SAUBER Neckartor GRU Multivar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function get_engine at 0x7f5440041f28>\n"
     ]
    }
   ],
   "source": [
    "#Import database setup\n",
    "import setup_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# SARIMA example\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Input, Dense, GRU, Embedding\n",
    "from tensorflow.python.keras.optimizers import RMSprop\n",
    "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid search sarima hyperparameters\n",
    "from math import sqrt\n",
    "from multiprocessing import cpu_count\n",
    "from joblib import Parallel\n",
    "from joblib import delayed\n",
    "from warnings import catch_warnings\n",
    "from warnings import filterwarnings\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import RobustScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import isnan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = \"*\"\n",
    "sel_link_id = 563934220\n",
    "sel_min_confidence = 0\n",
    "sel_max_weekday = 8\n",
    "sel_func_classes = ('4','3')\n",
    "sel_dir_travel = 'F'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_query = f\"\"\"\n",
    "    SELECT tr.*  \n",
    "    FROM here_traffic.stuttgart_traffic tr\n",
    "    JOIN here_streets.fc_streets_all_2018q3 st on tr.link_id = st.link_id\n",
    "    WHERE tr.link_id = {sel_link_id}\n",
    "    AND tr.confidence > {sel_min_confidence}\n",
    "    AND tr.weekday_n < {sel_max_weekday}\n",
    "    --AND st.func_class in {sel_func_classes}\n",
    "    AND tr.dir_travel = 'F'\n",
    "    LIMIT 100000\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DB Engine läuft\n",
      "Engine(postgresql://postgres:***@192.168.10.184:5430/here)\n"
     ]
    }
   ],
   "source": [
    "engine = setup_env.get_database()\n",
    "print(engine)\n",
    "try:\n",
    "    con = engine.raw_connection()\n",
    "    con.cursor().execute(\"SET SCHEMA '{}'\".format(\"here_traffic\"))\n",
    "except:\n",
    "    print(\"Con: DB Verbindung prüfen!\") \n",
    "    exit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 235 ms, sys: 48 ms, total: 283 ms\n",
      "Wall time: 32.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "    pd_read = pd.read_sql_query(sql_query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    pd_read,\n",
    "    columns=[\n",
    "        \"id_pk\",\n",
    "        \"link_id\",\n",
    "        \"dir_travel\",\n",
    "        \"mean_kmh\",     #2\n",
    "        \"datum_zeit\",   #5\n",
    "        \"weekday_n\",    #6\n",
    "        \"epoch_60\",     #7\n",
    "        \"confidence\",\n",
    "        \"count_n\",\n",
    "        \"holiday\"\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('datum_zeit', inplace=True, drop=True)\n",
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(25, 4))\n",
    "# sns.lineplot(data=df['count_n'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%time\n",
    "#total_plt = sns.lineplot(x=df.index,y=df['count_n'], data=df, legend=False)\n",
    "#sns.set()\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_pickle('link_id_'+str(sel_link_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#daily_mean = df4.groupby(pd.Grouper(freq='1D')).mean()\n",
    "#daily_mean.dropna(inplace = True)\n",
    "#daily_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_acf(df['count_n'], lags=72)\n",
    "\n",
    "# plot_pacf(df['count_n'], lags=72)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #df.set_index('datum_zeit', inplace=True, drop=True)\n",
    "# plt.figure(figsize=(40,20))\n",
    "# result = seasonal_decompose(df4['count_n'], model='add',freq=24)\n",
    "# result.plot()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if df.shape[0] < 1: print(\"No data in dataframe. Exiting\"),exit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fill_index = pd.date_range('2018-05-01 00:00:00', '2019-04-30 23:00:00', freq='1H')\n",
    "df = df.reindex(fill_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['link_id']=sel_link_id\n",
    "df['dir_travel']=df['dir_travel'].fillna(method='backfill')\n",
    "df['epoch_60']=df.index.hour\n",
    "df['weekday_n']=df.index.dayofweek+1\n",
    "\n",
    "df[\"hourweek\"] = (df[\"weekday_n\"]-1)*24+df[\"epoch_60\"]\n",
    "df[\"hourweek\"] = pd.to_numeric(df.hourweek, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_mean = df['count_n'].groupby(df['hourweek']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df.iterrows():\n",
    "    m_idx = row['hourweek']\n",
    "    if isnan(row['count_n']):\n",
    "        row['count_n'] = count_mean[m_idx]\n",
    "        df['count_n'].at[index] = count_mean[m_idx].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "remain_nan = df['count_n'].isna().sum()\n",
    "if remain_nan > 0:\n",
    "    print(\"%i Remaining cols without value. Aborting.\" % (remain_nan))\n",
    "    exit(1)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model[[(0, 0, 0), (2, 0, 1, 0), 'n']] 32.762"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['count_diff'] = df['count_n'].diff()\n",
    "df['count_diff'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:00:00</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 01:00:00</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 02:00:00</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 03:00:00</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 04:00:00</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 19:00:00</th>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 20:00:00</th>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 21:00:00</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 22:00:00</th>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 23:00:00</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>8760 rows × 1 columns</p>\n",
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      "text/plain": [
       "                     count_n\n",
       "2018-05-01 00:00:00      5.0\n",
       "2018-05-01 01:00:00      7.0\n",
       "2018-05-01 02:00:00      5.0\n",
       "2018-05-01 03:00:00      8.0\n",
       "2018-05-01 04:00:00      6.0\n",
       "...                      ...\n",
       "2019-04-30 19:00:00     94.0\n",
       "2019-04-30 20:00:00     19.0\n",
       "2019-04-30 21:00:00      8.0\n",
       "2019-04-30 22:00:00     20.0\n",
       "2019-04-30 23:00:00     13.0\n",
       "\n",
       "[8760 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['count_n']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(df[['count_diff']], lags=72)\n",
    "\n",
    "plot_pacf(df[['count_diff']], lags=72)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_cols = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_steps = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names=['count_n']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_params = df[['count_n','weekday_n','epoch_60','holiday']]#,'holiday']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>count_diff</th>\n",
       "      <th>weekday_n</th>\n",
       "      <th>epoch_60</th>\n",
       "      <th>holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 01:00:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 02:00:00</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 03:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 04:00:00</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 19:00:00</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 20:00:00</th>\n",
       "      <td>-75.0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 21:00:00</th>\n",
       "      <td>-11.0</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 22:00:00</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 23:00:00</th>\n",
       "      <td>-7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count_diff  weekday_n  epoch_60  holiday\n",
       "2018-05-01 00:00:00         0.0          2         0    -99.0\n",
       "2018-05-01 01:00:00         2.0          2         1    -99.0\n",
       "2018-05-01 02:00:00        -2.0          2         2    -99.0\n",
       "2018-05-01 03:00:00         3.0          2         3      0.0\n",
       "2018-05-01 04:00:00        -2.0          2         4    -99.0\n",
       "...                         ...        ...       ...      ...\n",
       "2019-04-30 19:00:00         2.0          2        19     -1.0\n",
       "2019-04-30 20:00:00       -75.0          2        20     -1.0\n",
       "2019-04-30 21:00:00       -11.0          2        21     -1.0\n",
       "2019-04-30 22:00:00        12.0          2        22     -1.0\n",
       "2019-04-30 23:00:00        -7.0          2        23     -1.0\n",
       "\n",
       "[8760 rows x 4 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['count_diff','weekday_n','epoch_60','holiday']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['holiday'].fillna(-99,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# target_val = df['count_diff'].shift(-prediction_steps)\n",
    "target_val = df['count_n'].shift(-prediction_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-04-30 19:00:00    19.0\n",
       "2019-04-30 20:00:00     8.0\n",
       "2019-04-30 21:00:00    20.0\n",
       "2019-04-30 22:00:00    13.0\n",
       "2019-04-30 23:00:00     NaN\n",
       "Freq: H, Name: count_n, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_val.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_values = input_params.values[0:-prediction_steps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8759"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_data = len(x_values)\n",
    "num_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "Shape: (8759, 4)\n"
     ]
    }
   ],
   "source": [
    "print(type(x_values))\n",
    "print(\"Shape:\", x_values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_values = target_val.values[:-prediction_steps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "Shape: (8759,)\n"
     ]
    }
   ],
   "source": [
    "print(type(y_values))\n",
    "print(\"Shape:\", y_values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_date = '2019-04-01 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8040"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_idx = df.index.get_loc(test_date)\n",
    "test_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8040"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = x_values[0:test_idx]\n",
    "x_test = x_values[test_idx:]\n",
    "len(x_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8040"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = y_values[0:test_idx]\n",
    "y_test = y_values[test_idx:]\n",
    "len(y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((719,), (8040,))"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "719"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_train = num_data - test_idx\n",
    "num_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8040"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_test = num_data - num_train\n",
    "num_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8040"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaler = MinMaxScaler() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled = x_scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: 0.0\n",
      "Max: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Min:\", np.min(x_train_scaled))\n",
    "print(\"Max:\", np.max(x_train_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_scaled = x_scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scaler = MinMaxScaler()\n",
    "y_train_scaled = y_scaler.fit_transform(y_train.reshape(-1,1))\n",
    "y_test_scaled = y_scaler.transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8040, 4)\n",
      "(8040, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_scaled.shape)\n",
    "print(y_train_scaled.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "###########"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_data(vals, robust=True):\n",
    "    if robust: scaler = RobustScaler() \n",
    "    else: scaler = MinMaxScaler()\n",
    "    \n",
    "    if vals.ndim < 2: \n",
    "        vals = vals.reshape(-1,1)\n",
    "    else:\n",
    "        pass\n",
    "    vals_scaled = scaler.fit_transform(vals)\n",
    "    return vals_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled = scale_data(x_train, False)\n",
    "x_test_scaled = scale_data(x_test, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_scaled = scale_data(y_train)\n",
    "y_test_scaled = scale_data(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.03053435 0.16666667 0.         0.        ]\n",
      " [0.04580153 0.16666667 0.04347826 0.        ]\n",
      " [0.03053435 0.16666667 0.08695652 0.        ]\n",
      " ...\n",
      " [0.20610687 1.         0.91304348 0.        ]\n",
      " [0.03053435 1.         0.95652174 0.        ]\n",
      " [0.05343511 1.         1.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(x_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input signals:  4\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    num_x_signals = x_values.shape[1]\n",
    "    print('Input signals: ',num_x_signals)\n",
    "except:\n",
    "    num_x_signals = 1\n",
    "    print('Univar Input')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_y_signals = y_values.ndim\n",
    "num_y_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "719"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8040"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_generator(batch_size, sequence_length):\n",
    "    \"\"\"\n",
    "    Generator function for creating random batches of training-data.\n",
    "    \"\"\"\n",
    "\n",
    "    # Infinite loop.\n",
    "    while True:\n",
    "        # Allocate a new array for the batch of input-signals.\n",
    "        x_shape = (batch_size, sequence_length, num_x_signals)\n",
    "        x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n",
    "\n",
    "        # Allocate a new array for the batch of output-signals.\n",
    "        y_shape = (batch_size, sequence_length, num_y_signals)\n",
    "        y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n",
    "\n",
    "        # Fill the batch with random sequences of data.\n",
    "        for i in range(batch_size):\n",
    "            # Get a random start-index.\n",
    "            # This points somewhere into the training-data.\n",
    "            idx = np.random.randint(num_train - sequence_length)\n",
    "            \n",
    "            # Copy the sequences of data starting at this index.\n",
    "            x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n",
    "            y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n",
    "        \n",
    "        yield (x_batch, y_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_length = 72\n",
    "sequence_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = batch_generator(batch_size=batch_size,\n",
    "                            sequence_length=sequence_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_batch, y_batch = next(generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 72, 4)\n",
      "(256, 72, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_batch.shape)\n",
    "print(y_batch.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.0916  0.      0.4783  0.     ]\n",
      "  [0.206   0.      0.522   0.     ]\n",
      "  [0.336   0.      0.5654  0.     ]\n",
      "  ...\n",
      "  [0.313   0.5     0.348   0.     ]\n",
      "  [0.42    0.5     0.3914  0.     ]\n",
      "  [0.351   0.5     0.4348  0.     ]]\n",
      "\n",
      " [[0.3665  0.6665  0.609   0.     ]\n",
      "  [0.2137  0.6665  0.6523  0.     ]\n",
      "  [0.435   0.6665  0.696   0.     ]\n",
      "  ...\n",
      "  [0.084   0.      0.4783  0.99   ]\n",
      "  [0.0916  0.      0.522   0.99   ]\n",
      "  [0.267   0.      0.5654  0.99   ]]\n",
      "\n",
      " [[0.0229  0.6665  0.174   0.     ]\n",
      "  [0.1069  0.6665  0.2174  0.     ]\n",
      "  [0.4045  0.6665  0.261   0.     ]\n",
      "  ...\n",
      "  [0.0229  0.      0.0435  0.     ]\n",
      "  [0.01527 0.      0.087   0.     ]\n",
      "  [0.07635 0.      0.1305  0.     ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.03053 1.      0.1305  0.     ]\n",
      "  [0.03053 1.      0.174   0.     ]\n",
      "  [0.03818 1.      0.2174  0.     ]\n",
      "  ...\n",
      "  [0.05344 0.3333  0.      0.     ]\n",
      "  [0.03053 0.3333  0.0435  0.     ]\n",
      "  [0.0229  0.3333  0.087   0.     ]]\n",
      "\n",
      " [[0.03053 0.1666  0.      0.     ]\n",
      "  [0.0458  0.1666  0.0435  0.     ]\n",
      "  [0.03053 0.1666  0.087   0.     ]\n",
      "  ...\n",
      "  [0.1375  0.5     0.913   0.     ]\n",
      "  [0.0458  0.5     0.9565  0.     ]\n",
      "  [0.1145  0.5     1.      0.     ]]\n",
      "\n",
      " [[0.206   0.      0.2174  0.     ]\n",
      "  [0.3054  0.      0.261   0.     ]\n",
      "  [0.588   0.      0.3044  0.     ]\n",
      "  ...\n",
      "  [0.01527 0.5     0.087   0.     ]\n",
      "  [0.03818 0.5     0.1305  0.     ]\n",
      "  [0.0229  0.5     0.174   0.     ]]]\n"
     ]
    }
   ],
   "source": [
    "print(x_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.05263158, 0.        , 0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.04347826, 0.        ],\n",
       "        [0.01503759, 0.        , 0.08695652, 0.        ],\n",
       "        ...,\n",
       "        [0.13533835, 0.16666667, 0.86956522, 0.98      ],\n",
       "        [0.05263158, 0.16666667, 0.91304348, 0.98      ],\n",
       "        [0.14285714, 0.16666667, 0.95652174, 0.98      ]]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.expand_dims(x_test_scaled,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n",
    "                   np.expand_dims(y_test_scaled, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[0.05263158, 0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.04347826, 0.        ],\n",
       "         [0.01503759, 0.        , 0.08695652, 0.        ],\n",
       "         ...,\n",
       "         [0.13533835, 0.16666667, 0.86956522, 0.98      ],\n",
       "         [0.05263158, 0.16666667, 0.91304348, 0.98      ],\n",
       "         [0.14285714, 0.16666667, 0.95652174, 0.98      ]]]),\n",
       " array([[[-0.51764706],\n",
       "         [-0.47058824],\n",
       "         [-0.28235294],\n",
       "         [-0.42352941],\n",
       "         [-0.32941176],\n",
       "         [ 0.42352941],\n",
       "         [ 2.61176471],\n",
       "         [ 1.27058824],\n",
       "         [ 0.68235294],\n",
       "         [ 0.07058824],\n",
       "         [ 0.07058824],\n",
       "         [ 0.09411765],\n",
       "         [-0.04705882],\n",
       "         [ 0.02352941],\n",
       "         [ 0.25882353],\n",
       "         [ 0.37647059],\n",
       "         [ 1.03529412],\n",
       "         [ 0.96470588],\n",
       "         [ 0.        ],\n",
       "         [-0.47058824],\n",
       "         [ 0.09411765],\n",
       "         [-0.47058824],\n",
       "         [-0.49411765],\n",
       "         [-0.42352941],\n",
       "         [-0.37647059],\n",
       "         [-0.42352941],\n",
       "         [-0.44705882],\n",
       "         [-0.4       ],\n",
       "         [-0.49411765],\n",
       "         [ 0.89411765],\n",
       "         [ 1.22352941],\n",
       "         [ 0.77647059],\n",
       "         [ 0.72941176],\n",
       "         [ 0.84705882],\n",
       "         [ 0.42352941],\n",
       "         [ 0.49411765],\n",
       "         [-0.23529412],\n",
       "         [-0.04705882],\n",
       "         [ 0.30588235],\n",
       "         [ 1.24705882],\n",
       "         [ 0.72941176],\n",
       "         [ 0.65882353],\n",
       "         [ 0.42352941],\n",
       "         [-0.11764706],\n",
       "         [-0.21176471],\n",
       "         [ 0.09411765],\n",
       "         [-0.37647059],\n",
       "         [-0.02352941],\n",
       "         [-0.42352941],\n",
       "         [-0.44705882],\n",
       "         [-0.42352941],\n",
       "         [-0.42352941],\n",
       "         [-0.35294118],\n",
       "         [ 0.47058824],\n",
       "         [ 2.11764706],\n",
       "         [ 2.25882353],\n",
       "         [ 0.82352941],\n",
       "         [ 1.03529412],\n",
       "         [ 1.76470588],\n",
       "         [ 0.98823529],\n",
       "         [-0.21176471],\n",
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       "         [ 0.47058824],\n",
       "         [ 1.10588235],\n",
       "         [ 0.89411765],\n",
       "         [ 0.84705882],\n",
       "         [ 0.4       ],\n",
       "         [ 0.65882353],\n",
       "         [ 0.44705882],\n",
       "         [ 0.42352941],\n",
       "         [-0.18823529],\n",
       "         [-0.11764706],\n",
       "         [-0.35294118],\n",
       "         [-0.42352941],\n",
       "         [-0.42352941],\n",
       "         [-0.47058824],\n",
       "         [-0.49411765],\n",
       "         [-0.42352941],\n",
       "         [-0.4       ],\n",
       "         [-0.44705882],\n",
       "         [-0.44705882],\n",
       "         [-0.32941176],\n",
       "         [-0.21176471],\n",
       "         [ 0.14117647],\n",
       "         [-0.16470588],\n",
       "         [-0.04705882],\n",
       "         [ 0.18823529],\n",
       "         [-0.4       ],\n",
       "         [ 0.09411765],\n",
       "         [-0.28235294],\n",
       "         [ 0.32941176],\n",
       "         [ 0.        ],\n",
       "         [-0.4       ],\n",
       "         [ 0.        ],\n",
       "         [-0.30588235],\n",
       "         [-0.23529412],\n",
       "         [-0.37647059],\n",
       "         [-0.42352941],\n",
       "         [-0.44705882],\n",
       "         [-0.47058824],\n",
       "         [-0.28235294],\n",
       "         [-0.42352941],\n",
       "         [-0.04705882],\n",
       "         [ 0.44705882],\n",
       "         [ 1.52941176],\n",
       "         [ 1.27058824],\n",
       "         [ 0.61176471],\n",
       "         [ 0.25882353],\n",
       "         [ 0.54117647],\n",
       "         [ 0.70588235],\n",
       "         [ 0.21176471],\n",
       "         [ 0.02352941],\n",
       "         [-0.02352941],\n",
       "         [ 0.4       ],\n",
       "         [ 0.30588235],\n",
       "         [ 1.01176471],\n",
       "         [ 0.49411765],\n",
       "         [ 0.11764706],\n",
       "         [-0.21176471],\n",
       "         [-0.32941176],\n",
       "         [-0.28235294],\n",
       "         [-0.44705882],\n",
       "         [-0.49411765],\n",
       "         [-0.42352941],\n",
       "         [-0.44705882],\n",
       "         [-0.4       ],\n",
       "         [-0.32941176],\n",
       "         [ 0.47058824],\n",
       "         [ 2.61176471],\n",
       "         [ 0.94117647],\n",
       "         [ 0.8       ],\n",
       "         [ 0.28235294],\n",
       "         [ 0.25882353],\n",
       "         [-0.07058824],\n",
       "         [ 0.14117647],\n",
       "         [-0.16470588],\n",
       "         [ 0.51764706],\n",
       "         [ 0.51764706],\n",
       "         [ 1.24705882],\n",
       "         [ 1.62352941],\n",
       "         [ 1.67058824],\n",
       "         [-0.09411765],\n",
       "         [-0.35294118],\n",
       "         [-0.07058824],\n",
       "         [-0.23529412]]]))"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>id_pk</th>\n",
       "      <th>link_id</th>\n",
       "      <th>dir_travel</th>\n",
       "      <th>mean_kmh</th>\n",
       "      <th>weekday_n</th>\n",
       "      <th>epoch_60</th>\n",
       "      <th>confidence</th>\n",
       "      <th>count_n</th>\n",
       "      <th>holiday</th>\n",
       "      <th>hourweek</th>\n",
       "      <th>count_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>563934220</td>\n",
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       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>2018-05-01 01:00:00</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>25</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 02:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>26</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 03:00:00</th>\n",
       "      <td>57778393.0</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>51.6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>28</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 19:00:00</th>\n",
       "      <td>13293108.0</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>51.5</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>40.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 20:00:00</th>\n",
       "      <td>13293109.0</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>51.9</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>40.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>44</td>\n",
       "      <td>-75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 21:00:00</th>\n",
       "      <td>13293110.0</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>50.8</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>-11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 22:00:00</th>\n",
       "      <td>13293111.0</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>50.8</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>46</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30 23:00:00</th>\n",
       "      <td>13293112.0</td>\n",
       "      <td>563934220</td>\n",
       "      <td>F</td>\n",
       "      <td>62.6</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>47</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8760 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          id_pk    link_id dir_travel  mean_kmh  weekday_n  \\\n",
       "2018-05-01 00:00:00         NaN  563934220          F       NaN          2   \n",
       "2018-05-01 01:00:00         NaN  563934220          F       NaN          2   \n",
       "2018-05-01 02:00:00         NaN  563934220          F       NaN          2   \n",
       "2018-05-01 03:00:00  57778393.0  563934220          F      51.6          2   \n",
       "2018-05-01 04:00:00         NaN  563934220          F       NaN          2   \n",
       "...                         ...        ...        ...       ...        ...   \n",
       "2019-04-30 19:00:00  13293108.0  563934220          F      51.5          2   \n",
       "2019-04-30 20:00:00  13293109.0  563934220          F      51.9          2   \n",
       "2019-04-30 21:00:00  13293110.0  563934220          F      50.8          2   \n",
       "2019-04-30 22:00:00  13293111.0  563934220          F      50.8          2   \n",
       "2019-04-30 23:00:00  13293112.0  563934220          F      62.6          2   \n",
       "\n",
       "                     epoch_60  confidence  count_n  holiday  hourweek  \\\n",
       "2018-05-01 00:00:00         0         NaN      5.0    -99.0        24   \n",
       "2018-05-01 01:00:00         1         NaN      7.0    -99.0        25   \n",
       "2018-05-01 02:00:00         2         NaN      5.0    -99.0        26   \n",
       "2018-05-01 03:00:00         3        40.0      8.0      0.0        27   \n",
       "2018-05-01 04:00:00         4         NaN      6.0    -99.0        28   \n",
       "...                       ...         ...      ...      ...       ...   \n",
       "2019-04-30 19:00:00        19        40.0     94.0     -1.0        43   \n",
       "2019-04-30 20:00:00        20        40.0     19.0     -1.0        44   \n",
       "2019-04-30 21:00:00        21        40.0      8.0     -1.0        45   \n",
       "2019-04-30 22:00:00        22        40.0     20.0     -1.0        46   \n",
       "2019-04-30 23:00:00        23        40.0     13.0     -1.0        47   \n",
       "\n",
       "                     count_diff  \n",
       "2018-05-01 00:00:00         0.0  \n",
       "2018-05-01 01:00:00         2.0  \n",
       "2018-05-01 02:00:00        -2.0  \n",
       "2018-05-01 03:00:00         3.0  \n",
       "2018-05-01 04:00:00        -2.0  \n",
       "...                         ...  \n",
       "2019-04-30 19:00:00         2.0  \n",
       "2019-04-30 20:00:00       -75.0  \n",
       "2019-04-30 21:00:00       -11.0  \n",
       "2019-04-30 22:00:00        12.0  \n",
       "2019-04-30 23:00:00        -7.0  \n",
       "\n",
       "[8760 rows x 11 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['holiday'].fillna(-99)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_pk         1689\n",
       "link_id          0\n",
       "dir_travel       0\n",
       "mean_kmh      1689\n",
       "weekday_n        0\n",
       "epoch_60         0\n",
       "confidence    1689\n",
       "count_n          0\n",
       "holiday          0\n",
       "hourweek         0\n",
       "count_diff       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(GRU(units=128,\n",
    "              return_sequences=True,\n",
    "              input_shape=(None, num_x_signals,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.add(Dense(num_y_signals, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    from tensorflow.python.keras.initializers import RandomUniform\n",
    "\n",
    "    # Maybe use lower init-ranges.\n",
    "    init = RandomUniform(minval=-0.0005, maxval=0.0005)\n",
    "\n",
    "    model.add(Dense(num_y_signals,\n",
    "                    activation='linear',\n",
    "                    kernel_initializer=init))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5388769470>]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch = 1   # First sequence in the batch.\n",
    "signal = 0  # First signal from the input-signals.\n",
    "seq = x_batch[batch, :, signal]\n",
    "plt.plot(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f53886c8438>]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seq = y_batch[batch, :, 0]\n",
    "plt.plot(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "warmup_steps = 48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_mse_warmup(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the Mean Squared Error between y_true and y_pred,\n",
    "    but ignore the beginning \"warmup\" part of the sequences.\n",
    "    \n",
    "    y_true is the desired output.\n",
    "    y_pred is the model's output.\n",
    "    \"\"\"\n",
    "\n",
    "    y_true_slice = y_true[:, warmup_steps:, :]\n",
    "    y_pred_slice = y_pred[:, warmup_steps:, :]\n",
    "    loss = tf.losses.mse(y_true_slice,y_pred_slice)\n",
    "    loss_mean = tf.reduce_mean(loss)\n",
    "\n",
    "    return loss_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = RMSprop(lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=loss_mse_warmup, optimizer='RMSprop') #RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, None, 128)         51072     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, None, 1)           129       \n",
      "=================================================================\n",
      "Total params: 51,201\n",
      "Trainable params: 51,201\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_checkpoint = 'checkpoint_link_'+str(sel_link_id)+'_.keras'\n",
    "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n",
    "                                      monitor='val_loss',\n",
    "                                      verbose=1,\n",
    "                                      save_weights_only=True,\n",
    "                                      save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback_early_stopping = EarlyStopping(monitor='val_loss',\n",
    "                                        patience=5, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback_tensorboard = TensorBoard(log_dir='./'+str(sel_link_id)+'23_logs/',\n",
    "                                   histogram_freq=0,\n",
    "                                   write_graph=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n",
    "                                       factor=0.01,\n",
    "                                       min_lr=1e-5,\n",
    "                                       patience=0,\n",
    "                                       verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [callback_early_stopping,\n",
    "             callback_checkpoint,\n",
    "             callback_tensorboard,\n",
    "             callback_reduce_lr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 30 steps, validate on 1 samples\n",
      "Epoch 1/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.4431\n",
      "Epoch 00001: val_loss improved from inf to 0.32380, saving model to checkpoint_link_563934220_.keras\n",
      "30/30 [==============================] - 58s 2s/step - loss: 0.4412 - val_loss: 0.3238\n",
      "Epoch 2/100\n",
      "29/30 [============================>.] - ETA: 0s - loss: 0.3676\n",
      "Epoch 00002: val_loss improved from 0.32380 to 0.29998, saving model to checkpoint_link_563934220_.keras\n",
      "30/30 [==============================] - 21s 706ms/step - loss: 0.3666 - val_loss: 0.3000\n",
      "Epoch 3/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.3467\n",
      "Epoch 00003: val_loss improved from 0.29998 to 0.28744, saving model to checkpoint_link_563934220_.keras\n",
      "30/30 [==============================] - 32s 1s/step - loss: 0.3464 - val_loss: 0.2874\n",
      "Epoch 4/100\n",
      "29/30 [============================>.] - ETA: 0s - loss: 0.3393\n",
      "Epoch 00004: val_loss improved from 0.28744 to 0.28743, saving model to checkpoint_link_563934220_.keras\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "30/30 [==============================] - 24s 806ms/step - loss: 0.3397 - val_loss: 0.2874\n",
      "Epoch 5/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.3443\n",
      "Epoch 00005: val_loss improved from 0.28743 to 0.28019, saving model to checkpoint_link_563934220_.keras\n",
      "30/30 [==============================] - 41s 1s/step - loss: 0.3444 - val_loss: 0.2802\n",
      "Epoch 6/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.3351\n",
      "Epoch 00006: val_loss improved from 0.28019 to 0.27677, saving model to checkpoint_link_563934220_.keras\n",
      "30/30 [==============================] - 42s 1s/step - loss: 0.3351 - val_loss: 0.2768\n",
      "Epoch 7/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.3309\n",
      "Epoch 00007: val_loss did not improve from 0.27677\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "30/30 [==============================] - 39s 1s/step - loss: 0.3313 - val_loss: 0.2776\n",
      "Epoch 8/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.3286\n",
      "Epoch 00008: val_loss did not improve from 0.27677\n",
      "30/30 [==============================] - 41s 1s/step - loss: 0.3292 - val_loss: 0.2795\n",
      "Epoch 9/100\n",
      "29/30 [============================>.] - ETA: 0s - loss: 0.3273\n",
      "Epoch 00009: val_loss did not improve from 0.27677\n",
      "30/30 [==============================] - 18s 612ms/step - loss: 0.3277 - val_loss: 0.2798\n",
      "Epoch 10/100\n",
      "29/30 [============================>.] - ETA: 0s - loss: 0.3252\n",
      "Epoch 00010: val_loss did not improve from 0.27677\n",
      "30/30 [==============================] - 27s 896ms/step - loss: 0.3246 - val_loss: 0.2794\n",
      "Epoch 11/100\n",
      "29/30 [============================>.] - ETA: 1s - loss: 0.3280\n",
      "Epoch 00011: val_loss did not improve from 0.27677\n",
      "30/30 [==============================] - 38s 1s/step - loss: 0.3285 - val_loss: 0.2788\n",
      "Epoch 00011: early stopping\n",
      "CPU times: user 9min 4s, sys: 1min 4s, total: 10min 9s\n",
      "Wall time: 6min 21s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f538864f518>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit_generator(generator=generator,\n",
    "                    epochs=100,\n",
    "                    steps_per_epoch=30,\n",
    "                    validation_data=validation_data,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, None, 128)         51072     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, None, 1)           129       \n",
      "=================================================================\n",
      "Total params: 51,201\n",
      "Trainable params: 51,201\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - 0s 164ms/sample - loss: 0.2788\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n",
    "                        y=np.expand_dims(y_test_scaled, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss (test-set): 0.27881109714508057\n"
     ]
    }
   ],
   "source": [
    "print(\"loss (test-set):\", result)"
   ]
  },
  {
   "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": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_comparison(start_idx, length=100, train=True):\n",
    "    \"\"\"\n",
    "    Plot the predicted and true output-signals.\n",
    "    \n",
    "    :param start_idx: Start-index for the time-series.\n",
    "    :param length: Sequence-length to process and plot.\n",
    "    :param train: Boolean whether to use training- or test-set.\n",
    "    \"\"\"\n",
    "    \n",
    "    if train:\n",
    "        # Use training-data.\n",
    "        x = x_train_scaled\n",
    "        y_true = y_train\n",
    "    else:\n",
    "        # Use test-data.\n",
    "        x = x_test_scaled\n",
    "        y_true = y_test\n",
    "    \n",
    "    # End-index for the sequences.\n",
    "    end_idx = start_idx + length\n",
    "    \n",
    "    # Select the sequences from the given start-index and\n",
    "    # of the given length.\n",
    "    x = x[start_idx:end_idx]\n",
    "    y_true = y_true[start_idx:end_idx]\n",
    "    \n",
    "    # Input-signals for the model.\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "\n",
    "    # Use the model to predict the output-signals.\n",
    "    y_pred = model.predict(x)\n",
    "    \n",
    "    # The output of the model is between 0 and 1.\n",
    "    # Do an inverse map to get it back to the scale\n",
    "    # of the original data-set.\n",
    "    y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n",
    "    \n",
    "    # For each output-signal.\n",
    "    for signal in range(len(target_names)):\n",
    "        # Get the output-signal predicted by the model.\n",
    "        print(y_pred_rescaled.shape)\n",
    "        print(y_true.shape)\n",
    "        signal_pred = y_pred_rescaled[:, signal] \n",
    "        \n",
    "        # Get the true output-signal from the data-set.\n",
    "        signal_true = y_true                     ## BEI MULTIVAR ÄNDERN AUF y_true[:, signal]   \n",
    "\n",
    "        # Make the plotting-canvas bigger.\n",
    "        plt.figure(figsize=(15,5))\n",
    "        \n",
    "        # Plot and compare the two signals.\n",
    "        plt.plot(signal_true, label='true')\n",
    "        plt.plot(signal_pred, label='pred')\n",
    "        \n",
    "        # Plot grey box for warmup-period.\n",
    "        p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n",
    "        \n",
    "        # Plot labels etc.\n",
    "        plt.ylabel(target_names[signal])\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1)\n",
      "(1000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_comparison(start_idx=1000, length=1000, train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scaled():\n",
    "    scaler_plt = sns.lineplot(data=scale_data(df['count_n'],True)[:50],color='red')\n",
    "    rob_scaler_plt = sns.lineplot(data=scale_data(df['weekday_n'],True)[:50])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "count_plt = sns.lineplot(data=df['count_n'][:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=False):\n",
    "    n_vars=1 if type(data) is list else data.shape[1]\n",
    "    df = pd.DataFrame(data)\n",
    "    cols,names = list(), list()\n",
    "    for i in range(n_in,0,-1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('+ä%d(t-%d)') % (j+1, i) for j in range(n_vars)]\n",
    "    for i in range (0,n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)') % (j+1) for j in range(n_vars)]\n",
    "        else: \n",
    "            [('var%d(t+%d)') % (j+1, i) for j in range(n_vars)]\n",
    "    agg = pd.concat(cols,axis=1)\n",
    "\n",
    "    agg.columns = names\n",
    "  \n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "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": [
    "pd_read = pd.read_sql_query(\"\"\"SELECT here_traffic.find_touching_traffic(563934220)\"\"\", con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pd_read[\"find_touching_traffic\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pd_read,columns=[\"link_id\",\"dir\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled.shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}