DLwithPython/chapter10_dl_for_timeseries.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/theaveas/DeepLearning/blob/main/DLwithPython/chapter10_dl_for_timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "159e8c6e-58b7-400f-8c98-1d4607ee6601",
      "metadata": {
        "id": "159e8c6e-58b7-400f-8c98-1d4607ee6601"
      },
      "source": [
        "# Deep learning for Timeseries\n",
        "## A temperature-forecasting \n",
        "#### Download and Inspect the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67725c12-78b5-4274-b430-5df2bda43f14",
      "metadata": {
        "id": "67725c12-78b5-4274-b430-5df2bda43f14",
        "outputId": "63a0fa3b-a4c6-4195-d912-2833b5501bb4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-03-30 23:07:44--  https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 54.231.201.120\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|54.231.201.120|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13565642 (13M) [application/zip]\n",
            "Saving to: ‘jena_climate_2009_2016.csv.zip’\n",
            "\n",
            "jena_climate_2009_2 100%[===================>]  12.94M   438KB/s    in 48s     \n",
            "\n",
            "2022-03-30 23:08:34 (276 KB/s) - ‘jena_climate_2009_2016.csv.zip’ saved [13565642/13565642]\n",
            "\n",
            "Archive:  jena_climate_2009_2016.csv.zip\n",
            "  inflating: jena_climate_2009_2016.csv  \n",
            "  inflating: __MACOSX/._jena_climate_2009_2016.csv  \n"
          ]
        }
      ],
      "source": [
        "# download\n",
        "!wget https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
        "\n",
        "# unzip\n",
        "!unzip jena_climate_2009_2016.csv.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "734342ad-56ce-48ad-88cb-bb03cdc97c0d",
      "metadata": {
        "id": "734342ad-56ce-48ad-88cb-bb03cdc97c0d"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('jena_climate_2009_2016.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c310f54e-73c8-4392-bc0e-6bbad2edd15d",
      "metadata": {
        "id": "c310f54e-73c8-4392-bc0e-6bbad2edd15d",
        "outputId": "8bcc76a3-8984-407f-e1f9-4ef9a313905d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 420451 entries, 0 to 420450\n",
            "Data columns (total 15 columns):\n",
            " #   Column           Non-Null Count   Dtype  \n",
            "---  ------           --------------   -----  \n",
            " 0   Date Time        420451 non-null  object \n",
            " 1   p (mbar)         420451 non-null  float64\n",
            " 2   T (degC)         420451 non-null  float64\n",
            " 3   Tpot (K)         420451 non-null  float64\n",
            " 4   Tdew (degC)      420451 non-null  float64\n",
            " 5   rh (%)           420451 non-null  float64\n",
            " 6   VPmax (mbar)     420451 non-null  float64\n",
            " 7   VPact (mbar)     420451 non-null  float64\n",
            " 8   VPdef (mbar)     420451 non-null  float64\n",
            " 9   sh (g/kg)        420451 non-null  float64\n",
            " 10  H2OC (mmol/mol)  420451 non-null  float64\n",
            " 11  rho (g/m**3)     420451 non-null  float64\n",
            " 12  wv (m/s)         420451 non-null  float64\n",
            " 13  max. wv (m/s)    420451 non-null  float64\n",
            " 14  wd (deg)         420451 non-null  float64\n",
            "dtypes: float64(14), object(1)\n",
            "memory usage: 48.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdb42bbe-7a96-45b9-bf59-c5851a2941af",
      "metadata": {
        "id": "fdb42bbe-7a96-45b9-bf59-c5851a2941af"
      },
      "source": [
        "#### Pharsing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "408ee312-7c7e-4848-9033-f1ad64745ce4",
      "metadata": {
        "id": "408ee312-7c7e-4848-9033-f1ad64745ce4"
      },
      "outputs": [],
      "source": [
        "temperature = df['T (degC)']\n",
        "raw_data = df.drop('T (degC)', axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d38e2a17-f5f5-44ff-87d7-caea185279ee",
      "metadata": {
        "id": "d38e2a17-f5f5-44ff-87d7-caea185279ee"
      },
      "source": [
        "#### Ploting the temperature timeseries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "18b9cfdf-15bc-4cae-aeb5-acd600cc1eee",
      "metadata": {
        "id": "18b9cfdf-15bc-4cae-aeb5-acd600cc1eee",
        "outputId": "332badf5-954e-4a1d-a16a-d8b237308caf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x178a3f970>]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(range(len(temperature)), temperature)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15eff246-3b0d-4bee-bd5e-fecc7d37b955",
      "metadata": {
        "id": "15eff246-3b0d-4bee-bd5e-fecc7d37b955"
      },
      "source": [
        "#### Ploting the first 10 days of the temp timeseries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "72ba9d8d-9185-4b04-b669-a292cd367978",
      "metadata": {
        "id": "72ba9d8d-9185-4b04-b669-a292cd367978",
        "outputId": "608a6f2c-1683-4647-c782-7c5f6f3237fe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x17ec22700>]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(range(1440), temperature[:1440])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04376062-40c2-4336-b698-641256dd1dc7",
      "metadata": {
        "id": "04376062-40c2-4336-b698-641256dd1dc7"
      },
      "source": [
        "#### Computing the number of samples for train test valid split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02388ca5-9882-4e54-b479-b21d6ad102e3",
      "metadata": {
        "id": "02388ca5-9882-4e54-b479-b21d6ad102e3",
        "outputId": "2c747ff5-a09c-4910-f471-8526a99f703c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of train samples 210225\n",
            "Number of validation samples 105112\n",
            "Number of test samples 105114\n"
          ]
        }
      ],
      "source": [
        "train_samples = int(.5 * len(raw_data))\n",
        "valid_samples = int(.25 * len(raw_data))\n",
        "test_samples = len(raw_data) - train_samples - valid_samples\n",
        "\n",
        "# print\n",
        "print(f'Number of train samples {train_samples}\\nNumber of validation samples {valid_samples}\\nNumber of test samples {test_samples}')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9d1289b-7d86-4913-8c5e-c0bc53028c10",
      "metadata": {
        "id": "f9d1289b-7d86-4913-8c5e-c0bc53028c10"
      },
      "source": [
        "### Preparing the dataset\n",
        "#### Normalizing the train dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1fd13ff-8159-48b8-bfe3-8388e45b8c1f",
      "metadata": {
        "id": "f1fd13ff-8159-48b8-bfe3-8388e45b8c1f"
      },
      "outputs": [],
      "source": [
        "# to normalize the dataset: minus the mean and divide by the standard deviation\n",
        "norm_data = raw_data.drop('Date Time', axis=1)\n",
        "\n",
        "mean = norm_data[:train_samples].mean(axis=0)\n",
        "norm_data -= mean\n",
        "std = norm_data[:train_samples].std(axis=0)\n",
        "norm_data /= std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a1b0a85-d6c9-465b-9a3b-c90fcd6dcbc7",
      "metadata": {
        "id": "6a1b0a85-d6c9-465b-9a3b-c90fcd6dcbc7",
        "outputId": "b0a4f8c3-b294-4b10-ca62-65614b230f63"
      },
      "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>p (mbar)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.913649</td>\n",
              "      <td>-1.974488</td>\n",
              "      <td>-1.866254</td>\n",
              "      <td>1.048015</td>\n",
              "      <td>-1.291316</td>\n",
              "      <td>-1.467152</td>\n",
              "      <td>-0.782343</td>\n",
              "      <td>-1.470122</td>\n",
              "      <td>-1.472032</td>\n",
              "      <td>2.124151</td>\n",
              "      <td>-0.730165</td>\n",
              "      <td>-0.779351</td>\n",
              "      <td>-0.281192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.919528</td>\n",
              "      <td>-2.018478</td>\n",
              "      <td>-1.919925</td>\n",
              "      <td>1.054028</td>\n",
              "      <td>-1.304472</td>\n",
              "      <td>-1.488855</td>\n",
              "      <td>-0.784440</td>\n",
              "      <td>-1.489114</td>\n",
              "      <td>-1.493462</td>\n",
              "      <td>2.172914</td>\n",
              "      <td>-0.932305</td>\n",
              "      <td>-0.886968</td>\n",
              "      <td>-0.469893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.914825</td>\n",
              "      <td>-2.029758</td>\n",
              "      <td>-1.924162</td>\n",
              "      <td>1.084097</td>\n",
              "      <td>-1.307103</td>\n",
              "      <td>-1.491266</td>\n",
              "      <td>-0.786537</td>\n",
              "      <td>-1.492912</td>\n",
              "      <td>-1.495843</td>\n",
              "      <td>2.183381</td>\n",
              "      <td>-1.277899</td>\n",
              "      <td>-1.261473</td>\n",
              "      <td>-0.056383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.912474</td>\n",
              "      <td>-2.006071</td>\n",
              "      <td>-1.890265</td>\n",
              "      <td>1.102138</td>\n",
              "      <td>-1.300525</td>\n",
              "      <td>-1.476798</td>\n",
              "      <td>-0.788633</td>\n",
              "      <td>-1.477719</td>\n",
              "      <td>-1.481556</td>\n",
              "      <td>2.158404</td>\n",
              "      <td>-1.180089</td>\n",
              "      <td>-1.317434</td>\n",
              "      <td>0.251128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.912474</td>\n",
              "      <td>-2.002687</td>\n",
              "      <td>-1.886027</td>\n",
              "      <td>1.096124</td>\n",
              "      <td>-1.299210</td>\n",
              "      <td>-1.474386</td>\n",
              "      <td>-0.788633</td>\n",
              "      <td>-1.477719</td>\n",
              "      <td>-1.479175</td>\n",
              "      <td>2.153885</td>\n",
              "      <td>-1.193130</td>\n",
              "      <td>-1.261473</td>\n",
              "      <td>0.440993</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   p (mbar)  Tpot (K)  Tdew (degC)    rh (%)  VPmax (mbar)  VPact (mbar)  \\\n",
              "0  0.913649 -1.974488    -1.866254  1.048015     -1.291316     -1.467152   \n",
              "1  0.919528 -2.018478    -1.919925  1.054028     -1.304472     -1.488855   \n",
              "2  0.914825 -2.029758    -1.924162  1.084097     -1.307103     -1.491266   \n",
              "3  0.912474 -2.006071    -1.890265  1.102138     -1.300525     -1.476798   \n",
              "4  0.912474 -2.002687    -1.886027  1.096124     -1.299210     -1.474386   \n",
              "\n",
              "   VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  wv (m/s)  \\\n",
              "0     -0.782343  -1.470122        -1.472032      2.124151 -0.730165   \n",
              "1     -0.784440  -1.489114        -1.493462      2.172914 -0.932305   \n",
              "2     -0.786537  -1.492912        -1.495843      2.183381 -1.277899   \n",
              "3     -0.788633  -1.477719        -1.481556      2.158404 -1.180089   \n",
              "4     -0.788633  -1.477719        -1.479175      2.153885 -1.193130   \n",
              "\n",
              "   max. wv (m/s)  wd (deg)  \n",
              "0      -0.779351 -0.281192  \n",
              "1      -0.886968 -0.469893  \n",
              "2      -1.261473 -0.056383  \n",
              "3      -1.317434  0.251128  \n",
              "4      -1.261473  0.440993  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "norm_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59e5d74e-9a9f-41e4-801a-d2503068bbb8",
      "metadata": {
        "id": "59e5d74e-9a9f-41e4-801a-d2503068bbb8"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
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