{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2b5535f9",
      "metadata": {
        "id": "2b5535f9"
      },
      "outputs": [],
      "source": [
        "!pip -q install kaggle pandas numpy scikit-learn statsmodels tensorflow openpyxl matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1cdac472",
      "metadata": {
        "id": "1cdac472"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import glob\n",
        "import zipfile\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7381e4a8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "id": "7381e4a8",
        "outputId": "7a6a01c5-949b-4a39-abf4-f88aaa0abba0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-74eef110-ad0d-46c9-a6bf-a9c3a282686b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-74eef110-ad0d-46c9-a6bf-a9c3a282686b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "json_files = [name for name in uploaded.keys() if name.endswith(\".json\")]\n",
        "assert len(json_files) > 0, \"Загрузите kaggle.json\"\n",
        "\n",
        "src_json = json_files[0]\n",
        "\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp \"$src_json\" ~/.kaggle/kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d5a4e950",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5a4e950",
        "outputId": "4cd8da0f-81e2-4b42-f534-c8b4e6826ff4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/mashlyn/online-retail-ii-uci\n",
            "License(s): CC0-1.0\n",
            "Downloading online-retail-ii-uci.zip to /content/online_retail_data\n",
            "100% 14.5M/14.5M [00:00<00:00, 90.3MB/s]\n",
            "\n",
            "Файлы в папке:\n",
            "/content/online_retail_data ['online-retail-ii-uci.zip', 'online_retail_II.csv']\n"
          ]
        }
      ],
      "source": [
        "DATA_DIR = \"/content/online_retail_data\"\n",
        "os.makedirs(DATA_DIR, exist_ok=True)\n",
        "\n",
        "!kaggle datasets download -d mashlyn/online-retail-ii-uci -p $DATA_DIR\n",
        "\n",
        "zip_files = glob.glob(f\"{DATA_DIR}/*.zip\")\n",
        "for z in zip_files:\n",
        "    with zipfile.ZipFile(z, \"r\") as zip_ref:\n",
        "        zip_ref.extractall(DATA_DIR)\n",
        "\n",
        "print(\"Файлы в папке:\")\n",
        "for root, dirs, files_ in os.walk(DATA_DIR):\n",
        "    print(root, files_[:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "30ecf5e2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "30ecf5e2",
        "outputId": "141b2891-11c7-4a0c-f86d-23d92c590a5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Найден файл: /content/online_retail_data/online_retail_II.csv\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Invoice StockCode                          Description  Quantity  \\\n",
              "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
              "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
              "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
              "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
              "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
              "\n",
              "           InvoiceDate  Price  Customer ID         Country  \n",
              "0  2009-12-01 07:45:00   6.95      13085.0  United Kingdom  \n",
              "1  2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
              "2  2009-12-01 07:45:00   6.75      13085.0  United Kingdom  \n",
              "3  2009-12-01 07:45:00   2.10      13085.0  United Kingdom  \n",
              "4  2009-12-01 07:45:00   1.25      13085.0  United Kingdom  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab8c7be8-4950-4fce-9761-ea1e377d73a9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
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              "      <td>United Kingdom</td>\n",
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              "    <tr>\n",
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              "      <td>489434</td>\n",
              "      <td>79323P</td>\n",
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              "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
              "      <td>48</td>\n",
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              "      <td>2.10</td>\n",
              "      <td>13085.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-ab8c7be8-4950-4fce-9761-ea1e377d73a9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ab8c7be8-4950-4fce-9761-ea1e377d73a9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(raw\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Invoice\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"489434\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"79323P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"PINK CHERRY LIGHTS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 12,\n        \"max\": 48,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2009-12-01 07:45:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8333725487482226,\n        \"min\": 1.25,\n        \"max\": 6.95,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 13085.0,\n        \"max\": 13085.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          13085.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1067371, 8)\n",
            "['Invoice', 'StockCode', 'Description', 'Quantity', 'InvoiceDate', 'Price', 'Customer ID', 'Country']\n"
          ]
        }
      ],
      "source": [
        "def find_first(patterns, base_dir=DATA_DIR):\n",
        "    for pattern in patterns:\n",
        "        matches = glob.glob(f\"{base_dir}/**/{pattern}\", recursive=True)\n",
        "        if matches:\n",
        "            return matches[0]\n",
        "    raise FileNotFoundError(f\"Не найден ни один файл из шаблонов: {patterns}\")\n",
        "\n",
        "data_file = find_first([\"*.xlsx\", \"*.xls\", \"*.csv\"])\n",
        "print(\"Найден файл:\", data_file)\n",
        "\n",
        "if data_file.endswith((\".xlsx\", \".xls\")):\n",
        "    raw = pd.read_excel(data_file)\n",
        "else:\n",
        "    raw = pd.read_csv(data_file, encoding_errors=\"ignore\")\n",
        "\n",
        "raw.columns = [str(c).strip() for c in raw.columns]\n",
        "display(raw.head())\n",
        "print(raw.shape)\n",
        "print(raw.columns.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "98740e32",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "98740e32",
        "outputId": "631c3921-aceb-4fe7-8667-fffe846baf2f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  Invoice StockCode                          Description  Quantity  \\\n",
              "0  489434     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
              "1  489434    79323P                   PINK CHERRY LIGHTS        12   \n",
              "2  489434    79323W                  WHITE CHERRY LIGHTS        12   \n",
              "3  489434     22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
              "4  489434     21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
              "\n",
              "          InvoiceDate  Price  Customer ID         Country       date  revenue  \n",
              "0 2009-12-01 07:45:00   6.95      13085.0  United Kingdom 2009-12-01     83.4  \n",
              "1 2009-12-01 07:45:00   6.75      13085.0  United Kingdom 2009-12-01     81.0  \n",
              "2 2009-12-01 07:45:00   6.75      13085.0  United Kingdom 2009-12-01     81.0  \n",
              "3 2009-12-01 07:45:00   2.10      13085.0  United Kingdom 2009-12-01    100.8  \n",
              "4 2009-12-01 07:45:00   1.25      13085.0  United Kingdom 2009-12-01     30.0  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
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              "      <th>revenue</th>\n",
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              "      <th>0</th>\n",
              "      <td>489434</td>\n",
              "      <td>85048</td>\n",
              "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.95</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009-12-01</td>\n",
              "      <td>83.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323P</td>\n",
              "      <td>PINK CHERRY LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009-12-01</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323W</td>\n",
              "      <td>WHITE CHERRY LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009-12-01</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>489434</td>\n",
              "      <td>22041</td>\n",
              "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
              "      <td>48</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009-12-01</td>\n",
              "      <td>100.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>489434</td>\n",
              "      <td>21232</td>\n",
              "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
              "      <td>24</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009-12-01</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-40929ca2-4c77-4181-a968-0159eb81918f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-40929ca2-4c77-4181-a968-0159eb81918f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(\\\"\\u041f\\u043e\\u0441\\u043b\\u0435 \\u043e\\u0447\\u0438\\u0441\\u0442\\u043a\\u0438:\\\", df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Invoice\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"489434\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StockCode\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"79323P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"PINK CHERRY LIGHTS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 12,\n        \"max\": 48,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InvoiceDate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2009-12-01 07:45:00\",\n        \"max\": \"2009-12-01 07:45:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2009-12-01 07:45:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8333725487482226,\n        \"min\": 1.25,\n        \"max\": 6.95,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 13085.0,\n        \"max\": 13085.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          13085.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2009-12-01 00:00:00\",\n        \"max\": \"2009-12-01 00:00:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2009-12-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.6125534287862,\n        \"min\": 30.0,\n        \"max\": 100.80000000000001,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          81.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "После очистки: (1041671, 10)\n"
          ]
        }
      ],
      "source": [
        "# Нормализация названий столбцов и поиск нужных полей\n",
        "col_map = {c.lower().replace(\" \", \"\").replace(\"_\", \"\"): c for c in raw.columns}\n",
        "\n",
        "date_col = None\n",
        "for key in [\"invoicedate\", \"date\"]:\n",
        "    if key in col_map:\n",
        "        date_col = col_map[key]\n",
        "        break\n",
        "\n",
        "qty_col = None\n",
        "for key in [\"quantity\", \"qty\"]:\n",
        "    if key in col_map:\n",
        "        qty_col = col_map[key]\n",
        "        break\n",
        "\n",
        "price_col = None\n",
        "for key in [\"unitprice\", \"price\"]:\n",
        "    if key in col_map:\n",
        "        price_col = col_map[key]\n",
        "        break\n",
        "\n",
        "country_col = None\n",
        "for key in [\"country\"]:\n",
        "    if key in col_map:\n",
        "        country_col = col_map[key]\n",
        "        break\n",
        "\n",
        "customer_col = None\n",
        "for key in [\"customerid\", \"customer id\"]:\n",
        "    norm = key.replace(\" \", \"\")\n",
        "    if norm in col_map:\n",
        "        customer_col = col_map[norm]\n",
        "        break\n",
        "\n",
        "assert date_col is not None, \"Не найден столбец с датой\"\n",
        "assert qty_col is not None, \"Не найден столбец quantity\"\n",
        "assert price_col is not None, \"Не найден столбец price / unit price\"\n",
        "\n",
        "df = raw.copy()\n",
        "df[date_col] = pd.to_datetime(df[date_col], errors=\"coerce\")\n",
        "df = df.dropna(subset=[date_col, qty_col, price_col]).copy()\n",
        "\n",
        "df[qty_col] = pd.to_numeric(df[qty_col], errors=\"coerce\")\n",
        "df[price_col] = pd.to_numeric(df[price_col], errors=\"coerce\")\n",
        "df = df.dropna(subset=[qty_col, price_col]).copy()\n",
        "\n",
        "# Убираем возвраты и некорректные значения\n",
        "df = df[(df[qty_col] > 0) & (df[price_col] > 0)].copy()\n",
        "\n",
        "df[\"date\"] = df[date_col].dt.floor(\"D\")\n",
        "df[\"revenue\"] = df[qty_col] * df[price_col]\n",
        "\n",
        "if country_col is None:\n",
        "    df[\"Country\"] = \"Unknown\"\n",
        "    country_col = \"Country\"\n",
        "\n",
        "display(df.head())\n",
        "print(\"После очистки:\", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "377b577e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "377b577e",
        "outputId": "98537142-7995-4385-d931-ddcabf910572"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        date  daily_revenue  avg_quantity  avg_price  transactions_count  \\\n",
              "0 2009-12-01       54513.50      8.439291   4.502309                3105   \n",
              "1 2009-12-02       63352.51      9.814154   4.062046                3250   \n",
              "2 2009-12-03       74037.91     16.715207   3.783265                2946   \n",
              "3 2009-12-04       40732.92      8.435522   3.762979                2528   \n",
              "4 2009-12-05        9803.05     12.797500   3.608150                 400   \n",
              "\n",
              "   unique_customers    main_country  dayofweek  month  dayofyear  is_weekend  \n",
              "0                91  United Kingdom          1     12        335           0  \n",
              "1                94  United Kingdom          2     12        336           0  \n",
              "2               106  United Kingdom          3     12        337           0  \n",
              "3                76  United Kingdom          4     12        338           0  \n",
              "4                26  United Kingdom          5     12        339           1  "
            ],
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>daily_revenue</th>\n",
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              "      <td>2009-12-01</td>\n",
              "      <td>54513.50</td>\n",
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              "      <th>1</th>\n",
              "      <td>2009-12-02</td>\n",
              "      <td>63352.51</td>\n",
              "      <td>9.814154</td>\n",
              "      <td>4.062046</td>\n",
              "      <td>3250</td>\n",
              "      <td>94</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>336</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-12-03</td>\n",
              "      <td>74037.91</td>\n",
              "      <td>16.715207</td>\n",
              "      <td>3.783265</td>\n",
              "      <td>2946</td>\n",
              "      <td>106</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-12-04</td>\n",
              "      <td>40732.92</td>\n",
              "      <td>8.435522</td>\n",
              "      <td>3.762979</td>\n",
              "      <td>2528</td>\n",
              "      <td>76</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>338</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-12-05</td>\n",
              "      <td>9803.05</td>\n",
              "      <td>12.797500</td>\n",
              "      <td>3.608150</td>\n",
              "      <td>400</td>\n",
              "      <td>26</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>339</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e995c293-02aa-40d6-bd30-eb9198c366dc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e995c293-02aa-40d6-bd30-eb9198c366dc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e995c293-02aa-40d6-bd30-eb9198c366dc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(\\\"\\u041f\\u0435\\u0440\\u0438\\u043e\\u0434:\\\", daily[\\\"date\\\"]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2009-12-01 00:00:00\",\n        \"max\": \"2009-12-05 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2009-12-02 00:00:00\",\n          \"2009-12-05 00:00:00\",\n          \"2009-12-03 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"daily_revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24833.066821803746,\n        \"min\": 9803.05,\n        \"max\": 74037.91,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          63352.51,\n          9803.05,\n          74037.91\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5406849476657642,\n        \"min\": 8.435522151898734,\n        \"max\": 16.71520706042091,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.814153846153847,\n          12.7975,\n          16.71520706042091\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.352506479920555,\n        \"min\": 3.60815,\n        \"max\": 4.502309178743961,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.062046153846154,\n          3.60815,\n          3.78326544467074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"transactions_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1175,\n        \"min\": 400,\n        \"max\": 3250,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3250,\n          400,\n          2946\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique_customers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 26,\n        \"max\": 106,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          94,\n          26,\n          106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"main_country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United Kingdom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofweek\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dayofyear\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_weekend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Число дней: 604\n",
            "Период: 2009-12-01 00:00:00 — 2011-12-09 00:00:00\n"
          ]
        }
      ],
      "source": [
        "# Агрегация по дням\n",
        "agg_dict = {\n",
        "    \"revenue\": \"sum\",\n",
        "    qty_col: \"mean\",\n",
        "    price_col: \"mean\",\n",
        "}\n",
        "\n",
        "daily = df.groupby(\"date\").agg(agg_dict)\n",
        "daily = daily.rename(columns={\n",
        "    \"revenue\": \"daily_revenue\",\n",
        "    qty_col: \"avg_quantity\",\n",
        "    price_col: \"avg_price\"\n",
        "})\n",
        "\n",
        "daily[\"transactions_count\"] = df.groupby(\"date\").size()\n",
        "\n",
        "if customer_col is not None and customer_col in df.columns:\n",
        "    daily[\"unique_customers\"] = df.groupby(\"date\")[customer_col].nunique()\n",
        "else:\n",
        "    daily[\"unique_customers\"] = 0\n",
        "\n",
        "country_mode = df.groupby(\"date\")[country_col].agg(lambda x: x.mode().iloc[0] if not x.mode().empty else \"Unknown\")\n",
        "daily[\"main_country\"] = country_mode\n",
        "\n",
        "daily = daily.sort_index().reset_index()\n",
        "\n",
        "# Оставляем только самые частые страны, остальные объединяем\n",
        "top_countries = daily[\"main_country\"].value_counts().head(8).index\n",
        "daily[\"main_country\"] = daily[\"main_country\"].where(daily[\"main_country\"].isin(top_countries), \"Other\")\n",
        "\n",
        "# Календарные признаки\n",
        "daily[\"dayofweek\"] = daily[\"date\"].dt.dayofweek\n",
        "daily[\"month\"] = daily[\"date\"].dt.month\n",
        "daily[\"dayofyear\"] = daily[\"date\"].dt.dayofyear\n",
        "daily[\"is_weekend\"] = (daily[\"dayofweek\"] >= 5).astype(int)\n",
        "\n",
        "display(daily.head())\n",
        "print(\"Число дней:\", len(daily))\n",
        "print(\"Период:\", daily[\"date\"].min(), \"—\", daily[\"date\"].max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cea5be6c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "cea5be6c",
        "outputId": "98510b65-1e53-4d15-f6bb-3c1ca0b9581e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(14, 5))\n",
        "plt.plot(daily[\"date\"], daily[\"daily_revenue\"])\n",
        "plt.title(\"Исходный временной ряд: ежедневная выручка\")\n",
        "plt.xlabel(\"Дата\")\n",
        "plt.ylabel(\"Выручка\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "502a3538",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "502a3538",
        "outputId": "14da80f4-396a-4247-efbc-35177505591f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Всего наблюдений: 604\n",
            "Размер train: 302\n",
            "Размер одного test-блока: 30\n",
            "Количество test-блоков: 10\n",
            "Сумма строк train + test: 602\n"
          ]
        }
      ],
      "source": [
        "encoded_daily = pd.get_dummies(daily, columns=[\"main_country\"], drop_first=False)\n",
        "\n",
        "TARGET_COL = \"daily_revenue\"\n",
        "DATE_COL = \"date\"\n",
        "LAG = 14\n",
        "\n",
        "n = len(encoded_daily)\n",
        "train_size = int(n * 0.5)\n",
        "chunk_size = int(n * 0.05)\n",
        "\n",
        "train_df = encoded_daily.iloc[:train_size].copy()\n",
        "\n",
        "test_chunks = []\n",
        "for i in range(10):\n",
        "    start = train_size + i * chunk_size\n",
        "    end = start + chunk_size\n",
        "    chunk = encoded_daily.iloc[start:end].copy()\n",
        "    test_chunks.append(chunk)\n",
        "\n",
        "print(f\"Всего наблюдений: {n}\")\n",
        "print(f\"Размер train: {len(train_df)}\")\n",
        "print(f\"Размер одного test-блока: {chunk_size}\")\n",
        "print(f\"Количество test-блоков: {len(test_chunks)}\")\n",
        "print(\"Сумма строк train + test:\", len(train_df) + sum(len(x) for x in test_chunks))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e27da614",
      "metadata": {
        "id": "e27da614"
      },
      "outputs": [],
      "source": [
        "def mae(y_true, y_pred):\n",
        "    return mean_absolute_error(y_true, y_pred)\n",
        "\n",
        "def rmse(y_true, y_pred):\n",
        "    return np.sqrt(mean_squared_error(y_true, y_pred))\n",
        "\n",
        "def mape(y_true, y_pred):\n",
        "    y_true = np.asarray(y_true)\n",
        "    y_pred = np.asarray(y_pred)\n",
        "    mask = np.abs(y_true) > 1e-8\n",
        "    return np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100 if mask.sum() > 0 else np.nan\n",
        "\n",
        "def build_tree_supervised(df_part, target_col=TARGET_COL, date_col=DATE_COL, lag=LAG):\n",
        "    temp = df_part.copy()\n",
        "\n",
        "    for i in range(1, lag + 1):\n",
        "        temp[f\"lag_{i}\"] = temp[target_col].shift(i)\n",
        "\n",
        "    temp = temp.dropna().reset_index(drop=True)\n",
        "\n",
        "    excluded = [date_col, target_col]\n",
        "    feature_cols = [c for c in temp.columns if c not in excluded]\n",
        "\n",
        "    lag_cols = [f\"lag_{i}\" for i in range(1, lag + 1)]\n",
        "    other_feature_cols = [c for c in feature_cols if c not in lag_cols]\n",
        "\n",
        "    numeric_feature_cols = [c for c in lag_cols + other_feature_cols if pd.api.types.is_numeric_dtype(temp[c])]\n",
        "    lag_cols = [c for c in lag_cols if c in numeric_feature_cols]\n",
        "    other_feature_cols = [c for c in other_feature_cols if c in numeric_feature_cols]\n",
        "\n",
        "    X = temp[lag_cols + other_feature_cols]\n",
        "    y = temp[target_col]\n",
        "    return X, y, lag_cols, other_feature_cols\n",
        "\n",
        "def fit_extra_trees(train_part):\n",
        "    X_train, y_train, lag_cols, other_feature_cols = build_tree_supervised(train_part)\n",
        "    model = ExtraTreesRegressor(\n",
        "        n_estimators=300,\n",
        "        max_depth=12,\n",
        "        min_samples_leaf=2,\n",
        "        random_state=42,\n",
        "        n_jobs=-1\n",
        "    )\n",
        "    model.fit(X_train, y_train)\n",
        "    return model, lag_cols, other_feature_cols\n",
        "\n",
        "def recursive_tree_forecast(model, history_df, steps, lag_cols, other_feature_cols, target_col=TARGET_COL):\n",
        "    history = history_df.copy().reset_index(drop=True)\n",
        "    preds = []\n",
        "\n",
        "    for step_idx in range(steps):\n",
        "        row = history.iloc[-1:].copy()\n",
        "\n",
        "        lag_values = history[target_col].tolist()\n",
        "        current_lags = lag_values[-len(lag_cols):] if len(lag_values) >= len(lag_cols) else [lag_values[0]] * (len(lag_cols) - len(lag_values)) + lag_values\n",
        "        current_lags = list(reversed(current_lags))\n",
        "\n",
        "        feat_dict = {}\n",
        "        for idx, col in enumerate(lag_cols, start=1):\n",
        "            feat_dict[col] = current_lags[idx - 1]\n",
        "        for col in other_feature_cols:\n",
        "            feat_dict[col] = row[col].values[0] if col in row.columns else 0\n",
        "\n",
        "        x_input = pd.DataFrame([feat_dict])[lag_cols + other_feature_cols]\n",
        "        pred = model.predict(x_input)[0]\n",
        "        preds.append(pred)\n",
        "\n",
        "        new_row = row.copy()\n",
        "        new_row[target_col] = pred\n",
        "        history = pd.concat([history, new_row], ignore_index=True)\n",
        "\n",
        "    return np.array(preds)\n",
        "\n",
        "def fit_sarimax(train_series):\n",
        "    model = SARIMAX(\n",
        "        train_series,\n",
        "        order=(1, 1, 1),\n",
        "        seasonal_order=(1, 0, 1, 7),\n",
        "        enforce_stationarity=False,\n",
        "        enforce_invertibility=False\n",
        "    )\n",
        "    return model.fit(disp=False)\n",
        "\n",
        "def create_lstm_sequences(series, lookback=14):\n",
        "    X, y = [], []\n",
        "    for i in range(lookback, len(series)):\n",
        "        X.append(series[i-lookback:i])\n",
        "        y.append(series[i])\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "def fit_lstm(train_series, lookback=14, epochs=20, batch_size=16):\n",
        "    scaler = MinMaxScaler()\n",
        "    scaled = scaler.fit_transform(np.array(train_series).reshape(-1, 1)).flatten()\n",
        "\n",
        "    X_train, y_train = create_lstm_sequences(scaled, lookback=lookback)\n",
        "    X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
        "\n",
        "    model = Sequential([\n",
        "        LSTM(32, input_shape=(lookback, 1)),\n",
        "        Dense(16, activation=\"relu\"),\n",
        "        Dense(1)\n",
        "    ])\n",
        "\n",
        "    model.compile(optimizer=\"adam\", loss=\"mse\")\n",
        "\n",
        "    es = EarlyStopping(monitor=\"loss\", patience=3, restore_best_weights=True)\n",
        "    model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0, callbacks=[es])\n",
        "\n",
        "    return model, scaler\n",
        "\n",
        "def recursive_lstm_forecast(model, scaler, history_series, steps, lookback=14):\n",
        "    history = list(history_series)\n",
        "    scaled_history = scaler.transform(np.array(history).reshape(-1, 1)).flatten().tolist()\n",
        "\n",
        "    preds = []\n",
        "    for _ in range(steps):\n",
        "        if len(scaled_history) < lookback:\n",
        "            seq = [scaled_history[0]] * (lookback - len(scaled_history)) + scaled_history\n",
        "        else:\n",
        "            seq = scaled_history[-lookback:]\n",
        "\n",
        "        x_input = np.array(seq).reshape(1, lookback, 1)\n",
        "        pred_scaled = model.predict(x_input, verbose=0)[0, 0]\n",
        "        pred = scaler.inverse_transform([[pred_scaled]])[0, 0]\n",
        "\n",
        "        preds.append(pred)\n",
        "        scaled_history.append(pred_scaled)\n",
        "\n",
        "    return np.array(preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af39dbe6",
      "metadata": {
        "id": "af39dbe6"
      },
      "outputs": [],
      "source": [
        "def tree_forecast_with_known_features(model, history_df, future_df, lag_cols, other_feature_cols, target_col=TARGET_COL):\n",
        "    history_values = history_df[target_col].tolist()\n",
        "    preds = []\n",
        "\n",
        "    for i in range(len(future_df)):\n",
        "        feat_dict = {}\n",
        "\n",
        "        recent = history_values[-len(lag_cols):] if len(history_values) >= len(lag_cols) else [history_values[0]] * (len(lag_cols) - len(history_values)) + history_values\n",
        "        recent = list(reversed(recent))\n",
        "\n",
        "        for idx, col in enumerate(lag_cols, start=1):\n",
        "            feat_dict[col] = recent[idx - 1]\n",
        "\n",
        "        for col in other_feature_cols:\n",
        "            feat_dict[col] = future_df.iloc[i][col] if col in future_df.columns else 0\n",
        "\n",
        "        x_input = pd.DataFrame([feat_dict])[lag_cols + other_feature_cols]\n",
        "        pred = model.predict(x_input)[0]\n",
        "        preds.append(pred)\n",
        "        history_values.append(pred)\n",
        "\n",
        "    return np.array(preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6902f63e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6902f63e",
        "outputId": "339b9d7c-fbb4-4516-fffa-09bdfd7c58be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Первичное сравнение на первом тестовом блоке:\n",
            "SARIMAX: MAE = 40993.5263\n",
            "ExtraTrees: MAE = 13811.7127\n",
            "LSTM: MAE = 43972.2548\n",
            "\n",
            "Базовая модель: ExtraTrees\n",
            "Начальный лучший MAE: 13811.7127\n",
            "Порог переобучения (MAE): 18645.8122\n"
          ]
        }
      ],
      "source": [
        "train_series = train_df[TARGET_COL].values\n",
        "\n",
        "sarimax_model = fit_sarimax(train_series)\n",
        "tree_model, lag_cols, other_feature_cols = fit_extra_trees(train_df)\n",
        "lstm_model, lstm_scaler = fit_lstm(train_series, epochs=20)\n",
        "\n",
        "first_test = test_chunks[0]\n",
        "y_true_first = first_test[TARGET_COL].values\n",
        "\n",
        "sarimax_pred_first = sarimax_model.forecast(steps=len(first_test))\n",
        "tree_pred_first = tree_forecast_with_known_features(tree_model, train_df, first_test, lag_cols, other_feature_cols)\n",
        "lstm_pred_first = recursive_lstm_forecast(lstm_model, lstm_scaler, train_series, steps=len(first_test))\n",
        "\n",
        "initial_scores = {\n",
        "    \"SARIMAX\": mae(y_true_first, sarimax_pred_first),\n",
        "    \"ExtraTrees\": mae(y_true_first, tree_pred_first),\n",
        "    \"LSTM\": mae(y_true_first, lstm_pred_first),\n",
        "}\n",
        "\n",
        "base_model = min(initial_scores, key=initial_scores.get)\n",
        "initial_best_mae = initial_scores[base_model]\n",
        "retrain_threshold = initial_best_mae * 1.35\n",
        "\n",
        "print(\"Первичное сравнение на первом тестовом блоке:\")\n",
        "for model_name, score in initial_scores.items():\n",
        "    print(f\"{model_name}: MAE = {score:.4f}\")\n",
        "\n",
        "print(f\"\\nБазовая модель: {base_model}\")\n",
        "print(f\"Начальный лучший MAE: {initial_best_mae:.4f}\")\n",
        "print(f\"Порог переобучения (MAE): {retrain_threshold:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3672266d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3672266d",
        "outputId": "d4173b55-83a9-45de-d7e4-3a37f3bfab85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 1/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 40993.5263\n",
            "ExtraTrees: 13811.7127\n",
            "LSTM: 43972.2548\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 47019.4740\n",
            "ExtraTrees: 25708.9131\n",
            "LSTM: 49684.3578\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 209.33%\n",
            "ExtraTrees: 35.04%\n",
            "LSTM: 225.59%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 2/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 49521.8206\n",
            "ExtraTrees: 3324.5868\n",
            "LSTM: 16220.0559\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 50266.8924\n",
            "ExtraTrees: 4148.9645\n",
            "LSTM: 18275.3470\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 346.21%\n",
            "ExtraTrees: 27.61%\n",
            "LSTM: 137.84%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 3/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 46458.0989\n",
            "ExtraTrees: 3526.8893\n",
            "LSTM: 7884.0571\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 47901.8085\n",
            "ExtraTrees: 5952.5992\n",
            "LSTM: 11640.4786\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 275.71%\n",
            "ExtraTrees: 18.54%\n",
            "LSTM: 54.80%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 4/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 45363.1896\n",
            "ExtraTrees: 3982.3874\n",
            "LSTM: 8537.5243\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 46386.7626\n",
            "ExtraTrees: 4680.9999\n",
            "LSTM: 11426.4948\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 239.51%\n",
            "ExtraTrees: 20.09%\n",
            "LSTM: 58.25%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 5/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 39298.7283\n",
            "ExtraTrees: 5336.6276\n",
            "LSTM: 11322.7056\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 41566.2096\n",
            "ExtraTrees: 9145.3937\n",
            "LSTM: 14303.0162\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 183.40%\n",
            "ExtraTrees: 16.74%\n",
            "LSTM: 45.29%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 6/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 44695.7706\n",
            "ExtraTrees: 4725.4616\n",
            "LSTM: 11392.3394\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 46239.2919\n",
            "ExtraTrees: 5680.7592\n",
            "LSTM: 13334.0501\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 263.90%\n",
            "ExtraTrees: 23.87%\n",
            "LSTM: 78.53%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 7/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 41998.8013\n",
            "ExtraTrees: 5894.8336\n",
            "LSTM: 11327.9429\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 44710.2944\n",
            "ExtraTrees: 7952.7269\n",
            "LSTM: 15502.7285\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 225.04%\n",
            "ExtraTrees: 23.25%\n",
            "LSTM: 55.68%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 8/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 36733.7990\n",
            "ExtraTrees: 6585.9703\n",
            "LSTM: 13044.0429\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 38754.5080\n",
            "ExtraTrees: 13867.2894\n",
            "LSTM: 20931.9599\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 135.96%\n",
            "ExtraTrees: 15.54%\n",
            "LSTM: 32.66%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 9/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 27836.3188\n",
            "ExtraTrees: 5906.3387\n",
            "LSTM: 12840.0569\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 31667.8278\n",
            "ExtraTrees: 8155.5591\n",
            "LSTM: 16083.5776\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 104.79%\n",
            "ExtraTrees: 18.73%\n",
            "LSTM: 43.62%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n",
            "\n",
            "======================================================================\n",
            "Обработка тестового блока 10/10\n",
            "======================================================================\n",
            "MAE:\n",
            "SARIMAX: 17755.6538\n",
            "ExtraTrees: 9034.6361\n",
            "LSTM: 23084.9174\n",
            "\n",
            "RMSE:\n",
            "SARIMAX: 22022.7353\n",
            "ExtraTrees: 12267.5322\n",
            "LSTM: 27471.3960\n",
            "\n",
            "MAPE:\n",
            "SARIMAX: 41.61%\n",
            "ExtraTrees: 15.77%\n",
            "LSTM: 36.96%\n",
            "\n",
            "Базовая модель остается: ExtraTrees\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   chunk_number  date_start    date_end   SARIMAX_MAE        ET_MAE  \\\n",
              "0             1  2010-12-05  2011-01-18  40993.526267  13811.712713   \n",
              "1             2  2011-01-19  2011-02-22  49521.820602   3324.586787   \n",
              "2             3  2011-02-23  2011-03-29  46458.098936   3526.889310   \n",
              "3             4  2011-03-30  2011-05-09  45363.189569   3982.387430   \n",
              "4             5  2011-05-10  2011-06-14  39298.728269   5336.627642   \n",
              "\n",
              "       LSTM_MAE  SARIMAX_RMSE       ET_RMSE     LSTM_RMSE  SARIMAX_MAPE  \\\n",
              "0  43972.254823  47019.473992  25708.913052  49684.357757    209.329536   \n",
              "1  16220.055931  50266.892398   4148.964529  18275.346988    346.211930   \n",
              "2   7884.057149  47901.808467   5952.599191  11640.478606    275.712200   \n",
              "3   8537.524272  46386.762552   4680.999859  11426.494780    239.505482   \n",
              "4  11322.705573  41566.209583   9145.393694  14303.016209    183.401888   \n",
              "\n",
              "     ET_MAPE   LSTM_MAPE  best_model base_model_after_step  model_changed  \\\n",
              "0  35.035909  225.585447  ExtraTrees            ExtraTrees          False   \n",
              "1  27.612080  137.842794  ExtraTrees            ExtraTrees          False   \n",
              "2  18.537464   54.795241  ExtraTrees            ExtraTrees          False   \n",
              "3  20.094263   58.248722  ExtraTrees            ExtraTrees          False   \n",
              "4  16.743695   45.294615  ExtraTrees            ExtraTrees          False   \n",
              "\n",
              "   retrained  \n",
              "0      False  \n",
              "1      False  \n",
              "2      False  \n",
              "3      False  \n",
              "4      False  "
            ],
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              "      <th>chunk_number</th>\n",
              "      <th>date_start</th>\n",
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              "      <th>ET_MAPE</th>\n",
              "      <th>LSTM_MAPE</th>\n",
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              "      <th>base_model_after_step</th>\n",
              "      <th>model_changed</th>\n",
              "      <th>retrained</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-12-05</td>\n",
              "      <td>2011-01-18</td>\n",
              "      <td>40993.526267</td>\n",
              "      <td>13811.712713</td>\n",
              "      <td>43972.254823</td>\n",
              "      <td>47019.473992</td>\n",
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              "      <td>209.329536</td>\n",
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              "      <td>225.585447</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-19</td>\n",
              "      <td>2011-02-22</td>\n",
              "      <td>49521.820602</td>\n",
              "      <td>3324.586787</td>\n",
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              "      <td>50266.892398</td>\n",
              "      <td>4148.964529</td>\n",
              "      <td>18275.346988</td>\n",
              "      <td>346.211930</td>\n",
              "      <td>27.612080</td>\n",
              "      <td>137.842794</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-02-23</td>\n",
              "      <td>2011-03-29</td>\n",
              "      <td>46458.098936</td>\n",
              "      <td>3526.889310</td>\n",
              "      <td>7884.057149</td>\n",
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              "      <td>54.795241</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-03-30</td>\n",
              "      <td>2011-05-09</td>\n",
              "      <td>45363.189569</td>\n",
              "      <td>3982.387430</td>\n",
              "      <td>8537.524272</td>\n",
              "      <td>46386.762552</td>\n",
              "      <td>4680.999859</td>\n",
              "      <td>11426.494780</td>\n",
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              "      <td>20.094263</td>\n",
              "      <td>58.248722</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-05-10</td>\n",
              "      <td>2011-06-14</td>\n",
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              "      <td>45.294615</td>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>ExtraTrees</td>\n",
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              "      <td>False</td>\n",
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              "   chunk_number       date     y_true  SARIMAX_pred  ExtraTrees_pred  \\\n",
              "0             1 2010-12-05   63549.90  73715.511000     57393.283040   \n",
              "1             1 2010-12-06  109660.92  72431.308419     85547.798593   \n",
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              "4             1 2010-12-09  107172.36  70571.874203     89475.346094   \n",
              "\n",
              "      LSTM_pred base_model_after_step  \n",
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      "source": [
        "logs = []\n",
        "pred_rows = []\n",
        "\n",
        "current_base_model = base_model\n",
        "expanded_train_df = train_df.copy()\n",
        "\n",
        "for i, test_chunk in enumerate(test_chunks):\n",
        "    print(\"\\n\" + \"=\"*70)\n",
        "    print(f\"Обработка тестового блока {i+1}/10\")\n",
        "    print(\"=\"*70)\n",
        "\n",
        "    y_true = test_chunk[TARGET_COL].values\n",
        "    history_series = expanded_train_df[TARGET_COL].values\n",
        "\n",
        "    sarimax_pred = sarimax_model.forecast(steps=len(test_chunk))\n",
        "    tree_pred = tree_forecast_with_known_features(tree_model, expanded_train_df, test_chunk, lag_cols, other_feature_cols)\n",
        "    lstm_pred = recursive_lstm_forecast(lstm_model, lstm_scaler, history_series, steps=len(test_chunk))\n",
        "\n",
        "    scores = {\n",
        "        \"SARIMAX\": mae(y_true, sarimax_pred),\n",
        "        \"ExtraTrees\": mae(y_true, tree_pred),\n",
        "        \"LSTM\": mae(y_true, lstm_pred),\n",
        "    }\n",
        "\n",
        "    rmses = {\n",
        "        \"SARIMAX\": rmse(y_true, sarimax_pred),\n",
        "        \"ExtraTrees\": rmse(y_true, tree_pred),\n",
        "        \"LSTM\": rmse(y_true, lstm_pred),\n",
        "    }\n",
        "\n",
        "    mapes = {\n",
        "        \"SARIMAX\": mape(y_true, sarimax_pred),\n",
        "        \"ExtraTrees\": mape(y_true, tree_pred),\n",
        "        \"LSTM\": mape(y_true, lstm_pred),\n",
        "    }\n",
        "\n",
        "    best_model = min(scores, key=scores.get)\n",
        "    best_score = scores[best_model]\n",
        "\n",
        "    print(\"MAE:\")\n",
        "    for k, v in scores.items():\n",
        "        print(f\"{k}: {v:.4f}\")\n",
        "\n",
        "    print(\"\\nRMSE:\")\n",
        "    for k, v in rmses.items():\n",
        "        print(f\"{k}: {v:.4f}\")\n",
        "\n",
        "    print(\"\\nMAPE:\")\n",
        "    for k, v in mapes.items():\n",
        "        print(f\"{k}: {v:.2f}%\")\n",
        "\n",
        "    model_changed = False\n",
        "    retrained = False\n",
        "\n",
        "    if best_model != current_base_model:\n",
        "        print(f\"\\nСмена базовой модели: {current_base_model} -> {best_model}\")\n",
        "        current_base_model = best_model\n",
        "        model_changed = True\n",
        "    else:\n",
        "        print(f\"\\nБазовая модель остается: {current_base_model}\")\n",
        "\n",
        "    if best_score > retrain_threshold:\n",
        "        print(f\"\\nКачество ухудшилось: MAE = {best_score:.4f} > {retrain_threshold:.4f}. Запускается переобучение.\")\n",
        "        retrained = True\n",
        "\n",
        "        expanded_train_df = encoded_daily.iloc[:train_size + (i + 1) * chunk_size].copy()\n",
        "        expanded_train_series = expanded_train_df[TARGET_COL].values\n",
        "\n",
        "        sarimax_model = fit_sarimax(expanded_train_series)\n",
        "        tree_model, lag_cols, other_feature_cols = fit_extra_trees(expanded_train_df)\n",
        "        lstm_model, lstm_scaler = fit_lstm(expanded_train_series, epochs=20)\n",
        "\n",
        "        sarimax_pred_re = sarimax_model.forecast(steps=len(test_chunk))\n",
        "        tree_pred_re = tree_forecast_with_known_features(tree_model, expanded_train_df, test_chunk, lag_cols, other_feature_cols)\n",
        "        lstm_pred_re = recursive_lstm_forecast(lstm_model, lstm_scaler, expanded_train_series, steps=len(test_chunk))\n",
        "\n",
        "        scores_after_retrain = {\n",
        "            \"SARIMAX\": mae(y_true, sarimax_pred_re),\n",
        "            \"ExtraTrees\": mae(y_true, tree_pred_re),\n",
        "            \"LSTM\": mae(y_true, lstm_pred_re),\n",
        "        }\n",
        "\n",
        "        current_base_model = min(scores_after_retrain, key=scores_after_retrain.get)\n",
        "\n",
        "        print(\"\\nРезультаты после переобучения:\")\n",
        "        for k, v in scores_after_retrain.items():\n",
        "            print(f\"{k}: {v:.4f}\")\n",
        "\n",
        "        print(f\"Новая базовая модель: {current_base_model}\")\n",
        "    else:\n",
        "        expanded_train_df = pd.concat([expanded_train_df, test_chunk], ignore_index=True)\n",
        "\n",
        "    logs.append({\n",
        "        \"chunk_number\": i + 1,\n",
        "        \"date_start\": test_chunk[DATE_COL].min().date(),\n",
        "        \"date_end\": test_chunk[DATE_COL].max().date(),\n",
        "        \"SARIMAX_MAE\": scores[\"SARIMAX\"],\n",
        "        \"ET_MAE\": scores[\"ExtraTrees\"],\n",
        "        \"LSTM_MAE\": scores[\"LSTM\"],\n",
        "        \"SARIMAX_RMSE\": rmses[\"SARIMAX\"],\n",
        "        \"ET_RMSE\": rmses[\"ExtraTrees\"],\n",
        "        \"LSTM_RMSE\": rmses[\"LSTM\"],\n",
        "        \"SARIMAX_MAPE\": mapes[\"SARIMAX\"],\n",
        "        \"ET_MAPE\": mapes[\"ExtraTrees\"],\n",
        "        \"LSTM_MAPE\": mapes[\"LSTM\"],\n",
        "        \"best_model\": best_model,\n",
        "        \"base_model_after_step\": current_base_model,\n",
        "        \"model_changed\": model_changed,\n",
        "        \"retrained\": retrained\n",
        "    })\n",
        "\n",
        "    block_preds = pd.DataFrame({\n",
        "        \"chunk_number\": i + 1,\n",
        "        \"date\": test_chunk[DATE_COL].values,\n",
        "        \"y_true\": y_true,\n",
        "        \"SARIMAX_pred\": sarimax_pred,\n",
        "        \"ExtraTrees_pred\": tree_pred,\n",
        "        \"LSTM_pred\": lstm_pred,\n",
        "        \"base_model_after_step\": current_base_model\n",
        "    })\n",
        "    pred_rows.append(block_preds)\n",
        "\n",
        "logs_df = pd.DataFrame(logs)\n",
        "predictions_df = pd.concat(pred_rows, ignore_index=True)\n",
        "\n",
        "display(logs_df.head())\n",
        "display(predictions_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cca50805",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "cca50805",
        "outputId": "54824d79-0f24-449b-de2a-d0d5dd87127a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Model      Mean_MAE     Mean_RMSE   Mean_MAPE\n",
              "0     SARIMAX  39065.570699  41653.580428  202.545321\n",
              "1  ExtraTrees   6212.944419   9756.073708   21.518841\n",
              "2        LSTM  15962.589722  19865.340649   76.921895"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d436747-e1d4-4130-90c3-4f2fe831aebc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Mean_MAE</th>\n",
              "      <th>Mean_RMSE</th>\n",
              "      <th>Mean_MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SARIMAX</td>\n",
              "      <td>39065.570699</td>\n",
              "      <td>41653.580428</td>\n",
              "      <td>202.545321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ExtraTrees</td>\n",
              "      <td>6212.944419</td>\n",
              "      <td>9756.073708</td>\n",
              "      <td>21.518841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LSTM</td>\n",
              "      <td>15962.589722</td>\n",
              "      <td>19865.340649</td>\n",
              "      <td>76.921895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d436747-e1d4-4130-90c3-4f2fe831aebc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d436747-e1d4-4130-90c3-4f2fe831aebc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d436747-e1d4-4130-90c3-4f2fe831aebc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "summary_metrics",
              "summary": "{\n  \"name\": \"summary_metrics\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"SARIMAX\",\n          \"ExtraTrees\",\n          \"LSTM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16872.55348709007,\n        \"min\": 6212.944418824744,\n        \"max\": 39065.57069894436,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39065.57069894436,\n          6212.944418824744,\n          15962.589722437866\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16301.204483787184,\n        \"min\": 9756.07370750035,\n        \"max\": 41653.58042782737,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          41653.58042782737,\n          9756.07370750035,\n          19865.340648744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean_MAPE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92.75535013948851,\n        \"min\": 21.518841192640885,\n        \"max\": 202.54532094080346,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          202.54532094080346,\n          21.518841192640885,\n          76.9218950662408\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "summary_metrics = pd.DataFrame({\n",
        "    \"Model\": [\"SARIMAX\", \"ExtraTrees\", \"LSTM\"],\n",
        "    \"Mean_MAE\": [\n",
        "        logs_df[\"SARIMAX_MAE\"].mean(),\n",
        "        logs_df[\"ET_MAE\"].mean(),\n",
        "        logs_df[\"LSTM_MAE\"].mean()\n",
        "    ],\n",
        "    \"Mean_RMSE\": [\n",
        "        logs_df[\"SARIMAX_RMSE\"].mean(),\n",
        "        logs_df[\"ET_RMSE\"].mean(),\n",
        "        logs_df[\"LSTM_RMSE\"].mean()\n",
        "    ],\n",
        "    \"Mean_MAPE\": [\n",
        "        logs_df[\"SARIMAX_MAPE\"].mean(),\n",
        "        logs_df[\"ET_MAPE\"].mean(),\n",
        "        logs_df[\"LSTM_MAPE\"].mean()\n",
        "    ]\n",
        "})\n",
        "\n",
        "display(summary_metrics)\n",
        "\n",
        "logs_df.to_csv(\"model_logs_v3.csv\", index=False)\n",
        "predictions_df.to_csv(\"predictions_all_blocks_v3.csv\", index=False)\n",
        "summary_metrics.to_csv(\"summary_metrics_v3.csv\", index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c96ab23",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "3c96ab23",
        "outputId": "63b4ee4c-b5d3-4371-cfa6-34d2152f8e42"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(logs_df[\"chunk_number\"], logs_df[\"SARIMAX_MAE\"], marker=\"o\", label=\"SARIMAX\")\n",
        "plt.plot(logs_df[\"chunk_number\"], logs_df[\"ET_MAE\"], marker=\"o\", label=\"ExtraTrees\")\n",
        "plt.plot(logs_df[\"chunk_number\"], logs_df[\"LSTM_MAE\"], marker=\"o\", label=\"LSTM\")\n",
        "plt.xlabel(\"Test block\")\n",
        "plt.ylabel(\"MAE\")\n",
        "plt.title(\"MAE моделей по тестовым блокам\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c5229ce",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "0c5229ce",
        "outputId": "cf72682d-184b-4732-8412-937ec03b62af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(14, 6))\n",
        "plt.plot(predictions_df[\"date\"], predictions_df[\"y_true\"], label=\"Истинные значения\")\n",
        "plt.plot(predictions_df[\"date\"], predictions_df[\"SARIMAX_pred\"], label=\"SARIMAX\")\n",
        "plt.plot(predictions_df[\"date\"], predictions_df[\"ExtraTrees_pred\"], label=\"ExtraTrees\")\n",
        "plt.plot(predictions_df[\"date\"], predictions_df[\"LSTM_pred\"], label=\"LSTM\")\n",
        "plt.xlabel(\"Дата\")\n",
        "plt.ylabel(\"daily_revenue\")\n",
        "plt.title(\"Сравнение истинных значений и прогнозов\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9777cb59",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "9777cb59",
        "outputId": "d20b696a-8a5a-4bfb-c9d7-4f1cc0eae3a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "base_mapping = {\"SARIMAX\": 0, \"ExtraTrees\": 1, \"LSTM\": 2}\n",
        "reverse_mapping = {v: k for k, v in base_mapping.items()}\n",
        "\n",
        "base_numeric = logs_df[\"base_model_after_step\"].map(base_mapping)\n",
        "\n",
        "plt.figure(figsize=(10, 4))\n",
        "plt.step(logs_df[\"chunk_number\"], base_numeric, where=\"mid\")\n",
        "plt.yticks(list(reverse_mapping.keys()), list(reverse_mapping.values()))\n",
        "plt.xlabel(\"Test block\")\n",
        "plt.ylabel(\"Base model\")\n",
        "plt.title(\"Изменение базовой модели по тестовым блокам\")\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7980654f",
      "metadata": {
        "id": "7980654f"
      },
      "source": [
        "В ходе работы были сравнены три модели прогнозирования временного ряда: SARIMAX, ExtraTrees и LSTM. По результатам эксперимента на всех 10 тестовых блоках наилучшее качество показала модель ExtraTrees. Она имела наименьшие значения ошибок по метрикам MAE, RMSE и MAPE, а также сохраняла лидерство на протяжении всего тестового периода. Базовая модель ни разу не менялась, переобучение не потребовалось. Следовательно, для данного набора данных наиболее эффективной моделью прогнозирования оказалась ExtraTrees."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU",
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}