_wkendtr.columns,X_wkendtr.shape)\n",
    "print(X_wkendts.columns,X_wkendts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(array([    0,     1,     2, ..., 22592, 22593, 22594]), array([   13,    14,    26, ..., 22540, 22552, 22554])), (array([    0,     1,     2, ..., 22592, 22593, 22594]), array([    4,    18,    19, ..., 22564, 22574, 22584])), (array([    0,     1,     2, ..., 22591, 22592, 22594]), array([    6,     8,    23, ..., 22586, 22588, 22593])), (array([    0,     1,     2, ..., 22592, 22593, 22594]), array([    5,     9,    29, ..., 22560, 22565, 22570])), (array([    3,     4,     5, ..., 22592, 22593, 22594]), array([    0,     1,     2, ..., 22566, 22571, 22583])), (array([    0,     1,     2, ..., 22590, 22593, 22594]), array([   16,    42,    58, ..., 22587, 22591, 22592])), (array([    0,     1,     2, ..., 22592, 22593, 22594]), array([   11,    21,    38, ..., 22579, 22580, 22582])), (array([    0,     1,     2, ..., 22592, 22593, 22594]), array([   28,    39,    52, ..., 22549, 22562, 22585])), (array([    0,     1,     2, ..., 22591, 22592, 22593]), array([    3,    20,    32, ..., 22573, 22589, 22594])), (array([    0,     1,     2, ..., 22592, 22593, 22594]), array([   10,    15,    17, ..., 22575, 22577, 22590]))] [(array([    0,     3,     4, ..., 10442, 10444, 10445]), array([    1,     2,     5, ..., 10440, 10441, 10443])), (array([    1,     2,     3, ..., 10443, 10444, 10445]), array([    0,     6,    16, ..., 10386, 10389, 10408])), (array([    0,     1,     2, ..., 10443, 10444, 10445]), array([    8,    11,    28, ..., 10393, 10401, 10428])), (array([    0,     1,     2, ..., 10443, 10444, 10445]), array([   37,    56,    59, ..., 10420, 10426, 10432])), (array([    0,     1,     2, ..., 10443, 10444, 10445]), array([    9,    34,    39, ..., 10406, 10433, 10438])), (array([    0,     1,     2, ..., 10443, 10444, 10445]), array([   20,    27,    29, ..., 10414, 10429, 10431])), (array([    0,     1,     2, ..., 10442, 10443, 10445]), array([   12,    21,    23, ..., 10425, 10430, 10444])), (array([    0,     1,     2, ..., 10443, 10444, 10445]), array([    4,    13,    24, ..., 10369, 10384, 10388])), (array([    0,     1,     2, ..., 10443, 10444, 10445]), array([   17,    22,    53, ..., 10423, 10427, 10439])), (array([    0,     1,     2, ..., 10441, 10443, 10444]), array([    3,     7,    10, ..., 10437, 10442, 10445]))]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "KF=KFold(n_splits=10,shuffle=True,random_state=1234)\n",
    "KF_wkday=list(KF.split(X_wkdaytr))\n",
    "KF_wkend=list(KF.split(X_wkendtr))\n",
    "print(KF_wkday,KF_wkend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "#3. Modeling\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "import mlxtend\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Tue',\n",
      "       'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb', 'Month_Mar'],\n",
      "      dtype='object') Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Tue',\n",
      "       'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb', 'Month_Mar'],\n",
      "      dtype='object')\n",
      "Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY',\n",
      "       'DoW_Sat', 'Month_Feb', 'Month_Mar'],\n",
      "      dtype='object') Index(['PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY',\n",
      "       'DoW_Sat', 'Month_Feb', 'Month_Mar'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "##3.1 Robust Linear regression  \n",
    "dummydrop_1=['DoW_Mon','Month_Jan']\n",
    "dummydrop_2=['DoW_Sun','Month_Jan']\n",
    "X_wkdaytr_rlm=X_wkdaytr.drop(dummydrop_1,axis=1)\n",
    "X_wkdayts_rlm=X_wkdayts.drop(dummydrop_1,axis=1)\n",
    "print(X_wkdaytr_rlm.columns,X_wkdayts_rlm.columns)\n",
    "X_wkendtr_rlm=X_wkendtr.drop(dummydrop_2,axis=1)\n",
    "X_wkendts_rlm=X_wkendts.drop(dummydrop_2,axis=1)\n",
    "print(X_wkendtr_rlm.columns,X_wkendts_rlm.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HuberRegressor(alpha=0.0001, epsilon=1.35, fit_intercept=True, max_iter=100,\n",
      "               tol=1e-05, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "### Weekday linear regression \n",
    "#### Huberregression with Sequential Forward Floating Selection (SFFS)\n",
    "\n",
    "##### Find the amount of key features\n",
    "from sklearn.linear_model import HuberRegressor\n",
    "hr=HuberRegressor()\n",
    "print(hr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SequentialFeatureSelector(clone_estimator=True,\n",
      "                          cv=[(array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([   13,    14,    26, ..., 22540, 22552, 22554])),\n",
      "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([    4,    18,    19, ..., 22564, 22574, 22584])),\n",
      "                              (array([    0,     1,     2, ..., 22591, 22592, 22594]),\n",
      "                               array([    6,     8,    23, ..., 22586, 22588, 22593])),\n",
      "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([    5,     9,    29, ..., 22560, 225...\n",
      "                               array([    3,    20,    32, ..., 22573, 22589, 22594])),\n",
      "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([   10,    15,    17, ..., 22575, 22577, 22590]))],\n",
      "                          estimator=HuberRegressor(alpha=0.0001, epsilon=1.35,\n",
      "                                                   fit_intercept=True,\n",
      "                                                   max_iter=100, tol=1e-05,\n",
      "                                                   warm_start=False),\n",
      "                          floating=False, forward=True, k_features=11,\n",
      "                          n_jobs=-1, pre_dispatch='2*n_jobs',\n",
      "                          scoring='neg_mean_squared_error', verbose=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SequentialFeatureSelector(clone_estimator=True,\n",
       "                          cv=[(array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
       "                               array([   13,    14,    26, ..., 22540, 22552, 22554])),\n",
       "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
       "                               array([    4,    18,    19, ..., 22564, 22574, 22584])),\n",
       "                              (array([    0,     1,     2, ..., 22591, 22592, 22594]),\n",
       "                               array([    6,     8,    23, ..., 22586, 22588, 22593])),\n",
       "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
       "                               array([    5,     9,    29, ..., 22560, 225...\n",
       "                               array([    3,    20,    32, ..., 22573, 22589, 22594])),\n",
       "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
       "                               array([   10,    15,    17, ..., 22575, 22577, 22590]))],\n",
       "                          estimator=HuberRegressor(alpha=0.0001, epsilon=1.35,\n",
       "                                                   fit_intercept=True,\n",
       "                                                   max_iter=100, tol=1e-05,\n",
       "                                                   warm_start=False),\n",
       "                          floating=False, forward=True, k_features=11,\n",
       "                          n_jobs=-1, pre_dispatch='2*n_jobs',\n",
       "                          scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rlm_wkday = SFS(estimator=hr, \n",
    "           k_features=X_wkdaytr_rlm.shape[1], \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           scoring='neg_mean_squared_error',n_jobs=-1,\n",
    "           cv=KF_wkday)\n",
    "print(rlm_wkday)\n",
    "rlm_wkday.fit(X_wkdaytr_rlm,Y_wkdaytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    -0.0937043\n",
      "2    -0.0918451\n",
      "3    -0.0943223\n",
      "4     -0.213114\n",
      "5     -0.183367\n",
      "6     -0.274103\n",
      "7     -0.271083\n",
      "8     -0.365815\n",
      "9     -0.364916\n",
      "10    -0.621491\n",
      "11     -1.00864\n",
      "Name: avg_score, dtype: object [('ShorPth',) ('DSTStrDen', 'ShorPth') ('PRCP', 'DSTStrDen', 'ShorPth')\n",
      " ('PRCP', 'DSTStrDen', 'ShorPth', 'DoW_Fri')\n",
      " ('PRCP', 'DSTStrDen', 'ShorPth', 'DoW_Wed', 'DoW_Fri')\n",
      " ('PRCP', 'DSTStrDen', 'ShorPth', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri')\n",
      " ('PRCP', 'DSTStrDen', 'ShorPth', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri')\n",
      " ('PRCP', 'DSTStrDen', 'ShorPth', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb')\n",
      " ('PRCP', 'DSTStrDen', 'ShorPth', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb', 'Month_Mar')]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 30))\n",
    "fig = plot_sfs(rlm_wkday.get_metric_dict(), kind='std_err')\n",
    "plt.title('SFS Huber Robust Regression Performance (w. StdErr)_Workday',fontsize=20)\n",
    "#plt.text(0.6,-0.45,'3 Key Features',fontsize=20,color='r')\n",
    "#plt.text(1.2,-0.55,'with Neg.MSE = -0.0948951',fontsize=20,color='r')\n",
    "\n",
    "#plt.arrow(2,-0.4,0.8,0.25,head_width=0.01,head_length=0.1,color='r',shape='full')\n",
    "plt.grid()\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('Number of Features',fontsize=20)\n",
    "plt.ylabel('Performance',fontsize=20)\n",
    "plt.show()\n",
    "rlm_wk_mtr=day=pd.DataFrame.from_dict(rlm_wkday.get_metric_dict()).T \n",
    "print(rlm_wk_mtr['avg_score'],rlm_wk_mtr['feature_names'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PRCP     DSTStrDen    ShorPth\n",
      "45565  0.04  13309.186513  18097.723\n",
      "41464  0.00   2186.370621  28218.574\n",
      "22817  0.00  18081.107572   8298.044\n",
      "13029  0.03   5307.496210  21643.232\n",
      "23743  0.09  17158.319401   7527.730\n",
      "       PRCP     DSTStrDen    ShorPth\n",
      "27504  0.00  10125.722764  12244.072\n",
      "17874  0.00  23904.197120   4679.861\n",
      "21152  0.00   8867.267590  15398.036\n",
      "43152  0.05  20738.316766   7351.749\n",
      "27512  0.00   4302.964676  17012.332\n"
     ]
    }
   ],
   "source": [
    "###transforming hr key featurs after SFS\n",
    "X_wkdaytr_hr3=X_wkdaytr[['PRCP', 'DSTStrDen', 'ShorPth']]\n",
    "print(X_wkdaytr_hr3.head())\n",
    "X_wkdayts_hr3=X_wkdayts[['PRCP', 'DSTStrDen', 'ShorPth']]\n",
    "print(X_wkdayts_hr3.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1, 10.0, 100.0, 1000.0, 10000.0, 100000.0, 1000000.0, 10000000.0]}\n",
      "GridSearchCV(cv=[(array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                  array([   13,    14,    26, ..., 22540, 22552, 22554])),\n",
      "                 (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                  array([    4,    18,    19, ..., 22564, 22574, 22584])),\n",
      "                 (array([    0,     1,     2, ..., 22591, 22592, 22594]),\n",
      "                  array([    6,     8,    23, ..., 22586, 22588, 22593])),\n",
      "                 (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                  array([    5,     9,    29, ..., 22560, 22565, 22570])),\n",
      "                 (array([    3,     4,     5, ..., 2259...\n",
      "             error_score='raise-deprecating',\n",
      "             estimator=HuberRegressor(alpha=0.0001, epsilon=1.35,\n",
      "                                      fit_intercept=True, max_iter=100,\n",
      "                                      tol=1e-05, warm_start=False),\n",
      "             iid='warn', n_jobs=-1,\n",
      "             param_grid={'alpha': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1, 10.0,\n",
      "                                   100.0, 1000.0, 10000.0, 100000.0, 1000000.0,\n",
      "                                   10000000.0]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "             scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "hr_wkday_paragrid_1 = {\n",
    "    'alpha': [1e-05,1e-04,1e-03,1e-02,1e-01,1,1e01,1e02,1e03,1e04,1e05,1e06,1e07]\n",
    "                }\n",
    "print(hr_wkday_paragrid_1)\n",
    "hr_Wkday_gs_1=GridSearchCV(\n",
    "            hr,\n",
    "            hr_wkday_paragrid_1,\n",
    "            scoring='neg_mean_squared_error',\n",
    "            cv=KF_wkday,\n",
    "            return_train_score=True,\n",
    "            n_jobs=-1\n",
    "            )\n",
    "print(hr_Wkday_gs_1)\n",
    "hr_Wkday_gs_1.fit(X_wkdaytr_hr3,Y_wkdaytr)\n",
    "print('Fit Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1e-05} mse.: -0.09432256786181045\n",
      "{'alpha': 0.0001} mse.: -0.0943225608405192\n",
      "{'alpha': 0.001} mse.: -0.0943225617577126\n",
      "{'alpha': 0.01} mse.: -0.09432256484545234\n",
      "{'alpha': 0.1} mse.: -0.09432255677485128\n",
      "{'alpha': 1} mse.: -0.09432248276837973\n",
      "{'alpha': 10.0} mse.: -0.09432173269656338\n",
      "{'alpha': 100.0} mse.: -0.0943143070795719\n",
      "{'alpha': 1000.0} mse.: -0.09424294602656264\n",
      "{'alpha': 10000.0} mse.: -0.09334422711262608\n",
      "{'alpha': 100000.0} mse.: -0.09184526133126965\n",
      "{'alpha': 1000000.0} mse.: -0.09184550455983945\n",
      "{'alpha': 10000000.0} mse.: -0.09184561636360508\n",
      "Best score: -0.09184526133126965\n",
      "Best parameters via GridSearch {'alpha': 100000.0}\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(hr_Wkday_gs_1.cv_results_['params'])):\n",
    "    print(hr_Wkday_gs_1.cv_results_['params'][i], 'mse.:', hr_Wkday_gs_1.cv_results_['mean_test_score'][i])\n",
    "print('Best score:', hr_Wkday_gs_1.best_score_)\n",
    "print(\"Best parameters via GridSearch\", hr_Wkday_gs_1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.27951387 6.84343541 7.41280063 6.97908999 7.52343292]\n",
      "27504    7.766417\n",
      "17874    6.974479\n",
      "21152    7.379632\n",
      "43152    7.206377\n",
      "27512    7.381502\n",
      "Name: Log_Mean Travel Time, dtype: float64\n",
      "Weekday Huber Robust Regression Prediction (Best features=3): 0.09067587815865827\n"
     ]
    }
   ],
   "source": [
    "Y_hr3_wkday=hr_Wkday_gs_1.predict(X_wkdayts_hr3)\n",
    "print(Y_hr3_wkday[0:5])\n",
    "print(Y_wkdayts[0:5])\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Weekday Huber Robust Regression Prediction (Best features=3):',mean_squared_error(Y_wkdayts,Y_hr3_wkday))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SequentialFeatureSelector(clone_estimator=True,\n",
      "                          cv=[(array([    0,     3,     4, ..., 10442, 10444, 10445]),\n",
      "                               array([    1,     2,     5, ..., 10440, 10441, 10443])),\n",
      "                              (array([    1,     2,     3, ..., 10443, 10444, 10445]),\n",
      "                               array([    0,     6,    16, ..., 10386, 10389, 10408])),\n",
      "                              (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                               array([    8,    11,    28, ..., 10393, 10401, 10428])),\n",
      "                              (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                               array([   37,    56,    59, ..., 10420, 1042...\n",
      "                              (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                               array([   17,    22,    53, ..., 10423, 10427, 10439])),\n",
      "                              (array([    0,     1,     2, ..., 10441, 10443, 10444]),\n",
      "                               array([    3,     7,    10, ..., 10437, 10442, 10445]))],\n",
      "                          estimator=HuberRegressor(alpha=0.0001, epsilon=1.35,\n",
      "                                                   fit_intercept=True,\n",
      "                                                   max_iter=100, tol=1e-05,\n",
      "                                                   warm_start=False),\n",
      "                          floating=False, forward=True, k_features=9, n_jobs=-1,\n",
      "                          pre_dispatch='2*n_jobs',\n",
      "                          scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "### Weekend linear regression \n",
    "#### Huberregression with Sequential Forward Floating Selection (SFFS)\n",
    "rlm_wkend = SFS(estimator=hr, \n",
    "           k_features=X_wkendtr_rlm.shape[1], \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           scoring='neg_mean_squared_error',n_jobs=-1,\n",
    "           cv=KF_wkend)\n",
    "print(rlm_wkend)\n",
    "rlm_wkend.fit(X_wkendtr_rlm,Y_wkendtr)\n",
    "print(\"Fit Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    -0.173003\n",
      "2    -0.172603\n",
      "3   -0.0993647\n",
      "4    -0.140147\n",
      "5    -0.114259\n",
      "6    -0.142556\n",
      "7    -0.379534\n",
      "8     -1.60202\n",
      "9     -1.10947\n",
      "Name: avg_score, dtype: object [('Month_Mar',) ('DoW_Sat', 'Month_Mar')\n",
      " ('ShorPth', 'DoW_Sat', 'Month_Mar')\n",
      " ('PRCP', 'ShorPth', 'DoW_Sat', 'Month_Mar')\n",
      " ('PRCP', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'Month_Mar')\n",
      " ('PRCP', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'Month_Feb', 'Month_Mar')]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 30))\n",
    "fig = plot_sfs(rlm_wkend.get_metric_dict(), kind='std_err')\n",
    "plt.title('SFS Huber Robust Regression Performance (w. StdErr)_Weekend/holiday',fontsize=20)\n",
    "plt.grid()\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('Number of Features',fontsize=20)\n",
    "plt.ylabel('Performance',fontsize=20)\n",
    "plt.show()\n",
    "rlm_wkend_mtr=day=pd.DataFrame.from_dict(rlm_wkend.get_metric_dict()).T \n",
    "print(rlm_wkend_mtr['avg_score'],rlm_wkend_mtr['feature_names'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       PRCP    ShorPth  DoW_Sat  Month_Feb  Month_Mar\n",
      "10703  0.00  10861.897        1          0          0\n",
      "37027  0.00  12427.456        0          0          1\n",
      "7815   0.16   6722.084        0          0          0\n",
      "3599   1.06  12164.610        1          1          0\n",
      "32739  0.00  11078.130        1          0          1\n",
      "       PRCP    ShorPth  DoW_Sat  Month_Feb  Month_Mar\n",
      "31578  0.00   3558.627        1          0          1\n",
      "42048  0.17   9891.369        0          1          0\n",
      "2960   1.05  11369.827        0          1          0\n",
      "11688  0.11  13024.145        1          0          0\n",
      "11502  0.11   1243.901        1          0          0\n"
     ]
    }
   ],
   "source": [
    "###transforming hr key featurs after SFS\n",
    "X_wkendtr_hr3=X_wkendtr[['PRCP', 'ShorPth', 'DoW_Sat', 'Month_Feb', 'Month_Mar']]\n",
    "print(X_wkendtr_hr3.head())\n",
    "X_wkendts_hr3=X_wkendts[['PRCP', 'ShorPth', 'DoW_Sat', 'Month_Feb', 'Month_Mar']]\n",
    "print(X_wkendts_hr3.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1, 10.0, 100.0, 1000.0, 10000.0, 100000.0, 1000000.0, 10000000.0]}\n",
      "GridSearchCV(cv=[(array([    0,     3,     4, ..., 10442, 10444, 10445]),\n",
      "                  array([    1,     2,     5, ..., 10440, 10441, 10443])),\n",
      "                 (array([    1,     2,     3, ..., 10443, 10444, 10445]),\n",
      "                  array([    0,     6,    16, ..., 10386, 10389, 10408])),\n",
      "                 (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                  array([    8,    11,    28, ..., 10393, 10401, 10428])),\n",
      "                 (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                  array([   37,    56,    59, ..., 10420, 10426, 10432])),\n",
      "                 (array([    0,     1,     2, ..., 10443...\n",
      "             error_score='raise-deprecating',\n",
      "             estimator=HuberRegressor(alpha=0.0001, epsilon=1.35,\n",
      "                                      fit_intercept=True, max_iter=100,\n",
      "                                      tol=1e-05, warm_start=False),\n",
      "             iid='warn', n_jobs=-1,\n",
      "             param_grid={'alpha': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1, 10.0,\n",
      "                                   100.0, 1000.0, 10000.0, 100000.0, 1000000.0,\n",
      "                                   10000000.0]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "             scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "hr_wkend_paragrid_1 = {\n",
    "    'alpha': [1e-05,1e-04,1e-03,1e-02,1e-01,1,1e01,1e02,1e03,1e04,1e05,1e06,1e07]\n",
    "                }\n",
    "print(hr_wkend_paragrid_1)\n",
    "hr_Wkend_gs_1=GridSearchCV(\n",
    "            hr,\n",
    "            hr_wkend_paragrid_1,\n",
    "            scoring='neg_mean_squared_error',\n",
    "            cv=KF_wkend,\n",
    "            return_train_score=True,\n",
    "            n_jobs=-1\n",
    "            )\n",
    "print(hr_Wkend_gs_1)\n",
    "hr_Wkend_gs_1.fit(X_wkendtr_hr3,Y_wkendtr)\n",
    "print('Fit Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1e-05} mse.: -0.15383866568478033\n",
      "{'alpha': 0.0001} mse.: -0.14497662021369503\n",
      "{'alpha': 0.001} mse.: -0.14163948327787196\n",
      "{'alpha': 0.01} mse.: -0.13601579524529891\n",
      "{'alpha': 0.1} mse.: -0.16373040377009437\n",
      "{'alpha': 1} mse.: -0.2055380004446626\n",
      "{'alpha': 10.0} mse.: -0.21720245579050645\n",
      "{'alpha': 100.0} mse.: -0.13106093257999776\n",
      "{'alpha': 1000.0} mse.: -0.3196150585148888\n",
      "{'alpha': 10000.0} mse.: -0.10224452175308556\n",
      "{'alpha': 100000.0} mse.: -0.10394973966325212\n",
      "{'alpha': 1000000.0} mse.: -0.10122956937858393\n",
      "{'alpha': 10000000.0} mse.: -0.0993385778034208\n",
      "Best score: -0.0993385778034208\n",
      "Best parameters via GridSearch {'alpha': 10000000.0}\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(hr_Wkend_gs_1.cv_results_['params'])):\n",
    "    print(hr_Wkend_gs_1.cv_results_['params'][i], 'mse.:', hr_Wkend_gs_1.cv_results_['mean_test_score'][i])\n",
    "print('Best score:', hr_Wkend_gs_1.best_score_)\n",
    "print(\"Best parameters via GridSearch\", hr_Wkend_gs_1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.71781807 6.98967047 7.05315485 7.12418155 6.61841548]\n",
      "31578    6.870053\n",
      "42048    6.815640\n",
      "2960     7.033506\n",
      "11688    7.634337\n",
      "11502    5.267858\n",
      "Name: Log_Mean Travel Time, dtype: float64\n",
      "Weekend  Regression Prediction (Best features=5): 0.09491358177455467\n"
     ]
    }
   ],
   "source": [
    "Y_hr3_wkend_pred=hr_Wkend_gs_1.predict(X_wkendts_hr3)\n",
    "print(Y_hr3_wkend_pred[0:5])\n",
    "print(Y_wkendts[0:5])\n",
    "print('Weekend  Regression Prediction (Best features=5):', mean_squared_error(Y_wkendts,Y_hr3_wkend_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=32,\n",
      "                      max_features='log2', max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=2,\n",
      "                      min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=-1,\n",
      "                      oob_score=False, random_state=114, verbose=0,\n",
      "                      warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "##3.2 Random Forest \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor(max_depth=32,\n",
    "                           bootstrap=True,\n",
    "                           random_state=114,\n",
    "                           n_jobs=-1,\n",
    "                           max_features='log2',\n",
    "                           n_estimators=300)\n",
    "print(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SequentialFeatureSelector(clone_estimator=True,\n",
      "                          cv=[(array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([   13,    14,    26, ..., 22540, 22552, 22554])),\n",
      "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([    4,    18,    19, ..., 22564, 22574, 22584])),\n",
      "                              (array([    0,     1,     2, ..., 22591, 22592, 22594]),\n",
      "                               array([    6,     8,    23, ..., 22586, 22588, 22593])),\n",
      "                              (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                               array([    5,     9,    29, ..., 22560, 225...\n",
      "                                                          max_features='log2',\n",
      "                                                          max_leaf_nodes=None,\n",
      "                                                          min_impurity_decrease=0.0,\n",
      "                                                          min_impurity_split=None,\n",
      "                                                          min_samples_leaf=1,\n",
      "                                                          min_samples_split=2,\n",
      "                                                          min_weight_fraction_leaf=0.0,\n",
      "                                                          n_estimators=300,\n",
      "                                                          n_jobs=-1,\n",
      "                                                          oob_score=False,\n",
      "                                                          random_state=114,\n",
      "                                                          verbose=0,\n",
      "                                                          warm_start=False),\n",
      "                          floating=False, forward=True, k_features=13,\n",
      "                          n_jobs=-1, pre_dispatch='2*n_jobs',\n",
      "                          scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "###Random Forest with SFS_weekday \n",
    "rf_wkday = SFS(estimator=rf, \n",
    "           k_features=X_wkdaytr.shape[1], \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           scoring='neg_mean_squared_error',n_jobs=-1,\n",
    "           cv=KF_wkday)\n",
    "print(rf_wkday)\n",
    "rf_wkday.fit(X_wkdaytr,Y_wkdaytr)\n",
    "print(\"Fit Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    -0.00778955\n",
      "2    -0.00778621\n",
      "3    -0.00778584\n",
      "4    -0.00783764\n",
      "5    -0.00791848\n",
      "6    -0.00816395\n",
      "7    -0.00846142\n",
      "8    -0.00845617\n",
      "9    -0.00979183\n",
      "10    -0.0117778\n",
      "11    -0.0124713\n",
      "12    -0.0197039\n",
      "13    -0.0302405\n",
      "Name: avg_score, dtype: object [('ShorPth',) ('DSTStrDen', 'ShorPth')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Fri')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Fri')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Fri')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Thu', 'DoW_Fri')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Feb', 'Month_Mar')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri', 'Month_Jan', 'Month_Feb', 'Month_Mar')]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 30))\n",
    "fig = plot_sfs(rf_wkday.get_metric_dict(), kind='std_err')\n",
    "plt.grid()\n",
    "plt.title('SFS Random Forest Regression Performance (w. StdErr)_Workday',fontsize=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('Number of Features',fontsize=20)\n",
    "plt.ylabel('Performance',fontsize=20)\n",
    "plt.show()\n",
    "rf_wkday_mtr=day=pd.DataFrame.from_dict(rf_wkday.get_metric_dict()).T \n",
    "print(rf_wkday_mtr['avg_score'],rf_wkday_mtr['feature_names'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DSTStrDen  DSTRailDen    ShorPth  DoW_Mon  DoW_Tue  DoW_Wed  \\\n",
      "45565  13309.186513    0.000000  18097.723        0        0        0   \n",
      "41464   2186.370621    0.000000  28218.574        1        0        0   \n",
      "22817  18081.107572    0.000000   8298.044        0        0        0   \n",
      "13029   5307.496210    0.165623  21643.232        0        0        1   \n",
      "23743  17158.319401    0.000000   7527.730        0        0        0   \n",
      "\n",
      "       DoW_Thu  DoW_Fri  \n",
      "45565        0        1  \n",
      "41464        0        0  \n",
      "22817        0        1  \n",
      "13029        0        0  \n",
      "23743        1        0  \n",
      "          DSTStrDen  DSTRailDen    ShorPth  DoW_Mon  DoW_Tue  DoW_Wed  \\\n",
      "27504  10125.722764         0.0  12244.072        0        0        0   \n",
      "17874  23904.197120         0.0   4679.861        0        0        0   \n",
      "21152   8867.267590         0.0  15398.036        0        0        0   \n",
      "43152  20738.316766         0.0   7351.749        0        0        0   \n",
      "27512   4302.964676         0.0  17012.332        0        0        0   \n",
      "\n",
      "       DoW_Thu  DoW_Fri  \n",
      "27504        0        1  \n",
      "17874        0        1  \n",
      "21152        1        0  \n",
      "43152        0        1  \n",
      "27512        0        1  \n"
     ]
    }
   ],
   "source": [
    "###transforming key featurs after SFS\n",
    "X_wkdaytr_rf8=X_wkdaytr[['DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri']]\n",
    "print(X_wkdaytr_rf8.head())\n",
    "X_wkdayts_rf8=X_wkdayts[['DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_Mon', 'DoW_Tue', 'DoW_Wed', 'DoW_Thu', 'DoW_Fri']]\n",
    "print(X_wkdayts_rf8.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': [1, 2, 4, 8, 16, 24, 32, 48, 56, 64]}\n",
      "GridSearchCV(cv=[(array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                  array([   13,    14,    26, ..., 22540, 22552, 22554])),\n",
      "                 (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                  array([    4,    18,    19, ..., 22564, 22574, 22584])),\n",
      "                 (array([    0,     1,     2, ..., 22591, 22592, 22594]),\n",
      "                  array([    6,     8,    23, ..., 22586, 22588, 22593])),\n",
      "                 (array([    0,     1,     2, ..., 22592, 22593, 22594]),\n",
      "                  array([    5,     9,    29, ..., 22560, 22565, 22570])),\n",
      "                 (array([    3,     4,     5, ..., 2259...\n",
      "                                             min_impurity_split=None,\n",
      "                                             min_samples_leaf=1,\n",
      "                                             min_samples_split=2,\n",
      "                                             min_weight_fraction_leaf=0.0,\n",
      "                                             n_estimators=300, n_jobs=-1,\n",
      "                                             oob_score=False, random_state=114,\n",
      "                                             verbose=0, warm_start=False),\n",
      "             iid='warn', n_jobs=-1,\n",
      "             param_grid={'max_depth': [1, 2, 4, 8, 16, 24, 32, 48, 56, 64]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "             scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "###building gridsearch cv RF model with key features \n",
    "rf_wkday_paragrid_1 = {\n",
    "    'max_depth':[1,2,4,8,16,24,32,48,56,64],    \n",
    "                }\n",
    "print(rf_wkday_paragrid_1)\n",
    "rf_Wkday_gs_1=GridSearchCV(\n",
    "            rf,\n",
    "            rf_wkday_paragrid_1,\n",
    "            scoring='neg_mean_squared_error',\n",
    "            cv=KF_wkday,\n",
    "            return_train_score=True,\n",
    "            n_jobs=-1\n",
    "            )\n",
    "print(rf_Wkday_gs_1)\n",
    "rf_Wkday_gs_1.fit(X_wkdaytr_rf8,Y_wkdaytr)\n",
    "print('Fit Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 1} mse.: -0.10854647937555004\n",
      "{'max_depth': 2} mse.: -0.08005478605090731\n",
      "{'max_depth': 4} mse.: -0.05289761645110831\n",
      "{'max_depth': 8} mse.: -0.033211271519141675\n",
      "{'max_depth': 16} mse.: -0.011007052638523475\n",
      "{'max_depth': 24} mse.: -0.008448741034322402\n",
      "{'max_depth': 32} mse.: -0.008456181783336758\n",
      "{'max_depth': 48} mse.: -0.008458396934885756\n",
      "{'max_depth': 56} mse.: -0.008458396934885756\n",
      "{'max_depth': 64} mse.: -0.008458396934885754\n",
      "Best score: -0.008448741034322402\n",
      "Best parameters via GridSearch {'max_depth': 24}\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(rf_Wkday_gs_1.cv_results_['params'])):\n",
    "    print(rf_Wkday_gs_1.cv_results_['params'][i], 'mse.:', rf_Wkday_gs_1.cv_results_['mean_test_score'][i])\n",
    "print('Best score:', rf_Wkday_gs_1.best_score_)\n",
    "print(\"Best parameters via GridSearch\", rf_Wkday_gs_1.best_params_)\n",
    "#rf_Wkday_gs_1_output=pd.DataFrame(rf_Wkday_gs_1.cv_results_)\n",
    "#rf_Wkday_gs_1_hm=rf_Wkday_gs_1_output.pivot('param_max_depth','param_n_estimators','mean_test_score')\n",
    "#hm_rf=sns.heatmap(rf_Wkday_gs_1_hm,linewidths=0.1,annot=True,cmap='RdBu_r',center=-0.0076,fmt='.5g')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: -0.008448741034322402\n",
      "Best parameters via GridSearch {'max_depth': 24}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Best score:', rf_Wkday_gs_1.best_score_)\n",
    "print(\"Best parameters via GridSearch\", rf_Wkday_gs_1.best_params_)\n",
    "rf_Wkday_gs_1_cvrsl=pd.DataFrame(rf_Wkday_gs_1.cv_results_)\n",
    "x=rf_Wkday_gs_1_cvrsl['param_max_depth']\n",
    "y=rf_Wkday_gs_1_cvrsl['mean_test_score']\n",
    "#y_std = rf_Wkday_gs_1_cvrsl['std_test_score']\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(x,y,label='RF regression MSE Line',c='g')\n",
    "#std_error = y_std / np.sqrt(3)\n",
    "#plt.plot(x, y + std_error, 'b-.')\n",
    "#plt.plot(x, y - std_error, 'b-.')\n",
    "plt.axhline(np.max(y), linestyle='--', color='.5',label='Best MSE')\n",
    "plt.ylabel('CV score +/- std error')\n",
    "plt.xlabel('max_depth')\n",
    "plt.title('RF regression MSE Performance_Weekday')\n",
    "plt.xlim([0, np.max(x)])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "Prediction MSE of 8 SFS Key Features in Random Forest Model_Weekday: 0.008024640362951307\n"
     ]
    }
   ],
   "source": [
    "rf_Wkday_k8=rf.set_params(**rf_Wkday_gs_1.best_params_).fit(X_wkdaytr_rf8,Y_wkdaytr)\n",
    "importances = rf_Wkday_k8.feature_importances_\n",
    "rf_Wkday_k8_imp=pd.DataFrame(data={'Importance':importances,'Best Features':X_wkdaytr_rf8.columns})\n",
    "rf_Wkday_k8_imp=rf_Wkday_k8_imp.set_index('Best Features')\n",
    "rf_Wkday_k8_imp=rf_Wkday_k8_imp.sort_values('Importance',ascending=True)\n",
    "rf_Wkday_k8_imp.plot(kind='barh',figsize=(30,10))\n",
    "plt.title('Importance of 8 SFS Key Features in Random Forest Model_Workday',fontsize=50)\n",
    "plt.xticks(fontsize=35)\n",
    "plt.yticks(fontsize=35)\n",
    "plt.ylabel('Best Features',fontsize=35)\n",
    "plt.legend(loc='lower center',fontsize=30)\n",
    "plt.show()\n",
    "Y_rf_Wkday_pred_8=rf_Wkday_gs_1.predict(X_wkdayts_rf8)\n",
    "print('Done')\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Prediction MSE of 8 SFS Key Features in Random Forest Model_Weekday:',mean_squared_error(Y_wkdayts,Y_rf_Wkday_pred_8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SequentialFeatureSelector(clone_estimator=True,\n",
      "                          cv=[(array([    0,     3,     4, ..., 10442, 10444, 10445]),\n",
      "                               array([    1,     2,     5, ..., 10440, 10441, 10443])),\n",
      "                              (array([    1,     2,     3, ..., 10443, 10444, 10445]),\n",
      "                               array([    0,     6,    16, ..., 10386, 10389, 10408])),\n",
      "                              (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                               array([    8,    11,    28, ..., 10393, 10401, 10428])),\n",
      "                              (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                               array([   37,    56,    59, ..., 10420, 1042...\n",
      "                                                          max_features='log2',\n",
      "                                                          max_leaf_nodes=None,\n",
      "                                                          min_impurity_decrease=0.0,\n",
      "                                                          min_impurity_split=None,\n",
      "                                                          min_samples_leaf=1,\n",
      "                                                          min_samples_split=2,\n",
      "                                                          min_weight_fraction_leaf=0.0,\n",
      "                                                          n_estimators=300,\n",
      "                                                          n_jobs=-1,\n",
      "                                                          oob_score=False,\n",
      "                                                          random_state=114,\n",
      "                                                          verbose=0,\n",
      "                                                          warm_start=False),\n",
      "                          floating=False, forward=True, k_features=11,\n",
      "                          n_jobs=-1, pre_dispatch='2*n_jobs',\n",
      "                          scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "###Random Forest with SFS_weekend\n",
    "rf_wkend = SFS(estimator=rf, \n",
    "           k_features=X_wkendtr.shape[1], \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           scoring='neg_mean_squared_error',n_jobs=-1,\n",
    "           cv=KF_wkend)\n",
    "print(rf_wkend)\n",
    "rf_wkend.fit(X_wkendtr,Y_wkendtr)\n",
    "print(\"Fit Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    -0.00543957\n",
      "2    -0.00502827\n",
      "3    -0.00495451\n",
      "4    -0.00494131\n",
      "5    -0.00525154\n",
      "6    -0.00529648\n",
      "7    -0.00816989\n",
      "8    -0.00920222\n",
      "9      -0.010931\n",
      "10    -0.0211176\n",
      "11    -0.0365643\n",
      "Name: avg_score, dtype: object [('ShorPth',) ('ShorPth', 'Month_Jan')\n",
      " ('DSTStrDen', 'ShorPth', 'Month_Jan')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'Month_Jan')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'Month_Jan', 'Month_Mar')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'DoW_Sun', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'DoW_Sun', 'Month_Jan', 'Month_Feb', 'Month_Mar')\n",
      " ('PRCP', 'TAVG', 'DSTStrDen', 'DSTRailDen', 'ShorPth', 'DoW_HDY', 'DoW_Sat', 'DoW_Sun', 'Month_Jan', 'Month_Feb', 'Month_Mar')]\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 30))\n",
    "fig = plot_sfs(rf_wkend.get_metric_dict(), kind='std_err')\n",
    "plt.title('SFS Random Forest Regression Performance (w. StdErr)_Weekend/holiday',fontsize=20)\n",
    "plt.grid()\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel('Number of Features',fontsize=20)\n",
    "plt.ylabel('Performance',fontsize=20)\n",
    "plt.show()\n",
    "rf_wkend_mtr=day=pd.DataFrame.from_dict(rf_wkend.get_metric_dict()).T \n",
    "print(rf_wkend_mtr['avg_score'],rf_wkend_mtr['feature_names'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          DSTStrDen  DSTRailDen    ShorPth  Month_Jan  Month_Feb  Month_Mar\n",
      "10703   9538.542681         0.0  10861.897          1          0          0\n",
      "37027   7856.957170         0.0  12427.456          0          0          1\n",
      "7815    7382.135098         0.0   6722.084          1          0          0\n",
      "3599   17622.085717         0.0  12164.610          0          1          0\n",
      "32739  11353.765995         0.0  11078.130          0          0          1\n",
      "          DSTStrDen  DSTRailDen    ShorPth  Month_Jan  Month_Feb  Month_Mar\n",
      "31578  21961.455075    0.000000   3558.627          0          0          1\n",
      "42048  14327.368921    0.000000   9891.369          0          1          0\n",
      "2960   14047.519743    0.000000  11369.827          0          1          0\n",
      "11688  27373.915334    0.000000  13024.145          1          0          0\n",
      "11502  22605.887344    1.695493   1243.901          1          0          0\n"
     ]
    }
   ],
   "source": [
    "X_wkendtr_rf6=X_wkendtr[['DSTStrDen', 'DSTRailDen', 'ShorPth', 'Month_Jan', 'Month_Feb', 'Month_Mar']]\n",
    "print(X_wkendtr_rf6.head())\n",
    "X_wkendts_rf6=X_wkendts[['DSTStrDen', 'DSTRailDen', 'ShorPth', 'Month_Jan', 'Month_Feb', 'Month_Mar']]\n",
    "print(X_wkendts_rf6.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': [1, 2, 4, 8, 16, 24, 32, 48, 56, 64]}\n",
      "GridSearchCV(cv=[(array([    0,     3,     4, ..., 10442, 10444, 10445]),\n",
      "                  array([    1,     2,     5, ..., 10440, 10441, 10443])),\n",
      "                 (array([    1,     2,     3, ..., 10443, 10444, 10445]),\n",
      "                  array([    0,     6,    16, ..., 10386, 10389, 10408])),\n",
      "                 (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                  array([    8,    11,    28, ..., 10393, 10401, 10428])),\n",
      "                 (array([    0,     1,     2, ..., 10443, 10444, 10445]),\n",
      "                  array([   37,    56,    59, ..., 10420, 10426, 10432])),\n",
      "                 (array([    0,     1,     2, ..., 10443...\n",
      "                                             min_impurity_split=None,\n",
      "                                             min_samples_leaf=1,\n",
      "                                             min_samples_split=2,\n",
      "                                             min_weight_fraction_leaf=0.0,\n",
      "                                             n_estimators=300, n_jobs=-1,\n",
      "                                             oob_score=False, random_state=114,\n",
      "                                             verbose=0, warm_start=False),\n",
      "             iid='warn', n_jobs=-1,\n",
      "             param_grid={'max_depth': [1, 2, 4, 8, 16, 24, 32, 48, 56, 64]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
      "             scoring='neg_mean_squared_error', verbose=0)\n",
      "Fit Done\n"
     ]
    }
   ],
   "source": [
    "rf_wkend_paragrid_1 = {\n",
    "    'max_depth':[1,2,4,8,16,24,32,48,56,64],    \n",
    "                }\n",
    "print(rf_wkend_paragrid_1)\n",
    "rf_Wkend_gs_1=GridSearchCV(\n",
    "            rf,\n",
    "            rf_wkend_paragrid_1,\n",
    "            scoring='neg_mean_squared_error',\n",
    "            cv=KF_wkend,\n",
    "            return_train_score=True,\n",
    "            n_jobs=-1\n",
    "            )\n",
    "print(rf_Wkend_gs_1)\n",
    "rf_Wkend_gs_1.fit(X_wkendtr_rf6,Y_wkendtr)\n",
    "print('Fit Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 1} mse.: -0.11794083508655681\n",
      "{'max_depth': 2} mse.: -0.09058229549306052\n",
      "{'max_depth': 4} mse.: -0.06157446499259727\n",
      "{'max_depth': 8} mse.: -0.03772624610140084\n",
      "{'max_depth': 16} mse.: -0.007653338541164035\n",
      "{'max_depth': 24} mse.: -0.005296504638917986\n",
      "{'max_depth': 32} mse.: -0.005294413818735534\n",
      "{'max_depth': 48} mse.: -0.005293887535868478\n",
      "{'max_depth': 56} mse.: -0.00529388753586848\n",
      "{'max_depth': 64} mse.: -0.005293887535868481\n",
      "Best score: -0.005293887535868478\n",
      "Best parameters via GridSearch {'max_depth': 48}\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(rf_Wkend_gs_1.cv_results_['params'])):\n",
    "    print(rf_Wkend_gs_1.cv_results_['params'][i], 'mse.:', rf_Wkend_gs_1.cv_results_['mean_test_score'][i])\n",
    "print('Best score:', rf_Wkend_gs_1.best_score_)\n",
    "print(\"Best parameters via GridSearch\", rf_Wkend_gs_1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: -0.005293887535868478\n",
      "Best parameters via GridSearch {'max_depth': 48}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Best score:', rf_Wkend_gs_1.best_score_)\n",
    "print(\"Best parameters via GridSearch\", rf_Wkend_gs_1.best_params_)\n",
    "rf_Wkend_gs_1_cvrsl=pd.DataFrame(rf_Wkend_gs_1.cv_results_)\n",
    "x=rf_Wkend_gs_1_cvrsl['param_max_depth']\n",
    "y=rf_Wkend_gs_1_cvrsl['mean_test_score']\n",
    "#y_std = rf_Wkend_gs_1_cvrsl['std_test_score']\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(x,y,label='RF regression MSE Line',c='g')\n",
    "#std_error = y_std / np.sqrt(3)\n",
    "#plt.plot(x, y + std_error, 'b-.')\n",
    "#plt.plot(x, y - std_error, 'b-.')\n",
    "plt.axhline(np.max(y), linestyle='--', color='.5',label='Best MSE')\n",
    "plt.ylabel('CV score +/- std error')\n",
    "plt.xlabel('max_depth')\n",
    "plt.title('RF regression MSE Performance_Weekend')\n",
    "plt.xlim([0, np.max(x)])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "Prediction MSE of 6 SFS Key Features in Random Forest Model_Weekend/holiday: 0.0054451738836305726\n"
     ]
    }
   ],
   "source": [
    "rf_Wkend_k6=rf.set_params(**rf_Wkend_gs_1.best_params_).fit(X_wkendtr_rf6,Y_wkendtr)\n",
    "importances = rf_Wkend_k6.feature_importances_\n",
    "rf_Wkend_k6_imp=pd.DataFrame(data={'Importance':importances,'Best Features':X_wkendtr_rf6.columns})\n",
    "rf_Wkend_k6_imp=rf_Wkend_k6_imp.set_index('Best Features')\n",
    "rf_Wkend_k6_imp=rf_Wkend_k6_imp.sort_values('Importance',ascending=True)\n",
    "rf_Wkend_k6_imp.plot(kind='barh',figsize=(30,10))\n",
    "plt.title('Importance of 6 SFS Key Features in Random Forest Model_Weekend/holiday',fontsize=50)\n",
    "plt.xticks(fontsize=35)\n",
    "plt.yticks(fontsize=35)\n",
    "plt.ylabel('Best Features',fontsize=35)\n",
    "plt.legend(loc='lower center',fontsize=30)\n",
    "plt.show()\n",
    "Y_rf_Wkend_pred_6=rf_Wkend_gs_1.predict(X_wkendts_rf6)\n",
    "print('Done')\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Prediction MSE of 6 SFS Key Features in Random Forest Model_Weekend/holiday:',mean_squared_error(Y_wkendts,Y_rf_Wkend_pred_6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Day of Week</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Y_wkday_ts</td>\n",
       "      <td>7.766417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Y_wkday_ts</td>\n",
       "      <td>6.974479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Y_wkday_ts</td>\n",
       "      <td>7.379632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Y_wkday_ts</td>\n",
       "      <td>7.206377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Y_wkday_ts</td>\n",
       "      <td>7.381502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day of Week    variable     value\n",
       "0            4  Y_wkday_ts  7.766417\n",
       "1            4  Y_wkday_ts  6.974479\n",
       "2            3  Y_wkday_ts  7.379632\n",
       "3            4  Y_wkday_ts  7.206377\n",
       "4            4  Y_wkday_ts  7.381502"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#. Prediction comparison\n",
    "##.Weekday\n",
    "output={'Y_wkday_ts':Y_wkdayts,'Y_wkday_rlmpred':Y_hr3_wkday,'Y_wkday_rfpred':Y_rf_Wkday_pred_8}\n",
    "df_pred=pd.DataFrame(data=output)\n",
    "df_pred['Day of Week']=wkday_ts['Day of Week'].to_numpy()\n",
    "df_pred=df_pred.melt(id_vars=['Day of Week'], value_vars=['Y_wkday_ts', 'Y_wkday_rlmpred','Y_wkday_rfpred'])\n",
    "df_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log_Mean Travel Time')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "ax1 = sns.boxplot(x='variable',y='value',data=df_pred, orient=\"v\", palette=\"Set2\")\n",
    "plt.title('Predict Camparison_boxplot')\n",
    "plt.ylabel('Log_Mean Travel Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log_Mean Travel Time')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "ax1 = sns.violinplot(x='Day of Week',y='value',hue=\"variable\",data=df_pred, orient=\"v\", palette=\"Set2\")\n",
    "plt.xticks([0,1,2,3,4],['Mon', 'Tue','Wed', 'Thu', 'Fri'])\n",
    "plt.title('Predicted Log_Mean Time Camparison_violinplot')\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Log_Mean Travel Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Day of Week</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Y_wkend_ts</td>\n",
       "      <td>6.870053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Y_wkend_ts</td>\n",
       "      <td>6.815640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Y_wkend_ts</td>\n",
       "      <td>7.033506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Y_wkend_ts</td>\n",
       "      <td>7.634337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Y_wkend_ts</td>\n",
       "      <td>5.267858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day of Week    variable     value\n",
       "0            5  Y_wkend_ts  6.870053\n",
       "1            6  Y_wkend_ts  6.815640\n",
       "2            6  Y_wkend_ts  7.033506\n",
       "3            5  Y_wkend_ts  7.634337\n",
       "4            5  Y_wkend_ts  5.267858"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output={'Y_wkend_ts':Y_wkendts,'Y_wkend_rlmpred':Y_hr3_wkend_pred,'Y_wkend_rfpred':Y_rf_Wkend_pred_6}\n",
    "df_pred_wkend=pd.DataFrame(data=output)\n",
    "df_pred_wkend['Day of Week']=wkend_ts['Day of Week'].to_numpy()\n",
    "df_pred_wkend=df_pred_wkend.melt(id_vars=['Day of Week'], value_vars=['Y_wkend_ts', 'Y_wkend_rlmpred','Y_wkend_rfpred'])\n",
    "df_pred_wkend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log_Mean Travel Time')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "ax1 = sns.boxplot(x='variable',y='value',data=df_pred_wkend, orient=\"v\", palette=\"Set2\")\n",
    "plt.title('Predict Camparison_boxplot')\n",
    "plt.ylabel('Log_Mean Travel Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log_Mean Travel Time')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "ax1 = sns.violinplot(x='Day of Week',y='value',hue=\"variable\",data=df_pred_wkend, orient=\"v\", palette=\"Set2\")\n",
    "plt.xticks([0,1,2],['Holiday','Sat','Sun'])\n",
    "plt.title('Weekend Predicted Log_Mean Time Camparison_violinplot')\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Log_Mean Travel Time')"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}