OPTIMUS_PRIME_RF_GB.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "wvE12juMTOq5",
        "outputId": "df5b5708-b7d9-4752-f2be-392da5f1d393"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: h2o in /usr/local/lib/python3.7/dist-packages (3.36.1.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from h2o) (2.23.0)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from h2o) (0.8.9)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from h2o) (0.16.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->h2o) (3.0.4)\n",
            "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "--------------------------  ----------------------------------\n",
              "H2O_cluster_uptime:         3 hours 3 mins\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.36.1.1\n",
              "H2O_cluster_version_age:    17 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_bjj4b2\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    2.475 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         locked, healthy\n",
              "H2O_connection_url:         http://localhost:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "Python_version:             3.7.13 final\n",
              "--------------------------  ----------------------------------"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>3 hours 3 mins</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.36.1.1</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>17 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_bjj4b2</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>2.475 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>locked, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://localhost:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.7.13 final</td></tr></table></div>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "!pip3 install h2o\n",
        "import h2o\n",
        "from h2o.estimators import H2ORandomForestEstimator as RFE\n",
        "h2o.init()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "XF6Deu3IV4rQ"
      },
      "outputs": [],
      "source": [
        "sales = pd.read_csv(\"/sales.csv\")\n",
        "geo_params = pd.read_csv(\"/geo_params.csv\")\n",
        "sku = pd.read_csv(\"/sku.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 823
        },
        "id": "HVD5Rg_zgOmv",
        "outputId": "26d2d28a-0e15-4267-ab50-ea1f0056f1d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         geoCluster     SKU       date  price  sales  \\\n",
              "0                92   32485 2021-07-05  38.29    4.0   \n",
              "2               112   32485 2021-05-27  45.39    3.7   \n",
              "75              131   32485 2021-04-20  43.99    2.3   \n",
              "152             148   32485 2021-04-26  46.79    3.5   \n",
              "160             148   32485 2021-05-04  38.39    4.0   \n",
              "...             ...     ...        ...    ...    ...   \n",
              "4604750        3209  362721 2021-06-30  36.99    2.0   \n",
              "4604751        3209  362721 2021-07-01  36.99    1.0   \n",
              "4604752        3209  362721 2021-07-02  36.99    2.0   \n",
              "4604754        3209  362721 2021-07-04  36.99    8.0   \n",
              "4604755        3209  362721 2021-07-05  36.99    2.0   \n",
              "\n",
              "                                 Category            Type  brandId  \\\n",
              "0                                  Banana  Banana — Plain      NaN   \n",
              "2                                  Banana  Banana — Plain      NaN   \n",
              "75                                 Banana  Banana — Plain      NaN   \n",
              "152                                Banana  Banana — Plain      NaN   \n",
              "160                                Banana  Banana — Plain      NaN   \n",
              "...                                   ...             ...      ...   \n",
              "4604750  Plain fancy cake, own production         Cupcake      NaN   \n",
              "4604751  Plain fancy cake, own production         Cupcake      NaN   \n",
              "4604752  Plain fancy cake, own production         Cupcake      NaN   \n",
              "4604754  Plain fancy cake, own production         Cupcake      NaN   \n",
              "4604755  Plain fancy cake, own production         Cupcake      NaN   \n",
              "\n",
              "         lagerUnitQuantity  Units  trademark  countryOfOrigin  \\\n",
              "0                      1.0    NaN        NaN              NaN   \n",
              "2                      1.0    NaN        NaN              NaN   \n",
              "75                     1.0    NaN        NaN              NaN   \n",
              "152                    1.0    NaN        NaN              NaN   \n",
              "160                    1.0    NaN        NaN              NaN   \n",
              "...                    ...    ...        ...              ...   \n",
              "4604750              300.0    NaN     9666.0              1.0   \n",
              "4604751              300.0    NaN     9666.0              1.0   \n",
              "4604752              300.0    NaN     9666.0              1.0   \n",
              "4604754              300.0    NaN     9666.0              1.0   \n",
              "4604755              300.0    NaN     9666.0              1.0   \n",
              "\n",
              "                   Group  Year Month Day       dotw  \n",
              "0        Tropical fruits  2021    07  05     Monday  \n",
              "2        Tropical fruits  2021    05  27   Thursday  \n",
              "75       Tropical fruits  2021    04  20    Tuesday  \n",
              "152      Tropical fruits  2021    04  26     Monday  \n",
              "160      Tropical fruits  2021    05  04    Tuesday  \n",
              "...                  ...   ...   ...  ..        ...  \n",
              "4604750           Bakery  2021    06  30  Wednesday  \n",
              "4604751           Bakery  2021    07  01   Thursday  \n",
              "4604752           Bakery  2021    07  02     Friday  \n",
              "4604754           Bakery  2021    07  04     Sunday  \n",
              "4604755           Bakery  2021    07  05     Monday  \n",
              "\n",
              "[778366 rows x 17 columns]"
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              "\n",
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              "      <th></th>\n",
              "      <th>geoCluster</th>\n",
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              "      <th>0</th>\n",
              "      <td>92</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>38.29</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Banana</td>\n",
              "      <td>Banana — Plain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tropical fruits</td>\n",
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              "      <td>2021-05-27</td>\n",
              "      <td>45.39</td>\n",
              "      <td>3.7</td>\n",
              "      <td>Banana</td>\n",
              "      <td>Banana — Plain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tropical fruits</td>\n",
              "      <td>2021</td>\n",
              "      <td>05</td>\n",
              "      <td>27</td>\n",
              "      <td>Thursday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>131</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-04-20</td>\n",
              "      <td>43.99</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Banana</td>\n",
              "      <td>Banana — Plain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tropical fruits</td>\n",
              "      <td>2021</td>\n",
              "      <td>04</td>\n",
              "      <td>20</td>\n",
              "      <td>Tuesday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>148</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-04-26</td>\n",
              "      <td>46.79</td>\n",
              "      <td>3.5</td>\n",
              "      <td>Banana</td>\n",
              "      <td>Banana — Plain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tropical fruits</td>\n",
              "      <td>2021</td>\n",
              "      <td>04</td>\n",
              "      <td>26</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>148</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-05-04</td>\n",
              "      <td>38.39</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Banana</td>\n",
              "      <td>Banana — Plain</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2021</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4604750</th>\n",
              "      <td>3209</td>\n",
              "      <td>362721</td>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>36.99</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Plain fancy cake, own production</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>300.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9666.0</td>\n",
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              "      <td>06</td>\n",
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              "      <th>4604751</th>\n",
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              "      <td>362721</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>Plain fancy cake, own production</td>\n",
              "      <td>Cupcake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>300.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9666.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>362721</td>\n",
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              "      <td>8.0</td>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-41780382-25c7-4318-b8f1-96eb093b11ae button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-41780382-25c7-4318-b8f1-96eb093b11ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "joined = sales.join(sku.set_index('SKU'), on = 'SKU', how = 'inner')\n",
        "#joined = joined.drop(['Type','ID','SKU', 'Units', 'countryOfOrigin', 'trademark', 'brandId'], axis = 1)\n",
        "joined = joined.drop(['ID'], axis = 1)\n",
        "joined = joined.dropna(axis = 0, subset = ['sales', 'price'])\n",
        "joined[[\"Year\", \"Month\", \"Day\"]] = joined.date.str.split(pat='-',expand=True)\n",
        "joined['date'] = pd.to_datetime(joined['date'])\n",
        "joined['dotw']= joined['date'].dt.day_name()\n",
        "#joined = joined.drop(['date'], axis = 1)\n",
        "joined"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Random forest"
      ],
      "metadata": {
        "id": "sUrmMorj8lxa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "lhUHBB53k5Tu",
        "outputId": "5786eb91-ca1e-4e75-f8c7-fe49e96047ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  geoCluster</th><th style=\"text-align: right;\">   SKU</th><th>date               </th><th style=\"text-align: right;\">  price</th><th style=\"text-align: right;\">  sales</th><th>Category                                               </th><th>Type                                                           </th><th style=\"text-align: right;\">  brandId</th><th style=\"text-align: right;\">  lagerUnitQuantity</th><th style=\"text-align: right;\">  Units</th><th style=\"text-align: right;\">  trademark</th><th style=\"text-align: right;\">  countryOfOrigin</th><th>Group          </th><th style=\"text-align: right;\">  Year</th><th style=\"text-align: right;\">  Month</th><th style=\"text-align: right;\">  Day</th><th>dotw     </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">        2264</td><td style=\"text-align: right;\"> 16649</td><td>2021-02-26 00:00:00</td><td style=\"text-align: right;\">  18.09</td><td style=\"text-align: right;\">    6  </td><td>Water, still                                           </td><td>Table water — PET — from 1 to 2 L — Still                      </td><td style=\"text-align: right;\">     1241</td><td style=\"text-align: right;\">                1.5</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       1323</td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   26</td><td>Friday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2077</td><td style=\"text-align: right;\">543663</td><td>2021-04-01 00:00:00</td><td style=\"text-align: right;\"> 424.69</td><td style=\"text-align: right;\">    0.5</td><td>Soft cheese, white mold                                </td><td>soft cheese — More expensive — Import — White mold — Brie      </td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       8802</td><td style=\"text-align: right;\">                6</td><td>Cheese         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">    1</td><td>Thursday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2750</td><td style=\"text-align: right;\">224200</td><td>2020-10-11 00:00:00</td><td style=\"text-align: right;\">  23.49</td><td style=\"text-align: right;\">    3  </td><td>Plain wheat bread, toast-type, own production          </td><td>Bread — plain — wheat                                          </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">              550  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       9666</td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">     10</td><td style=\"text-align: right;\">   11</td><td>Sunday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2013</td><td style=\"text-align: right;\">567970</td><td>2021-01-21 00:00:00</td><td style=\"text-align: right;\">  26.39</td><td style=\"text-align: right;\">    1  </td><td>Plain baguette loaf baked in the hearth, own production</td><td>Baguette loaf — plain                                          </td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">              300  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       9974</td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">   21</td><td>Thursday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2053</td><td style=\"text-align: right;\"> 32546</td><td>2021-06-02 00:00:00</td><td style=\"text-align: right;\">  40.79</td><td style=\"text-align: right;\">    4  </td><td>Orange                                                 </td><td>Citrus fruit — Orange — Plain                                  </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      6</td><td style=\"text-align: right;\">    2</td><td>Wednesday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2383</td><td style=\"text-align: right;\">503007</td><td>2020-06-17 00:00:00</td><td style=\"text-align: right;\">  30.59</td><td style=\"text-align: right;\">    2  </td><td>Plain rye-wheat bread, parbaking                       </td><td>Bread — plain — rye-wheat                                      </td><td style=\"text-align: right;\">     8274</td><td style=\"text-align: right;\">              340  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">      6</td><td style=\"text-align: right;\">   17</td><td>Wednesday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2177</td><td style=\"text-align: right;\">787133</td><td>2021-01-01 00:00:00</td><td style=\"text-align: right;\">  81.69</td><td style=\"text-align: right;\">    1  </td><td>Avocado                                                </td><td>Tropical fruit — Avocado — Ready to Eat                        </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                2  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       9666</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">    1</td><td>Friday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2069</td><td style=\"text-align: right;\"> 47330</td><td>2020-04-09 00:00:00</td><td style=\"text-align: right;\"> 240.09</td><td style=\"text-align: right;\">    1  </td><td>Semi-hard coarse-pored cheese                          </td><td>semi-hard cheese — More expensive — National — Available import</td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        297</td><td style=\"text-align: right;\">               16</td><td>Cheese         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">    9</td><td>Thursday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        1999</td><td style=\"text-align: right;\"> 20872</td><td>2021-06-07 00:00:00</td><td style=\"text-align: right;\">  37.69</td><td style=\"text-align: right;\">    2  </td><td>Water, sparkling                                       </td><td>Therapeutic-table water — PET — from 1 to 2 L                  </td><td style=\"text-align: right;\">     2693</td><td style=\"text-align: right;\">                1.5</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       4384</td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      6</td><td style=\"text-align: right;\">    7</td><td>Monday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2542</td><td style=\"text-align: right;\"> 89074</td><td>2021-02-25 00:00:00</td><td style=\"text-align: right;\">  20.69</td><td style=\"text-align: right;\">    5  </td><td>Milk cakes                                             </td><td>Sponge cake                                                    </td><td style=\"text-align: right;\">     2724</td><td style=\"text-align: right;\">               28  </td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">       1835</td><td style=\"text-align: right;\">              nan</td><td>Yogurts        </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   25</td><td>Thursday </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "shuffled=joined.sample(frac=1, random_state=1)\n",
        "h2o_joined = h2o.H2OFrame(shuffled)\n",
        "h2o_joined"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "iqNZSfLVxctt",
        "outputId": "a78f0870-e03a-463c-a9b2-761a83c69a3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  geoCluster</th><th style=\"text-align: right;\">   SKU</th><th>date               </th><th style=\"text-align: right;\">  price</th><th style=\"text-align: right;\">  sales</th><th>Category                                               </th><th>Type                                                           </th><th style=\"text-align: right;\">  brandId</th><th style=\"text-align: right;\">  lagerUnitQuantity</th><th style=\"text-align: right;\">  Units</th><th>trademark  </th><th style=\"text-align: right;\">  countryOfOrigin</th><th>Group          </th><th style=\"text-align: right;\">  Year</th><th style=\"text-align: right;\">  Month</th><th style=\"text-align: right;\">  Day</th><th>dotw     </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">        2264</td><td style=\"text-align: right;\"> 16649</td><td>2021-02-26 00:00:00</td><td style=\"text-align: right;\">  18.09</td><td style=\"text-align: right;\">    6  </td><td>Water, still                                           </td><td>Table water — PET — from 1 to 2 L — Still                      </td><td style=\"text-align: right;\">     1241</td><td style=\"text-align: right;\">                1.5</td><td style=\"text-align: right;\">    nan</td><td>1323       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   26</td><td>Friday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2077</td><td style=\"text-align: right;\">543663</td><td>2021-04-01 00:00:00</td><td style=\"text-align: right;\"> 424.69</td><td style=\"text-align: right;\">    0.5</td><td>Soft cheese, white mold                                </td><td>soft cheese — More expensive — Import — White mold — Brie      </td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>8802       </td><td style=\"text-align: right;\">                6</td><td>Cheese         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">    1</td><td>Thursday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2750</td><td style=\"text-align: right;\">224200</td><td>2020-10-11 00:00:00</td><td style=\"text-align: right;\">  23.49</td><td style=\"text-align: right;\">    3  </td><td>Plain wheat bread, toast-type, own production          </td><td>Bread — plain — wheat                                          </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">              550  </td><td style=\"text-align: right;\">    nan</td><td>9666       </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">     10</td><td style=\"text-align: right;\">   11</td><td>Sunday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2013</td><td style=\"text-align: right;\">567970</td><td>2021-01-21 00:00:00</td><td style=\"text-align: right;\">  26.39</td><td style=\"text-align: right;\">    1  </td><td>Plain baguette loaf baked in the hearth, own production</td><td>Baguette loaf — plain                                          </td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">              300  </td><td style=\"text-align: right;\">    nan</td><td>9974       </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">   21</td><td>Thursday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2053</td><td style=\"text-align: right;\"> 32546</td><td>2021-06-02 00:00:00</td><td style=\"text-align: right;\">  40.79</td><td style=\"text-align: right;\">    4  </td><td>Orange                                                 </td><td>Citrus fruit — Orange — Plain                                  </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>           </td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      6</td><td style=\"text-align: right;\">    2</td><td>Wednesday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2383</td><td style=\"text-align: right;\">503007</td><td>2020-06-17 00:00:00</td><td style=\"text-align: right;\">  30.59</td><td style=\"text-align: right;\">    2  </td><td>Plain rye-wheat bread, parbaking                       </td><td>Bread — plain — rye-wheat                                      </td><td style=\"text-align: right;\">     8274</td><td style=\"text-align: right;\">              340  </td><td style=\"text-align: right;\">    nan</td><td>           </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">      6</td><td style=\"text-align: right;\">   17</td><td>Wednesday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2177</td><td style=\"text-align: right;\">787133</td><td>2021-01-01 00:00:00</td><td style=\"text-align: right;\">  81.69</td><td style=\"text-align: right;\">    1  </td><td>Avocado                                                </td><td>Tropical fruit — Avocado — Ready to Eat                        </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                2  </td><td style=\"text-align: right;\">    nan</td><td>9666       </td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">    1</td><td>Friday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2069</td><td style=\"text-align: right;\"> 47330</td><td>2020-04-09 00:00:00</td><td style=\"text-align: right;\"> 240.09</td><td style=\"text-align: right;\">    1  </td><td>Semi-hard coarse-pored cheese                          </td><td>semi-hard cheese — More expensive — National — Available import</td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>297        </td><td style=\"text-align: right;\">               16</td><td>Cheese         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">    9</td><td>Thursday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        1999</td><td style=\"text-align: right;\"> 20872</td><td>2021-06-07 00:00:00</td><td style=\"text-align: right;\">  37.69</td><td style=\"text-align: right;\">    2  </td><td>Water, sparkling                                       </td><td>Therapeutic-table water — PET — from 1 to 2 L                  </td><td style=\"text-align: right;\">     2693</td><td style=\"text-align: right;\">                1.5</td><td style=\"text-align: right;\">    nan</td><td>4384       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      6</td><td style=\"text-align: right;\">    7</td><td>Monday   </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2542</td><td style=\"text-align: right;\"> 89074</td><td>2021-02-25 00:00:00</td><td style=\"text-align: right;\">  20.69</td><td style=\"text-align: right;\">    5  </td><td>Milk cakes                                             </td><td>Sponge cake                                                    </td><td style=\"text-align: right;\">     2724</td><td style=\"text-align: right;\">               28  </td><td style=\"text-align: right;\">    nan</td><td>1835       </td><td style=\"text-align: right;\">              nan</td><td>Yogurts        </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   25</td><td>Thursday </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "# Set the predictors and response;\n",
        "predictors = [\"geoCluster\",\"price\",\"Category\",\"Type\", \"brandId\", \"lagerUnitQuantity\",\"Units\", \"trademark\", \"countryOfOrigin\",\"Group\", \"Month\", \"Year\", \"dotw\"]\n",
        "response = \"sales\"\n",
        "# set the response as a factor:\n",
        "h2o_joined[\"Category\"] = h2o_joined[\"Category\"].asfactor()\n",
        "h2o_joined[\"countryOfOrigin\"] = h2o_joined[\"countryOfOrigin\"].asfactor()\n",
        "h2o_joined[\"trademark\"] = h2o_joined[\"trademark\"].asfactor()\n",
        "h2o_joined[\"geoCluster\"] = h2o_joined[\"geoCluster\"].asfactor()\n",
        "h2o_joined[\"Group\"] = h2o_joined[\"Group\"].asfactor()\n",
        "h2o_joined[\"Type\"] = h2o_joined[\"Type\"].asfactor()\n",
        "h2o_joined[\"dotw\"] = h2o_joined[\"dotw\"].asfactor()\n",
        "h2o_joined"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8JwixiQwoga",
        "outputId": "09d9ebaa-f87f-4a74-8f06-213da826b3a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drf Model Build progress: |"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/h2o/estimators/estimator_base.py:208: RuntimeWarning: Dropping bad and constant columns: [Units]\n",
            "  warnings.warn(mesg[\"message\"], RuntimeWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/h2o/estimators/estimator_base.py:208: RuntimeWarning: Calibration frame was specified but calibration was not requested.\n",
            "  warnings.warn(mesg[\"message\"], RuntimeWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "██████████████████████████████████████████████████████| (done) 100%\n",
            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ],
      "source": [
        "# Split the dataset into a train and valid set:\n",
        "train, valid = h2o_joined.split_frame(ratios=[.9], seed=1234)\n",
        "\n",
        "# Build and train the model:\n",
        "model = RFE(ntrees=10,\n",
        "            max_depth=5,\n",
        "            min_rows=5000,\n",
        "            calibration_frame=valid)\n",
        "\n",
        "model.train(x=predictors,\n",
        "               y=response,\n",
        "               training_frame=train,\n",
        "               validation_frame=valid)\n",
        "\n",
        "# Eval performance:\n",
        "perf = model.model_performance()\n",
        "\n",
        "# Generate predictions on a validation set (if necessary):\n",
        "pred =  model.predict(valid)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.download_mojo(\"my_gbm_mojo.zip\")\n",
        "valid"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "DsRR6Nr4Dq4t",
        "outputId": "5497cb9a-ae0d-430a-8e04-46814b9ba43c"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  geoCluster</th><th style=\"text-align: right;\">   SKU</th><th>date               </th><th style=\"text-align: right;\">  price</th><th style=\"text-align: right;\">  sales</th><th>Category                                                </th><th>Type                                                           </th><th style=\"text-align: right;\">  brandId</th><th style=\"text-align: right;\">  lagerUnitQuantity</th><th style=\"text-align: right;\">  Units</th><th>trademark  </th><th style=\"text-align: right;\">  countryOfOrigin</th><th>Group          </th><th style=\"text-align: right;\">  Year</th><th style=\"text-align: right;\">  Month</th><th style=\"text-align: right;\">  Day</th><th>dotw    </th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">        2013</td><td style=\"text-align: right;\">567970</td><td>2021-01-21 00:00:00</td><td style=\"text-align: right;\">  26.39</td><td style=\"text-align: right;\">    1  </td><td>Plain baguette loaf baked in the hearth, own production </td><td>Baguette loaf — plain                                          </td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">              300  </td><td style=\"text-align: right;\">    nan</td><td>9974       </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">   21</td><td>Thursday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2542</td><td style=\"text-align: right;\"> 89074</td><td>2021-02-25 00:00:00</td><td style=\"text-align: right;\">  20.69</td><td style=\"text-align: right;\">    5  </td><td>Milk cakes                                              </td><td>Sponge cake                                                    </td><td style=\"text-align: right;\">     2724</td><td style=\"text-align: right;\">               28  </td><td style=\"text-align: right;\">    nan</td><td>1835       </td><td style=\"text-align: right;\">              nan</td><td>Yogurts        </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   25</td><td>Thursday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        1999</td><td style=\"text-align: right;\">369071</td><td>2021-04-24 00:00:00</td><td style=\"text-align: right;\">  15.69</td><td style=\"text-align: right;\">    6  </td><td>Water, still                                            </td><td>Table water — PET — from 1 to 2 L — Still                      </td><td style=\"text-align: right;\">     1330</td><td style=\"text-align: right;\">                2  </td><td style=\"text-align: right;\">    nan</td><td>2781       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">   24</td><td>Saturday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2270</td><td style=\"text-align: right;\">690818</td><td>2020-11-13 00:00:00</td><td style=\"text-align: right;\">  15.99</td><td style=\"text-align: right;\">    4  </td><td>Ciabatta, light, plain, parbaking                       </td><td>Ciabatta                                                       </td><td style=\"text-align: right;\">     8274</td><td style=\"text-align: right;\">              250  </td><td style=\"text-align: right;\">    nan</td><td>4043       </td><td style=\"text-align: right;\">              nan</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">     11</td><td style=\"text-align: right;\">   13</td><td>Friday  </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2259</td><td style=\"text-align: right;\"> 32550</td><td>2020-12-25 00:00:00</td><td style=\"text-align: right;\">  58.29</td><td style=\"text-align: right;\">    4.7</td><td>Lemon                                                   </td><td>Citrus fruit — Lemon — Plain                                   </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>           </td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">     12</td><td style=\"text-align: right;\">   25</td><td>Friday  </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2120</td><td style=\"text-align: right;\">369071</td><td>2021-02-14 00:00:00</td><td style=\"text-align: right;\">  19.69</td><td style=\"text-align: right;\">    4  </td><td>Water, still                                            </td><td>Table water — PET — from 1 to 2 L — Still                      </td><td style=\"text-align: right;\">     1330</td><td style=\"text-align: right;\">                2  </td><td style=\"text-align: right;\">    nan</td><td>2781       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   14</td><td>Sunday  </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2577</td><td style=\"text-align: right;\">591899</td><td>2021-05-15 00:00:00</td><td style=\"text-align: right;\">  82.49</td><td style=\"text-align: right;\">    0.5</td><td>Flavored wheat bread baked in the hearth, own production</td><td>Bread — flavored                                               </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>15156      </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">   15</td><td>Saturday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2171</td><td style=\"text-align: right;\">690818</td><td>2020-09-01 00:00:00</td><td style=\"text-align: right;\">  17.09</td><td style=\"text-align: right;\">    2  </td><td>Ciabatta, light, plain, parbaking                       </td><td>Ciabatta                                                       </td><td style=\"text-align: right;\">     8274</td><td style=\"text-align: right;\">              250  </td><td style=\"text-align: right;\">    nan</td><td>4043       </td><td style=\"text-align: right;\">              nan</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">      9</td><td style=\"text-align: right;\">    1</td><td>Tuesday </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2542</td><td style=\"text-align: right;\">571255</td><td>2021-02-27 00:00:00</td><td style=\"text-align: right;\"> 255.59</td><td style=\"text-align: right;\">    2  </td><td>Semi-hard coarse-pored cheese                           </td><td>semi-hard cheese — More expensive — National — Available import</td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>1037       </td><td style=\"text-align: right;\">               33</td><td>Cheese         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   27</td><td>Saturday</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2735</td><td style=\"text-align: right;\">   208</td><td>2021-02-01 00:00:00</td><td style=\"text-align: right;\">  15.59</td><td style=\"text-align: right;\">    2  </td><td>Water, sparkling                                        </td><td>Therapeutic-table water — PET — from 1 to 2 L                  </td><td style=\"text-align: right;\">     1241</td><td style=\"text-align: right;\">                1.5</td><td style=\"text-align: right;\">    nan</td><td>3670       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">    1</td><td>Monday  </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ufXOjVjr7_qC",
        "outputId": "469ca36d-4df0-4c0f-8bc3-9d30ffd647e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ModelMetricsRegression: drf\n",
            "** Reported on train data. **\n",
            "\n",
            "MSE: 14.979483152187747\n",
            "RMSE: 3.8703337262034325\n",
            "MAE: 1.9401738551033412\n",
            "RMSLE: 0.49526404077733666\n",
            "Mean Residual Deviance: 14.979483152187747\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "perf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "E5kLR8-V7Qch",
        "outputId": "866b2a74-34b1-4945-fe62-d3c3463f03ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\"> 3.15915 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 3.94017 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 4.60775 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 2.76589 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 1.99803 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 4.56954 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 0.992484</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 2.76589 </td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 0.763989</td></tr>\n",
              "<tr><td style=\"text-align: right;\"> 4.82277 </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "WivDJwSmAlT6",
        "outputId": "fb163dc8-d9c8-495b-e87a-309c62d26c32"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  geoCluster</th><th style=\"text-align: right;\">   SKU</th><th>date               </th><th style=\"text-align: right;\">  price</th><th style=\"text-align: right;\">  sales</th><th>Category                                                </th><th>Type                                                           </th><th style=\"text-align: right;\">  brandId</th><th style=\"text-align: right;\">  lagerUnitQuantity</th><th style=\"text-align: right;\">  Units</th><th>trademark  </th><th style=\"text-align: right;\">  countryOfOrigin</th><th>Group          </th><th style=\"text-align: right;\">  Year</th><th style=\"text-align: right;\">  Month</th><th style=\"text-align: right;\">  Day</th><th>dotw    </th><th style=\"text-align: right;\">  prediction</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">        2013</td><td style=\"text-align: right;\">567970</td><td>2021-01-21 00:00:00</td><td style=\"text-align: right;\">  26.39</td><td style=\"text-align: right;\">    1  </td><td>Plain baguette loaf baked in the hearth, own production </td><td>Baguette loaf — plain                                          </td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">              300  </td><td style=\"text-align: right;\">    nan</td><td>9974       </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      1</td><td style=\"text-align: right;\">   21</td><td>Thursday</td><td style=\"text-align: right;\">    3.15915 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2542</td><td style=\"text-align: right;\"> 89074</td><td>2021-02-25 00:00:00</td><td style=\"text-align: right;\">  20.69</td><td style=\"text-align: right;\">    5  </td><td>Milk cakes                                              </td><td>Sponge cake                                                    </td><td style=\"text-align: right;\">     2724</td><td style=\"text-align: right;\">               28  </td><td style=\"text-align: right;\">    nan</td><td>1835       </td><td style=\"text-align: right;\">              nan</td><td>Yogurts        </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   25</td><td>Thursday</td><td style=\"text-align: right;\">    3.94017 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        1999</td><td style=\"text-align: right;\">369071</td><td>2021-04-24 00:00:00</td><td style=\"text-align: right;\">  15.69</td><td style=\"text-align: right;\">    6  </td><td>Water, still                                            </td><td>Table water — PET — from 1 to 2 L — Still                      </td><td style=\"text-align: right;\">     1330</td><td style=\"text-align: right;\">                2  </td><td style=\"text-align: right;\">    nan</td><td>2781       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      4</td><td style=\"text-align: right;\">   24</td><td>Saturday</td><td style=\"text-align: right;\">    4.60775 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2270</td><td style=\"text-align: right;\">690818</td><td>2020-11-13 00:00:00</td><td style=\"text-align: right;\">  15.99</td><td style=\"text-align: right;\">    4  </td><td>Ciabatta, light, plain, parbaking                       </td><td>Ciabatta                                                       </td><td style=\"text-align: right;\">     8274</td><td style=\"text-align: right;\">              250  </td><td style=\"text-align: right;\">    nan</td><td>4043       </td><td style=\"text-align: right;\">              nan</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">     11</td><td style=\"text-align: right;\">   13</td><td>Friday  </td><td style=\"text-align: right;\">    2.76589 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2259</td><td style=\"text-align: right;\"> 32550</td><td>2020-12-25 00:00:00</td><td style=\"text-align: right;\">  58.29</td><td style=\"text-align: right;\">    4.7</td><td>Lemon                                                   </td><td>Citrus fruit — Lemon — Plain                                   </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>           </td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">     12</td><td style=\"text-align: right;\">   25</td><td>Friday  </td><td style=\"text-align: right;\">    1.99803 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2120</td><td style=\"text-align: right;\">369071</td><td>2021-02-14 00:00:00</td><td style=\"text-align: right;\">  19.69</td><td style=\"text-align: right;\">    4  </td><td>Water, still                                            </td><td>Table water — PET — from 1 to 2 L — Still                      </td><td style=\"text-align: right;\">     1330</td><td style=\"text-align: right;\">                2  </td><td style=\"text-align: right;\">    nan</td><td>2781       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   14</td><td>Sunday  </td><td style=\"text-align: right;\">    4.56954 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2577</td><td style=\"text-align: right;\">591899</td><td>2021-05-15 00:00:00</td><td style=\"text-align: right;\">  82.49</td><td style=\"text-align: right;\">    0.5</td><td>Flavored wheat bread baked in the hearth, own production</td><td>Bread — flavored                                               </td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>15156      </td><td style=\"text-align: right;\">                1</td><td>Bakery         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      5</td><td style=\"text-align: right;\">   15</td><td>Saturday</td><td style=\"text-align: right;\">    0.992484</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2171</td><td style=\"text-align: right;\">690818</td><td>2020-09-01 00:00:00</td><td style=\"text-align: right;\">  17.09</td><td style=\"text-align: right;\">    2  </td><td>Ciabatta, light, plain, parbaking                       </td><td>Ciabatta                                                       </td><td style=\"text-align: right;\">     8274</td><td style=\"text-align: right;\">              250  </td><td style=\"text-align: right;\">    nan</td><td>4043       </td><td style=\"text-align: right;\">              nan</td><td>Bakery         </td><td style=\"text-align: right;\">  2020</td><td style=\"text-align: right;\">      9</td><td style=\"text-align: right;\">    1</td><td>Tuesday </td><td style=\"text-align: right;\">    2.76589 </td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2542</td><td style=\"text-align: right;\">571255</td><td>2021-02-27 00:00:00</td><td style=\"text-align: right;\"> 255.59</td><td style=\"text-align: right;\">    2  </td><td>Semi-hard coarse-pored cheese                           </td><td>semi-hard cheese — More expensive — National — Available import</td><td style=\"text-align: right;\">     2737</td><td style=\"text-align: right;\">                1  </td><td style=\"text-align: right;\">    nan</td><td>1037       </td><td style=\"text-align: right;\">               33</td><td>Cheese         </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">   27</td><td>Saturday</td><td style=\"text-align: right;\">    0.763989</td></tr>\n",
              "<tr><td style=\"text-align: right;\">        2735</td><td style=\"text-align: right;\">   208</td><td>2021-02-01 00:00:00</td><td style=\"text-align: right;\">  15.59</td><td style=\"text-align: right;\">    2  </td><td>Water, sparkling                                        </td><td>Therapeutic-table water — PET — from 1 to 2 L                  </td><td style=\"text-align: right;\">     1241</td><td style=\"text-align: right;\">                1.5</td><td style=\"text-align: right;\">    nan</td><td>3670       </td><td style=\"text-align: right;\">                1</td><td>Mineral water  </td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      2</td><td style=\"text-align: right;\">    1</td><td>Monday  </td><td style=\"text-align: right;\">    4.82277 </td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "valid[\"prediction\"] = pred\n",
        "valid[[\"prediction\", \"sales\"]]\n",
        "valid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jkResu9aL32P",
        "outputId": "56994c8b-7d4e-4206-ab15-c9483063c746"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb018e38190>]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "new = valid[(valid[\"SKU\"]==32485) & (valid[\"geoCluster\"] == '2069')]\n",
        "new = new.as_data_frame().sort_values(by = \"date\")\n",
        "plt.plot(new[\"date\"],new[\"sales\"])\n",
        "plt.plot(new[\"date\"],new[\"prediction\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualize the tree"
      ],
      "metadata": {
        "id": "ARGFK6mxMOkk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "from IPython.display import Image\n",
        "\n",
        "mojo_file_name = \"/content/my_gbm_mojo.zip\"\n",
        "h2o_jar_path= '/content/tools/h2o-3/h2o-3.14.0.3/h2o.jar'\n",
        "mojo_full_path = mojo_file_name\n",
        "gv_file_path = \"/content//my_gbm_graph.gv\"\n",
        "image_file_name = \" /content/my_gbm_tree\"\n",
        "model.download_mojo(mojo_file_name)\n",
        "\n",
        "def generateTree(h2o_jar_path, mojo_full_path, gv_file_path, image_file_path, tree_id = 0):\n",
        "    image_file_path = image_file_path + \"_\" + str(tree_id) + \".png\"\n",
        "    result = subprocess.call([\"java\", \"-cp\", h2o_jar_path, \"hex.genmodel.tools.PrintMojo\", \"--tree\", str(tree_id), \"-i\", mojo_full_path , \"-o\", gv_file_path ], shell=False)\n",
        "    result = subprocess.call([\"ls\",gv_file_path], shell = False)\n",
        "    if result is 0:\n",
        "        print(\"Success: Graphviz file \" + gv_file_path + \" is generated.\")\n",
        "    else: \n",
        "        print(\"Error: Graphviz file \" + gv_file_path + \" could not be generated.\")\n",
        "\n",
        "def generateTreeImage(gv_file_path, image_file_path, tree_id):\n",
        "    image_file_path = image_file_path + \"_\" + str(tree_id) + \".png\"\n",
        "    result = subprocess.call([\"dot\", \"-Tpng\", gv_file_path, \"-o\", image_file_path], shell=False)\n",
        "    result = subprocess.call([\"ls\",image_file_path], shell = False)\n",
        "    if result is 0:\n",
        "        print(\"Success: Image File \" + image_file_path + \" is generated.\")\n",
        "        print(\"Now you can execute the follow line as-it-is to see the tree graph:\") \n",
        "        print(\"Image(filename='\" + image_file_path + \"\\')\")\n",
        "    else:\n",
        "        print(\"Error: Image file \" + image_file_path + \" could not be generated.\")\n",
        "\n",
        "generateTree(h2o_jar_path, mojo_full_path, gv_file_path, image_file_name, 3)\n",
        "generateTreeImage(gv_file_path, image_file_name, 3)\n",
        "# Just pass the Tree Image file name depending on your tree\n",
        "#Image(filename='/content/my_gbm_tree_0.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5ELL2rm1wAb",
        "outputId": "7e7c13f1-6e7b-49db-e156-e1e8c01f9cbb"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: Graphviz file /content//my_gbm_graph.gv could not be generated.\n",
            "Error: Image file  /content/my_gbm_tree_3.png could not be generated.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = pd.read_csv(\"test.csv\")\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "yf5mBwm5B3N5",
        "outputId": "9f8a4a90-6836-4465-814c-761fabba5380"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                ID  geoCluster     SKU        date  price_filled  sales\n",
              "0       RR27956447          21   32485  2021-07-08         39.69    NaN\n",
              "1       RR27956448          21   32485  2021-07-09         39.69    NaN\n",
              "2       RR27956449          21   32485  2021-07-10         39.69    NaN\n",
              "3       RR27956450          21   32485  2021-07-11         39.69    NaN\n",
              "4       RR27956451          21   32485  2021-07-12         39.69    NaN\n",
              "...            ...         ...     ...         ...           ...    ...\n",
              "202732  RR55912539        3230  711838  2021-07-15         28.49    NaN\n",
              "202733  RR55912540        3230  711838  2021-07-16         28.49    NaN\n",
              "202734  RR55912541        3230  711838  2021-07-17         28.49    NaN\n",
              "202735  RR55912542        3230  711838  2021-07-18         28.49    NaN\n",
              "202736  RR55912543        3230  711838  2021-07-19         28.49    NaN\n",
              "\n",
              "[202737 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3906ffc5-eff7-4ba4-9f65-8778e97f4684\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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</th>\n",
              "      <th>geoCluster</th>\n",
              "      <th>SKU</th>\n",
              "      <th>date</th>\n",
              "      <th>price_filled</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RR27956447</td>\n",
              "      <td>21</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-07-08</td>\n",
              "      <td>39.69</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RR27956448</td>\n",
              "      <td>21</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-07-09</td>\n",
              "      <td>39.69</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RR27956449</td>\n",
              "      <td>21</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-07-10</td>\n",
              "      <td>39.69</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RR27956450</td>\n",
              "      <td>21</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-07-11</td>\n",
              "      <td>39.69</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RR27956451</td>\n",
              "      <td>21</td>\n",
              "      <td>32485</td>\n",
              "      <td>2021-07-12</td>\n",
              "      <td>39.69</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202732</th>\n",
              "      <td>RR55912539</td>\n",
              "      <td>3230</td>\n",
              "      <td>711838</td>\n",
              "      <td>2021-07-15</td>\n",
              "      <td>28.49</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202733</th>\n",
              "      <td>RR55912540</td>\n",
              "      <td>3230</td>\n",
              "      <td>711838</td>\n",
              "      <td>2021-07-16</td>\n",
              "      <td>28.49</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202734</th>\n",
              "      <td>RR55912541</td>\n",
              "      <td>3230</td>\n",
              "      <td>711838</td>\n",
              "      <td>2021-07-17</td>\n",
              "      <td>28.49</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202735</th>\n",
              "      <td>RR55912542</td>\n",
              "      <td>3230</td>\n",
              "      <td>711838</td>\n",
              "      <td>2021-07-18</td>\n",
              "      <td>28.49</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202736</th>\n",
              "      <td>RR55912543</td>\n",
              "      <td>3230</td>\n",
              "      <td>711838</td>\n",
              "      <td>2021-07-19</td>\n",
              "      <td>28.49</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>202737 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3906ffc5-eff7-4ba4-9f65-8778e97f4684')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3906ffc5-eff7-4ba4-9f65-8778e97f4684 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3906ffc5-eff7-4ba4-9f65-8778e97f4684');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joined_test = test.join(sku.set_index('SKU'), on = 'SKU', how = 'inner')\n",
        "joined_test = joined_test.drop(['ID'], axis = 1)\n",
        "joined_test[[\"Year\", \"Month\", \"Day\"]] = joined_test.date.str.split(pat='-',expand=True)\n",
        "joined_test['date'] = pd.to_datetime(joined_test['date'])\n",
        "joined_test['dotw']= joined_test['date'].dt.day_name()\n",
        "joined_test['price'] = joined_test['price_filled']\n",
        "joined_test.drop(['price_filled'], axis = 1)\n",
        "h2o_joined_test = h2o.H2OFrame(joined_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btdY7yhmCVg-",
        "outputId": "dcc990b5-0685-4fa3-a3eb-347eeb4dc6c7"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h2o_joined_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "khLQBCXuDkI1",
        "outputId": "82f50ae7-cf47-4e68-d68c-1b1d5c962e0c"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th style=\"text-align: right;\">  geoCluster</th><th style=\"text-align: right;\">  SKU</th><th>date               </th><th style=\"text-align: right;\">  price_filled</th><th style=\"text-align: right;\">  sales</th><th>Category  </th><th>Type          </th><th style=\"text-align: right;\">  brandId</th><th style=\"text-align: right;\">  lagerUnitQuantity</th><th style=\"text-align: right;\">  Units</th><th style=\"text-align: right;\">  trademark</th><th style=\"text-align: right;\">  countryOfOrigin</th><th>Group          </th><th style=\"text-align: right;\">  Year</th><th style=\"text-align: right;\">  Month</th><th style=\"text-align: right;\">  Day</th><th>dotw     </th><th style=\"text-align: right;\">  price</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-08 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">    8</td><td>Thursday </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-09 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">    9</td><td>Friday   </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-10 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   10</td><td>Saturday </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-11 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   11</td><td>Sunday   </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-12 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   12</td><td>Monday   </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-13 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   13</td><td>Tuesday  </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-14 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   14</td><td>Wednesday</td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-15 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   15</td><td>Thursday </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-16 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   16</td><td>Friday   </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "<tr><td style=\"text-align: right;\">          21</td><td style=\"text-align: right;\">32485</td><td>2021-07-17 00:00:00</td><td style=\"text-align: right;\">         39.69</td><td style=\"text-align: right;\">    nan</td><td>Banana    </td><td>Banana — Plain</td><td style=\"text-align: right;\">      nan</td><td style=\"text-align: right;\">                  1</td><td style=\"text-align: right;\">    nan</td><td style=\"text-align: right;\">        nan</td><td style=\"text-align: right;\">              nan</td><td>Tropical fruits</td><td style=\"text-align: right;\">  2021</td><td style=\"text-align: right;\">      7</td><td style=\"text-align: right;\">   17</td><td>Saturday </td><td style=\"text-align: right;\">  39.69</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h2o_joined_test[\"Category\"] = h2o_joined_test[\"Category\"].asfactor()\n",
        "h2o_joined_test[\"countryOfOrigin\"] = h2o_joined_test[\"countryOfOrigin\"].asfactor()\n",
        "h2o_joined_test[\"trademark\"] = h2o_joined_test[\"trademark\"].asfactor()\n",
        "h2o_joined_test[\"geoCluster\"] = h2o_joined_test[\"geoCluster\"].asfactor()\n",
        "h2o_joined_test[\"Group\"] = h2o_joined_test[\"Group\"].asfactor()\n",
        "h2o_joined_test[\"Type\"] = h2o_joined_test[\"Type\"].asfactor()\n",
        "h2o_joined_test[\"dotw\"] = h2o_joined_test[\"dotw\"].asfactor()\n",
        "h2o_joined_test = h2o_joined_test.drop(['sales'], axis = 1)\n",
        "h2o_joined_test\n",
        "pred =  model.predict(h2o_joined_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CRr2Fst87oS",
        "outputId": "0e2fddcb-26ca-4153-9dbc-58cab471c3c2"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drf prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/h2o/job.py:83: UserWarning: Test/Validation dataset column 'geoCluster' has levels not trained on: [\"21\", \"298\", \"447\", \"493\", \"645\", \"1673\", \"1887\", \"2723\", \"2953\", \"3000\", \"3079\", \"3086\", \"3095\", \"3154\", \"3167\", \"3183\"]\n",
            "  warnings.warn(w)\n",
            "/usr/local/lib/python3.7/dist-packages/h2o/job.py:83: UserWarning: Test/Validation dataset column 'trademark' has levels not trained on: [\"1037.0\", \"1120.0\", \"1173.0\", \"1192.0\", \"11960.0\", \"1323.0\", \"15156.0\", \"1835.0\", \"1839.0\", \"1921.0\", ...4 not listed..., \"4043.0\", \"4384.0\", \"4891.0\", \"4970.0\", \"5070.0\", \"6133.0\", \"8802.0\", \"9666.0\", \"9974.0\", \"nan\"]\n",
            "  warnings.warn(w)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJTDf1mwMzZu",
        "outputId": "826e8018-9a6a-47af-b3ce-5aeb3947f1fb"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['geoCluster',\n",
              " 'price',\n",
              " 'Category',\n",
              " 'Type',\n",
              " 'brandId',\n",
              " 'lagerUnitQuantity',\n",
              " 'Units',\n",
              " 'trademark',\n",
              " 'countryOfOrigin',\n",
              " 'Group',\n",
              " 'Month',\n",
              " 'Year',\n",
              " 'dotw']"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Gradient Boosting Estimator"
      ],
      "metadata": {
        "id": "s5LYcN2MMYb5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from h2o.estimators import H2OGradientBoostingEstimator\n",
        "h2o.init()\n",
        "\n",
        "# Build and train the model:\n",
        "pros_gbm = H2OGradientBoostingEstimator(nfolds=5,\n",
        "                                        seed=1111,\n",
        "                                        keep_cross_validation_predictions = True)\n",
        "pros_gbm.train(x=predictors, y=response, training_frame=h2o_joined)\n",
        "\n",
        "# Eval performance:\n",
        "perf = pros_gbm.model_performance(h2o_joined)\n",
        "\n",
        "# Generate predictions on a test set (if necessary):\n",
        "pred = pros_gbm.predict(h2o_joined)\n",
        "\n",
        "# Extract feature interactions:\n",
        "feature_interactions = pros_gbm.feature_interaction()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "XFFJXRrhIYGs",
        "outputId": "71adb82c-6abf-4e26-f3e8-537c150f8046"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "--------------------------  ----------------------------------\n",
              "H2O_cluster_uptime:         3 hours 4 mins\n",
              "H2O_cluster_timezone:       Etc/UTC\n",
              "H2O_data_parsing_timezone:  UTC\n",
              "H2O_cluster_version:        3.36.1.1\n",
              "H2O_cluster_version_age:    17 days\n",
              "H2O_cluster_name:           H2O_from_python_unknownUser_bjj4b2\n",
              "H2O_cluster_total_nodes:    1\n",
              "H2O_cluster_free_memory:    2.389 Gb\n",
              "H2O_cluster_total_cores:    2\n",
              "H2O_cluster_allowed_cores:  2\n",
              "H2O_cluster_status:         locked, healthy\n",
              "H2O_connection_url:         http://localhost:54321\n",
              "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
              "H2O_internal_security:      False\n",
              "Python_version:             3.7.13 final\n",
              "--------------------------  ----------------------------------"
            ],
            "text/html": [
              "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
              "<td>3 hours 4 mins</td></tr>\n",
              "<tr><td>H2O_cluster_timezone:</td>\n",
              "<td>Etc/UTC</td></tr>\n",
              "<tr><td>H2O_data_parsing_timezone:</td>\n",
              "<td>UTC</td></tr>\n",
              "<tr><td>H2O_cluster_version:</td>\n",
              "<td>3.36.1.1</td></tr>\n",
              "<tr><td>H2O_cluster_version_age:</td>\n",
              "<td>17 days </td></tr>\n",
              "<tr><td>H2O_cluster_name:</td>\n",
              "<td>H2O_from_python_unknownUser_bjj4b2</td></tr>\n",
              "<tr><td>H2O_cluster_total_nodes:</td>\n",
              "<td>1</td></tr>\n",
              "<tr><td>H2O_cluster_free_memory:</td>\n",
              "<td>2.389 Gb</td></tr>\n",
              "<tr><td>H2O_cluster_total_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_allowed_cores:</td>\n",
              "<td>2</td></tr>\n",
              "<tr><td>H2O_cluster_status:</td>\n",
              "<td>locked, healthy</td></tr>\n",
              "<tr><td>H2O_connection_url:</td>\n",
              "<td>http://localhost:54321</td></tr>\n",
              "<tr><td>H2O_connection_proxy:</td>\n",
              "<td>{\"http\": null, \"https\": null}</td></tr>\n",
              "<tr><td>H2O_internal_security:</td>\n",
              "<td>False</td></tr>\n",
              "<tr><td>Python_version:</td>\n",
              "<td>3.7.13 final</td></tr></table></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gbm Model Build progress: |"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/h2o/estimators/estimator_base.py:208: RuntimeWarning: Dropping bad and constant columns: [Units]\n",
            "  warnings.warn(mesg[\"message\"], RuntimeWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "██████████████████████████████████████████████████████| (done) 100%\n",
            "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "perf"
      ],
      "metadata": {
        "id": "LPcrw0pMOalP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fef32865-1430-4737-c4da-df132c9ba84a"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "ModelMetricsRegression: gbm\n",
            "** Reported on test data. **\n",
            "\n",
            "MSE: 11.487788620197085\n",
            "RMSE: 3.389364043621913\n",
            "MAE: 1.7214298534019936\n",
            "RMSLE: 0.4490033562071233\n",
            "Mean Residual Deviance: 11.487788620197085\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7bNnPi-uIVig"
      }
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "hack.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}