task_1_rnn.ipynb

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  "metadata": {
    "colab": {
      "name": "lenta_rnn.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shattl/HackPromoAnalytics/blob/main/task_1_rnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlE5snY4OoeJ"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYFjMLBtOyHL"
      },
      "source": [
        "train = pd.read_csv('lenta.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJZbH9qDO1Nu"
      },
      "source": [
        "train = train[train.columns[1:]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt7NhFpdPEkZ"
      },
      "source": [
        "off = pd.read_csv('/content/20210521_offers.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "OVu-zN40PIYW",
        "outputId": "b138ca58-b5c1-45d5-9ca0-bf259acb848c"
      },
      "source": [
        "off"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Promo_type</th>\n",
              "      <th>Offer_ID</th>\n",
              "      <th>sku</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>train_test_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>71c770923102af052f64c6036c9dd590</td>\n",
              "      <td>20200213</td>\n",
              "      <td>20200219</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F2</td>\n",
              "      <td>e87788b585ead792a4d40f7de18f2f76</td>\n",
              "      <td>20200102</td>\n",
              "      <td>20200108</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F9</td>\n",
              "      <td>5ae92b5e465d630285677e80b43fe5f7</td>\n",
              "      <td>20200116</td>\n",
              "      <td>20200122</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F45</td>\n",
              "      <td>0790b472112a765339f0e5c3f66816ee</td>\n",
              "      <td>20200319</td>\n",
              "      <td>20200325</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F191</td>\n",
              "      <td>e87788b585ead792a4d40f7de18f2f76</td>\n",
              "      <td>20201112</td>\n",
              "      <td>20201118</td>\n",
              "      <td>test</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>9771</th>\n",
              "      <td>Biweekly</td>\n",
              "      <td>683</td>\n",
              "      <td>0f368be7897dd1531e43dc37115eeaf0</td>\n",
              "      <td>20191114</td>\n",
              "      <td>20191127</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9772</th>\n",
              "      <td>Biweekly</td>\n",
              "      <td>691</td>\n",
              "      <td>47c28bf957fe120f2d016e46911e5bdb</td>\n",
              "      <td>20191114</td>\n",
              "      <td>20191127</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9773</th>\n",
              "      <td>Biweekly</td>\n",
              "      <td>683</td>\n",
              "      <td>00432d021eca6059fee2ed69a732c2b2</td>\n",
              "      <td>20191114</td>\n",
              "      <td>20191127</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9774</th>\n",
              "      <td>Biweekly</td>\n",
              "      <td>690</td>\n",
              "      <td>40d78b3648a4ea15f300070a0ffd9752</td>\n",
              "      <td>20191114</td>\n",
              "      <td>20191127</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9775</th>\n",
              "      <td>Biweekly</td>\n",
              "      <td>687</td>\n",
              "      <td>0d73ba0d16f74777b696657bf69a9ef0</td>\n",
              "      <td>20191114</td>\n",
              "      <td>20191127</td>\n",
              "      <td>train</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9776 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Promo_type Offer_ID  ...  end_date  train_test_group\n",
              "0       Facades      F26  ...  20200219             train\n",
              "1       Facades       F2  ...  20200108             train\n",
              "2       Facades       F9  ...  20200122             train\n",
              "3       Facades      F45  ...  20200325             train\n",
              "4       Facades     F191  ...  20201118              test\n",
              "...         ...      ...  ...       ...               ...\n",
              "9771   Biweekly      683  ...  20191127             train\n",
              "9772   Biweekly      691  ...  20191127             train\n",
              "9773   Biweekly      683  ...  20191127             train\n",
              "9774   Biweekly      690  ...  20191127             train\n",
              "9775   Biweekly      687  ...  20191127             train\n",
              "\n",
              "[9776 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bu2CUSGPI8d"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qv0dso-P3DO1"
      },
      "source": [
        "off.start_date = pd.to_datetime(off.start_date.map(lambda x: str(x)[:4]+ '-' + str(x)[4:6]+'-' +str(x)[6:]))\n",
        "off.end_date = pd.to_datetime(off.end_date.map(lambda x: str(x)[:4]+ '-' + str(x)[4:6]+'-' +str(x)[6:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdkL-HXs3DPE"
      },
      "source": [
        "off_train, off_test = off[off.train_test_group == 'train'].drop(['train_test_group'], axis=1), off[off.train_test_group == 'test'].drop(['train_test_group'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6jt7ACJPjkB"
      },
      "source": [
        "train = train.merge(off_train[['Offer_ID', 'start_date', 'end_date']].drop_duplicates(), on=['Offer_ID'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "mkhQfwz2Pq7T",
        "outputId": "ba6ab445-d579-4cd1-fe04-4b54216ad100"
      },
      "source": [
        "off_train[off_train['Offer_ID'] == 'F26']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Promo_type</th>\n",
              "      <th>Offer_ID</th>\n",
              "      <th>sku</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>71c770923102af052f64c6036c9dd590</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>b178ad83d2ed46e4ca3b7d6bf3e50baa</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>bc169501599f610c529db4962942d80d</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>bc5f3166f87117e513a68ba6bad1d2da</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>aa4dfbef8de268ea041b073c0b06c460</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>3dccf48e3bb5c393c5c8c7d479b8395b</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>e015fab8b16757eacf67128138d35569</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>ddeb01bce36693b6f1d3bfec12c8b1e5</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>9d34169d44a6dbbd3f00f6a734805a5f</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>652832505970c306c01774f74b0a92de</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>cf7b6210471e7ceb441878b7807679ee</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>6907aa7b733ab2874e3b57a0c1ed5007</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>3d4a0f26b31151b6b6a9d2e47aab896d</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>17305c71018699ab2184d3dc41c1ee3e</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>558</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>c0421baadeade309fdbd1e00fc8cf86c</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>4e345989f6052be4235fb89293c682d2</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>789</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>268a85ca34f0675e9605160c2f1a33fc</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>f6b72300227d7ab53699f5f39905dba5</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>939</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F26</td>\n",
              "      <td>ceb7a3e97b8f1f038ad475fde4efd28a</td>\n",
              "      <td>2020-02-13</td>\n",
              "      <td>2020-02-19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Promo_type Offer_ID                               sku start_date   end_date\n",
              "0      Facades      F26  71c770923102af052f64c6036c9dd590 2020-02-13 2020-02-19\n",
              "112    Facades      F26  b178ad83d2ed46e4ca3b7d6bf3e50baa 2020-02-13 2020-02-19\n",
              "156    Facades      F26  bc169501599f610c529db4962942d80d 2020-02-13 2020-02-19\n",
              "163    Facades      F26  bc5f3166f87117e513a68ba6bad1d2da 2020-02-13 2020-02-19\n",
              "204    Facades      F26  aa4dfbef8de268ea041b073c0b06c460 2020-02-13 2020-02-19\n",
              "228    Facades      F26  3dccf48e3bb5c393c5c8c7d479b8395b 2020-02-13 2020-02-19\n",
              "247    Facades      F26  e015fab8b16757eacf67128138d35569 2020-02-13 2020-02-19\n",
              "254    Facades      F26  ddeb01bce36693b6f1d3bfec12c8b1e5 2020-02-13 2020-02-19\n",
              "258    Facades      F26  9d34169d44a6dbbd3f00f6a734805a5f 2020-02-13 2020-02-19\n",
              "266    Facades      F26  652832505970c306c01774f74b0a92de 2020-02-13 2020-02-19\n",
              "267    Facades      F26  cf7b6210471e7ceb441878b7807679ee 2020-02-13 2020-02-19\n",
              "315    Facades      F26  6907aa7b733ab2874e3b57a0c1ed5007 2020-02-13 2020-02-19\n",
              "413    Facades      F26  3d4a0f26b31151b6b6a9d2e47aab896d 2020-02-13 2020-02-19\n",
              "487    Facades      F26  17305c71018699ab2184d3dc41c1ee3e 2020-02-13 2020-02-19\n",
              "558    Facades      F26  c0421baadeade309fdbd1e00fc8cf86c 2020-02-13 2020-02-19\n",
              "711    Facades      F26  4e345989f6052be4235fb89293c682d2 2020-02-13 2020-02-19\n",
              "789    Facades      F26  268a85ca34f0675e9605160c2f1a33fc 2020-02-13 2020-02-19\n",
              "859    Facades      F26  f6b72300227d7ab53699f5f39905dba5 2020-02-13 2020-02-19\n",
              "939    Facades      F26  ceb7a3e97b8f1f038ad475fde4efd28a 2020-02-13 2020-02-19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "LJtpfRG1QJch",
        "outputId": "d6a227f0-efe3-4bd8-b9e3-b4043ec9967d"
      },
      "source": [
        "off_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Promo_type</th>\n",
              "      <th>Offer_ID</th>\n",
              "      <th>sku</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F191</td>\n",
              "      <td>e87788b585ead792a4d40f7de18f2f76</td>\n",
              "      <td>2020-11-12</td>\n",
              "      <td>2020-11-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Billboards</td>\n",
              "      <td>B86</td>\n",
              "      <td>cdab3403355b7fda81a1329b2f05cdec</td>\n",
              "      <td>2020-10-29</td>\n",
              "      <td>2020-11-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F172</td>\n",
              "      <td>723dad25536c2cf3fb4fd3f577ee2b7c</td>\n",
              "      <td>2020-10-15</td>\n",
              "      <td>2020-10-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Billboards</td>\n",
              "      <td>B96</td>\n",
              "      <td>9817112604594b376adf135efe787bbc</td>\n",
              "      <td>2020-12-03</td>\n",
              "      <td>2020-12-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Facades</td>\n",
              "      <td>F173</td>\n",
              "      <td>b8169608d99f3a31891379d394735732</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>2020-10-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7861</th>\n",
              "      <td>Seasonal</td>\n",
              "      <td>99</td>\n",
              "      <td>fc2fe174d3547a2d077bb94742977bdb</td>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>2020-10-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8003</th>\n",
              "      <td>Billboards</td>\n",
              "      <td>B140</td>\n",
              "      <td>7a8675a8f6988ebcbefd062fabaccc85</td>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>2020-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8004</th>\n",
              "      <td>Billboards</td>\n",
              "      <td>B140</td>\n",
              "      <td>787762f7d829ce318599bf015baba175</td>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>2020-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8008</th>\n",
              "      <td>Billboards</td>\n",
              "      <td>B140</td>\n",
              "      <td>c8ac520562ba934dd67cbf0f060c4252</td>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>2020-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8010</th>\n",
              "      <td>Billboards</td>\n",
              "      <td>B140</td>\n",
              "      <td>5abc0869d756fa1c6831651776138baa</td>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>2020-12-30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2060 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Promo_type Offer_ID  ... start_date   end_date\n",
              "4        Facades     F191  ... 2020-11-12 2020-11-18\n",
              "6     Billboards      B86  ... 2020-10-29 2020-11-11\n",
              "18       Facades     F172  ... 2020-10-15 2020-10-21\n",
              "23    Billboards      B96  ... 2020-12-03 2020-12-09\n",
              "26       Facades     F173  ... 2020-10-14 2020-10-21\n",
              "...          ...      ...  ...        ...        ...\n",
              "7861    Seasonal       99  ... 2020-10-06 2020-10-19\n",
              "8003  Billboards     B140  ... 2020-12-24 2020-12-30\n",
              "8004  Billboards     B140  ... 2020-12-24 2020-12-30\n",
              "8008  Billboards     B140  ... 2020-12-24 2020-12-30\n",
              "8010  Billboards     B140  ... 2020-12-24 2020-12-30\n",
              "\n",
              "[2060 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8Hv67GGRcDM"
      },
      "source": [
        "test = off_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gDV1KbbQtLO"
      },
      "source": [
        "hie = pd.read_csv('/content/20210518_hierarchy.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ot-dwlvYs1h"
      },
      "source": [
        "new_hie = hie.drop(['hierarchy_level1','hierarchy_level3', 'hierarchy_level4'], axis=1).drop_duplicates()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccFiJDH0arZ2"
      },
      "source": [
        "d_categories = {}\n",
        "for sku, category in zip(new_hie['sku'], new_hie['hierarchy_level2']):\n",
        "  d_categories[sku] = category"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWj25cC03DPH"
      },
      "source": [
        "test['category'] = test.sku.map(lambda x: d_categories[x])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hW2ao3wl3DPH"
      },
      "source": [
        "test = test.drop(['sku'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMkFVoFY3DPI"
      },
      "source": [
        "days = []\n",
        "for start, end in zip(test['start_date'], test['end_date']):\n",
        "  days.append((end - start).days + 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JKvHIbZ3DPI"
      },
      "source": [
        "test['days'] = days"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqXUmx9G3d0D"
      },
      "source": [
        "test_hot = pd.get_dummies(test.drop(['Offer_ID'], axis=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WioYcoj13d0D"
      },
      "source": [
        "test_hot['Offer_ID'] = test['Offer_ID']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cd5RPGN3d0E"
      },
      "source": [
        "main_columns = ['start_date', 'end_date', 'Promo_type_Billboards', 'Promo_type_Biweekly', 'Promo_type_Facades', 'Promo_type_Seasonal', 'Offer_ID', 'days']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJP48Rra3d0E"
      },
      "source": [
        "test_group = test_hot.groupby(['Offer_ID']).agg({col: ['sum'] for col in set(test_hot.columns) - set(main_columns)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYFlqYCf3d0E"
      },
      "source": [
        "offer_count = test.groupby(['Offer_ID']).Offer_ID.count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upFUuRTa3d0E"
      },
      "source": [
        "sub_d = {}\n",
        "sub_d['Offer_ID'] = list(test_group.index)\n",
        "for col in set(test_hot.columns) - set(main_columns):\n",
        "  sub_d[col] = test_group[col]['sum']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fipjatY3d0E"
      },
      "source": [
        "sub_test = pd.DataFrame(sub_d)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqyGTszF3d0F"
      },
      "source": [
        "sub_test.index = range(149)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TxvWB2d3d0F"
      },
      "source": [
        "ready_test = test_hot[main_columns].drop_duplicates().merge(sub_test, on=['Offer_ID'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EO0RuHp03d0F"
      },
      "source": [
        "ready_test = ready_test.merge(pd.DataFrame({'count': list(offer_count), 'Offer_ID': list(offer_count.index)}, index=range(149)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "ElIEvov9Rjk8",
        "outputId": "eec1ac34-81b3-43d8-e115-278f3d8a9419"
      },
      "source": [
        "ready_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>start_date</th>\n",
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              "      <th>Promo_type_Billboards</th>\n",
              "      <th>Promo_type_Biweekly</th>\n",
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              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-11-12</td>\n",
              "      <td>2020-11-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>18</td>\n",
              "    </tr>\n",
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              "      <td>2020-10-21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-12-03</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>2020-10-21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>F173</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>2020-10-19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>95</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>2020-10-19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>2020-10-19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>2020-10-19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>97</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2020-12-24</td>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>B140</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 42 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    start_date   end_date  ...  category_17e62166fc8586dfa4d1bc0e1742c08b  count\n",
              "0   2020-11-12 2020-11-18  ...                                          0     22\n",
              "1   2020-10-29 2020-11-11  ...                                          0     18\n",
              "2   2020-10-15 2020-10-21  ...                                          0      6\n",
              "3   2020-12-03 2020-12-09  ...                                          0      3\n",
              "4   2020-10-14 2020-10-21  ...                                          0     23\n",
              "..         ...        ...  ...                                        ...    ...\n",
              "144 2020-10-06 2020-10-19  ...                                          0     21\n",
              "145 2020-10-06 2020-10-19  ...                                          0     79\n",
              "146 2020-10-06 2020-10-19  ...                                          0     12\n",
              "147 2020-10-06 2020-10-19  ...                                          0      2\n",
              "148 2020-12-24 2020-12-30  ...                                          0      4\n",
              "\n",
              "[149 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiJJvgdZRkuO"
      },
      "source": [
        "train = train[list(set(train.columns) & set(ready_test.columns) | set(['UpLift']))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWhv_lRETFvk"
      },
      "source": [
        "ready_test = ready_test[list(set(train.columns) & set(ready_test.columns))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dw4yWSGXTKFh"
      },
      "source": [
        "train = train.sort_values(['start_date']).drop(['Offer_ID'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9SPdDZygJpY",
        "outputId": "3f5da806-8319-473d-9dc2-a193d00a7c3e"
      },
      "source": [
        "train[592:]['start_date']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "654   2020-06-29\n",
              "653   2020-06-29\n",
              "475   2020-06-29\n",
              "664   2020-06-29\n",
              "214   2020-06-29\n",
              "         ...    \n",
              "546   2020-09-10\n",
              "120   2020-09-10\n",
              "557   2020-09-10\n",
              "287   2020-09-10\n",
              "609   2020-09-10\n",
              "Name: start_date, Length: 148, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rz6Onv68kI6d"
      },
      "source": [
        "train['month'] = train['start_date'].map(lambda x: x.month)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWaaRLOHp5cn"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range = (0, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPbScii2q1BU"
      },
      "source": [
        "train = train[list(set(train.columns) - set(['UpLift'])) + ['UpLift']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfBeO9tlp2EZ"
      },
      "source": [
        "train_scaled = scaler.fit_transform(train.drop(['start_date', 'end_date'], axis=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtvNeyAOqcVZ",
        "outputId": "0f9bd232-2ff4-4579-a6fb-a36d6d6d62d1"
      },
      "source": [
        "list(train.columns).index('UpLift')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdfNBcnVTbFF"
      },
      "source": [
        "import numpy as np\n",
        "X_train, y_train, X_val, y_val = np.array(train_scaled[:592, :-1]), np.array(train_scaled[:592, -1]), np.array(train_scaled[592:, :-1]), np.array(train_scaled[592:, -1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIzogCGljeBn"
      },
      "source": [
        "need = ['count', 'days', 'Promo_type_Biweekly', 'Promo_type_Seasonal', 'Promo_type_Facades', 'Promo_type_Billboards']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILFzoK_LjM7U"
      },
      "source": [
        "X_train, y_train, X_val, y_val = np.array(train[need][:592]), np.array(train['UpLift'][:592]), np.array(train[need][592:]), np.array(train['UpLift'][592:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "njKpbflmUXYb",
        "outputId": "7ca49b8e-5dc7-4f62-cdfd-222551885f1c"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(592,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xgoMcwwVCRR"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkPUqITipeKG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyX7pfFBVj0K"
      },
      "source": [
        "BATCH_SIZE = 256\n",
        "BUFFER_SIZE = 10000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bC7-cqUjVS2z"
      },
      "source": [
        "train_data_multi = tf.data.Dataset.from_tensor_slices((X_train.reshape(X_train.shape[0],  X_train.shape[1], 1), y_train))\n",
        "train_data_multi = train_data_multi.cache().batch(BATCH_SIZE).repeat()\n",
        "\n",
        "val_data_multi = tf.data.Dataset.from_tensor_slices((X_val.reshape(X_val.shape[0], X_val.shape[1], 1), y_val))\n",
        "val_data_multi = val_data_multi.batch(BATCH_SIZE).repeat()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ee0E0DGwVkWP"
      },
      "source": [
        "multi_step_model = tf.keras.models.Sequential()\n",
        "multi_step_model.add(tf.keras.layers.LSTM(32,\n",
        "                                          return_sequences=True,\n",
        "                                          input_shape=[X_train.shape[1], 1]))\n",
        "multi_step_model.add(tf.keras.layers.LSTM(16, activation='relu'))\n",
        "multi_step_model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQX-TWwPduet"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8RgRmWGbQ2r"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=50, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=50, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=50))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(units = 1))\n",
        "model.compile(optimizer = 'adam', loss = 'mae')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AhfZfvBWxTR"
      },
      "source": [
        "EVALUATION_INTERVAL = 300\n",
        "EPOCHS = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NuF6q01BWE7h",
        "outputId": "5eb8468c-d35b-455b-d187-baaab8832141"
      },
      "source": [
        "multi_step_history = multi_step_model.fit(train_data_multi, epochs=EPOCHS,\n",
        "                                          steps_per_epoch=EVALUATION_INTERVAL,\n",
        "                                          validation_data=val_data_multi,\n",
        "                                          validation_steps=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "300/300 [==============================] - 15s 44ms/step - loss: 0.1360 - val_loss: 0.1102\n",
            "Epoch 2/10\n",
            "300/300 [==============================] - 12s 40ms/step - loss: 0.1299 - val_loss: 0.1095\n",
            "Epoch 3/10\n",
            "300/300 [==============================] - 12s 42ms/step - loss: 0.1278 - val_loss: 0.1115\n",
            "Epoch 4/10\n",
            "300/300 [==============================] - 13s 42ms/step - loss: 0.1257 - val_loss: 0.1114\n",
            "Epoch 5/10\n",
            "300/300 [==============================] - 13s 43ms/step - loss: 0.1237 - val_loss: 0.1110\n",
            "Epoch 6/10\n",
            "300/300 [==============================] - 13s 42ms/step - loss: 0.1221 - val_loss: 0.1132\n",
            "Epoch 7/10\n",
            "300/300 [==============================] - 13s 42ms/step - loss: 0.1207 - val_loss: 0.1128\n",
            "Epoch 8/10\n",
            "300/300 [==============================] - 13s 42ms/step - loss: 0.1193 - val_loss: 0.1097\n",
            "Epoch 9/10\n",
            "300/300 [==============================] - 13s 43ms/step - loss: 0.1179 - val_loss: 0.1067\n",
            "Epoch 10/10\n",
            "300/300 [==============================] - 13s 44ms/step - loss: 0.1165 - val_loss: 0.1145\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pwpBpMBWx_T",
        "outputId": "a16e6e7b-311f-4c05-a883-c4664f96d6b5"
      },
      "source": [
        "train_data_multi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<RepeatDataset shapes: ((None, 37, 1), (None,)), types: (tf.float64, tf.float64)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGlk8l4HXBLY",
        "outputId": "8e2728e4-57e9-402b-b325-a431abe9d7b4"
      },
      "source": [
        "X_train.shape[-2:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(592, 37)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnmRcAyxhdWN"
      },
      "source": [
        "X, y = np.array(train.drop(['UpLift', 'start_date', 'end_date'], axis=1)), np.array(train['UpLift'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouYhAGoxit_7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69ijylX3XQy8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_train_history(history, title):\n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "\n",
        "  epochs = range(len(loss))\n",
        "\n",
        "  plt.figure()\n",
        "\n",
        "  plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "  plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "  plt.title(title)\n",
        "  plt.legend()\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "eV45DWngXxWf",
        "outputId": "72c4a038-8671-4689-fa5b-4ef0f89258e0"
      },
      "source": [
        "plot_train_history(multi_step_history, 'Multi-Step Training and validation loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4K9EnqAaDzZ",
        "outputId": "c54d7e48-e1e5-44e4-f021-39b66f1ef446"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['category_9bf31c7ff062936a96d3c8bd1f8f2ff3',\n",
              "       'category_a5bfc9e07964f8dddeb95fc584cd965d',\n",
              "       'category_b6d767d2f8ed5d21a44b0e5886680cb9',\n",
              "       'category_1ff1de774005f8da13f42943881c655f',\n",
              "       'category_44f683a84163b3523afe57c2e008bc8c', 'count',\n",
              "       'category_fbd7939d674997cdb4692d34de8633c4',\n",
              "       'category_f457c545a9ded88f18ecee47145a72c0',\n",
              "       'category_34173cb38f07f89ddbebc2ac9128303f',\n",
              "       'category_6512bd43d9caa6e02c990b0a82652dca',\n",
              "       'category_a5771bce93e200c36f7cd9dfd0e5deaa',\n",
              "       'category_33e75ff09dd601bbe69f351039152189',\n",
              "       'category_6f4922f45568161a8cdf4ad2299f6d23',\n",
              "       'category_37693cfc748049e45d87b8c7d8b9aacd', 'start_date',\n",
              "       'Promo_type_Biweekly', 'category_d82c8d1619ad8176d665453cfb2e55f0',\n",
              "       'category_6c8349cc7260ae62e3b1396831a8398f', 'Promo_type_Seasonal',\n",
              "       'category_6ea9ab1baa0efb9e19094440c317e21b', 'Promo_type_Facades',\n",
              "       'category_a1d0c6e83f027327d8461063f4ac58a6',\n",
              "       'category_c16a5320fa475530d9583c34fd356ef5',\n",
              "       'category_c51ce410c124a10e0db5e4b97fc2af39', 'UpLift', 'end_date',\n",
              "       'category_d645920e395fedad7bbbed0eca3fe2e0',\n",
              "       'category_fc490ca45c00b1249bbe3554a4fdf6fb',\n",
              "       'category_e369853df766fa44e1ed0ff613f563bd',\n",
              "       'category_1c383cd30b7c298ab50293adfecb7b18',\n",
              "       'category_f7177163c833dff4b38fc8d2872f1ec6',\n",
              "       'category_8e296a067a37563370ded05f5a3bf3ec',\n",
              "       'category_182be0c5cdcd5072bb1864cdee4d3d6e',\n",
              "       'category_d67d8ab4f4c10bf22aa353e27879133c',\n",
              "       'category_19ca14e7ea6328a42e0eb13d585e4c22', 'Promo_type_Billboards',\n",
              "       'category_c20ad4d76fe97759aa27a0c99bff6710', 'days',\n",
              "       'category_6364d3f0f495b6ab9dcf8d3b5c6e0b01',\n",
              "       'category_17e62166fc8586dfa4d1bc0e1742c08b'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-pVKtPBjZ-M",
        "outputId": "94cefdee-5bf8-41b2-a4f2-744da5980d47"
      },
      "source": [
        "ready_test['month'] = ready_test['start_date'].map(lambda x: x.month)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiUlVUG2sA5c"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range = (0, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "1e4FdaQTsBrC",
        "outputId": "4c1bee83-d6fc-44db-f2f8-962174bda4fa"
      },
      "source": [
        "train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>2019-09-24</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>2019-10-07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>15.601001</td>\n",
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              "    <tr>\n",
              "      <th>725</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019-09-24</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019-10-07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>19.979078</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>546</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2554</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2554.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.375943</td>\n",
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              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>337</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-10</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.436841</td>\n",
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              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>3.088375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>605</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-10</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>298.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.875797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1126</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-10</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-09-16</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>2.453359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>740 rows × 41 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     category_9bf31c7ff062936a96d3c8bd1f8f2ff3  ...     UpLift\n",
              "685                                        0.0  ...   8.690918\n",
              "568                                        0.0  ...  17.189989\n",
              "723                                        0.0  ...   6.699233\n",
              "724                                        0.0  ...  15.601001\n",
              "725                                        0.0  ...  19.979078\n",
              "..                                         ...  ...        ...\n",
              "546                                        0.0  ...   0.375943\n",
              "120                                        0.0  ...   0.436841\n",
              "557                                        0.0  ...   3.088375\n",
              "287                                        0.0  ...   0.875797\n",
              "609                                        0.0  ...   2.453359\n",
              "\n",
              "[740 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "HGtc3j7Ymjz5",
        "outputId": "d1abe41b-4eda-48c4-92fe-75eb6fa8d630"
      },
      "source": [
        "ready_test[train.drop(['UpLift', 'start_date', 'end_date'], axis=1).columns]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 38 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     category_9bf31c7ff062936a96d3c8bd1f8f2ff3  ...  month\n",
              "0                                            0  ...     11\n",
              "1                                            0  ...     10\n",
              "2                                            0  ...     10\n",
              "3                                            0  ...     12\n",
              "4                                            0  ...     10\n",
              "..                                         ...  ...    ...\n",
              "144                                          0  ...     10\n",
              "145                                          0  ...     10\n",
              "146                                          0  ...     10\n",
              "147                                          0  ...     10\n",
              "148                                          0  ...     12\n",
              "\n",
              "[149 rows x 38 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lXDYfQXmv3z"
      },
      "source": [
        "train_scaled = scaler.fit_transform(train.drop(['start_date', 'end_date'], axis=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5tLMintmy6V"
      },
      "source": [
        "X, y = np.array(train.drop(['UpLift', 'start_date', 'end_date'], axis=1)), np.array(train['UpLift'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMYnb4x9m9Uc"
      },
      "source": [
        "X_test = ready_test[train.drop(['UpLift',  'end_date'], axis=1).columns]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Np0HHwEMnBtO",
        "outputId": "22295aff-bb30-4aec-aa1e-0dfc8c6f4e2f"
      },
      "source": [
        "X_test['UpLift'] = np.zeros(149)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3TjBfdMtILj"
      },
      "source": [
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkggGq3rnKgq"
      },
      "source": [
        "BATCH_SIZE = 256\n",
        "BUFFER_SIZE = 10000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ViM6jk8ynKgr"
      },
      "source": [
        "train_data_multi = tf.data.Dataset.from_tensor_slices((X.reshape(X.shape[0],  X.shape[1], 1), y))\n",
        "train_data_multi = train_data_multi.cache().batch(BATCH_SIZE).repeat()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fr-8bfq0nKgr"
      },
      "source": [
        "multi_step_model = tf.keras.models.Sequential()\n",
        "multi_step_model.add(tf.keras.layers.LSTM(32,\n",
        "                                          return_sequences=True,\n",
        "                                          input_shape=[X.shape[1], 1]))\n",
        "multi_step_model.add(tf.keras.layers.LSTM(16, activation='relu'))\n",
        "multi_step_model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2Hcue-OnKgs",
        "outputId": "934c95e7-2b72-4571-b9c9-5d3eda42c770"
      },
      "source": [
        "multi_step_history = multi_step_model.fit(train_data_multi, epochs=5,\n",
        "                                          steps_per_epoch=EVALUATION_INTERVAL,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "300/300 [==============================] - 16s 43ms/step - loss: 4.2537\n",
            "Epoch 2/5\n",
            "300/300 [==============================] - 13s 44ms/step - loss: 4.0118\n",
            "Epoch 3/5\n",
            "300/300 [==============================] - 13s 44ms/step - loss: 3.7581\n",
            "Epoch 4/5\n",
            "300/300 [==============================] - 13s 43ms/step - loss: 3.5963\n",
            "Epoch 5/5\n",
            "300/300 [==============================] - 14s 46ms/step - loss: 3.5022\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Qz8Wri3tR3b"
      },
      "source": [
        "X_test = np.array(X_test.sort_values(['start_date']).drop(['start_date'], axis=1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGJ8EZnAnXQ-"
      },
      "source": [
        "y_pred = multi_step_model.predict(X_test.reshape(X_test.shape[0],  X_test.shape[1], 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQk7uaW3oCEU",
        "outputId": "5e749f60-dfa6-4ef1-a587-fffe0159563e"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [ 4.3430409e+00],\n",
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              "       [ 3.8778226e+00]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M49koonQoHhv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHiYs8xj-EKY"
      },
      "source": [
        "res_df = pd.DataFrame({'Offer_ID': ready_test.sort_values(['start_date'])['Offer_ID'], 'UpLift': y_pred.reshape(149)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWAQNAJV-KJL"
      },
      "source": [
        "ss = pd.read_csv('/content/20210521_sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hsx8CZ2r-30C"
      },
      "source": [
        "res_df = ss.merge(res_df, on=['Offer_ID']).drop(['UpLift_x'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozlIh-Fv-9k6"
      },
      "source": [
        "res_df.columns = ['Offer_ID', 'UpLift']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0EUQLjr_Yb0"
      },
      "source": [
        "res_df.to_csv('submit_lenta_9.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "85UdhD1L_dqB",
        "outputId": "97ddf444-3f7f-4a7c-c606-7f59be040773"
      },
      "source": [
        "pd.read_csv('/content/submit_lenta_9.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Offer_ID</th>\n",
              "      <th>UpLift</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>386</td>\n",
              "      <td>3.625404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>182</td>\n",
              "      <td>6.088385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>173</td>\n",
              "      <td>2.699683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>375</td>\n",
              "      <td>6.274321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>379</td>\n",
              "      <td>1.931651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>536</td>\n",
              "      <td>2.051840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>99</td>\n",
              "      <td>4.550816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>100</td>\n",
              "      <td>5.047436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>95</td>\n",
              "      <td>2.225247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>97</td>\n",
              "      <td>3.955635</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Offer_ID    UpLift\n",
              "0        386  3.625404\n",
              "1        182  6.088385\n",
              "2        173  2.699683\n",
              "3        375  6.274321\n",
              "4        379  1.931651\n",
              "..       ...       ...\n",
              "144      536  2.051840\n",
              "145       99  4.550816\n",
              "146      100  5.047436\n",
              "147       95  2.225247\n",
              "148       97  3.955635\n",
              "\n",
              "[149 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbE_4qtW_06p"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jpQjqvw_t3F"
      },
      "source": [
        "off = pd.read_csv('/content/20210521_offers.csv')\n",
        "upl = pd.read_csv('/content/20210518_uplift.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMywzqsHADeA"
      },
      "source": [
        "off.start_date = pd.to_datetime(off.start_date.map(lambda x: str(x)[:4] + '-'+str(x)[4:6]+'-'+str(x)[6:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2MCryMEAXyR"
      },
      "source": [
        "off.end_date = pd.to_datetime(off.end_date.map(lambda x: str(x)[:4] + '-'+str(x)[4:6]+'-'+str(x)[6:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilV3FxyaAoL5"
      },
      "source": [
        "train = off[off.train_test_group == 'train'].drop(['train_test_group'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osLM6BgTAwGu"
      },
      "source": [
        "train = train.drop(['sku'], axis=1).drop_duplicates().sort_values(['start_date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhCBL-OoA8AZ"
      },
      "source": [
        "train = train.merge(upl, on=['Offer_ID']).sort_values(['start_date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-T8Ph3ky448"
      },
      "source": [
        "import numpy as np\n",
        "train_uplift = np.array(train['UpLift'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "tZjQsdWn2M_4",
        "outputId": "723edb69-4d29-4082-93fe-d2c014725bca"
      },
      "source": [
        "plt.plot(train['start_date'][::2], train_uplift[::2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd3d50cc0d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 297
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRtnrpy84Uz2"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range = (0, 1))\n",
        "train_scale = scaler.fit_transform(np.array(train_uplift).reshape(-1, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xRw7aZC2NuY"
      },
      "source": [
        "X_time, y_time = [], []\n",
        "for i in range(24, 740):\n",
        "    X_time.append(train_uplift[i-24:i])\n",
        "    y_time.append(train_uplift[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7KWGFvU5K5h"
      },
      "source": [
        "X_time, y_time = np.array(X_time), np.array(y_time)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b50gBwrO4513"
      },
      "source": [
        "single_step_model = tf.keras.models.Sequential()\n",
        "single_step_model.add(tf.keras.layers.LSTM(32,\n",
        "                                           input_shape=[X_time.shape[1], 1]))\n",
        "single_step_model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "single_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='mae')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Od7mYi9N5aVZ"
      },
      "source": [
        "train_data_single = tf.data.Dataset.from_tensor_slices((X_time.reshape(X_time.shape[0], X_time.shape[1], 1), y_time))\n",
        "train_data_single = train_data_single.cache().shuffle(1000).batch(256).repeat()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3It5t095HV6",
        "outputId": "cbb30fab-c2ce-4468-d407-d7e4a2f0aa4c"
      },
      "source": [
        "single_step_history = single_step_model.fit(train_data_single, epochs=10,\n",
        "                                            steps_per_epoch=300,)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "300/300 [==============================] - 9s 4ms/step - loss: 4.2656\n",
            "Epoch 2/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 4.1346\n",
            "Epoch 3/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 4.0237\n",
            "Epoch 4/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.9330\n",
            "Epoch 5/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.8506\n",
            "Epoch 6/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.7723\n",
            "Epoch 7/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.6909\n",
            "Epoch 8/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.6083\n",
            "Epoch 9/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.5262\n",
            "Epoch 10/10\n",
            "300/300 [==============================] - 1s 4ms/step - loss: 3.4384\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58hcIB2U5g3P"
      },
      "source": [
        "X_test = y_time[-24:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6QMaJryN6bV9",
        "outputId": "84e83991-afcc-4ad2-e2b1-fb407f66028b"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ToHbU5Y6d2m",
        "outputId": "133f3573-bbbc-4535-ab47-c1e8cb23b63e"
      },
      "source": [
        "y_pred = []\n",
        "for i in range(149):\n",
        "  pred = single_step_model.predict(X_test.reshape(1, X_test.shape[0], 1).astype(float))\n",
        "  print(pred)\n",
        "  y_pred.append(pred[0][0])\n",
        "  X_test = np.array(list(X_test)[1:] + list(pred[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-1.5169643]]\n",
            "[[0.56931394]]\n",
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            "[[2.1480901]]\n",
            "[[4.611166]]\n",
            "[[6.3253493]]\n",
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            "[[0.03320919]]\n",
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            "[[0.44125473]]\n",
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            "[[1.8334503]]\n",
            "[[6.552726]]\n",
            "[[6.132186]]\n",
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            "[[2.740086]]\n",
            "[[2.2306287]]\n",
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            "[[2.615209]]\n",
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            "[[-0.03062882]]\n",
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            "[[3.3237376]]\n",
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            "[[2.5159125]]\n",
            "[[6.102037]]\n",
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            "[[4.956606]]\n",
            "[[9.197537]]\n",
            "[[7.695013]]\n",
            "[[9.29369]]\n",
            "[[5.4254494]]\n",
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            "[[3.0036342]]\n",
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            "[[2.6902785]]\n",
            "[[6.7428403]]\n",
            "[[2.639331]]\n",
            "[[6.6725974]]\n",
            "[[2.5256958]]\n",
            "[[6.571551]]\n",
            "[[2.5487022]]\n",
            "[[6.620137]]\n",
            "[[2.9479399]]\n",
            "[[7.4163594]]\n",
            "[[4.065956]]\n",
            "[[8.347542]]\n",
            "[[4.024358]]\n",
            "[[8.055912]]\n",
            "[[3.6890597]]\n",
            "[[7.8574758]]\n",
            "[[3.5516155]]\n",
            "[[7.7511983]]\n",
            "[[3.6329327]]\n",
            "[[7.923552]]\n",
            "[[3.5148535]]\n",
            "[[7.6676226]]\n",
            "[[3.1542115]]\n",
            "[[7.278029]]\n",
            "[[2.8740878]]\n",
            "[[6.937065]]\n",
            "[[2.6813557]]\n",
            "[[6.6899137]]\n",
            "[[2.595063]]\n",
            "[[6.580763]]\n",
            "[[2.5758204]]\n",
            "[[6.5957775]]\n",
            "[[2.614868]]\n",
            "[[6.7084217]]\n",
            "[[2.8472254]]\n",
            "[[7.094709]]\n",
            "[[3.3552773]]\n",
            "[[7.6843596]]\n",
            "[[3.830917]]\n",
            "[[8.053397]]\n",
            "[[4.0151286]]\n",
            "[[8.168789]]\n",
            "[[4.0480843]]\n",
            "[[8.221383]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSICul9VCO6N",
        "outputId": "0a9d526f-0c40-4387-adc1-ba6979d8a6b9"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-1.5169643,\n",
              " 0.56931394,\n",
              " 1.6326733,\n",
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              " 1.5567209,\n",
              " 2.1480901,\n",
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              " 6.3253493,\n",
              " 5.186191,\n",
              " 3.074622,\n",
              " 0.03320919,\n",
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              " 0.5394455,\n",
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              " 0.63953656,\n",
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              " 0.7370904,\n",
              " 0.7869969,\n",
              " 0.84059966,\n",
              " 0.895878,\n",
              " 4.3498707,\n",
              " 7.225669,\n",
              " 6.1592264,\n",
              " 0.38161945,\n",
              " 1.8334503,\n",
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              " 2.740086,\n",
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              " 2.615209,\n",
              " 2.138497,\n",
              " 6.1167374,\n",
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              " 0.9941275,\n",
              " 0.64953893,\n",
              " 0.830689,\n",
              " 0.88018227,\n",
              " 4.316862,\n",
              " 3.2371469,\n",
              " 5.4527817,\n",
              " 5.3258305,\n",
              " 7.524158,\n",
              " 7.4360127,\n",
              " 4.273766,\n",
              " 1.7806392,\n",
              " 2.580057,\n",
              " 5.571567,\n",
              " 5.040399,\n",
              " 3.332156,\n",
              " 3.403741,\n",
              " 3.9452007,\n",
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              " 5.296316,\n",
              " 0.57965416,\n",
              " 3.7506835,\n",
              " 4.1143174,\n",
              " 5.07716,\n",
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              " 9.609621,\n",
              " 8.87196,\n",
              " 7.5376334,\n",
              " 4.7647233,\n",
              " 2.2590864,\n",
              " 2.6622107,\n",
              " 4.939243,\n",
              " 8.094944,\n",
              " 9.358607,\n",
              " 5.6736712,\n",
              " 6.869083,\n",
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              " 6.3729334,\n",
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              " 9.197537,\n",
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              " 3.0036342,\n",
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              " 3.0618844,\n",
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              " 3.1542115,\n",
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              " 2.8740878,\n",
              " 6.937065,\n",
              " 2.6813557,\n",
              " 6.6899137,\n",
              " 2.595063,\n",
              " 6.580763,\n",
              " 2.5758204,\n",
              " 6.5957775,\n",
              " 2.614868,\n",
              " 6.7084217,\n",
              " 2.8472254,\n",
              " 7.094709,\n",
              " 3.3552773,\n",
              " 7.6843596,\n",
              " 3.830917,\n",
              " 8.053397,\n",
              " 4.0151286,\n",
              " 8.168789,\n",
              " 4.0480843,\n",
              " 8.221383]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWL4eHQu7LSj"
      },
      "source": [
        "res = scaler.inverse_transform(np.array(y_pred).reshape(-1, 1)).reshape(149)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jipUpvOm7dR0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdtjskAj8NSe"
      },
      "source": [
        "res_df = pd.DataFrame({'Offer_ID': off[off.train_test_group =='test'].drop(['sku'],axis=1).drop_duplicates().sort_values(['start_date'])['Offer_ID'], 'UpLift': y_pred})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qxe3XpcX8NSp"
      },
      "source": [
        "ss = pd.read_csv('/content/20210521_sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IepkVnof8NSp"
      },
      "source": [
        "res_df = ss.merge(res_df, on=['Offer_ID']).drop(['UpLift_x'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuwCqUPy8NSq"
      },
      "source": [
        "res_df.columns = ['Offer_ID', 'UpLift']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAcjnsUw8NSq"
      },
      "source": [
        "res_df.to_csv('submit_lenta_100.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "-xVeztiB8NSq",
        "outputId": "9c30efa1-0aa4-44af-ec1f-fc3009f0d9e7"
      },
      "source": [
        "pd.read_csv('/content/submit_lenta_100.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Offer_ID</th>\n",
              "      <th>UpLift</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>386</td>\n",
              "      <td>6.571551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>182</td>\n",
              "      <td>7.259575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>173</td>\n",
              "      <td>3.323738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>375</td>\n",
              "      <td>2.548702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>379</td>\n",
              "      <td>3.689060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>536</td>\n",
              "      <td>4.114317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>99</td>\n",
              "      <td>1.119886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>100</td>\n",
              "      <td>0.569314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>95</td>\n",
              "      <td>1.058684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>97</td>\n",
              "      <td>1.632673</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>149 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Offer_ID    UpLift\n",
              "0        386  6.571551\n",
              "1        182  7.259575\n",
              "2        173  3.323738\n",
              "3        375  2.548702\n",
              "4        379  3.689060\n",
              "..       ...       ...\n",
              "144      536  4.114317\n",
              "145       99  1.119886\n",
              "146      100  0.569314\n",
              "147       95  1.058684\n",
              "148       97  1.632673\n",
              "\n",
              "[149 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYbIEmAO8SWy"
      },
      "source": [
        "single_step_model.save('lenta.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzJQsjZ1DTRL"
      },
      "source": [
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pw-cJN-tDjW3"
      },
      "source": [
        "model = load_model('lenta.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KeF9nvKMDyED",
        "outputId": "0bd75e0c-7419-49b7-f26b-d93204db5c31"
      },
      "source": [
        "model.predict(X_test.reshape(1, X_test.shape[0], 1).astype(float))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.9746995]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBjjMbZqDy8N"
      },
      "source": [
        "pd.DataFrame({'UpLift': X_test}).to_csv('example.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SecxtzYEol5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}