Chapter 6/2. Bidirectional LSTM Univarient Horizon Style.ipynb

{
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     "end_time": "2020-03-02T15:30:23.724935Z",
     "start_time": "2020-03-02T15:30:18.765192Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "tf.random.set_seed(123)\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "end_time": "2020-03-02T15:30:24.632509Z",
     "start_time": "2020-03-02T15:30:24.556713Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'\\Data\\Metro_Interstate_Traffic_Volume.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2020-03-02T15:30:27.008162Z",
     "start_time": "2020-03-02T15:30:26.988251Z"
    }
   },
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "      <td>5026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  holiday    temp  rain_1h  snow_1h  clouds_all weather_main  \\\n",
       "0    None  288.28      0.0      0.0          40       Clouds   \n",
       "1    None  289.36      0.0      0.0          75       Clouds   \n",
       "2    None  289.58      0.0      0.0          90       Clouds   \n",
       "3    None  290.13      0.0      0.0          90       Clouds   \n",
       "4    None  291.14      0.0      0.0          75       Clouds   \n",
       "\n",
       "  weather_description            date_time  traffic_volume  \n",
       "0    scattered clouds  2012-10-02 09:00:00            5545  \n",
       "1       broken clouds  2012-10-02 10:00:00            4516  \n",
       "2     overcast clouds  2012-10-02 11:00:00            4767  \n",
       "3     overcast clouds  2012-10-02 12:00:00            5026  \n",
       "4       broken clouds  2012-10-02 13:00:00            4918  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:28.410420Z",
     "start_time": "2020-03-02T15:30:28.371521Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48204.000000</td>\n",
       "      <td>48204.000000</td>\n",
       "      <td>48204.000000</td>\n",
       "      <td>48204.000000</td>\n",
       "      <td>48204.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>281.205870</td>\n",
       "      <td>0.334264</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>49.362231</td>\n",
       "      <td>3259.818355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.338232</td>\n",
       "      <td>44.789133</td>\n",
       "      <td>0.008168</td>\n",
       "      <td>39.015750</td>\n",
       "      <td>1986.860670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>272.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>282.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>3380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>291.806000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>4933.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>310.070000</td>\n",
       "      <td>9831.300000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>7280.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               temp       rain_1h       snow_1h    clouds_all  traffic_volume\n",
       "count  48204.000000  48204.000000  48204.000000  48204.000000    48204.000000\n",
       "mean     281.205870      0.334264      0.000222     49.362231     3259.818355\n",
       "std       13.338232     44.789133      0.008168     39.015750     1986.860670\n",
       "min        0.000000      0.000000      0.000000      0.000000        0.000000\n",
       "25%      272.160000      0.000000      0.000000      1.000000     1193.000000\n",
       "50%      282.450000      0.000000      0.000000     64.000000     3380.000000\n",
       "75%      291.806000      0.000000      0.000000     90.000000     4933.000000\n",
       "max      310.070000   9831.300000      0.510000    100.000000     7280.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:30.576626Z",
     "start_time": "2020-03-02T15:30:30.555685Z"
    }
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset=['date_time'], keep=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:31.251821Z",
     "start_time": "2020-03-02T15:30:31.240855Z"
    }
   },
   "outputs": [],
   "source": [
    "validate = df['traffic_volume'].tail(10)\n",
    "df.drop(df['traffic_volume'].tail(10).index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:32.179344Z",
     "start_time": "2020-03-02T15:30:32.171367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time\n",
       "2012-10-02 09:00:00    5545\n",
       "2012-10-02 10:00:00    4516\n",
       "2012-10-02 11:00:00    4767\n",
       "2012-10-02 12:00:00    5026\n",
       "2012-10-02 13:00:00    4918\n",
       "Name: traffic_volume, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_data = df['traffic_volume']\n",
    "uni_data.index = df['date_time']\n",
    "uni_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:34.845219Z",
     "start_time": "2020-03-02T15:30:34.839236Z"
    }
   },
   "outputs": [],
   "source": [
    "uni_data = uni_data.values\n",
    "scaler_x = preprocessing.MinMaxScaler()\n",
    "x_rescaled = scaler_x.fit_transform(uni_data.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:41.283051Z",
     "start_time": "2020-03-02T15:30:41.276040Z"
    }
   },
   "outputs": [],
   "source": [
    "def custom_ts_univariate_data_prep(dataset, start, end, window, horizon):\n",
    "  X = []\n",
    "  y = []\n",
    "\n",
    "  start = start + window\n",
    "  if end is None:\n",
    "    end = len(dataset) - horizon\n",
    "\n",
    "  for i in range(start, end):\n",
    "    indicesx = range(i-window, i)\n",
    "    X.append(np.reshape(dataset[indicesx], (window, 1)))\n",
    "    indicesy = range(i,i+horizon)\n",
    "    y.append(dataset[indicesy])\n",
    "  return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:44.121464Z",
     "start_time": "2020-03-02T15:30:43.080216Z"
    }
   },
   "outputs": [],
   "source": [
    "univar_hist_window = 48\n",
    "horizon = 10\n",
    "TRAIN_SPLIT = 30000\n",
    "x_train_uni, y_train_uni = custom_ts_univariate_data_prep(x_rescaled, 0, TRAIN_SPLIT,univar_hist_window, horizon)\n",
    "x_val_uni, y_val_uni = custom_ts_univariate_data_prep(x_rescaled, TRAIN_SPLIT, None,univar_hist_window,horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:45.741102Z",
     "start_time": "2020-03-02T15:30:45.731129Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single window of past history\n",
      "[[0.76167582]\n",
      " [0.62032967]\n",
      " [0.65480769]\n",
      " [0.69038462]\n",
      " [0.67554945]\n",
      " [0.71167582]\n",
      " [0.76703297]\n",
      " [0.82623626]\n",
      " [0.79546703]\n",
      " [0.65521978]\n",
      " [0.48612637]\n",
      " [0.38241758]\n",
      " [0.32431319]\n",
      " [0.21002747]\n",
      " [0.13228022]\n",
      " [0.06950549]\n",
      " [0.04409341]\n",
      " [0.0375    ]\n",
      " [0.05041209]\n",
      " [0.11181319]\n",
      " [0.37335165]\n",
      " [0.77925824]\n",
      " [0.89436813]\n",
      " [0.75151099]\n",
      " [0.70013736]\n",
      " [0.67129121]\n",
      " [0.7331044 ]\n",
      " [0.78186813]\n",
      " [0.84299451]\n",
      " [0.63502747]\n",
      " [0.49326923]\n",
      " [0.39807692]\n",
      " [0.36222527]\n",
      " [0.24409341]\n",
      " [0.13942308]\n",
      " [0.08214286]\n",
      " [0.05068681]\n",
      " [0.04285714]\n",
      " [0.05041209]\n",
      " [0.1146978 ]\n",
      " [0.37445055]\n",
      " [0.78145604]\n",
      " [0.96016484]\n",
      " [0.82211538]\n",
      " [0.72925824]\n",
      " [0.63228022]\n",
      " [0.67087912]\n",
      " [0.7010989 ]]\n",
      "\n",
      " Target horizon\n",
      "[[0.71126374]\n",
      " [0.75563187]\n",
      " [0.78475275]\n",
      " [0.86428571]\n",
      " [0.83200549]\n",
      " [0.67403846]\n",
      " [0.48118132]\n",
      " [0.41717033]\n",
      " [0.38763736]\n",
      " [0.27362637]]\n"
     ]
    }
   ],
   "source": [
    "print ('Single window of past history')\n",
    "print (x_train_uni[0])\n",
    "print ('\\n Target horizon')\n",
    "print (y_train_uni[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:49.106110Z",
     "start_time": "2020-03-02T15:30:47.963168Z"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 256\n",
    "BUFFER_SIZE = 150\n",
    "\n",
    "train_univariate = tf.data.Dataset.from_tensor_slices((x_train_uni, y_train_uni))\n",
    "train_univariate = train_univariate.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()\n",
    "\n",
    "val_univariate = tf.data.Dataset.from_tensor_slices((x_val_uni, y_val_uni))\n",
    "val_univariate = val_univariate.batch(BATCH_SIZE).repeat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:30:51.614443Z",
     "start_time": "2020-03-02T15:30:50.241079Z"
    }
   },
   "outputs": [],
   "source": [
    "Bi_lstm_model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100, return_sequences=True), \n",
    "                               input_shape=x_train_uni.shape[-2:]),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(50)),\n",
    "    tf.keras.layers.Dense(20, activation='softmax'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(units=horizon),\n",
    "])\n",
    "Bi_lstm_model.compile(optimizer='adam', loss='mse')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:32:28.956286Z",
     "start_time": "2020-03-02T15:32:28.952299Z"
    }
   },
   "outputs": [],
   "source": [
    "model_path = r'\\Chapter 6\\Bidirectional_LSTM_Univarient_1.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:24.459987Z",
     "start_time": "2020-03-02T15:32:31.100555Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 100 steps, validate for 50 steps\n",
      "Epoch 1/100\n",
      "100/100 [==============================] - 12s 123ms/step - loss: 0.1805 - val_loss: 0.1294\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 5s 49ms/step - loss: 0.1019 - val_loss: 0.0849\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0819 - val_loss: 0.0723\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 3s 33ms/step - loss: 0.0738 - val_loss: 0.0666\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0715 - val_loss: 0.0652\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0694 - val_loss: 0.0633\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 3s 30ms/step - loss: 0.0679 - val_loss: 0.0612\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0666 - val_loss: 0.0583\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0618 - val_loss: 0.0525\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0564 - val_loss: 0.0683\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0543 - val_loss: 0.0413\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0522 - val_loss: 0.0399\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0488 - val_loss: 0.0407\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0485 - val_loss: 0.0366\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0483 - val_loss: 0.0392\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0468 - val_loss: 0.0433\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0438 - val_loss: 0.0479\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0431 - val_loss: 0.0361\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0446 - val_loss: 0.0337\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0420 - val_loss: 0.0363\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0424 - val_loss: 0.0318\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0422 - val_loss: 0.0337\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0415 - val_loss: 0.0354\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0383 - val_loss: 0.0333\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0380 - val_loss: 0.0309\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0392 - val_loss: 0.0293\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0375 - val_loss: 0.0315\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0384 - val_loss: 0.0288\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0377 - val_loss: 0.0303\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0378 - val_loss: 0.0323\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 4s 40ms/step - loss: 0.0338 - val_loss: 0.0278\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0355 - val_loss: 0.0290\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0356 - val_loss: 0.0277\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0347 - val_loss: 0.0286\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0360 - val_loss: 0.0273\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0355 - val_loss: 0.0296\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0353 - val_loss: 0.0316\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 3s 32ms/step - loss: 0.0316 - val_loss: 0.0266\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0341 - val_loss: 0.0282\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0331 - val_loss: 0.0260\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0323 - val_loss: 0.0268\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0338 - val_loss: 0.0257\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0330 - val_loss: 0.0280\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0334 - val_loss: 0.0319\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 3s 29ms/step - loss: 0.0294 - val_loss: 0.0256\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0325 - val_loss: 0.0262\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0311 - val_loss: 0.0250\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0308 - val_loss: 0.0247\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0318 - val_loss: 0.0244\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0317 - val_loss: 0.0261\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0321 - val_loss: 0.0302\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0294 - val_loss: 0.0257\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0321 - val_loss: 0.0257\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0298 - val_loss: 0.0247\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0297 - val_loss: 0.0241\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0307 - val_loss: 0.0241\n",
      "Epoch 57/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0303 - val_loss: 0.0255\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0312 - val_loss: 0.0294\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0272 - val_loss: 0.0236\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0302 - val_loss: 0.0247\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0282 - val_loss: 0.0245\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0286 - val_loss: 0.0232\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0296 - val_loss: 0.0241\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0294 - val_loss: 0.0260\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0297 - val_loss: 0.0248\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0266 - val_loss: 0.0243\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0291 - val_loss: 0.0244\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0273 - val_loss: 0.0253\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 3s 28ms/step - loss: 0.0277 - val_loss: 0.0229\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0282 - val_loss: 0.0236\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0279 - val_loss: 0.0257\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0274 - val_loss: 0.0254\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0261 - val_loss: 0.0242\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0277 - val_loss: 0.0246\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0259 - val_loss: 0.0246\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0267 - val_loss: 0.0237\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0270 - val_loss: 0.0247\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0274 - val_loss: 0.0262\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 3s 27ms/step - loss: 0.0262 - val_loss: 0.0250\n",
      "Epoch 00079: early stopping\n"
     ]
    }
   ],
   "source": [
    "EVALUATION_INTERVAL = 100\n",
    "EPOCHS = 100\n",
    "history = Bi_lstm_model.fit(train_univariate, epochs=EPOCHS,steps_per_epoch=EVALUATION_INTERVAL,validation_data=val_univariate, validation_steps=50,verbose =1,\n",
    "                              callbacks =[tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1, mode='min'),tf.keras.callbacks.ModelCheckpoint(model_path,monitor='val_loss', save_best_only=True, mode='min', verbose=0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:31.851237Z",
     "start_time": "2020-03-02T15:36:28.857214Z"
    }
   },
   "outputs": [],
   "source": [
    "# Recreate the exact same model, including its weights and the optimizer\n",
    "Trained_model = tf.keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:32.754794Z",
     "start_time": "2020-03-02T15:36:32.746817Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional (Bidirectional (None, 48, 200)           81600     \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 100)               100400    \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 20)                2020      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                210       \n",
      "=================================================================\n",
      "Total params: 184,230\n",
      "Trainable params: 184,230\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Show the model architecture\n",
    "Trained_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:36.371130Z",
     "start_time": "2020-03-02T15:36:36.115847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train loss', 'validation loss'], loc='upper left')\n",
    "plt.rcParams[\"figure.figsize\"] = [16,9]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:38.087546Z",
     "start_time": "2020-03-02T15:36:38.083556Z"
    }
   },
   "outputs": [],
   "source": [
    "uni = df['traffic_volume']\n",
    "validatehori = uni.tail(48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:39.123809Z",
     "start_time": "2020-03-02T15:36:39.118787Z"
    }
   },
   "outputs": [],
   "source": [
    "validatehist = validatehori.values\n",
    "scaler_val = preprocessing.StandardScaler()\n",
    "val_rescaled = scaler_x.fit_transform(validatehist.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:40.447271Z",
     "start_time": "2020-03-02T15:36:40.441257Z"
    }
   },
   "outputs": [],
   "source": [
    "val_rescaled = val_rescaled.reshape((1, val_rescaled.shape[0], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:42.026055Z",
     "start_time": "2020-03-02T15:36:41.371772Z"
    }
   },
   "outputs": [],
   "source": [
    "Predicted_results = Trained_model.predict(val_rescaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:43.422288Z",
     "start_time": "2020-03-02T15:36:43.415311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5834908 , 0.58690774, 0.59064263, 0.6029938 , 0.6163158 ,\n",
       "        0.60863507, 0.57046133, 0.5115059 , 0.4549055 , 0.4061918 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predicted_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:45.544619Z",
     "start_time": "2020-03-02T15:36:45.540653Z"
    }
   },
   "outputs": [],
   "source": [
    "Predicted_inver_res = scaler_x.inverse_transform(Predicted_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:47.113424Z",
     "start_time": "2020-03-02T15:36:47.107439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3750.1716, 3770.171 , 3792.0312, 3864.3225, 3942.2961, 3897.341 ,\n",
       "        3673.9102, 3328.844 , 2997.562 , 2712.4404]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predicted_inver_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:48.664279Z",
     "start_time": "2020-03-02T15:36:48.632370Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
    "    \n",
    "    def mean_absolute_percentage_error(y_true, y_pred): \n",
    "        y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "        return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "    print('Evaluation metric results:-')\n",
    "    print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')\n",
    "    print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')\n",
    "    print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')\n",
    "    print(f'MAPE is : {mean_absolute_percentage_error(y_true, y_pred)}')\n",
    "    print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:50.195189Z",
     "start_time": "2020-03-02T15:36:50.187213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation metric results:-\n",
      "MSE is : 915543.7118907511\n",
      "MAE is : 794.2105224609375\n",
      "RMSE is : 956.8404840362635\n",
      "MAPE is : 43.781638221483625\n",
      "R2 is : 0.40561524153688866\n",
      "\n"
     ]
    }
   ],
   "source": [
    "timeseries_evaluation_metrics_func(validate,Predicted_inver_res[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T15:36:52.955849Z",
     "start_time": "2020-03-02T15:36:52.715460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( list(validate))\n",
    "plt.plot( list(Predicted_inver_res[0]))\n",
    "plt.title(\"Actual vs Predicted\")\n",
    "plt.ylabel(\"Traffic volume\")\n",
    "plt.legend(('Actual','predicted'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "nbTranslate": {
   "displayLangs": [
    "*"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "en",
   "targetLang": "fr",
   "useGoogleTranslate": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}