NN Model-Train1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train = pd.read_csv(\"DataSets/Train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>air_pollution_index</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>visibility_in_miles</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rain_p_h</th>\n",
       "      <th>snow_p_h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_type</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>121</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "      <td>329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>178</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>113</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>5026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>281</td>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time is_holiday  air_pollution_index  humidity  wind_speed  \\\n",
       "0  2012-10-02 09:00:00       None                  121        89           2   \n",
       "1  2012-10-02 10:00:00       None                  178        67           3   \n",
       "2  2012-10-02 11:00:00       None                  113        66           3   \n",
       "3  2012-10-02 12:00:00       None                   20        66           3   \n",
       "4  2012-10-02 13:00:00       None                  281        65           3   \n",
       "\n",
       "   wind_direction  visibility_in_miles  dew_point  temperature  rain_p_h  \\\n",
       "0             329                    1          1       288.28       0.0   \n",
       "1             330                    1          1       289.36       0.0   \n",
       "2             329                    2          2       289.58       0.0   \n",
       "3             329                    5          5       290.13       0.0   \n",
       "4             329                    7          7       291.14       0.0   \n",
       "\n",
       "   snow_p_h  clouds_all weather_type weather_description  traffic_volume  \n",
       "0       0.0          40       Clouds    scattered clouds            5545  \n",
       "1       0.0          75       Clouds       broken clouds            4516  \n",
       "2       0.0          90       Clouds     overcast clouds            4767  \n",
       "3       0.0          90       Clouds     overcast clouds            5026  \n",
       "4       0.0          75       Clouds       broken clouds            4918  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train['is_holiday'] = np.where(Train['is_holiday'] == \"None\", \"Working Day\", Train['is_holiday'])\n",
    "\n",
    "dateTimeSplits = Train[\"date_time\"].str.split(\" \")\n",
    "Train[\"Year\"] = pd.DataFrame([ item[0].split(\"-\")[0] for item in dateTimeSplits ])\n",
    "Train[\"Month\"] = pd.DataFrame([ item[0].split(\"-\")[1] for item in dateTimeSplits ])\n",
    "Train[\"Day\"] = pd.DataFrame([ item[0].split(\"-\")[2] for item in dateTimeSplits ])\n",
    "Train[\"hour\"] = pd.DataFrame([ item[1].split(\":\")[0] for item in dateTimeSplits ])\n",
    "\n",
    "# Train = Train.set_index('date_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'traffic_volume':Train['traffic_volume'], 'date_time':Train['date_time']}\n",
    "target = pd.DataFrame(data).set_index('date_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# target['traffic_volume'] = Train['traffic_volume']\n",
    "Train.drop(columns =[\"traffic_volume\",\"date_time\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>air_pollution_index</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>visibility_in_miles</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rain_p_h</th>\n",
       "      <th>snow_p_h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_type</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Working Day</td>\n",
       "      <td>121</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "      <td>329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>02</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Working Day</td>\n",
       "      <td>178</td>\n",
       "      <td>67</td>\n",
       "      <td>3</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>02</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Working Day</td>\n",
       "      <td>113</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>02</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Working Day</td>\n",
       "      <td>20</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>02</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Working Day</td>\n",
       "      <td>281</td>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>329</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>02</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    is_holiday  air_pollution_index  humidity  wind_speed  wind_direction  \\\n",
       "0  Working Day                  121        89           2             329   \n",
       "1  Working Day                  178        67           3             330   \n",
       "2  Working Day                  113        66           3             329   \n",
       "3  Working Day                   20        66           3             329   \n",
       "4  Working Day                  281        65           3             329   \n",
       "\n",
       "   visibility_in_miles  dew_point  temperature  rain_p_h  snow_p_h  \\\n",
       "0                    1          1       288.28       0.0       0.0   \n",
       "1                    1          1       289.36       0.0       0.0   \n",
       "2                    2          2       289.58       0.0       0.0   \n",
       "3                    5          5       290.13       0.0       0.0   \n",
       "4                    7          7       291.14       0.0       0.0   \n",
       "\n",
       "   clouds_all weather_type weather_description  Year Month Day hour  \n",
       "0          40       Clouds    scattered clouds  2012    10  02   09  \n",
       "1          75       Clouds       broken clouds  2012    10  02   10  \n",
       "2          90       Clouds     overcast clouds  2012    10  02   11  \n",
       "3          90       Clouds     overcast clouds  2012    10  02   12  \n",
       "4          75       Clouds       broken clouds  2012    10  02   13  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_holiday               12\n",
       "air_pollution_index     290\n",
       "humidity                 88\n",
       "wind_speed               17\n",
       "wind_direction          361\n",
       "visibility_in_miles       9\n",
       "dew_point                 9\n",
       "temperature            5611\n",
       "rain_p_h                353\n",
       "snow_p_h                 12\n",
       "clouds_all               60\n",
       "weather_type             11\n",
       "weather_description      38\n",
       "Year                      6\n",
       "Month                    12\n",
       "Day                      31\n",
       "hour                     24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler\n",
    "column_tuples = [\n",
    "    ('is_holiday', LabelEncoder()),\n",
    "    (['air_pollution_index'], StandardScaler()),\n",
    "    (['humidity'], StandardScaler()),\n",
    "    (['wind_speed'], StandardScaler()),\n",
    "    (['wind_direction'], StandardScaler()),\n",
    "    (['visibility_in_miles'], StandardScaler()),\n",
    "    (['dew_point'], StandardScaler()),\n",
    "    (['temperature'], StandardScaler()),\n",
    "    (['rain_p_h'], StandardScaler()), \n",
    "    (['snow_p_h'], StandardScaler()),\n",
    "    (['clouds_all'], StandardScaler()),\n",
    "    ('weather_type', LabelEncoder()),\n",
    "    ('weather_description', LabelEncoder()),\n",
    "    (['Year'], StandardScaler()),\n",
    "    (['Month'], MinMaxScaler()),\n",
    "    (['Day'], MinMaxScaler()),\n",
    "    (['hour'], MinMaxScaler())\n",
    "]\n",
    "mapper = DataFrameMapper(column_tuples,df_out=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper_fit = mapper.fit(Train)\n",
    "Train = mapper_fit.transform(Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>air_pollution_index</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>visibility_in_miles</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rain_p_h</th>\n",
       "      <th>snow_p_h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_type</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>-0.404153</td>\n",
       "      <td>1.055720</td>\n",
       "      <td>-0.670405</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>0.612030</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>-0.269063</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0.276572</td>\n",
       "      <td>-0.249763</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.307378</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>0.692536</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>0.631348</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>-0.499693</td>\n",
       "      <td>-0.309103</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>-1.163334</td>\n",
       "      <td>-1.163334</td>\n",
       "      <td>0.708936</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>1.017238</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>-1.610349</td>\n",
       "      <td>-0.309103</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.749934</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>1.017238</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1.506654</td>\n",
       "      <td>-0.368443</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>0.782204</td>\n",
       "      <td>0.782204</td>\n",
       "      <td>0.825223</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>0.631348</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.565217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_holiday  air_pollution_index  humidity  wind_speed  wind_direction  \\\n",
       "0          11            -0.404153  1.055720   -0.670405        1.297362   \n",
       "1          11             0.276572 -0.249763   -0.183967        1.307378   \n",
       "2          11            -0.499693 -0.309103   -0.183967        1.297362   \n",
       "3          11            -1.610349 -0.309103   -0.183967        1.297362   \n",
       "4          11             1.506654 -0.368443   -0.183967        1.297362   \n",
       "\n",
       "   visibility_in_miles  dew_point  temperature  rain_p_h  snow_p_h  \\\n",
       "0            -1.552441  -1.552441     0.612030 -0.008384 -0.032546   \n",
       "1            -1.552441  -1.552441     0.692536 -0.008384 -0.032546   \n",
       "2            -1.163334  -1.163334     0.708936 -0.008384 -0.032546   \n",
       "3             0.003989   0.003989     0.749934 -0.008384 -0.032546   \n",
       "4             0.782204   0.782204     0.825223 -0.008384 -0.032546   \n",
       "\n",
       "   clouds_all  weather_type  weather_description      Year     Month  \\\n",
       "0   -0.269063             1                   24 -1.678399  0.818182   \n",
       "1    0.631348             1                    2 -1.678399  0.818182   \n",
       "2    1.017238             1                   19 -1.678399  0.818182   \n",
       "3    1.017238             1                   19 -1.678399  0.818182   \n",
       "4    0.631348             1                    2 -1.678399  0.818182   \n",
       "\n",
       "        Day      hour  \n",
       "0  0.033333  0.391304  \n",
       "1  0.033333  0.434783  \n",
       "2  0.033333  0.478261  \n",
       "3  0.033333  0.521739  \n",
       "4  0.033333  0.565217  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "# fig_size[0] = 20\n",
    "# fig_size[1] = 5\n",
    "# plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ba155d4908>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(target[:200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>is_holiday</th>\n",
       "      <th>air_pollution_index</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>visibility_in_miles</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rain_p_h</th>\n",
       "      <th>snow_p_h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_type</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>-0.404153</td>\n",
       "      <td>1.055720</td>\n",
       "      <td>-0.670405</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>0.612030</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>-0.269063</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0.276572</td>\n",
       "      <td>-0.249763</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.307378</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>-1.552441</td>\n",
       "      <td>0.692536</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>0.631348</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>-0.499693</td>\n",
       "      <td>-0.309103</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>-1.163334</td>\n",
       "      <td>-1.163334</td>\n",
       "      <td>0.708936</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>1.017238</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>-1.610349</td>\n",
       "      <td>-0.309103</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>0.749934</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>1.017238</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1.506654</td>\n",
       "      <td>-0.368443</td>\n",
       "      <td>-0.183967</td>\n",
       "      <td>1.297362</td>\n",
       "      <td>0.782204</td>\n",
       "      <td>0.782204</td>\n",
       "      <td>0.825223</td>\n",
       "      <td>-0.008384</td>\n",
       "      <td>-0.032546</td>\n",
       "      <td>0.631348</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.678399</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.565217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_holiday  air_pollution_index  humidity  wind_speed  wind_direction  \\\n",
       "0          11            -0.404153  1.055720   -0.670405        1.297362   \n",
       "1          11             0.276572 -0.249763   -0.183967        1.307378   \n",
       "2          11            -0.499693 -0.309103   -0.183967        1.297362   \n",
       "3          11            -1.610349 -0.309103   -0.183967        1.297362   \n",
       "4          11             1.506654 -0.368443   -0.183967        1.297362   \n",
       "\n",
       "   visibility_in_miles  dew_point  temperature  rain_p_h  snow_p_h  \\\n",
       "0            -1.552441  -1.552441     0.612030 -0.008384 -0.032546   \n",
       "1            -1.552441  -1.552441     0.692536 -0.008384 -0.032546   \n",
       "2            -1.163334  -1.163334     0.708936 -0.008384 -0.032546   \n",
       "3             0.003989   0.003989     0.749934 -0.008384 -0.032546   \n",
       "4             0.782204   0.782204     0.825223 -0.008384 -0.032546   \n",
       "\n",
       "   clouds_all  weather_type  weather_description      Year     Month  \\\n",
       "0   -0.269063             1                   24 -1.678399  0.818182   \n",
       "1    0.631348             1                    2 -1.678399  0.818182   \n",
       "2    1.017238             1                   19 -1.678399  0.818182   \n",
       "3    1.017238             1                   19 -1.678399  0.818182   \n",
       "4    0.631348             1                    2 -1.678399  0.818182   \n",
       "\n",
       "        Day      hour  \n",
       "0  0.033333  0.391304  \n",
       "1  0.033333  0.434783  \n",
       "2  0.033333  0.478261  \n",
       "3  0.033333  0.521739  \n",
       "4  0.033333  0.565217  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train[:200].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(mapper_fit, open('fitted_mapper.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preProcessingScript(dataFrame):\n",
    "    dataFrame['is_holiday'] = np.where(dataFrame['is_holiday'] == \"None\", \"Working Day\", dataFrame['is_holiday'])\n",
    "    dateTimeSplits = dataFrame[\"date_time\"].str.split(\" \")\n",
    "    dataFrame[\"Year\"] = pd.DataFrame([ item[0].split(\"-\")[0] for item in dateTimeSplits ])\n",
    "    dataFrame[\"Month\"] = pd.DataFrame([ item[0].split(\"-\")[1] for item in dateTimeSplits ])\n",
    "    dataFrame[\"Day\"] = pd.DataFrame([ item[0].split(\"-\")[2] for item in dateTimeSplits ])\n",
    "    dataFrame[\"hour\"] = pd.DataFrame([ item[1].split(\":\")[0] for item in dateTimeSplits ])\n",
    "    dataFrame.drop(columns =[\"date_time\"], inplace = True)\n",
    "    mapper_fit = pickle.load(open('fitted_mapper.pkl', 'rb'))\n",
    "    return mapper_fit.transform(dataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33750, 17)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, Activation, Flatten, TimeDistributed, AveragePooling1D\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "import time\n",
    "\n",
    "\n",
    "def baseline_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(130, input_dim=17, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(116, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(100, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(66, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(60, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(54, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(45, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal'))\n",
    "    model.compile(loss='mean_squared_error', optimizer='rmsprop')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_LSTM_model():\n",
    "    sequence_length = 100\n",
    "    model = Sequential()\n",
    "    layers = {'input': 17, 'hidden1': 64, 'hidden2': 256, 'hidden3': 100, 'output': 1}\n",
    "    \n",
    "    model.add(Dense(100, input_dim=17, kernel_initializer='normal', activation='relu'))\n",
    "\n",
    "#     model.add(LSTM(\n",
    "# #                     input_length=sequence_length - 1,\n",
    "#                     input_shape = (100,17),\n",
    "# #                     input_dim=layers['input'],\n",
    "#                     units=100,\n",
    "#                     return_sequences=True))\n",
    "#     model.add(TimeDistributed(Dense(1)))\n",
    "#     model.add(AveragePooling1D())\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(LSTM(\n",
    "            layers['hidden2'],\n",
    "            return_sequences=True))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(LSTM(\n",
    "            layers['hidden3'],\n",
    "            return_sequences=False))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(layers['output']))\n",
    "    model.add(Activation(\"linear\"))\n",
    "\n",
    "    start = time.time()\n",
    "    model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n",
    "    print (\"Compilation Time : \"+ str(time.time() - start))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = baseline_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 130)               2340      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 116)               15196     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 100)               11700     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 66)                6666      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 66)                0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 66)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 60)                4020      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 54)                3294      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 45)                2475      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 46        \n",
      "=================================================================\n",
      "Total params: 45,737\n",
      "Trainable params: 45,737\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataForT = Train\n",
    "targetForT = target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 33412 samples, validate on 338 samples\n",
      "Epoch 1/500\n"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "2 root error(s) found.\n  (0) Internal: Blas GEMM launch failed : a.shape=(1100, 17), b.shape=(17, 130), m=1100, n=130, k=17\n\t [[{{node dense/MatMul}}]]\n\t [[loss/mul/_259]]\n  (1) Internal: Blas GEMM launch failed : a.shape=(1100, 17), b.shape=(17, 130), m=1100, n=130, k=17\n\t [[{{node dense/MatMul}}]]\n0 successful operations.\n0 derived errors ignored.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-abde2faaa23b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mNAME\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Traffic-{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtensorboard\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTensorBoard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlog_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"logs\\{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNAME\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mfM\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataForT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtargetForT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtensorboard\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    778\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m           \u001b[0mvalidation_freq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m           steps_name='steps_per_epoch')\n\u001b[0m\u001b[0;32m    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[1;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m         \u001b[1;31m# Get outputs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   3290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3291\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[1;32m-> 3292\u001b[1;33m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[0;32m   3293\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3294\u001b[0m     output_structure = nest.pack_sequence_as(\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1456\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[0;32m   1457\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1458\u001b[1;33m                                                run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1459\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1460\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInternalError\u001b[0m: 2 root error(s) found.\n  (0) Internal: Blas GEMM launch failed : a.shape=(1100, 17), b.shape=(17, 130), m=1100, n=130, k=17\n\t [[{{node dense/MatMul}}]]\n\t [[loss/mul/_259]]\n  (1) Internal: Blas GEMM launch failed : a.shape=(1100, 17), b.shape=(17, 130), m=1100, n=130, k=17\n\t [[{{node dense/MatMul}}]]\n0 successful operations.\n0 derived errors ignored."
     ]
    }
   ],
   "source": [
    "NAME = \"Traffic-{}\".format(int(time.time()))\n",
    "tensorboard = TensorBoard(log_dir=\"logs\\{}\".format(NAME))\n",
    "fM= model.fit(dataForT, targetForT, epochs=500, batch_size=1100, validation_split=0.01, verbose=2,callbacks=[tensorboard] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"Train1\\DLModel-{}.h5\".format(int(time.time()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "InternalError",
     "evalue": "GPU sync failed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1355\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1356\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1357\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1340\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1341\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1429\u001b[1;33m         run_metadata)\n\u001b[0m\u001b[0;32m   1430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInternalError\u001b[0m: GPU sync failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-dc2bcf68777b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Train1\\DLModel-1564821463.h5\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\save.py\u001b[0m in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[0;32m    144\u001b[0m       h5py is not None and (\n\u001b[0;32m    145\u001b[0m           isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):\n\u001b[1;32m--> 146\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mhdf5_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_model_from_hdf5\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\hdf5_format.py\u001b[0m in \u001b[0;36mload_model_from_hdf5\u001b[1;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;31m# set weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 215\u001b[1;33m     \u001b[0mload_weights_from_hdf5_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'model_weights'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\hdf5_format.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[1;34m(f, layers)\u001b[0m\n\u001b[0;32m    757\u001b[0m                        str(len(weight_values)) + ' elements.')\n\u001b[0;32m    758\u001b[0m     \u001b[0mweight_value_tuples\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 759\u001b[1;33m   \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    760\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    761\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[1;34m(tuples)\u001b[0m\n\u001b[0;32m   3069\u001b[0m           \u001b[0massign_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0massign_op\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3070\u001b[0m           \u001b[0mfeed_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0massign_placeholder\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3071\u001b[1;33m         \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0massign_ops\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3072\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3073\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36mget_session\u001b[1;34m(op_input_list)\u001b[0m\n\u001b[0;32m    460\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_MANUAL_VAR_INIT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m       \u001b[0m_initialize_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36m_initialize_variables\u001b[1;34m(session)\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;31m# marked as initialized.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    878\u001b[0m     is_initialized = session.run(\n\u001b[1;32m--> 879\u001b[1;33m         [variables_module.is_variable_initialized(v) for v in candidate_vars])\n\u001b[0m\u001b[0;32m    880\u001b[0m     \u001b[0muninitialized_vars\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_initialized\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcandidate_vars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    948\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    949\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 950\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    951\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    952\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1171\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1172\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1173\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1174\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1175\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1348\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1349\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1350\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1351\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1352\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\chira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1368\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1369\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1370\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1371\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1372\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInternalError\u001b[0m: GPU sync failed"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model(\"Train1\\DLModel-1564821463.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcmodel = keras.models.load_model(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig = pd.Series(list(target[\"traffic_volume\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(pd.DataFrame(Train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "ins = dataForT.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_list = [p[0] for p in pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.Series(pred_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcpred = rcmodel.predict(pd.DataFrame(Train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcpredList = pd.Series([p[0] for p in rcpred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(rcpredList)==np.all(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, j in zip(predictions,rcpredList):\n",
    "    if i!=j:\n",
    "        print(\"Fail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Comaprison of flow Between Original and predictions')"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(orig[:300],label=\"Original Data\")\n",
    "plt.plot(predictions[:300],label=\"Predictions\")\n",
    "# plt.plot(rcpredList,label=\"RC Predictions\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Comaprison of flow Between Original and predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Comaprison of flow Between Original and predictions')"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(orig,label=\"Original Data\")\n",
    "plt.plot(predictions,label=\"Predictions\")\n",
    "# plt.plot(rcpredList,label=\"RC Predictions\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Comaprison of flow Between Original and predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.engine.sequential.Sequential at 0x250c14b56d8>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LRmodel = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-d4b5fe75f95a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mLRmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "LRmodel.fit(np.array(predictions.tolist()).reshape(-1, 1),np.array(orig.tolist()).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"Train1\\LRModel{}.pkl\".format(int(time.time()))\n",
    "pickle.dump(LRmodel, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RCLRModel = pickle.load(open('Train1\\LRModel1564824033.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuedPred = LRmodel.predict(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuedPredSeries = pd.Series([p[0] for p in continuedPred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-c20961a57070>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcontinuedPred\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.scatter(orig[:300], pred[:300],color='r')\n",
    "plt.scatter(orig[:300],continuedPred[:300],color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.scatter(predictions.tolist()[:300], orig[:300],color='g')\n",
    "plt.plot(np.array(predictions.tolist())[:200], continuedPred[:200],color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.scatter(predictions.tolist(), orig,color='g')\n",
    "plt.plot(predictions.tolist(), continuedPred,color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'orig' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-522c1f9466ee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Original Data\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m# plt.plot(predictions,label=\"Predictions\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontinuedPredSeries\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Continued Predictions\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'upper left'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'orig' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(orig[-500:],label=\"Original Data\")\n",
    "# plt.plot(predictions,label=\"Predictions\")\n",
    "plt.plot(continuedPredSeries[-500:],label=\"Continued Predictions\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Comaprison of flow Between Original and predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Comaprison of flow Between Original and predictions')"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(orig,label=\"Original Data\")\n",
    "# plt.plot(predictions,label=\"Predictions\")\n",
    "plt.plot(continuedPredSeries,label=\"Continued Predictions\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Comaprison of flow Between Original and predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test_data = pd.read_csv(\"DataSets/Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "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>date_time</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>air_pollution_index</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>visibility_in_miles</th>\n",
       "      <th>dew_point</th>\n",
       "      <th>temperature</th>\n",
       "      <th>rain_p_h</th>\n",
       "      <th>snow_p_h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_type</th>\n",
       "      <th>weather_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>73</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>285.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>Rain</td>\n",
       "      <td>moderate rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>251</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>285.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>Mist</td>\n",
       "      <td>mist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>75</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>285.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>Drizzle</td>\n",
       "      <td>light intensity drizzle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-18 01:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>98</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>284.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>Rain</td>\n",
       "      <td>heavy intensity rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-18 01:00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>283</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>284.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>Mist</td>\n",
       "      <td>mist</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time is_holiday  air_pollution_index  humidity  wind_speed  \\\n",
       "0  2017-05-18 00:00:00       None                   73        63           1   \n",
       "1  2017-05-18 00:00:00       None                  251        63           1   \n",
       "2  2017-05-18 00:00:00       None                   75        56           1   \n",
       "3  2017-05-18 01:00:00       None                   98        56           1   \n",
       "4  2017-05-18 01:00:00       None                  283        56           1   \n",
       "\n",
       "   wind_direction  visibility_in_miles  dew_point  temperature  rain_p_h  \\\n",
       "0              27                    4          4       285.15       0.0   \n",
       "1              27                    4          4       285.15       0.0   \n",
       "2               0                    1          1       285.15       0.0   \n",
       "3             351                    2          2       284.79       0.0   \n",
       "4             351                    1          1       284.79       0.0   \n",
       "\n",
       "   snow_p_h  clouds_all weather_type      weather_description  \n",
       "0         0          90         Rain            moderate rain  \n",
       "1         0          90         Mist                     mist  \n",
       "2         0          90      Drizzle  light intensity drizzle  \n",
       "3         0          90         Rain     heavy intensity rain  \n",
       "4         0          90         Mist                     mist  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputDate = Test_data[\"date_time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "preProcessedtestData = preProcessingScript(Test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "firstPrediction = model.predict(pd.DataFrame(preProcessedtestData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "testDataPredictions = LRmodel.predict(firstPrediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test Data predictions')"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(testDataPredictions[:300],label=\"Predictions\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Test Data predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Test Data predictions')"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(testDataPredictions[:300],label=\"Predictions\")\n",
    "plt.plot(firstPrediction[:300],label=\"First Predictions\")\n",
    "# plt.plot(continuedPredSeries,label=\"Continued Predictions\")\n",
    "plt.legend(loc='upper left')\n",
    "plt.title(\"Test Data predictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "testDataPredictionsIntList = [int(round(item[0])) for item in testDataPredictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'date_time': outputDate,'traffic_volume':testDataPredictionsIntList}\n",
    "filename = \"Train1\\Prediction{}.csv\".format(int(time.time()))\n",
    "output = pd.DataFrame(data)\n",
    "output.to_csv(filename, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "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>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-05-18 00:00:00</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-05-18 01:00:00</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-05-18 01:00:00</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time  traffic_volume\n",
       "0  2017-05-18 00:00:00             498\n",
       "1  2017-05-18 00:00:00             448\n",
       "2  2017-05-18 00:00:00             788\n",
       "3  2017-05-18 01:00:00             718\n",
       "4  2017-05-18 01:00:00             337"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model,data):\n",
    "    outputDate = data[\"date_time\"]\n",
    "    return postProcessingScript(model.predict(pd.DataFrame(preProcessingScript(data))),outputDate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def postProcessingScript(predictions,index):\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    RCLRModel = pickle.load(open('Train1\\LRModel1564824033.pkl', 'rb'))\n",
    "    filteredPredictionsIntList = [int(round(item[0])) for item in RCLRModel.predict(predictions)]\n",
    "    data = {'date_time': index,'traffic_volume':filteredPredictionsIntList}\n",
    "    output = pd.DataFrame(data)\n",
    "    np.where(output['traffic_volume']  <= 0 , output['traffic_volume'].rolling(2).median(), output['traffic_volume'])\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "'break' outside loop (<ipython-input-248-6aaf1f276005>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-248-6aaf1f276005>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    break\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'break' outside loop\n"
     ]
    }
   ],
   "source": [
    "break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataForT = Train[:10000]\n",
    "targetForT = target[:10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 7\n",
    "np.random.seed(seed)\n",
    "# evaluate model with standardized dataset\n",
    "estimator = KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=25, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=10, random_state=seed)\n",
    "results = cross_val_score(estimator, dataForT, targetForT, cv=kfold)\n",
    "print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(processedTestData[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Test = pd.read_csv(\"DataSets/Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "processedTestData = preProcessingScript(Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "processedTestData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_LSTM_model():\n",
    "    sequence_length = 100\n",
    "    model = Sequential()\n",
    "    layers = {'input': 17, 'hidden1': 64, 'hidden2': 256, 'hidden3': 100, 'output': 1}\n",
    "\n",
    "    model.add(LSTM(\n",
    "#                     input_length=sequence_length - 1,\n",
    "                    input_shape = (100,17),\n",
    "#                     input_dim=layers['input'],\n",
    "                    units=100,\n",
    "                    return_sequences=True))\n",
    "#     model.add(TimeDistributed(Dense(1)))\n",
    "#     model.add(AveragePooling1D())\n",
    "    \n",
    "#     model.add(Dropout(0.2))\n",
    "\n",
    "#     model.add(LSTM(\n",
    "#             layers['hidden2'],\n",
    "#             return_sequences=True))\n",
    "#     model.add(Dropout(0.2))\n",
    "\n",
    "#     model.add(LSTM(\n",
    "#             layers['hidden3'],\n",
    "#             return_sequences=False))\n",
    "#     model.add(Dropout(0.2))\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(layers['output']))\n",
    "    model.add(Activation(\"linear\"))\n",
    "\n",
    "    start = time.time()\n",
    "    model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n",
    "    print (\"Compilation Time : \"+ str(time.time() - start))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_network(model=None, data=None):\n",
    "    global_start_time = time.time()\n",
    "\n",
    "    if data is None:\n",
    "        print('Loading data... ')\n",
    "        # train on first 700 samples and test on next 300 samples (has anomaly)\n",
    "        X_train, y_train, X_test, y_test = get_split_prep_data(0, 700, 500, 1000)\n",
    "    else:\n",
    "        X_train, y_train, X_test, y_test = data\n",
    "\n",
    "    print('\\nData Loaded. Compiling...\\n')\n",
    "\n",
    "    if model is None:\n",
    "        model = build_model()\n",
    "\n",
    "    try:\n",
    "        print(\"Training...\")\n",
    "        model.fit(\n",
    "                X_train, y_train,\n",
    "                batch_size=batch_size, nb_epoch=epochs, validation_split=0.05)\n",
    "        print(\"Predicting...\")\n",
    "        predicted = model.predict(X_test)\n",
    "        print(\"Reshaping predicted\")\n",
    "        predicted = np.reshape(predicted, (predicted.size,))\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"prediction exception\")\n",
    "        print ('Training duration (s) : '+ str(time.time() - global_start_time))\n",
    "        return model, y_test, 0\n",
    "\n",
    "    try:\n",
    "        plt.figure(1)\n",
    "        plt.subplot(311)\n",
    "        plt.title(\"Actual Test Signal w/Anomalies\")\n",
    "        plt.plot(y_test[:len(y_test)], 'b')\n",
    "        plt.subplot(312)\n",
    "        plt.title(\"Predicted Signal\")\n",
    "        plt.plot(predicted[:len(y_test)], 'g')\n",
    "        plt.subplot(313)\n",
    "        plt.title(\"Squared Error\")\n",
    "        mse = ((y_test - predicted) ** 2)\n",
    "        plt.plot(mse, 'r')\n",
    "        plt.show()\n",
    "    except Exception as e:\n",
    "        print(\"plotting exception\")\n",
    "        print(str(e))\n",
    "    print ('Training duration (s) : '+ str(time.time() - global_start_time))\n",
    "\n",
    "    return model, y_test, predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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