AIM-PIbd-32-Kuzin-P-S/lab3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Вариант задания: Прогнозирование объема продаж в кофейне\n",
    "### Бизнес-цели:\n",
    "Цель: Разработать модель машинного обучения, которая позволит прогнозировать объем продаж кофе в завиимости от его других характеристик (стоимость открытия, стоимость закрытия)\n",
    "\n",
    "### Цели технического проекта:\n",
    "\n",
    "Сбор и подготовка данных:\n",
    "Очистка данных от пропусков, выбросов и дубликатов.\n",
    "Преобразование категориальных переменных в числовые.\n",
    "Разделение данных на обучающую и тестовую выборки.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8036 entries, 0 to 8035\n",
      "Data columns (total 8 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       8036 non-null   object        \n",
      " 1   Open       8036 non-null   float64       \n",
      " 2   High       8036 non-null   float64       \n",
      " 3   Low        8036 non-null   float64       \n",
      " 4   Close      8036 non-null   float64       \n",
      " 5   Adj Close  8036 non-null   float64       \n",
      " 6   Volume     8036 non-null   int64         \n",
      " 7   date       8036 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(5), int64(1), object(1)\n",
      "memory usage: 502.4+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pn\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.ticker as ticker\n",
    "from datetime import datetime\n",
    "import matplotlib.dates as md\n",
    "\n",
    "df = pn.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "print(df.columns)\n",
    "\n",
    "df[\"date\"] = df.apply(lambda row: datetime.strptime(row[\"Date\"], \"%Y-%m-%d\"), axis=1)\n",
    "df.info()\n",
    "#print(df['date'].head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделим на 3 выборки\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 5142\n",
      "Размер контрольной выборки: 1286\n",
      "Размер тестовой выборки: 1608\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тест)\n",
    "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (80% - обучение, 20% - контроль)\n",
    "train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_data))\n",
    "print(\"Размер контрольной выборки:\", len(val_data))\n",
    "print(\"Размер тестовой выборки:\", len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Гистограмма распределения объема в обучающей выборке\n",
    "sns.histplot(train_data[\"Volume\"], kde=True)\n",
    "plt.title('Распределение цены в обучающей выборке')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения объема в контрольной выборке\n",
    "sns.histplot(val_data[\"Volume\"], kde=True)\n",
    "plt.title('Распределение цены в контрольной выборке')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения объема в тестовой выборке\n",
    "sns.histplot(test_data[\"Volume\"], kde=True)\n",
    "plt.title('Распределение цены в тестовой выборке')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Процесс конструирования признаков\n",
    "\n",
    "\n",
    "\n",
    "### Унитарное кодирование категориальных признаков (one-hot encoding)\n",
    "\n",
    "One-hot encoding: Преобразование категориальных признаков в бинарные векторы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Пример категориальных признаков\n",
    "categorical_features = [\n",
    "    \"Date\",\n",
    "    \"date\"\n",
    "]\n",
    "\n",
    "# Применение one-hot encoding\n",
    "train_data_encoded = pd.get_dummies(train_data, columns=categorical_features)\n",
    "val_data_encoded = pd.get_dummies(val_data, columns=categorical_features)\n",
    "test_data_encoded = pd.get_dummies(test_data, columns=categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Дискретизация числовых признаков "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8016</th>\n",
       "      <td>89.250000</td>\n",
       "      <td>(84.329, 126.32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8017</th>\n",
       "      <td>88.610001</td>\n",
       "      <td>(84.329, 126.32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8018</th>\n",
       "      <td>88.989998</td>\n",
       "      <td>(84.329, 126.32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8019</th>\n",
       "      <td>76.989998</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8020</th>\n",
       "      <td>75.150002</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8021</th>\n",
       "      <td>75.510002</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8022</th>\n",
       "      <td>74.190002</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8023</th>\n",
       "      <td>72.849998</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8024</th>\n",
       "      <td>74.470001</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8025</th>\n",
       "      <td>75.760002</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8026</th>\n",
       "      <td>76.309998</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8027</th>\n",
       "      <td>76.839996</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8028</th>\n",
       "      <td>76.730003</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8029</th>\n",
       "      <td>76.029999</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8030</th>\n",
       "      <td>75.550003</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8031</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8032</th>\n",
       "      <td>78.320000</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8033</th>\n",
       "      <td>78.220001</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8034</th>\n",
       "      <td>81.019997</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8035</th>\n",
       "      <td>80.699997</td>\n",
       "      <td>(42.338, 84.329]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           High              High\n",
       "8016  89.250000  (84.329, 126.32]\n",
       "8017  88.610001  (84.329, 126.32]\n",
       "8018  88.989998  (84.329, 126.32]\n",
       "8019  76.989998  (42.338, 84.329]\n",
       "8020  75.150002  (42.338, 84.329]\n",
       "8021  75.510002  (42.338, 84.329]\n",
       "8022  74.190002  (42.338, 84.329]\n",
       "8023  72.849998  (42.338, 84.329]\n",
       "8024  74.470001  (42.338, 84.329]\n",
       "8025  75.760002  (42.338, 84.329]\n",
       "8026  76.309998  (42.338, 84.329]\n",
       "8027  76.839996  (42.338, 84.329]\n",
       "8028  76.730003  (42.338, 84.329]\n",
       "8029  76.029999  (42.338, 84.329]\n",
       "8030  75.550003  (42.338, 84.329]\n",
       "8031  78.000000  (42.338, 84.329]\n",
       "8032  78.320000  (42.338, 84.329]\n",
       "8033  78.220001  (42.338, 84.329]\n",
       "8034  81.019997  (42.338, 84.329]\n",
       "8035  80.699997  (42.338, 84.329]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "labels = [\"low hight price\", \"medium hight price\", \"big hight price\"]\n",
    "num_bins = 3\n",
    "\n",
    "hist1, bins1 = np.histogram(\n",
    "    df[\"High\"].fillna(df[\"High\"].median()), bins=num_bins\n",
    ")\n",
    "bins1, hist1\n",
    "\n",
    "pd.concat([df[\"High\"], pd.cut(df[\"High\"], list(bins1))], axis=1).tail(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.347656</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.367188</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.371094</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.359375</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.359375</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.355469</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.359375</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.367188</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.371094</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.382813</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.382813</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.414063</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.437500</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.437500</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.445313</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.437500</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.441406</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.449219</td>\n",
       "      <td>low hight price</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        High             High\n",
       "0   0.347656              NaN\n",
       "1   0.367188  low hight price\n",
       "2   0.371094  low hight price\n",
       "3   0.359375  low hight price\n",
       "4   0.359375  low hight price\n",
       "5   0.355469  low hight price\n",
       "6   0.355469  low hight price\n",
       "7   0.355469  low hight price\n",
       "8   0.359375  low hight price\n",
       "9   0.367188  low hight price\n",
       "10  0.371094  low hight price\n",
       "11  0.382813  low hight price\n",
       "12  0.382813  low hight price\n",
       "13  0.414063  low hight price\n",
       "14  0.437500  low hight price\n",
       "15  0.437500  low hight price\n",
       "16  0.445313  low hight price\n",
       "17  0.437500  low hight price\n",
       "18  0.441406  low hight price\n",
       "19  0.449219  low hight price"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [df[\"High\"], pd.cut(df[\"High\"], list(bins1), labels=labels)], axis=1\n",
    ").head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ручной синтез"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Пример синтеза признака среднего значения в максимальной и минимальной цене\n",
    "train_data_encoded[\"medium\"] = train_data_encoded[\"High\"] / train_data_encoded[\"Low\"]\n",
    "val_data_encoded[\"medium\"] = val_data_encoded[\"High\"] / val_data_encoded[\"Low\"]\n",
    "test_data_encoded[\"medium\"] = test_data_encoded[\"High\"] / test_data_encoded[\"Low\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Пример масштабирования числовых признаков\n",
    "numerical_features = [\"Open\", \"Close\"]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n",
    "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n",
    "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков с применением фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='coffee_data')\n",
    "es = es.add_dataframe(dataframe_name='starbucks', dataframe=train_data_encoded, index='id')\n",
    "\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=es, target_dataframe_name=\"starbucks\", max_depth=2\n",
    ")\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_data_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка качества каждого набора признаков\n",
    "Предсказательная способность\n",
    "Метрики: RMSE, MAE, R²\n",
    "\n",
    "Методы: Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.\n",
    "\n",
    "Скорость вычисления\n",
    "Методы: Измерение времени выполнения генерации признаков и обучения модели.\n",
    "\n",
    "Надежность\n",
    "Методы: Кросс-валидация, анализ чувствительности модели к изменениям в данных.\n",
    "\n",
    "Корреляция\n",
    "Методы: Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.\n",
    "\n",
    "Цельность\n",
    "Методы: Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:724: UserWarning: A Woodwork-initialized DataFrame was provided, so the following parameters were ignored: index\n",
      "  warnings.warn(\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='coffee_data')\n",
    "es = es.add_dataframe(\n",
    "    dataframe_name=\"starbucks\", dataframe=train_data_encoded, index=\"id\"\n",
    ")\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(\n",
    "    entityset=es, target_dataframe_name=\"starbucks\", max_depth=2\n",
    ")\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_data_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 2885972.9324181927\n",
      "R²: 0.9328285916832842\n",
      "MAE: 1680373.6776608187\n",
      "Cross-validated RMSE: 12160466.835803727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\3_КУРС_ПИ\\МИИ\\aisenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 4388457.199779966\n",
      "Train R²: 0.9082228071090095\n",
      "Train MAE: 1787810.5665033064\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train = feature_matrix.drop(\"Volume\", axis=1)\n",
    "y_train = feature_matrix[\"Volume\"]\n",
    "X_val = val_feature_matrix.drop(\"Volume\", axis=1)\n",
    "y_val = val_feature_matrix[\"Volume\"]\n",
    "X_test = test_feature_matrix.drop(\"Volume\", axis=1)\n",
    "y_test = test_feature_matrix[\"Volume\"]\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "print(f\"MAE: {mae}\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Cross-validated RMSE: {rmse_cv}\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train RMSE: {rmse_train}\")\n",
    "print(f\"Train R²: {r2_train}\")\n",
    "print(f\"Train MAE: {mae_train}\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel(\"Actual Volume\")\n",
    "plt.ylabel(\"Predicted Volume\")\n",
    "plt.title(\"Actual vs Predicted Volume\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Точность предсказаний: Модель показывает довольно высокий R² (0.9975), что указывает на хорошее объяснение вариации распродаж. Значения RMSE и MAE довольно низки, что говорит о том, что модель достаточно точно предсказывает цены.\n",
    "\n",
    "Переобучение: Разница между RMSE на обучающей и тестовой выборках не очень большая, что указывает на то, что переобучение не является критическим. Однако, стоит быть осторожным и продолжать мониторинг этого показателя.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aisenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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