SAUBER Neckartor LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function get_engine at 0x7f32e4788f28>\n"
     ]
    }
   ],
   "source": [
    "#Import database setup\n",
    "import setup_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# SARIMA example\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import array\n",
    "from numpy import mean\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = \"*\"\n",
    "sel_link_id = 52852327\n",
    "sel_min_confidence = 0\n",
    "sel_max_weekday = 6\n",
    "sel_func_classes = ('4','3')\n",
    "sel_dir_travel = ('F','B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_query = f\"\"\"\n",
    "    SELECT DISTINCT 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",
    "    --LEFT JOIN public.holidays hd on date_trunc('day',tr.datum_zeit) = hd.hdate\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 in {sel_dir_travel}\n",
    "    --AND hd.hdate is null\n",
    "    ORDER BY tr.datum_zeit\n",
    "    LIMIT 100000;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39.1 ms, sys: 5.31 ms, total: 44.4 ms\n",
      "Wall time: 24.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pd_read = pd.read_sql_query(sql_query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    pd_read,\n",
    "    columns=[\n",
    "        \"id_pk\",        #0\n",
    "        \"link_id\",      #1\n",
    "        \"mean_kmh\",     #2\n",
    "        \"min_kmh\",      #3\n",
    "        \"max_kmh\",      #4\n",
    "        \"datum_zeit\",   #5\n",
    "        \"weekday_n\",    #6\n",
    "        \"epoch_60\",     #7\n",
    "        \"freeflow_kmh\",\n",
    "        \"confidence\",\n",
    "        \"count_n\"\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Laenge Datensatz:  (6002, 11)\n",
      "Ohne Duplikate:  (6002, 11)\n",
      "Ohne NaN:  (6002, 11)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_pk</th>\n",
       "      <th>link_id</th>\n",
       "      <th>mean_kmh</th>\n",
       "      <th>min_kmh</th>\n",
       "      <th>max_kmh</th>\n",
       "      <th>datum_zeit</th>\n",
       "      <th>weekday_n</th>\n",
       "      <th>epoch_60</th>\n",
       "      <th>freeflow_kmh</th>\n",
       "      <th>confidence</th>\n",
       "      <th>count_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164586295</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.8</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>2018-05-01 02:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164586296</td>\n",
       "      <td>52852327</td>\n",
       "      <td>60.2</td>\n",
       "      <td>48</td>\n",
       "      <td>69</td>\n",
       "      <td>2018-05-01 03:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164586297</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39</td>\n",
       "      <td>48</td>\n",
       "      <td>2018-05-01 04:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164586298</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>31</td>\n",
       "      <td>64</td>\n",
       "      <td>2018-05-01 05:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164586299</td>\n",
       "      <td>52852327</td>\n",
       "      <td>55.8</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>2018-05-01 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>164594730</td>\n",
       "      <td>52852327</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "      <td>2019-04-30 19:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>164594731</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.6</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>2019-04-30 20:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>164594732</td>\n",
       "      <td>52852327</td>\n",
       "      <td>42.5</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>2019-04-30 21:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>164594733</td>\n",
       "      <td>52852327</td>\n",
       "      <td>46.7</td>\n",
       "      <td>37</td>\n",
       "      <td>62</td>\n",
       "      <td>2019-04-30 22:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6001</th>\n",
       "      <td>164594734</td>\n",
       "      <td>52852327</td>\n",
       "      <td>45.2</td>\n",
       "      <td>32</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-30 23:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6002 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_pk   link_id  mean_kmh  min_kmh  max_kmh          datum_zeit  \\\n",
       "0     164586295  52852327      43.8       31       52 2018-05-01 02:00:00   \n",
       "1     164586296  52852327      60.2       48       69 2018-05-01 03:00:00   \n",
       "2     164586297  52852327      43.0       39       48 2018-05-01 04:00:00   \n",
       "3     164586298  52852327      43.0       31       64 2018-05-01 05:00:00   \n",
       "4     164586299  52852327      55.8       49       59 2018-05-01 06:00:00   \n",
       "...         ...       ...       ...      ...      ...                 ...   \n",
       "5997  164594730  52852327      44.0       22       73 2019-04-30 19:00:00   \n",
       "5998  164594731  52852327      43.6       20       59 2019-04-30 20:00:00   \n",
       "5999  164594732  52852327      42.5       22       61 2019-04-30 21:00:00   \n",
       "6000  164594733  52852327      46.7       37       62 2019-04-30 22:00:00   \n",
       "6001  164594734  52852327      45.2       32       60 2019-04-30 23:00:00   \n",
       "\n",
       "      weekday_n  epoch_60 freeflow_kmh  confidence  count_n  \n",
       "0             2         2         None          40        6  \n",
       "1             2         3         None          40       14  \n",
       "2             2         4         None          40        5  \n",
       "3             2         5         None          40       10  \n",
       "4             2         6         None          40        6  \n",
       "...         ...       ...          ...         ...      ...  \n",
       "5997          2        19         None          40      154  \n",
       "5998          2        20         None          40      134  \n",
       "5999          2        21         None          40       98  \n",
       "6000          2        22         None          40       85  \n",
       "6001          2        23         None          40      118  \n",
       "\n",
       "[6002 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Laenge Datensatz: \",df.shape)\n",
    "df.drop_duplicates(inplace=True)\n",
    "print(\"Ohne Duplikate: \",df.shape)\n",
    "df.dropna(subset=['count_n'],inplace=True)\n",
    "print(\"Ohne NaN: \",df.shape)\n",
    "df.reindex()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>mean_kmh</th>\n",
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       "      <th>max_kmh</th>\n",
       "      <th>datum_zeit</th>\n",
       "      <th>weekday_n</th>\n",
       "      <th>epoch_60</th>\n",
       "      <th>freeflow_kmh</th>\n",
       "      <th>confidence</th>\n",
       "      <th>count_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164586295</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.8</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>2018-05-01 02:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
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       "      <th>1</th>\n",
       "      <td>164586296</td>\n",
       "      <td>52852327</td>\n",
       "      <td>60.2</td>\n",
       "      <td>48</td>\n",
       "      <td>69</td>\n",
       "      <td>2018-05-01 03:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>39</td>\n",
       "      <td>48</td>\n",
       "      <td>2018-05-01 04:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164586298</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>31</td>\n",
       "      <td>64</td>\n",
       "      <td>2018-05-01 05:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164586299</td>\n",
       "      <td>52852327</td>\n",
       "      <td>55.8</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>2018-05-01 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>164594730</td>\n",
       "      <td>52852327</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "      <td>2019-04-30 19:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>164594731</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.6</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>2019-04-30 20:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>164594732</td>\n",
       "      <td>52852327</td>\n",
       "      <td>42.5</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>2019-04-30 21:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>164594733</td>\n",
       "      <td>52852327</td>\n",
       "      <td>46.7</td>\n",
       "      <td>37</td>\n",
       "      <td>62</td>\n",
       "      <td>2019-04-30 22:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6001</th>\n",
       "      <td>164594734</td>\n",
       "      <td>52852327</td>\n",
       "      <td>45.2</td>\n",
       "      <td>32</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-30 23:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6002 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_pk   link_id  mean_kmh  min_kmh  max_kmh          datum_zeit  \\\n",
       "0     164586295  52852327      43.8       31       52 2018-05-01 02:00:00   \n",
       "1     164586296  52852327      60.2       48       69 2018-05-01 03:00:00   \n",
       "2     164586297  52852327      43.0       39       48 2018-05-01 04:00:00   \n",
       "3     164586298  52852327      43.0       31       64 2018-05-01 05:00:00   \n",
       "4     164586299  52852327      55.8       49       59 2018-05-01 06:00:00   \n",
       "...         ...       ...       ...      ...      ...                 ...   \n",
       "5997  164594730  52852327      44.0       22       73 2019-04-30 19:00:00   \n",
       "5998  164594731  52852327      43.6       20       59 2019-04-30 20:00:00   \n",
       "5999  164594732  52852327      42.5       22       61 2019-04-30 21:00:00   \n",
       "6000  164594733  52852327      46.7       37       62 2019-04-30 22:00:00   \n",
       "6001  164594734  52852327      45.2       32       60 2019-04-30 23:00:00   \n",
       "\n",
       "      weekday_n  epoch_60 freeflow_kmh  confidence  count_n  \n",
       "0             2         2         None          40        6  \n",
       "1             2         3         None          40       14  \n",
       "2             2         4         None          40        5  \n",
       "3             2         5         None          40       10  \n",
       "4             2         6         None          40        6  \n",
       "...         ...       ...          ...         ...      ...  \n",
       "5997          2        19         None          40      154  \n",
       "5998          2        20         None          40      134  \n",
       "5999          2        21         None          40       98  \n",
       "6000          2        22         None          40       85  \n",
       "6001          2        23         None          40      118  \n",
       "\n",
       "[6002 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.set_index('datum_zeit', inplace=True, drop=True)\n",
    "#df.set_index('datum_zeit', inplace=True, drop=True)\n",
    "df.sort_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#avg_plt = sns.lineplot(x=df['epoch_60'],y=df['count_n'], data=df,legend='brief')\n",
    "#plt.show()\n",
    "\n",
    "time_plt = sns.lineplot(x=df.index,y=df['count_n'], data=df)\n",
    "sns.set()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_pickle('link_id_'+str(sel_link_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(df['count_n'], lags=48)\n",
    "\n",
    "plot_pacf(df['count_n'], lags=48)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df4 = df[(df.index  > '2018-05-01 00:00:00') & (df.index < '2018-09-1 00:00:00')]"
   ]
  },
  {
   "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>id_pk</th>\n",
       "      <th>link_id</th>\n",
       "      <th>mean_kmh</th>\n",
       "      <th>min_kmh</th>\n",
       "      <th>max_kmh</th>\n",
       "      <th>datum_zeit</th>\n",
       "      <th>weekday_n</th>\n",
       "      <th>epoch_60</th>\n",
       "      <th>freeflow_kmh</th>\n",
       "      <th>confidence</th>\n",
       "      <th>count_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164586295</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.8</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>2018-05-01 02:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164586296</td>\n",
       "      <td>52852327</td>\n",
       "      <td>60.2</td>\n",
       "      <td>48</td>\n",
       "      <td>69</td>\n",
       "      <td>2018-05-01 03:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164586297</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39</td>\n",
       "      <td>48</td>\n",
       "      <td>2018-05-01 04:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164586298</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>31</td>\n",
       "      <td>64</td>\n",
       "      <td>2018-05-01 05:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164586299</td>\n",
       "      <td>52852327</td>\n",
       "      <td>55.8</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>2018-05-01 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>5997</th>\n",
       "      <td>164594730</td>\n",
       "      <td>52852327</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "      <td>2019-04-30 19:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>164594731</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.6</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>2019-04-30 20:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>164594732</td>\n",
       "      <td>52852327</td>\n",
       "      <td>42.5</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>2019-04-30 21:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>164594733</td>\n",
       "      <td>52852327</td>\n",
       "      <td>46.7</td>\n",
       "      <td>37</td>\n",
       "      <td>62</td>\n",
       "      <td>2019-04-30 22:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6001</th>\n",
       "      <td>164594734</td>\n",
       "      <td>52852327</td>\n",
       "      <td>45.2</td>\n",
       "      <td>32</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-30 23:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6002 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_pk   link_id  mean_kmh  min_kmh  max_kmh          datum_zeit  \\\n",
       "0     164586295  52852327      43.8       31       52 2018-05-01 02:00:00   \n",
       "1     164586296  52852327      60.2       48       69 2018-05-01 03:00:00   \n",
       "2     164586297  52852327      43.0       39       48 2018-05-01 04:00:00   \n",
       "3     164586298  52852327      43.0       31       64 2018-05-01 05:00:00   \n",
       "4     164586299  52852327      55.8       49       59 2018-05-01 06:00:00   \n",
       "...         ...       ...       ...      ...      ...                 ...   \n",
       "5997  164594730  52852327      44.0       22       73 2019-04-30 19:00:00   \n",
       "5998  164594731  52852327      43.6       20       59 2019-04-30 20:00:00   \n",
       "5999  164594732  52852327      42.5       22       61 2019-04-30 21:00:00   \n",
       "6000  164594733  52852327      46.7       37       62 2019-04-30 22:00:00   \n",
       "6001  164594734  52852327      45.2       32       60 2019-04-30 23:00:00   \n",
       "\n",
       "      weekday_n  epoch_60 freeflow_kmh  confidence  count_n  \n",
       "0             2         2         None          40        6  \n",
       "1             2         3         None          40       14  \n",
       "2             2         4         None          40        5  \n",
       "3             2         5         None          40       10  \n",
       "4             2         6         None          40        6  \n",
       "...         ...       ...          ...         ...      ...  \n",
       "5997          2        19         None          40      154  \n",
       "5998          2        20         None          40      134  \n",
       "5999          2        21         None          40       98  \n",
       "6000          2        22         None          40       85  \n",
       "6001          2        23         None          40      118  \n",
       "\n",
       "[6002 rows x 11 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "if df.index.dtype:\n",
    "    df4 = df[(df.index > 30)&(df.index < 2880)]\n",
    "else:\n",
    "    df4 = df[(df.index > '2018-06-1 00:00:00')&(df.index < '2018-10-1 00:00:00')]\n",
    "#df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df4 = df[(df.index  > 0) & (df.index < 5000)]\n",
    "#df4.reindex\n",
    "#df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-step sarima forecast\n",
    "def sarima_forecast(history, config):\n",
    "\torder, sorder, trend = config\n",
    "\t# define model\n",
    "\tmodel = SARIMAX(history, order=order, seasonal_order=sorder, trend=trend, enforce_stationarity=False, enforce_invertibility=False)\n",
    "\t# fit model\n",
    "\tmodel_fit = model.fit(disp=False)\n",
    "\t# make one step forecast\n",
    "\tyhat = model_fit.predict(len(history), len(history))\n",
    "\treturn yhat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(data, n_test):\n",
    "\treturn data[:-n_test], data[-n_test:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max of Diff Series:  id_pk                     164590359\n",
      "link_id                    52852327\n",
      "mean_kmh                         82\n",
      "min_kmh                          74\n",
      "max_kmh                         289\n",
      "datum_zeit      2018-10-23 01:00:00\n",
      "weekday_n                         5\n",
      "epoch_60                         23\n",
      "freeflow_kmh                   None\n",
      "confidence                       40\n",
      "count_n                         432\n",
      "dtype: object\n",
      "Min of Diff Series:  id_pk                     164586326\n",
      "link_id                    52852327\n",
      "mean_kmh                        4.2\n",
      "min_kmh                           1\n",
      "max_kmh                          14\n",
      "datum_zeit      2018-05-02 10:00:00\n",
      "weekday_n                         1\n",
      "epoch_60                          0\n",
      "freeflow_kmh                   None\n",
      "confidence                       10\n",
      "count_n                           1\n",
      "dtype: object\n",
      "Mean of Diff Series:  id_pk           1.645883e+08\n",
      "link_id         5.285233e+07\n",
      "mean_kmh        4.236595e+01\n",
      "min_kmh         2.478519e+01\n",
      "max_kmh         6.242611e+01\n",
      "weekday_n       3.016146e+00\n",
      "epoch_60        1.189119e+01\n",
      "freeflow_kmh             NaN\n",
      "confidence      3.919972e+01\n",
      "count_n         7.804142e+01\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Max of Diff Series: \",df4.max())\n",
    "print(\"Min of Diff Series: \",df4.min())\n",
    "print(\"Mean of Diff Series: \",df4.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55       6.0\n",
       "56      26.0\n",
       "57      -5.0\n",
       "58      88.0\n",
       "59     -27.0\n",
       "        ... \n",
       "2875   -61.0\n",
       "2876   -52.0\n",
       "2877   -46.0\n",
       "2878   -40.0\n",
       "2879   -19.0\n",
       "Name: count_n, Length: 2825, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4['count_n'].diff(24)\n",
    "df4 = df4[24:]\n",
    "df4.fillna(method='backfill',inplace=True)\n",
    "df4\n",
    "#df4.reindex\n",
    "#df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max of Diff Series:  296.0\n",
      "Min of Diff Series:  -321.0\n",
      "Mean of Diff Series:  0.1511504424778761\n"
     ]
    }
   ],
   "source": [
    "print(\"Max of Diff Series: \",df4.max())\n",
    "print(\"Min of Diff Series: \",df4.min())\n",
    "print(\"Mean of Diff Series: \",df4.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4320x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(60,10))\n",
    "sns.lineplot(data=df4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(df4, lags=96)\n",
    "\n",
    "plot_acf(df4.diff(), lags=96)\n",
    "\n",
    "\n",
    "plot_pacf(df4, lags=96)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
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      "-20.0\n",
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      "-70.0\n",
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      "26.0\n",
      "-86.0\n",
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      "-86.0\n",
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      "-120.0\n",
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      "-25.0\n",
      "29.0\n",
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      "-4.0\n",
      "6.0\n",
      "-22.0\n",
      "-57.0\n",
      "56.0\n",
      "-10.0\n",
      "42.0\n",
      "-15.0\n",
      "-7.0\n",
      "-20.0\n",
      "28.0\n",
      "78.0\n",
      "-74.0\n",
      "154.0\n",
      "-204.0\n",
      "-2.0\n",
      "-12.0\n",
      "72.0\n",
      "-2.0\n",
      "-52.0\n",
      "30.0\n",
      "5.0\n",
      "1.0\n",
      "-4.0\n",
      "17.0\n",
      "-8.0\n",
      "11.0\n",
      "20.0\n",
      "-46.0\n",
      "100.0\n",
      "-143.0\n",
      "62.0\n",
      "-29.0\n",
      "-43.0\n",
      "19.0\n",
      "-51.0\n",
      "98.0\n",
      "15.0\n",
      "-62.0\n",
      "13.0\n",
      "39.0\n",
      "-39.0\n",
      "-18.0\n",
      "5.0\n",
      "59.0\n",
      "3.0\n",
      "2.0\n",
      "-17.0\n",
      "16.0\n",
      "99.0\n",
      "-65.0\n",
      "-118.0\n",
      "78.0\n",
      "-31.0\n",
      "93.0\n",
      "-91.0\n",
      "18.0\n",
      "42.0\n",
      "52.0\n",
      "104.0\n",
      "-137.0\n",
      "-164.0\n",
      "138.0\n",
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      "63.0\n",
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      "-4.0\n",
      "-32.0\n",
      "-2.0\n",
      "-1.0\n",
      "-3.0\n",
      "52.0\n",
      "65.0\n",
      "-100.0\n",
      "-78.0\n",
      "109.0\n",
      "37.0\n",
      "-122.0\n",
      "26.0\n",
      "62.0\n",
      "12.0\n",
      "120.0\n",
      "-70.0\n",
      "-148.0\n",
      "-13.0\n",
      "133.0\n",
      "-55.0\n",
      "16.0\n",
      "-16.0\n",
      "-89.0\n",
      "14.0\n",
      "48.0\n",
      "11.0\n",
      "0.0\n",
      "1.0\n",
      "13.0\n",
      "-22.0\n",
      "28.0\n",
      "21.0\n",
      "-7.0\n",
      "-107.0\n",
      "127.0\n",
      "-18.0\n",
      "-14.0\n",
      "18.0\n",
      "-37.0\n",
      "7.0\n",
      "11.0\n",
      "-52.0\n",
      "-8.0\n",
      "-16.0\n",
      "76.0\n",
      "-23.0\n",
      "29.0\n",
      "-8.0\n",
      "-8.0\n",
      "-6.0\n",
      "-3.0\n",
      "6.0\n",
      "4.0\n",
      "-24.0\n",
      "-44.0\n",
      "23.0\n",
      "3.0\n",
      "6.0\n",
      "-32.0\n",
      "116.0\n",
      "-109.0\n",
      "43.0\n",
      "-59.0\n",
      "-24.0\n",
      "-84.0\n",
      "210.0\n",
      "-58.0\n",
      "41.0\n",
      "-41.0\n",
      "-22.0\n",
      "64.0\n",
      "-21.0\n",
      "-1.0\n",
      "-2.0\n",
      "19.0\n",
      "8.0\n",
      "45.0\n",
      "-24.0\n",
      "-69.0\n",
      "41.0\n",
      "-50.0\n",
      "121.0\n",
      "-71.0\n",
      "-99.0\n",
      "125.0\n",
      "-75.0\n",
      "-39.0\n",
      "68.0\n",
      "68.0\n",
      "-108.0\n",
      "11.0\n",
      "-26.0\n",
      "9.0\n",
      "6.0\n",
      "6.0\n",
      "21.0\n"
     ]
    }
   ],
   "source": [
    "result = seasonal_decompose(df4.diff().dropna(), model='add',freq=1)\n",
    "result.plot()\n",
    "plt.show()\n",
    "for j in df4.diff():\n",
    "    print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max of Diff Series:  328.0\n",
      "Min of Diff Series:  -363.0\n",
      "Mean of Diff Series:  -0.008852691218130312\n"
     ]
    }
   ],
   "source": [
    "print(\"Max of Diff Series: \",df4.diff().max())\n",
    "print(\"Min of Diff Series: \",df4.diff().min())\n",
    "print(\"Mean of Diff Series: \",df4.diff().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_pk</th>\n",
       "      <th>link_id</th>\n",
       "      <th>mean_kmh</th>\n",
       "      <th>min_kmh</th>\n",
       "      <th>max_kmh</th>\n",
       "      <th>datum_zeit</th>\n",
       "      <th>weekday_n</th>\n",
       "      <th>epoch_60</th>\n",
       "      <th>freeflow_kmh</th>\n",
       "      <th>confidence</th>\n",
       "      <th>count_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164586295</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.8</td>\n",
       "      <td>31</td>\n",
       "      <td>52</td>\n",
       "      <td>2018-05-01 02:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164586296</td>\n",
       "      <td>52852327</td>\n",
       "      <td>60.2</td>\n",
       "      <td>48</td>\n",
       "      <td>69</td>\n",
       "      <td>2018-05-01 03:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164586297</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39</td>\n",
       "      <td>48</td>\n",
       "      <td>2018-05-01 04:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164586298</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.0</td>\n",
       "      <td>31</td>\n",
       "      <td>64</td>\n",
       "      <td>2018-05-01 05:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>164586299</td>\n",
       "      <td>52852327</td>\n",
       "      <td>55.8</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
       "      <td>2018-05-01 06:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>5997</th>\n",
       "      <td>164594730</td>\n",
       "      <td>52852327</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "      <td>2019-04-30 19:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>164594731</td>\n",
       "      <td>52852327</td>\n",
       "      <td>43.6</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>2019-04-30 20:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>164594732</td>\n",
       "      <td>52852327</td>\n",
       "      <td>42.5</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>2019-04-30 21:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>164594733</td>\n",
       "      <td>52852327</td>\n",
       "      <td>46.7</td>\n",
       "      <td>37</td>\n",
       "      <td>62</td>\n",
       "      <td>2019-04-30 22:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6001</th>\n",
       "      <td>164594734</td>\n",
       "      <td>52852327</td>\n",
       "      <td>45.2</td>\n",
       "      <td>32</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-30 23:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6002 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id_pk   link_id  mean_kmh  min_kmh  max_kmh          datum_zeit  \\\n",
       "0     164586295  52852327      43.8       31       52 2018-05-01 02:00:00   \n",
       "1     164586296  52852327      60.2       48       69 2018-05-01 03:00:00   \n",
       "2     164586297  52852327      43.0       39       48 2018-05-01 04:00:00   \n",
       "3     164586298  52852327      43.0       31       64 2018-05-01 05:00:00   \n",
       "4     164586299  52852327      55.8       49       59 2018-05-01 06:00:00   \n",
       "...         ...       ...       ...      ...      ...                 ...   \n",
       "5997  164594730  52852327      44.0       22       73 2019-04-30 19:00:00   \n",
       "5998  164594731  52852327      43.6       20       59 2019-04-30 20:00:00   \n",
       "5999  164594732  52852327      42.5       22       61 2019-04-30 21:00:00   \n",
       "6000  164594733  52852327      46.7       37       62 2019-04-30 22:00:00   \n",
       "6001  164594734  52852327      45.2       32       60 2019-04-30 23:00:00   \n",
       "\n",
       "      weekday_n  epoch_60 freeflow_kmh  confidence  count_n  \n",
       "0             2         2         None          40        6  \n",
       "1             2         3         None          40       14  \n",
       "2             2         4         None          40        5  \n",
       "3             2         5         None          40       10  \n",
       "4             2         6         None          40        6  \n",
       "...         ...       ...          ...         ...      ...  \n",
       "5997          2        19         None          40      154  \n",
       "5998          2        20         None          40      134  \n",
       "5999          2        21         None          40       98  \n",
       "6000          2        22         None          40       85  \n",
       "6001          2        23         None          40      118  \n",
       "\n",
       "[6002 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total configs: 2\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0mcfg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_configs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[0;31m# grid search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'done'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;31m# list top 10 configs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36mgrid_search\u001b[0;34m(data, cfg_list, n_test)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;31m# evaluate configs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrepeat_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcfg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m         \u001b[0;31m# sort configs by error, asc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    102\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;31m# evaluate configs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrepeat_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcfg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcfg_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m         \u001b[0;31m# sort configs by error, asc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36mrepeat_evaluate\u001b[0;34m(data, config, n_test, n_repeats)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;31m# fit and evaluate the model n times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mwalk_forward_validation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_repeats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;31m# summarize score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;31m# fit and evaluate the model n times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mwalk_forward_validation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_repeats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;31m# summarize score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36mwalk_forward_validation\u001b[0;34m(data, n_test, cfg)\u001b[0m\n\u001b[1;32m     84\u001b[0m                 \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;31m# estimate prediction error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeasure_rmse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' > %.3f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-31-3841ed8a7074>\u001b[0m in \u001b[0;36mmeasure_rmse\u001b[0;34m(actual, predicted)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# root mean squared error or rmse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmeasure_rmse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# difference dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    250\u001b[0m     \"\"\"\n\u001b[1;32m    251\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 252\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "# split a univariate dataset into train/test sets\n",
    "def train_test_split(data, n_test):\n",
    "\treturn data[:-n_test], data[-n_test:]\n",
    "\n",
    "# transform list into supervised learning format\n",
    "def series_to_supervised(data, n_in=1, n_out=1):\n",
    "\tdf = DataFrame(data)\n",
    "\tcols = list()\n",
    "\t# input sequence (t-n, ... t-1)\n",
    "\tfor i in range(n_in, 0, -1):\n",
    "\t\tcols.append(df.shift(i))\n",
    "\t# forecast sequence (t, t+1, ... t+n)\n",
    "\tfor i in range(0, n_out):\n",
    "\t\tcols.append(df.shift(-i))\n",
    "\t# put it all together\n",
    "\tagg = concat(cols, axis=1)\n",
    "\t# drop rows with NaN values\n",
    "\tagg.dropna(inplace=True)\n",
    "\treturn agg.values\n",
    "\n",
    "# root mean squared error or rmse\n",
    "def measure_rmse(actual, predicted):\n",
    "\treturn sqrt(mean_squared_error(actual, predicted))\n",
    "\n",
    "# difference dataset\n",
    "def difference(data, order):\n",
    "\treturn [data[i] - data[i - order] for i in range(order, len(data))]\n",
    "\n",
    "# fit a model\n",
    "def model_fit(train, config):\n",
    "\t# unpack config\n",
    "\tn_input, n_nodes, n_epochs, n_batch, n_diff = config\n",
    "\t# prepare data\n",
    "\tif n_diff > 0:\n",
    "\t\ttrain = difference(train, n_diff)\n",
    "\t# transform series into supervised format\n",
    "\tdata = series_to_supervised(train, n_in=n_input)\n",
    "\t# separate inputs and outputs\n",
    "\ttrain_x, train_y = data[:, :-1], data[:, -1]\n",
    "\t# reshape input data into [samples, timesteps, features]\n",
    "\tn_features = 1\n",
    "\ttrain_x = train_x.reshape((train_x.shape[0], train_x.shape[1], n_features))\n",
    "\t# define model\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(LSTM(n_nodes, activation='relu', input_shape=(n_input, n_features)))\n",
    "\tmodel.add(Dense(n_nodes, activation='relu'))\n",
    "\tmodel.add(Dense(1))\n",
    "\tmodel.compile(loss='mse', optimizer='adam')\n",
    "\t# fit model\n",
    "\tmodel.fit(train_x, train_y, epochs=n_epochs, batch_size=n_batch, verbose=0)\n",
    "\treturn model\n",
    "\n",
    "# forecast with the fit model\n",
    "def model_predict(model, history, config):\n",
    "\t# unpack config\n",
    "\tn_input, _, _, _, n_diff = config\n",
    "\t# prepare data\n",
    "\tcorrection = 0.0\n",
    "\tif n_diff > 0:\n",
    "\t\tcorrection = history[-n_diff]\n",
    "\t\thistory = difference(history, n_diff)\n",
    "\t# reshape sample into [samples, timesteps, features]\n",
    "\tx_input = array(history[-n_input:]).reshape((1, n_input, 1))\n",
    "\t# forecast\n",
    "\tyhat = model.predict(x_input, verbose=0)\n",
    "\treturn correction + yhat[0]\n",
    "\n",
    "# walk-forward validation for univariate data\n",
    "def walk_forward_validation(data, n_test, cfg):\n",
    "\tpredictions = list()\n",
    "\t# split dataset\n",
    "\ttrain, test = train_test_split(data, n_test)\n",
    "\t# fit model\n",
    "\tmodel = model_fit(train, cfg)\n",
    "\t# seed history with training dataset\n",
    "\thistory = [x for x in train]\n",
    "\t# step over each time-step in the test set\n",
    "\tfor i in range(len(test)):\n",
    "\t\t# fit model and make forecast for history\n",
    "\t\tyhat = model_predict(model, history, cfg)\n",
    "\t\t# store forecast in list of predictions\n",
    "\t\tpredictions.append(yhat)\n",
    "\t\t# add actual observation to history for the next loop\n",
    "\t\thistory.append(test[i])\n",
    "\t# estimate prediction error\n",
    "\terror = measure_rmse(test, predictions)\n",
    "\tprint(' > %.3f' % error)\n",
    "\treturn error\n",
    "\n",
    "# score a model, return None on failure\n",
    "def repeat_evaluate(data, config, n_test, n_repeats=10):\n",
    "\t# convert config to a key\n",
    "\tkey = str(config)\n",
    "\t# fit and evaluate the model n times\n",
    "\tscores = [walk_forward_validation(data, n_test, config) for _ in range(n_repeats)]\n",
    "\t# summarize score\n",
    "\tresult = mean(scores)\n",
    "\tprint('> Model[%s] %.3f' % (key, result))\n",
    "\treturn (key, result)\n",
    "\n",
    "# grid search configs\n",
    "def grid_search(data, cfg_list, n_test):\n",
    "\t# evaluate configs\n",
    "\tscores = [repeat_evaluate(data, cfg, n_test) for cfg in cfg_list]\n",
    "\t# sort configs by error, asc\n",
    "\tscores.sort(key=lambda tup: tup[1])\n",
    "\treturn scores\n",
    "\n",
    "# create a list of configs to try\n",
    "def model_configs():\n",
    "\t# define scope of configs\n",
    "\tn_input = [48]\n",
    "\tn_nodes = [100]\n",
    "\tn_epochs = [50]\n",
    "\tn_batch = [1, 150]\n",
    "\tn_diff = [24]\n",
    "\t# create configs\n",
    "\tconfigs = list()\n",
    "\tfor i in n_input:\n",
    "\t\tfor j in n_nodes:\n",
    "\t\t\tfor k in n_epochs:\n",
    "\t\t\t\tfor l in n_batch:\n",
    "\t\t\t\t\tfor m in n_diff:\n",
    "\t\t\t\t\t\tcfg = [i, j, k, l, m]\n",
    "\t\t\t\t\t\tconfigs.append(cfg)\n",
    "\tprint('Total configs: %d' % len(configs))\n",
    "\treturn configs\n",
    "\n",
    "# datensatz (values!)\n",
    "series = df['count_n']\n",
    "data = series.values\n",
    "# \n",
    "n_test = 24\n",
    "# model configs\n",
    "cfg_list = model_configs()\n",
    "# grid search\n",
    "scores = grid_search(data, cfg_list, n_test)\n",
    "print('done')c\n",
    "# list top 10 configs\n",
    "for cfg, error in scores[:3]:\n",
    "\tprint(cfg, error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = df[['count_n','weekday_n','epoch_60']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = series.values\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model[[(0, 0, 2), (2, 0, 2, 0), 'c']] 34.829\n",
    "# > Model[[(0, 0, 0), (0, 0, 1, 24), 'n']] 95.859"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Done\")"
   ]
  }
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