chapter10/RNN.ipynb

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    "<a href=\"https://colab.research.google.com/github/PacktPublishing/Machine-Learning-for-Time-Series-with-Python/blob/master/chapter10/RNN.ipynb\" target=\"_parent\\\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o47CT4PG6dsp"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "values = [         \n",
    "  112., 118., 132., 129., 121., 135., 148., 148., 136., 119., 104., 118., 115., 126.,\n",
    "  141., 135., 125., 149., 170., 170., 158., 133., 114., 140., 145., 150., 178., 163.,\n",
    "  172., 178., 199., 199., 184., 162., 146., 166., 171., 180., 193., 181., 183., 218.,\n",
    "  230., 242., 209., 191., 172., 194., 196., 196., 236., 235., 229., 243., 264., 272.,\n",
    "  237., 211., 180., 201., 204., 188., 235., 227., 234., 264., 302., 293., 259., 229.,\n",
    "  203., 229., 242., 233., 267., 269., 270., 315., 364., 347., 312., 274., 237., 278.,\n",
    "  284., 277., 317., 313., 318., 374., 413., 405., 355., 306., 271., 306., 315., 301.,\n",
    "  356., 348., 355., 422., 465., 467., 404., 347., 305., 336., 340., 318., 362., 348.,\n",
    "  363., 435., 491., 505., 404., 359., 310., 337., 360., 342., 406., 396., 420., 472.,\n",
    "  548., 559., 463., 407., 362., 405., 417., 391., 419., 461., 472., 535., 622., 606.,\n",
    "  508., 461., 390., 432.,\n",
    " ]\n",
    "idx = pd.date_range(\"1949-01-01\", periods=len(values), freq=\"M\")\n",
    "passengers = pd.Series(values, index=idx, name=\"passengers\").to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "id": "BNOhWQC46fJU",
    "outputId": "1687dde6-4e92-45f5-f40c-e4668cd7ba32"
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   "outputs": [],
   "source": [
    "passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1Fhde-Qg6pPj"
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   "source": [
    "LOOKBACK = 10\n",
    "\n",
    "def wrap_data(df, lookback):\n",
    "  dataset = []\n",
    "  for index in range(lookback, len(df)+1):\n",
    "    features = {\n",
    "        f\"col_{i}\": float(val) for i, val in enumerate(\n",
    "          df.iloc[index-lookback:index].values\n",
    "        )\n",
    "    }\n",
    "    row = pd.DataFrame.from_dict([features])\n",
    "    row.index = [df.index[index-1]]\n",
    "    dataset.append(row)\n",
    "  return pd.concat(dataset, axis=0)\n",
    "\n",
    "dataset = wrap_data(passengers, lookback=LOOKBACK)\n",
    "dataset = dataset.join(passengers.shift(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xkD2twYx7lxK"
   },
   "outputs": [],
   "source": [
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.layers import Input, Bidirectional, LSTM, Dense\n",
    "import tensorflow as tf\n",
    "\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
    "\n",
    "def create_model(passengers):\n",
    "    input_layer = Input(shape=(LOOKBACK, 1))\n",
    "    recurrent = Bidirectional(LSTM(20, activation=\"tanh\"))(input_layer)\n",
    "    output_layer = Dense(1)(recurrent)\n",
    "    model = keras.models.Model(inputs=input_layer, outputs=output_layer)\n",
    "    model.compile(\n",
    "        loss='mse', optimizer=keras.optimizers.Adagrad(),\n",
    "        metrics=[\n",
    "            keras.metrics.RootMeanSquaredError(),\n",
    "            keras.metrics.MeanAbsoluteError()\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "model = create_model(passengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uv0e7MJNBIrl"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    dataset.drop(columns=\"passengers\"),\n",
    "    dataset[\"passengers\"],\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bN4gzfAhAK4q",
    "outputId": "4c812a58-5a93-421a-fd20-e47bea56d52b"
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   "outputs": [],
   "source": [
    "model.fit(X_train, y_train, epochs=1000, callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o6lhB_7VApOJ"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def show_result(y_test, predicted):\n",
    "  plt.figure(figsize=(16, 6))\n",
    "  plt.plot(y_test.index, predicted, 'o-', label=\"predicted\")\n",
    "  plt.plot(y_test.index, y_test, '.-', label=\"actual\")\n",
    "\n",
    "  plt.ylabel(\"Passengers\")\n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "id": "9TN2UDG_Bu0c",
    "outputId": "4c84996c-8906-4d53-b84b-2204811b017c"
   },
   "outputs": [],
   "source": [
    "predicted = model.predict(X_test)\n",
    "show_result(y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V668DSiQBzV1"
   },
   "outputs": [],
   "source": []
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