From 264d0a4de81816adf23579d419cd7cceeb6f674f Mon Sep 17 00:00:00 2001
From: MaD <mdv_andrey@mail.ru>
Date: Mon, 9 Dec 2024 00:14:35 +0400
Subject: [PATCH 01/13] =?UTF-8?q?=D0=B3=D0=B5=D0=BD=D0=B5=D1=80=D0=B0?=
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 =?UTF-8?q?=D0=BE=D0=B2=20=D0=BA=D1=80=D0=B8=D0=BD=D0=B6,=20=D0=BF=D0=BE?=
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 .gitignore       | 1 +
 Lab_3/lab3.ipynb | 0
 2 files changed, 1 insertion(+)
 create mode 100644 Lab_3/lab3.ipynb

diff --git a/.gitignore b/.gitignore
index 1d91e1a..f0028f5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 data/jio_mart_items.csv
 /data
 /Lab_2/lab_2.ipynb
+/Lab_3/lab_3.ipynb
\ No newline at end of file
diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb
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index 0000000..e69de29

From 19073f816e2dc7c92de4b7af430cb1fee351e181 Mon Sep 17 00:00:00 2001
From: MaD <mdv_andrey@mail.ru>
Date: Sun, 15 Dec 2024 19:50:26 +0400
Subject: [PATCH 02/13] =?UTF-8?q?=D0=BF=D1=80=D0=B0=D0=B2=D0=BA=D0=B8?=
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---
 Lab_3/lab3.ipynb | 236 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 236 insertions(+)

diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb
index e69de29..b129952 100644
--- a/Lab_3/lab3.ipynb
+++ b/Lab_3/lab3.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, LabelEncoder\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "import featuretools as ft\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:1: SyntaxWarning: invalid escape sequence '\\j'\n",
+      "<>:1: SyntaxWarning: invalid escape sequence '\\j'\n",
+      "C:\\Users\\MaD\\AppData\\Local\\Temp\\ipykernel_6188\\750029597.py:1: SyntaxWarning: invalid escape sequence '\\j'\n",
+      "  df = pd.read_csv(\"../data\\jio_mart_items.csv\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 162313 entries, 0 to 162312\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count   Dtype  \n",
+      "---  ------        --------------   -----  \n",
+      " 0   category      162313 non-null  object \n",
+      " 1   sub_category  162313 non-null  object \n",
+      " 2   href          162313 non-null  object \n",
+      " 3   items         162280 non-null  object \n",
+      " 4   price         162282 non-null  float64\n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 6.2+ MB\n",
+      "None\n",
+      "Пропущенные значения:\n",
+      " category         0\n",
+      "sub_category     0\n",
+      "href             0\n",
+      "items           33\n",
+      "price           31\n",
+      "dtype: int64\n",
+      "              price\n",
+      "count  1.622820e+05\n",
+      "mean   1.991633e+03\n",
+      "std    1.593479e+04\n",
+      "min    5.000000e+00\n",
+      "25%    2.840000e+02\n",
+      "50%    4.990000e+02\n",
+      "75%    9.990000e+02\n",
+      "max    3.900000e+06\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../data\\jio_mart_items.csv\")\n",
+    "\n",
+    "print(df.info())\n",
+    "# print(df.head())\n",
+    "\n",
+    "print(\"Пропущенные значения:\\n\", df.isnull().sum())\n",
+    "\n",
+    "print(df.describe())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Бизнес-цели:\n",
+    "1. Предсказать категорию продукта (классификация), чтобы рекомендовать новые товары на основе текущей базы.\n",
+    "2. Определить ценовой диапазон (дискретизация + регрессия), чтобы лучше сегментировать продукты.\n",
+    "\n",
+    "Технические цели:\n",
+    "Для цели 1: Разработка модели классификации для предсказания категории продукта.\n",
+    "Для цели 2: Разработка модели, предсказывающей ценовой диапазон продукта.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Удаление бесполезных столбцов\n",
+    "df = df.drop(columns=[\"Product_ID\", \"Unnamed: 0\"], errors=\"ignore\")\n",
+    "\n",
+    "# Обработка пропущенных значений\n",
+    "imputer = SimpleImputer(strategy=\"most_frequent\")  # Для категориальных данных\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)\n",
+    "\n",
+    "# Преобразование числовых столбцов\n",
+    "numeric_cols = df.select_dtypes(include=[\"float64\", \"int64\"]).columns\n",
+    "categorical_cols = df.select_dtypes(include=[\"object\"]).columns\n",
+    "\n",
+    "# Дискретизация ценового диапазона, разобъём его на 10 категорий\n",
+    "df[\"Price_Range\"] = pd.qcut(df[\"price\"], q=10, labels=False)\n",
+    "\n",
+    "# Кодирование категорий\n",
+    "encoder = LabelEncoder()\n",
+    "for col in categorical_cols:\n",
+    "    df[col] = encoder.fit_transform(df[col])\n",
+    "\n",
+    "# Проверяем результат\n",
+    "# print(df.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Построем график распределение категорий чтобы убедится в верной дискретизации\n",
+    "# sns.countplot(data=df, x=\"Price_Range\", palette=\"viridis\")\n",
+    "# plt.title(\"Распределение значений Price_Range\")\n",
+    "# plt.xlabel(\"Диапазон цен (Price_Range)\")\n",
+    "# plt.ylabel(\"Количество товаров\")\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape (classification): (97387, 4)\n",
+      "Validation shape (classification): (32463, 4)\n",
+      "Test shape (classification): (32463, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Разделение данных на X и y для каждой задачи\n",
+    "X = df.drop(columns=[\"category\", \"Price_Range\"])  # Признаки\n",
+    "y_classification = df[\"category\"]  # Для первой цели (категория продукта)\n",
+    "y_regression = df[\"Price_Range\"]  # Для второй цели (ценовой диапазон)\n",
+    "\n",
+    "# Разбиение данных\n",
+    "X_train, X_temp, y_train_class, y_temp_class = train_test_split(X, y_classification, test_size=0.4, stratify=y_classification, random_state=42)\n",
+    "X_val, X_test, y_val_class, y_test_class = train_test_split(X_temp, y_temp_class, test_size=0.5, stratify=y_temp_class, random_state=42)\n",
+    "\n",
+    "X_train_reg, X_temp_reg, y_train_reg, y_temp_reg = train_test_split(X, y_regression, test_size=0.4, stratify=y_regression, random_state=42)\n",
+    "X_val_reg, X_test_reg, y_val_reg, y_test_reg = train_test_split(X_temp_reg, y_temp_reg, test_size=0.5, stratify=y_temp_reg, random_state=42)\n",
+    "\n",
+    "# Проверяем размеры выборок\n",
+    "print(\"Train shape (classification):\", X_train.shape)\n",
+    "print(\"Validation shape (classification):\", X_val.shape)\n",
+    "print(\"Test shape (classification):\", X_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение классов (Classification):\n",
+      " category\n",
+      "4    36201\n",
+      "3    27626\n",
+      "2    15660\n",
+      "1    11413\n",
+      "0     6445\n",
+      "5       42\n",
+      "Name: count, dtype: int64\n",
+      "Распределение классов после балансировки:\n",
+      " category\n",
+      "4    36201\n",
+      "3    36201\n",
+      "1    36201\n",
+      "2    36201\n",
+      "0    36201\n",
+      "5    36201\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Проверяем сбалансированность\n",
+    "print(\"Распределение классов (Classification):\\n\", y_train_class.value_counts())\n",
+    "\n",
+    "# Применяем SMOTE для балансировки классов\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train_class)\n",
+    "\n",
+    "# Проверяем результат\n",
+    "print(\"Распределение классов после балансировки:\\n\", pd.Series(y_train_balanced).value_counts())\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 2c9c5ed30b1b52d737c2179177a5a275225e7a26 Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Fri, 20 Dec 2024 12:16:47 +0400
Subject: [PATCH 03/13] =?UTF-8?q?=D0=B2=20=D0=BF=D1=80=D0=BE=D1=86=D0=B5?=
 =?UTF-8?q?=D1=81=D1=81=D0=B5?=
MIME-Version: 1.0
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Content-Transfer-Encoding: 8bit

---
 Lab_4/lab4.ipynb                    |   0
 Lab_4/lab_4.ipynb                   | 566 ++++++++++++++++++++++++++++
 Lab_4/lab_products_clustering.ipynb | 216 +++++++++++
 3 files changed, 782 insertions(+)
 create mode 100644 Lab_4/lab4.ipynb
 create mode 100644 Lab_4/lab_4.ipynb
 create mode 100644 Lab_4/lab_products_clustering.ipynb

diff --git a/Lab_4/lab4.ipynb b/Lab_4/lab4.ipynb
new file mode 100644
index 0000000..e69de29
diff --git a/Lab_4/lab_4.ipynb b/Lab_4/lab_4.ipynb
new file mode 100644
index 0000000..a6f7a98
--- /dev/null
+++ b/Lab_4/lab_4.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Вариант: Список людей. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Id                 10000 non-null  object \n",
+      " 1   Name               10000 non-null  object \n",
+      " 2   Short description  9996 non-null   object \n",
+      " 3   Gender             9927 non-null   object \n",
+      " 4   Country            9721 non-null   object \n",
+      " 5   Occupation         9836 non-null   object \n",
+      " 6   Birth year         10000 non-null  int64  \n",
+      " 7   Death year         9999 non-null   float64\n",
+      " 8   Manner of death    1893 non-null   object \n",
+      " 9   Age of death       9999 non-null   float64\n",
+      "dtypes: float64(2), int64(1), object(7)\n",
+      "memory usage: 781.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn import metrics\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.metrics import (\n",
+    "    precision_score, recall_score, accuracy_score, roc_auc_score, f1_score,\n",
+    "    matthews_corrcoef, cohen_kappa_score, confusion_matrix\n",
+    ")\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "import numpy as np\n",
+    "import featuretools as ft\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "\n",
+    "# Функция для применения oversampling\n",
+    "def apply_oversampling(X, y):\n",
+    "    oversampler = RandomOverSampler(random_state=42)\n",
+    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
+    "    return X_resampled, y_resampled\n",
+    "\n",
+    "# Функция для применения undersampling\n",
+    "def apply_undersampling(X, y):\n",
+    "    undersampler = RandomUnderSampler(random_state=42)\n",
+    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
+    "    return X_resampled, y_resampled\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
+    "    following fractional ratios provided by the user, where each subset is\n",
+    "    stratified by the values in a specific column (that is, each subset has\n",
+    "    the same relative frequency of the values in the column). It performs this\n",
+    "    splitting by running train_test_split() twice.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df_input : Pandas dataframe\n",
+    "        Input dataframe to be split.\n",
+    "    stratify_colname : str\n",
+    "        The name of the column that will be used for stratification. Usually\n",
+    "        this column would be for the label.\n",
+    "    frac_train : float\n",
+    "    frac_val   : float\n",
+    "    frac_test  : float\n",
+    "        The ratios with which the dataframe will be split into train, val, and\n",
+    "        test data. The values should be expressed as float fractions and should\n",
+    "        sum to 1.0.\n",
+    "    random_state : int, None, or RandomStateInstance\n",
+    "        Value to be passed to train_test_split().\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    df_train, df_val, df_test :\n",
+    "        Dataframes containing the three splits.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input  # Contains all columns.\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "\n",
+    "    # Split original dataframe into train and temp dataframes.\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    # Split the temp dataframe into val and test dataframes.\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/age.csv\", nrows=10000)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Как бизнес-цели выделим следующие 2 варианта:\n",
+    "    1) GameDev. Создание игры про конкретного персонажа, живущего в конкретном временном промежутке в конкретной стране. \n",
+    "    2) Исследование зависимости длительности жизни от страны проживания.\n",
+    "    \n",
+    "Поскольку именно эти бизнес-цели были выбраны в предыдущей лабораторной работе, будем их использовать.\n",
+    "Но возникает проблема с 1 целью: её невозможно использовать для задачи классификации. Заменим ее на классификацию людей по возрастным группам, что может быть полезно для рекламных целей."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выполним подготовку данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.fillna({\"Gender\": \"NaN\", \"Country\": \"NaN\", \"Occupation\" : \"NaN\", \"Manner of death\" : \"NaN\"}, inplace=True)\n",
+    "df = df.dropna()\n",
+    "df['Country'] = df['Country'].str.split('; ')\n",
+    "df = df.explode('Country')\n",
+    "data = df.copy()\n",
+    "\n",
+    "value_counts = data[\"Country\"].value_counts()\n",
+    "rare = value_counts[value_counts < 100].index\n",
+    "data = data[~data[\"Country\"].isin(rare)]\n",
+    "\n",
+    "data.drop(data[~data['Gender'].isin(['Male', 'Female'])].index, inplace=True)\n",
+    "\n",
+    "data1 = pd.get_dummies(data, columns=['Gender', 'Country', 'Occupation'], drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей не будет высоким, поскольку все таки длительность жизни лишь примерная и точно ее угадать невозможно."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выберем ориентиры для наших 2х задач:\n",
+    "    1)Регрессии - средний возраст человека\n",
+    "    2)Классификации - аиболее часто встречающаяся возрастная группа"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Построим конвейер."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Id', 'Name', 'Short description', 'Birth year', 'Death year',\n",
+      "       'Age of death', 'Gender_Male', 'Country_France',\n",
+      "       'Country_German Confederation', 'Country_German Democratic Republic',\n",
+      "       ...\n",
+      "       'Manner of death_euthanasia', 'Manner of death_homicide',\n",
+      "       'Manner of death_homicide; natural causes',\n",
+      "       'Manner of death_internal bleeding', 'Manner of death_natural causes',\n",
+      "       'Manner of death_suicide',\n",
+      "       'Manner of death_suicide; homicide; accident',\n",
+      "       'Manner of death_suicide; unfortunate accident',\n",
+      "       'Manner of death_summary execution', 'Manner of death_unnatural death'],\n",
+      "      dtype='object', length=400)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.columns)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters for Linear Regression: {}\n",
+      "Best parameters for Random Forest Regressor: {'model__max_depth': None, 'model__n_estimators': 100}\n",
+      "Best parameters for Gradient Boosting Regressor: {'model__learning_rate': 0.2, 'model__max_depth': 7, 'model__n_estimators': 300}\n",
+      "Linear Regression: MSE = 0.002807184047660083, R2 = 0.9999899555289343\n",
+      "Random Forest Regressor: MSE = 11.46917740409879, R2 = 0.9589617856804076\n",
+      "Gradient Boosting Regressor: MSE = 8.202651735797296, R2 = 0.9706498410424512\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_reg = data1.drop(['Id', 'Name', 'Age of death', 'Short description', 'Manner of death'], axis=1)\n",
+    "y_reg = data1['Age of death']\n",
+    "\n",
+    "# Разделение данных\n",
+    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Выбор моделей для регрессии\n",
+    "models_reg = {\n",
+    "    'Linear Regression': LinearRegression(),\n",
+    "    'Random Forest Regressor': RandomForestRegressor(random_state=42),\n",
+    "    'Gradient Boosting Regressor': GradientBoostingRegressor(random_state=42)\n",
+    "}\n",
+    "\n",
+    "# Создание конвейера для регрессии\n",
+    "pipelines_reg = {}\n",
+    "for name, model in models_reg.items():\n",
+    "    pipelines_reg[name] = Pipeline([\n",
+    "        ('scaler', StandardScaler()),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "\n",
+    "# Определение сетки гиперпараметров для регрессии\n",
+    "param_grids_reg = {\n",
+    "    'Linear Regression': {},\n",
+    "    'Random Forest Regressor': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__max_depth': [None, 10, 20, 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Regressor': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Настройка гиперпараметров для регрессии\n",
+    "best_models_reg = {}\n",
+    "for name, pipeline in pipelines_reg.items():\n",
+    "    grid_search = GridSearchCV(pipeline, param_grids_reg[name], cv=5, scoring='neg_mean_squared_error')\n",
+    "    grid_search.fit(X_train_reg, y_train_reg)\n",
+    "    best_models_reg[name] = grid_search.best_estimator_\n",
+    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n",
+    "\n",
+    "# Обучение моделей и оценка качества\n",
+    "for model_name in best_models_reg.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    model = best_models_reg[model_name][\"model\"]\n",
+    "\n",
+    "    model_pipeline = Pipeline([(\"scaler\", StandardScaler()), (\"model\", model)])\n",
+    "    model_pipeline = model_pipeline.fit(X_train_reg, y_train_reg)\n",
+    "\n",
+    "    y_train_predict = model_pipeline.predict(X_train_reg)\n",
+    "    y_test_predict = model_pipeline.predict(X_test_reg)\n",
+    "\n",
+    "    best_models_reg[model_name][\"pipeline\"] = model_pipeline\n",
+    "    best_models_reg[model_name][\"preds_train\"] = y_train_predict\n",
+    "    best_models_reg[model_name][\"preds_test\"] = y_test_predict\n",
+    "\n",
+    "    best_models_reg[model_name][\"MSE_train\"] = mean_squared_error(y_train_reg, y_train_predict)\n",
+    "    best_models_reg[model_name][\"MSE_test\"] = mean_squared_error(y_test_reg, y_test_predict)\n",
+    "    best_models_reg[model_name][\"R2_train\"] = r2_score(y_train_reg, y_train_predict)\n",
+    "    best_models_reg[model_name][\"R2_test\"] = r2_score(y_test_reg, y_test_predict)\n",
+    "    best_models_reg[model_name][\"MAE_train\"] = mean_absolute_error(y_train_reg, y_train_predict)\n",
+    "    best_models_reg[model_name][\"MAE_test\"] = mean_absolute_error(y_test_reg, y_test_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data2 = data.drop(['Short description', 'Manner of death', 'Gender', 'Country', 'Occupation'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Birth year', 'Death year'], dtype='object')\n",
+      "Best parameters for Logistic Regression: {'model__C': 10, 'model__solver': 'lbfgs'}\n",
+      "Best parameters for Random Forest Classifier: {'model__max_depth': 30, 'model__n_estimators': 200}\n",
+      "Best parameters for Gradient Boosting Classifier: {'model__learning_rate': 0.1, 'model__max_depth': 7, 'model__n_estimators': 200}\n",
+      "Model: Logistic Regression\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\89176\\sourse\\MII\\Labas\\AIM-PIbd-31-Kozyrev-S-S\\aimvenv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\89176\\sourse\\MII\\Labas\\AIM-PIbd-31-Kozyrev-S-S\\aimvenv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: Random Forest Classifier\n",
+      "Model: Gradient Boosting Classifier\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Создание возрастных групп\n",
+    "bins = [0, 18, 30, 50, 70, 100]\n",
+    "labels = ['0-18', '19-30', '31-50', '51-70', '71+']\n",
+    "data['Age Group'] = pd.cut(data['Age of death'], bins=bins, labels=labels)\n",
+    "\n",
+    "# Выбор признаков и целевой переменной для классификации\n",
+    "X_class = data2.drop(['Id', 'Name', 'Age of death', 'Age Group'], axis=1)\n",
+    "y_class = data['Age Group']  \n",
+    "print(X_class.columns)\n",
+    "# Разделение данных\n",
+    "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Выбор моделей для классификации\n",
+    "models_class = {\n",
+    "    'Logistic Regression': LogisticRegression(random_state=42, max_iter=5000, solver='liblinear'),\n",
+    "    'Random Forest Classifier': RandomForestClassifier(random_state=42),\n",
+    "    'Gradient Boosting Classifier': GradientBoostingClassifier(random_state=42)\n",
+    "}\n",
+    "\n",
+    "# Создание конвейера для классификации\n",
+    "pipelines_class = {}\n",
+    "for name, model in models_class.items():\n",
+    "    pipelines_class[name] = Pipeline([\n",
+    "        ('scaler', StandardScaler()),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "\n",
+    "# Определение сетки гиперпараметров для классификации\n",
+    "'''\n",
+    "param_grids_class = {\n",
+    "    'Logistic Regression': {\n",
+    "        'model__C': [0.1, 1, 10],\n",
+    "        'model__solver': ['lbfgs', 'liblinear']\n",
+    "    },\n",
+    "    'Random Forest Classifier': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__max_depth': [None, 10, 20, 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Classifier': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "}'''\n",
+    "# Убрал определение параметров поскольку уже был предподсчет данных, но вылетела ошибка. Сохранил лучшие параметры\n",
+    "\n",
+    "param_grids_class = {\n",
+    "    'Logistic Regression': {\n",
+    "        'model__C': [10],\n",
+    "        'model__solver': ['lbfgs']\n",
+    "    },\n",
+    "    'Random Forest Classifier': {\n",
+    "        'model__n_estimators': [200],\n",
+    "        'model__max_depth': [ 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Classifier': {\n",
+    "        'model__n_estimators': [200],\n",
+    "        'model__learning_rate': [0.1],\n",
+    "        'model__max_depth': [7]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Настройка гиперпараметров для классификации\n",
+    "best_models_class = {}\n",
+    "for name, pipeline in pipelines_class.items():\n",
+    "    grid_search = GridSearchCV(pipeline, param_grids_class[name], cv=5, scoring='accuracy', n_jobs=-1)\n",
+    "    grid_search.fit(X_train_class, y_train_class)\n",
+    "    best_models_class[name] = {\"model\": grid_search.best_estimator_}\n",
+    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n",
+    "\n",
+    "# Обучение моделей и оценка качества\n",
+    "for model_name in best_models_class.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    model = best_models_class[model_name][\"model\"]\n",
+    "\n",
+    "    model_pipeline = Pipeline([(\"scaler\", StandardScaler()), (\"model\", model)])\n",
+    "    model_pipeline = model_pipeline.fit(X_train_class, y_train_class)\n",
+    "\n",
+    "    y_train_predict = model_pipeline.predict(X_train_class)\n",
+    "    y_test_probs = model_pipeline.predict_proba(X_test_class)\n",
+    "    y_test_predict = model_pipeline.predict(X_test_class)\n",
+    "\n",
+    "    best_models_class[model_name][\"pipeline\"] = model_pipeline\n",
+    "    best_models_class[model_name][\"probs\"] = y_test_probs\n",
+    "    best_models_class[model_name][\"preds\"] = y_test_predict\n",
+    "\n",
+    "    best_models_class[model_name][\"Precision_train\"] = precision_score(y_train_class, y_train_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Precision_test\"] = precision_score(y_test_class, y_test_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Recall_train\"] = recall_score(y_train_class, y_train_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Recall_test\"] = recall_score(y_test_class, y_test_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Accuracy_train\"] = accuracy_score(y_train_class, y_train_predict)\n",
+    "    best_models_class[model_name][\"Accuracy_test\"] = accuracy_score(y_test_class, y_test_predict)\n",
+    "    best_models_class[model_name][\"ROC_AUC_test\"] = roc_auc_score(y_test_class, y_test_probs, multi_class='ovr')\n",
+    "    best_models_class[model_name][\"F1_train\"] = f1_score(y_train_class, y_train_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"F1_test\"] = f1_score(y_test_class, y_test_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"MCC_test\"] = matthews_corrcoef(y_test_class, y_test_predict)\n",
+    "    best_models_class[model_name][\"Cohen_kappa_test\"] = cohen_kappa_score(y_test_class, y_test_predict)\n",
+    "    best_models_class[model_name][\"Confusion_matrix\"] = confusion_matrix(y_test_class, y_test_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_models = len(best_models_class)\n",
+    "fig, ax = plt.subplots(num_models, 1, figsize=(12, 10), sharex=False, sharey=False)\n",
+    "for index, key in enumerate(best_models_class.keys()):\n",
+    "    c_matrix = best_models_class[key][\"Confusion_matrix\"]\n",
+    "    disp = ConfusionMatrixDisplay(\n",
+    "        confusion_matrix=c_matrix, display_labels=[\"0-18\", \"19-30\", \"31-50\", \"51-70\", \"71+\"]\n",
+    "    ).plot(ax=ax.flat[index])\n",
+    "    disp.ax_.set_title(key)\n",
+    "\n",
+    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax = plt.subplots(3, 2, figsize=(12, 10), sharex=False, sharey=False)\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "for index, (name, model) in enumerate(best_models_reg.items()):\n",
+    "    y_pred_reg = model.predict(X_test_reg)\n",
+    "\n",
+    "    # График фактических значений против предсказанных значений\n",
+    "    ax[index * 2].scatter(y_test_reg, y_pred_reg, alpha=0.5)\n",
+    "    ax[index * 2].plot([min(y_test_reg), max(y_test_reg)], [min(y_test_reg), max(y_test_reg)], color='red', linestyle='--')\n",
+    "    ax[index * 2].set_xlabel('Actual Values')\n",
+    "    ax[index * 2].set_ylabel('Predicted Values')\n",
+    "    ax[index * 2].set_title(f'{name}: Actual vs Predicted')\n",
+    "\n",
+    "    # График остатков\n",
+    "    residuals = y_test_reg - y_pred_reg\n",
+    "    ax[index * 2 + 1].scatter(y_pred_reg, residuals, alpha=0.5)\n",
+    "    ax[index * 2 + 1].axhline(y=0, color='red', linestyle='--')\n",
+    "    ax[index * 2 + 1].set_xlabel('Predicted Values')\n",
+    "    ax[index * 2 + 1].set_ylabel('Residuals')\n",
+    "    ax[index * 2 + 1].set_title(f'{name}: Residuals vs Predicted')\n",
+    "\n",
+    "\n",
+    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimvenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Lab_4/lab_products_clustering.ipynb b/Lab_4/lab_products_clustering.ipynb
new file mode 100644
index 0000000..e398c2e
--- /dev/null
+++ b/Lab_4/lab_products_clustering.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b33feadc",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа: Методы искусственного интеллекта\n",
+    "## Задача кластеризации продуктов\n",
+    "### Вариант: Продукты\n",
+    "В данной работе используется датасет с описанием продуктов. Цель: провести кластеризацию, чтобы выявить группы товаров на основе их характеристик. Это может быть полезно для сегментации ассортимента и оптимизации маркетинговых стратегий."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae0cdcc2",
+   "metadata": {},
+   "source": [
+    "### Бизнес-цель\n",
+    "Определить сегменты продуктов на основе характеристик из предоставленного датасета. Сегментация поможет:\n",
+    "- Разработать целевые рекламные кампании для различных групп товаров.\n",
+    "- Улучшить стратегию ценообразования."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29c45779",
+   "metadata": {},
+   "source": [
+    "### Загрузка и исследование данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dab41ace",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
+    "from sklearn.metrics import silhouette_score\n",
+    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv('your_dataset_path.csv')\n",
+    "df.info()  # Проверка структуры датасета\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "834f0e46",
+   "metadata": {},
+   "source": [
+    "### Предварительная обработка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14f5eb76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Обработка пропущенных значений\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# Проверим распределение числовых признаков\n",
+    "df.describe()\n",
+    "\n",
+    "# Нормализация данных\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "numeric_features = ['items', 'price']\n",
+    "df_scaled = scaler.fit_transform(df[numeric_features])\n",
+    "df_scaled = pd.DataFrame(df_scaled, columns=numeric_features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56fd1a00",
+   "metadata": {},
+   "source": [
+    "### Понижение размерности и визуализация данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c23ca5db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Применение PCA для понижения размерности\n",
+    "pca = PCA(n_components=2)\n",
+    "reduced_data = pca.fit_transform(df_scaled)\n",
+    "\n",
+    "# Визуализация данных\n",
+    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1])\n",
+    "plt.title('Визуализация данных после PCA')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd1339ee",
+   "metadata": {},
+   "source": [
+    "### Выбор оптимального количества кластеров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf6663df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Оценка инерции для выбора числа кластеров\n",
+    "inertia = []\n",
+    "silhouette_scores = []\n",
+    "k_range = range(2, 11)\n",
+    "for k in k_range:\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(reduced_data)\n",
+    "    inertia.append(kmeans.inertia_)\n",
+    "    silhouette_scores.append(silhouette_score(reduced_data, kmeans.labels_))\n",
+    "\n",
+    "# Построение графиков\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(k_range, inertia, marker='o')\n",
+    "plt.title('Критерий инерции')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Инерция')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(k_range, silhouette_scores, marker='o')\n",
+    "plt.title('Коэффициент силуэта')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Силуэт')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d55dd7f8",
+   "metadata": {},
+   "source": [
+    "### Кластерный анализ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d17981b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Кластеризация с KMeans\n",
+    "optimal_k = 4  # Выбираем на основе графиков\n",
+    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
+    "kmeans_labels = kmeans.fit_predict(reduced_data)\n",
+    "\n",
+    "# Визуализация кластеров\n",
+    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=kmeans_labels, cmap='viridis')\n",
+    "plt.title('Кластеры (KMeans)')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()\n",
+    "\n",
+    "# Иерархическая кластеризация\n",
+    "hierarchical = AgglomerativeClustering(n_clusters=optimal_k)\n",
+    "hierarchical_labels = hierarchical.fit_predict(reduced_data)\n",
+    "\n",
+    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=hierarchical_labels, cmap='viridis')\n",
+    "plt.title('Кластеры (Иерархическая кластеризация)')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98bd2b6e",
+   "metadata": {},
+   "source": [
+    "### Оценка качества кластеризации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82e73067",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Оценка коэффициента силуэта для KMeans и иерархической кластеризации\n",
+    "kmeans_silhouette = silhouette_score(reduced_data, kmeans_labels)\n",
+    "hierarchical_silhouette = silhouette_score(reduced_data, hierarchical_labels)\n",
+    "\n",
+    "print(f'Силуэт для KMeans: {kmeans_silhouette:.2f}')\n",
+    "print(f'Силуэт для иерархической кластеризации: {hierarchical_silhouette:.2f}')"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9546fabe3d5199e77f9c4237a75fde473a960609 Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Fri, 20 Dec 2024 12:24:06 +0400
Subject: [PATCH 04/13] =?UTF-8?q?=D0=B0=D0=B0=D0=B0=D0=B0=D0=B0=D0=B0?=
 =?UTF-8?q?=D0=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_4/lab_products_clustering.ipynb | 200 +++++++++++++++++++++++++---
 1 file changed, 185 insertions(+), 15 deletions(-)

diff --git a/Lab_4/lab_products_clustering.ipynb b/Lab_4/lab_products_clustering.ipynb
index e398c2e..a029afe 100644
--- a/Lab_4/lab_products_clustering.ipynb
+++ b/Lab_4/lab_products_clustering.ipynb
@@ -32,10 +32,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "dab41ace",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 162313 entries, 0 to 162312\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count   Dtype  \n",
+      "---  ------        --------------   -----  \n",
+      " 0   category      162313 non-null  object \n",
+      " 1   sub_category  162313 non-null  object \n",
+      " 2   href          162313 non-null  object \n",
+      " 3   items         162280 non-null  object \n",
+      " 4   price         162282 non-null  float64\n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 6.2+ MB\n"
+     ]
+    },
+    {
+     "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>category</th>\n",
+       "      <th>sub_category</th>\n",
+       "      <th>href</th>\n",
+       "      <th>items</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Groceries</td>\n",
+       "      <td>Fruits &amp; Vegetables</td>\n",
+       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
+       "      <td>Fresh Dates (Pack) (Approx 450 g - 500 g)</td>\n",
+       "      <td>109.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Groceries</td>\n",
+       "      <td>Fruits &amp; Vegetables</td>\n",
+       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
+       "      <td>Tender Coconut Cling Wrapped (1 pc) (Approx 90...</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Groceries</td>\n",
+       "      <td>Fruits &amp; Vegetables</td>\n",
+       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
+       "      <td>Mosambi 1 kg</td>\n",
+       "      <td>69.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Groceries</td>\n",
+       "      <td>Fruits &amp; Vegetables</td>\n",
+       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
+       "      <td>Orange Imported 1 kg</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Groceries</td>\n",
+       "      <td>Fruits &amp; Vegetables</td>\n",
+       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
+       "      <td>Banana Robusta 6 pcs (Box) (Approx 800 g - 110...</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    category         sub_category  \\\n",
+       "0  Groceries  Fruits & Vegetables   \n",
+       "1  Groceries  Fruits & Vegetables   \n",
+       "2  Groceries  Fruits & Vegetables   \n",
+       "3  Groceries  Fruits & Vegetables   \n",
+       "4  Groceries  Fruits & Vegetables   \n",
+       "\n",
+       "                                                href  \\\n",
+       "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+       "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+       "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+       "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+       "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+       "\n",
+       "                                               items  price  \n",
+       "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
+       "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
+       "2                                       Mosambi 1 kg   69.0  \n",
+       "3                               Orange Imported 1 kg  125.0  \n",
+       "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -44,9 +163,10 @@
     "from sklearn.metrics import silhouette_score\n",
     "from scipy.cluster.hierarchy import dendrogram, linkage\n",
     "import seaborn as sns\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
     "\n",
     "# Загрузка данных\n",
-    "df = pd.read_csv('your_dataset_path.csv')\n",
+    "df = pd.read_csv('../data/jio_mart_items.csv')\n",
     "df.info()  # Проверка структуры датасета\n",
     "df.head()"
    ]
@@ -61,23 +181,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "14f5eb76",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Обработка пропущенных значений\n",
-    "df.dropna(inplace=True)\n",
+    "# Преобразуем столбец 'items' в числовые категории\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
     "\n",
-    "# Проверим распределение числовых признаков\n",
-    "df.describe()\n",
+    "# Указываем числовые столбцы для нормализации\n",
+    "numeric_features = ['items_encoded', 'price']\n",
     "\n",
     "# Нормализация данных\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "scaler = StandardScaler()\n",
-    "numeric_features = ['items', 'price']\n",
     "df_scaled = scaler.fit_transform(df[numeric_features])\n",
-    "df_scaled = pd.DataFrame(df_scaled, columns=numeric_features)"
+    "\n",
+    "# Преобразуем обратно в DataFrame для удобства\n",
+    "df_scaled = pd.DataFrame(df_scaled, columns=numeric_features)\n",
+    "df_scaled = df_scaled.dropna()"
    ]
   },
   {
@@ -90,10 +213,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "c23ca5db",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Применение PCA для понижения размерности\n",
     "pca = PCA(n_components=2)\n",
@@ -117,10 +251,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "cf6663df",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[17], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m     kmeans\u001b[38;5;241m.\u001b[39mfit(reduced_data)\n\u001b[0;32m      8\u001b[0m     inertia\u001b[38;5;241m.\u001b[39mappend(kmeans\u001b[38;5;241m.\u001b[39minertia_)\n\u001b[1;32m----> 9\u001b[0m     silhouette_scores\u001b[38;5;241m.\u001b[39mappend(\u001b[43msilhouette_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduced_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabels_\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Построение графиков\u001b[39;00m\n\u001b[0;32m     12\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\_param_validation.py:216\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    211\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    212\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    213\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    214\u001b[0m         )\n\u001b[0;32m    215\u001b[0m     ):\n\u001b[1;32m--> 216\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    220\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    221\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    222\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    223\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    224\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    225\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    226\u001b[0m     )\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py:139\u001b[0m, in \u001b[0;36msilhouette_score\u001b[1;34m(X, labels, metric, sample_size, random_state, **kwds)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    138\u001b[0m         X, labels \u001b[38;5;241m=\u001b[39m X[indices], labels[indices]\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmean(silhouette_samples(X, labels, metric\u001b[38;5;241m=\u001b[39mmetric, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds))\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\_param_validation.py:189\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    187\u001b[0m global_skip_validation \u001b[38;5;241m=\u001b[39m get_config()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_parameter_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m global_skip_validation:\n\u001b[1;32m--> 189\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    191\u001b[0m func_sig \u001b[38;5;241m=\u001b[39m signature(func)\n\u001b[0;32m    193\u001b[0m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py:303\u001b[0m, in \u001b[0;36msilhouette_samples\u001b[1;34m(X, labels, metric, **kwds)\u001b[0m\n\u001b[0;32m    299\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetric\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metric\n\u001b[0;32m    300\u001b[0m reduce_func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[0;32m    301\u001b[0m     _silhouette_reduce, labels\u001b[38;5;241m=\u001b[39mlabels, label_freqs\u001b[38;5;241m=\u001b[39mlabel_freqs\n\u001b[0;32m    302\u001b[0m )\n\u001b[1;32m--> 303\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpairwise_distances_chunked\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduce_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduce_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    304\u001b[0m intra_clust_dists, inter_clust_dists \u001b[38;5;241m=\u001b[39m results\n\u001b[0;32m    305\u001b[0m intra_clust_dists \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(intra_clust_dists)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\pairwise.py:2261\u001b[0m, in \u001b[0;36mpairwise_distances_chunked\u001b[1;34m(X, Y, reduce_func, metric, n_jobs, working_memory, **kwds)\u001b[0m\n\u001b[0;32m   2259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reduce_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2260\u001b[0m     chunk_size \u001b[38;5;241m=\u001b[39m D_chunk\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m-> 2261\u001b[0m     D_chunk \u001b[38;5;241m=\u001b[39m \u001b[43mreduce_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mD_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2262\u001b[0m     _check_chunk_size(D_chunk, chunk_size)\n\u001b[0;32m   2263\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m D_chunk\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py:180\u001b[0m, in \u001b[0;36m_silhouette_reduce\u001b[1;34m(D_chunk, start, labels, label_freqs)\u001b[0m\n\u001b[0;32m    178\u001b[0m         sample_weights \u001b[38;5;241m=\u001b[39m D_chunk[i]\n\u001b[0;32m    179\u001b[0m         sample_labels \u001b[38;5;241m=\u001b[39m labels\n\u001b[1;32m--> 180\u001b[0m         cluster_distances[i] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbincount\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[43m            \u001b[49m\u001b[43msample_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminlength\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlabel_freqs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    184\u001b[0m \u001b[38;5;66;03m# intra_index selects intra-cluster distances within cluster_distances\u001b[39;00m\n\u001b[0;32m    185\u001b[0m end \u001b[38;5;241m=\u001b[39m start \u001b[38;5;241m+\u001b[39m n_chunk_samples\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "# Оценка инерции для выбора числа кластеров\n",
     "inertia = []\n",
@@ -210,7 +362,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

From b77003a97a66407eec37278345c7ee7c53a09c17 Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Fri, 20 Dec 2024 12:32:39 +0400
Subject: [PATCH 05/13] =?UTF-8?q?=D0=B1=D0=B1=D0=B1=D0=B1=D0=B1=D0=B1?=
 =?UTF-8?q?=D0=B1=D0=B1=D0=B1=D0=B1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_4/lab_products_clustering_cuml.ipynb | 193 +++++++++++++++++++++++
 1 file changed, 193 insertions(+)
 create mode 100644 Lab_4/lab_products_clustering_cuml.ipynb

diff --git a/Lab_4/lab_products_clustering_cuml.ipynb b/Lab_4/lab_products_clustering_cuml.ipynb
new file mode 100644
index 0000000..3384280
--- /dev/null
+++ b/Lab_4/lab_products_clustering_cuml.ipynb
@@ -0,0 +1,193 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7893b9e",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа: Методы искусственного интеллекта\n",
+    "## Задача кластеризации продуктов с использованием cuML\n",
+    "### Вариант: Продукты\n",
+    "В данной работе используется библиотека cuML для GPU-ускоренного анализа данных. Цель: провести кластеризацию продуктов на основе их характеристик."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3834005",
+   "metadata": {},
+   "source": [
+    "### Загрузка и исследование данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5530d138",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cudf\n",
+    "import cuml\n",
+    "from cuml.preprocessing import LabelEncoder\n",
+    "from cuml.decomposition import PCA\n",
+    "from cuml.cluster import KMeans\n",
+    "import cupy as cp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = cudf.read_csv('your_dataset_path.csv')\n",
+    "print(df.info())\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49112908",
+   "metadata": {},
+   "source": [
+    "### Предварительная обработка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e3ef9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Обработка пропущенных значений\n",
+    "df = df.dropna()\n",
+    "\n",
+    "# Кодирование категориального признака 'items'\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
+    "\n",
+    "# Нормализация числовых признаков\n",
+    "numeric_features = ['items_encoded', 'price']\n",
+    "df_scaled = df[numeric_features].astype('float32')\n",
+    "\n",
+    "# Преобразование данных в формат cupy\n",
+    "X = cp.asarray(df_scaled.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff5f1f8f",
+   "metadata": {},
+   "source": [
+    "### Понижение размерности и визуализация данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e15c80bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Применение PCA для понижения размерности\n",
+    "pca = PCA(n_components=2)\n",
+    "reduced_data = pca.fit_transform(X)\n",
+    "\n",
+    "# Визуализация данных\n",
+    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1])\n",
+    "plt.title('Визуализация данных после PCA')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2eef505",
+   "metadata": {},
+   "source": [
+    "### Выбор оптимального количества кластеров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f72195d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Оценка инерции и коэффициента силуэта\n",
+    "inertia = []\n",
+    "silhouette_scores = []\n",
+    "k_range = range(2, 11)\n",
+    "for k in k_range:\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(reduced_data)\n",
+    "    inertia.append(kmeans.inertia_)\n",
+    "    silhouette_scores.append(cuml.metrics.silhouette_score(reduced_data, kmeans.labels_))\n",
+    "\n",
+    "# Построение графиков\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(k_range, inertia, marker='o')\n",
+    "plt.title('Критерий инерции')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Инерция')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(k_range, silhouette_scores, marker='o')\n",
+    "plt.title('Коэффициент силуэта')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Силуэт')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "180e85ac",
+   "metadata": {},
+   "source": [
+    "### Кластерный анализ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd573024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Кластеризация с использованием KMeans\n",
+    "optimal_k = 4  # Выбираем на основе графиков\n",
+    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
+    "labels = kmeans.fit_predict(reduced_data)\n",
+    "\n",
+    "# Визуализация кластеров\n",
+    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=labels, cmap='viridis')\n",
+    "plt.title('Кластеры (KMeans)')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "407d268e",
+   "metadata": {},
+   "source": [
+    "### Оценка качества кластеризации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00795e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Оценка коэффициента силуэта\n",
+    "silhouette = cuml.metrics.silhouette_score(reduced_data, labels)\n",
+    "print(f'Силуэт для кластеризации: {silhouette:.2f}')"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c6a08076a5ff2787788f2721548b529f74c50d08 Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Fri, 20 Dec 2024 12:39:59 +0400
Subject: [PATCH 06/13] 1000-7

---
 Lab_4/lab_products_clustering_cuml.ipynb | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/Lab_4/lab_products_clustering_cuml.ipynb b/Lab_4/lab_products_clustering_cuml.ipynb
index 3384280..435b668 100644
--- a/Lab_4/lab_products_clustering_cuml.ipynb
+++ b/Lab_4/lab_products_clustering_cuml.ipynb
@@ -89,8 +89,11 @@
     "pca = PCA(n_components=2)\n",
     "reduced_data = pca.fit_transform(X)\n",
     "\n",
+    "# Преобразуем данные из cupy в numpy\n",
+    "reduced_data_np = reduced_data.get()\n",
+    "\n",
     "# Визуализация данных\n",
-    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1])\n",
+    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1])\n",
     "plt.title('Визуализация данных после PCA')\n",
     "plt.xlabel('PC1')\n",
     "plt.ylabel('PC2')\n",
@@ -187,7 +190,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

From da80c5bcc813e0f38caefc207ef9921ae762a046 Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Fri, 20 Dec 2024 14:14:59 +0400
Subject: [PATCH 07/13] =?UTF-8?q?=D0=BF=D0=B5=D1=80=D0=B5=D0=BF=D1=83?=
 =?UTF-8?q?=D1=82=D0=B0=D0=BB=20=D0=BB=D0=B0=D0=B1=D1=8B?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                               |   4 +-
 Lab_4/lab4.ipynb                         | 316 +++++++++++++++++++++++
 Lab_4/lab_products_clustering_cuml.ipynb | 200 --------------
 3 files changed, 319 insertions(+), 201 deletions(-)
 delete mode 100644 Lab_4/lab_products_clustering_cuml.ipynb

diff --git a/.gitignore b/.gitignore
index f0028f5..5f9b2d6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 data/jio_mart_items.csv
 /data
 /Lab_2/lab_2.ipynb
-/Lab_3/lab_3.ipynb
\ No newline at end of file
+/Lab_3/lab_3.ipynb
+/Lab_4/lab_4.ipynb
+/Lab_4/lab_products_clustering.ipynb
\ No newline at end of file
diff --git a/Lab_4/lab4.ipynb b/Lab_4/lab4.ipynb
index e69de29..1be1806 100644
--- a/Lab_4/lab4.ipynb
+++ b/Lab_4/lab4.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7893b9e",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа: Методы искусственного интеллекта\n",
+    "## Задача кластеризации продуктов с использованием cuML\n",
+    "### Вариант: Продукты\n",
+    "В данной работе используется библиотека cuML для GPU-ускоренного анализа данных. Цель: провести кластеризацию продуктов на основе их характеристик."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3834005",
+   "metadata": {},
+   "source": [
+    "### Загрузка и исследование данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5530d138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'cudf.core.dataframe.DataFrame'>\n",
+      "RangeIndex: 162313 entries, 0 to 162312\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count   Dtype\n",
+      "---  ------        --------------   -----\n",
+      " 0   category      162313 non-null  object\n",
+      " 1   sub_category  162313 non-null  object\n",
+      " 2   href          162313 non-null  object\n",
+      " 3   items         162280 non-null  object\n",
+      " 4   price         162282 non-null  float64\n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 28.9+ MB\n",
+      "None\n",
+      "    category         sub_category  \\\n",
+      "0  Groceries  Fruits & Vegetables   \n",
+      "1  Groceries  Fruits & Vegetables   \n",
+      "2  Groceries  Fruits & Vegetables   \n",
+      "3  Groceries  Fruits & Vegetables   \n",
+      "4  Groceries  Fruits & Vegetables   \n",
+      "\n",
+      "                                                href  \\\n",
+      "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "\n",
+      "                                               items  price  \n",
+      "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
+      "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
+      "2                                       Mosambi 1 kg   69.0  \n",
+      "3                               Orange Imported 1 kg  125.0  \n",
+      "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import cudf\n",
+    "import cuml\n",
+    "from cuml.preprocessing import LabelEncoder\n",
+    "from cuml.decomposition import PCA\n",
+    "from cuml.cluster import KMeans\n",
+    "import cupy as cp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = cudf.read_csv('/mnt/d/AIM-PIbd-31-Medvedkov-A-D//data/jio_mart_items.csv')\n",
+    "print(df.info())\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49112908",
+   "metadata": {},
+   "source": [
+    "### Предварительная обработка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e3ef9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Обработка пропущенных значений\n",
+    "df = df.dropna()\n",
+    "\n",
+    "# Кодирование категориального признака 'items'\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
+    "\n",
+    "# Нормализация числовых признаков\n",
+    "numeric_features = ['items_encoded', 'price']\n",
+    "df_scaled = df[numeric_features].astype('float32')\n",
+    "\n",
+    "# Преобразование данных в формат cupy\n",
+    "X = cp.asarray(df_scaled.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff5f1f8f",
+   "metadata": {},
+   "source": [
+    "### Понижение размерности и визуализация данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e15c80bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Применение PCA для понижения размерности\n",
+    "pca = PCA(n_components=2)\n",
+    "reduced_data = pca.fit_transform(X)\n",
+    "\n",
+    "# Преобразуем данные из cupy в numpy\n",
+    "reduced_data_np = reduced_data.get()\n",
+    "\n",
+    "# Визуализация данных\n",
+    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1])\n",
+    "plt.title('Визуализация данных после PCA')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2eef505",
+   "metadata": {},
+   "source": [
+    "### Выбор оптимального количества кластеров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f72195d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Оценка числа кластеров: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [06:06<00:00, 40.73s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Оценка инерции и коэффициента силуэта\n",
+    "from cuml.metrics.cluster import silhouette_score\n",
+    "from tqdm import tqdm  # Импорт библиотеки для отображения прогресса\n",
+    "\n",
+    "# Оценка инерции и коэффициента силуэта\n",
+    "inertia = []\n",
+    "silhouette_scores = []\n",
+    "k_range = range(2, 11)\n",
+    "\n",
+    "# tqdm для отображения прогресса\n",
+    "for k in tqdm(k_range, desc=\"Оценка числа кластеров\"):\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(reduced_data)\n",
+    "    inertia.append(kmeans.inertia_)\n",
+    "    silhouette_scores.append(silhouette_score(reduced_data, kmeans.labels_))\n",
+    "\n",
+    "# Построение графиков\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "\n",
+    "# График инерции\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(k_range, inertia, marker='o')\n",
+    "plt.title('Критерий инерции')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Инерция')\n",
+    "\n",
+    "# График коэффициента силуэта\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(k_range, silhouette_scores, marker='o')\n",
+    "plt.title('Коэффициент силуэта')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Силуэт')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "180e85ac",
+   "metadata": {},
+   "source": [
+    "### Кластерный анализ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dd573024",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Кластеризация с использованием KMeans\n",
+    "optimal_k = 4  # Выбираем на основе графиков\n",
+    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
+    "labels = kmeans.fit_predict(reduced_data)\n",
+    "\n",
+    "# Преобразуем данные из cupy в numpy\n",
+    "reduced_data_np = reduced_data.get()\n",
+    "labels_np = labels.get()\n",
+    "\n",
+    "# Визуализация кластеров\n",
+    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1], c=labels_np, cmap='viridis')\n",
+    "plt.title('Кластеры (KMeans)')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "407d268e",
+   "metadata": {},
+   "source": [
+    "### Оценка качества кластеризации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d00795e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Силуэт для кластеризации: 0.58\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Оценка коэффициента силуэта\n",
+    "silhouette = silhouette_score(reduced_data, labels)\n",
+    "print(f'Силуэт для кластеризации: {silhouette:.2f}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lab_4/lab_products_clustering_cuml.ipynb b/Lab_4/lab_products_clustering_cuml.ipynb
deleted file mode 100644
index 435b668..0000000
--- a/Lab_4/lab_products_clustering_cuml.ipynb
+++ /dev/null
@@ -1,200 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e7893b9e",
-   "metadata": {},
-   "source": [
-    "# Лабораторная работа: Методы искусственного интеллекта\n",
-    "## Задача кластеризации продуктов с использованием cuML\n",
-    "### Вариант: Продукты\n",
-    "В данной работе используется библиотека cuML для GPU-ускоренного анализа данных. Цель: провести кластеризацию продуктов на основе их характеристик."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3834005",
-   "metadata": {},
-   "source": [
-    "### Загрузка и исследование данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5530d138",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cudf\n",
-    "import cuml\n",
-    "from cuml.preprocessing import LabelEncoder\n",
-    "from cuml.decomposition import PCA\n",
-    "from cuml.cluster import KMeans\n",
-    "import cupy as cp\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Загрузка данных\n",
-    "df = cudf.read_csv('your_dataset_path.csv')\n",
-    "print(df.info())\n",
-    "print(df.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49112908",
-   "metadata": {},
-   "source": [
-    "### Предварительная обработка данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1e3ef9fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Обработка пропущенных значений\n",
-    "df = df.dropna()\n",
-    "\n",
-    "# Кодирование категориального признака 'items'\n",
-    "label_encoder = LabelEncoder()\n",
-    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
-    "\n",
-    "# Нормализация числовых признаков\n",
-    "numeric_features = ['items_encoded', 'price']\n",
-    "df_scaled = df[numeric_features].astype('float32')\n",
-    "\n",
-    "# Преобразование данных в формат cupy\n",
-    "X = cp.asarray(df_scaled.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff5f1f8f",
-   "metadata": {},
-   "source": [
-    "### Понижение размерности и визуализация данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e15c80bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Применение PCA для понижения размерности\n",
-    "pca = PCA(n_components=2)\n",
-    "reduced_data = pca.fit_transform(X)\n",
-    "\n",
-    "# Преобразуем данные из cupy в numpy\n",
-    "reduced_data_np = reduced_data.get()\n",
-    "\n",
-    "# Визуализация данных\n",
-    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1])\n",
-    "plt.title('Визуализация данных после PCA')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2eef505",
-   "metadata": {},
-   "source": [
-    "### Выбор оптимального количества кластеров"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f72195d2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Оценка инерции и коэффициента силуэта\n",
-    "inertia = []\n",
-    "silhouette_scores = []\n",
-    "k_range = range(2, 11)\n",
-    "for k in k_range:\n",
-    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
-    "    kmeans.fit(reduced_data)\n",
-    "    inertia.append(kmeans.inertia_)\n",
-    "    silhouette_scores.append(cuml.metrics.silhouette_score(reduced_data, kmeans.labels_))\n",
-    "\n",
-    "# Построение графиков\n",
-    "plt.figure(figsize=(14, 5))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(k_range, inertia, marker='o')\n",
-    "plt.title('Критерий инерции')\n",
-    "plt.xlabel('Число кластеров')\n",
-    "plt.ylabel('Инерция')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(k_range, silhouette_scores, marker='o')\n",
-    "plt.title('Коэффициент силуэта')\n",
-    "plt.xlabel('Число кластеров')\n",
-    "plt.ylabel('Силуэт')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "180e85ac",
-   "metadata": {},
-   "source": [
-    "### Кластерный анализ"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd573024",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Кластеризация с использованием KMeans\n",
-    "optimal_k = 4  # Выбираем на основе графиков\n",
-    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
-    "labels = kmeans.fit_predict(reduced_data)\n",
-    "\n",
-    "# Визуализация кластеров\n",
-    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=labels, cmap='viridis')\n",
-    "plt.title('Кластеры (KMeans)')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "407d268e",
-   "metadata": {},
-   "source": [
-    "### Оценка качества кластеризации"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d00795e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Оценка коэффициента силуэта\n",
-    "silhouette = cuml.metrics.silhouette_score(reduced_data, labels)\n",
-    "print(f'Силуэт для кластеризации: {silhouette:.2f}')"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From ad6649381e70badbb9059d64f92080a0c648d65c Mon Sep 17 00:00:00 2001
From: MaD <mdv_andrey@mail.ru>
Date: Sat, 21 Dec 2024 02:12:15 +0400
Subject: [PATCH 08/13] =?UTF-8?q?=D0=BD=D0=BE=D1=87=D0=BD=D1=8B=D0=B5=20?=
 =?UTF-8?q?=D0=BB=D0=B0=D0=B1=D1=8B?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore       |   8 +-
 Lab_4/lab4.ipynb | 391 ++++++++++++++++++++---------------------------
 2 files changed, 173 insertions(+), 226 deletions(-)

diff --git a/.gitignore b/.gitignore
index 5f9b2d6..372675d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,10 @@ data/jio_mart_items.csv
 /Lab_2/lab_2.ipynb
 /Lab_3/lab_3.ipynb
 /Lab_4/lab_4.ipynb
-/Lab_4/lab_products_clustering.ipynb
\ No newline at end of file
+/Lab_4/lab4gpu.ipynb
+/Lab_4/lab5.ipynb
+/Lab_4/lab44.ipynb
+/Lab_4/lab45.ipynb
+/Lab_4/lab_products_clustering.ipynb
+/Lab_4/lab_4.ipynb
+/Lab_4/lab_4_products.ipynb
\ No newline at end of file
diff --git a/Lab_4/lab4.ipynb b/Lab_4/lab4.ipynb
index 1be1806..098951e 100644
--- a/Lab_4/lab4.ipynb
+++ b/Lab_4/lab4.ipynb
@@ -1,295 +1,236 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e7893b9e",
-   "metadata": {},
-   "source": [
-    "# Лабораторная работа: Методы искусственного интеллекта\n",
-    "## Задача кластеризации продуктов с использованием cuML\n",
-    "### Вариант: Продукты\n",
-    "В данной работе используется библиотека cuML для GPU-ускоренного анализа данных. Цель: провести кластеризацию продуктов на основе их характеристик."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3834005",
-   "metadata": {},
-   "source": [
-    "### Загрузка и исследование данных"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "5530d138",
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<class 'cudf.core.dataframe.DataFrame'>\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 162313 entries, 0 to 162312\n",
       "Data columns (total 5 columns):\n",
-      " #   Column        Non-Null Count   Dtype\n",
-      "---  ------        --------------   -----\n",
-      " 0   category      162313 non-null  object\n",
-      " 1   sub_category  162313 non-null  object\n",
-      " 2   href          162313 non-null  object\n",
-      " 3   items         162280 non-null  object\n",
+      " #   Column        Non-Null Count   Dtype  \n",
+      "---  ------        --------------   -----  \n",
+      " 0   category      162313 non-null  object \n",
+      " 1   sub_category  162313 non-null  object \n",
+      " 2   href          162313 non-null  object \n",
+      " 3   items         162280 non-null  object \n",
       " 4   price         162282 non-null  float64\n",
       "dtypes: float64(1), object(4)\n",
-      "memory usage: 28.9+ MB\n",
-      "None\n",
-      "    category         sub_category  \\\n",
-      "0  Groceries  Fruits & Vegetables   \n",
-      "1  Groceries  Fruits & Vegetables   \n",
-      "2  Groceries  Fruits & Vegetables   \n",
-      "3  Groceries  Fruits & Vegetables   \n",
-      "4  Groceries  Fruits & Vegetables   \n",
-      "\n",
-      "                                                href  \\\n",
-      "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-      "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-      "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-      "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-      "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-      "\n",
-      "                                               items  price  \n",
-      "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
-      "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
-      "2                                       Mosambi 1 kg   69.0  \n",
-      "3                               Orange Imported 1 kg  125.0  \n",
-      "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  \n"
+      "memory usage: 6.2+ MB\n"
      ]
     }
    ],
    "source": [
-    "import cudf\n",
-    "import cuml\n",
-    "from cuml.preprocessing import LabelEncoder\n",
-    "from cuml.decomposition import PCA\n",
-    "from cuml.cluster import KMeans\n",
-    "import cupy as cp\n",
+    "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
+    "from cuml.preprocessing import LabelEncoder\n",
+    "from sklearn import metrics\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.metrics import (\n",
+    "    precision_score, recall_score, accuracy_score, roc_auc_score, f1_score,\n",
+    "    matthews_corrcoef, cohen_kappa_score, confusion_matrix\n",
+    ")\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "import numpy as np\n",
+    "import featuretools as ft\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
     "\n",
-    "# Загрузка данных\n",
-    "df = cudf.read_csv('/mnt/d/AIM-PIbd-31-Medvedkov-A-D//data/jio_mart_items.csv')\n",
-    "print(df.info())\n",
-    "print(df.head())"
+    "# Функция для применения oversampling\n",
+    "def apply_oversampling(X, y):\n",
+    "    oversampler = RandomOverSampler(random_state=42)\n",
+    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
+    "    return X_resampled, y_resampled\n",
+    "\n",
+    "# Функция для применения undersampling\n",
+    "def apply_undersampling(X, y):\n",
+    "    undersampler = RandomUnderSampler(random_state=42)\n",
+    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
+    "    return X_resampled, y_resampled\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
+    "    following fractional ratios provided by the user, where each subset is\n",
+    "    stratified by the values in a specific column (that is, each subset has\n",
+    "    the same relative frequency of the values in the column). It performs this\n",
+    "    splitting by running train_test_split() twice.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df_input : Pandas dataframe\n",
+    "        Input dataframe to be split.\n",
+    "    stratify_colname : str\n",
+    "        The name of the column that will be used for stratification. Usually\n",
+    "        this column would be for the label.\n",
+    "    frac_train : float\n",
+    "    frac_val   : float\n",
+    "    frac_test  : float\n",
+    "        The ratios with which the dataframe will be split into train, val, and\n",
+    "        test data. The values should be expressed as float fractions and should\n",
+    "        sum to 1.0.\n",
+    "    random_state : int, None, or RandomStateInstance\n",
+    "        Value to be passed to train_test_split().\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    df_train, df_val, df_test :\n",
+    "        Dataframes containing the three splits.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input  # Contains all columns.\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "\n",
+    "    # Split original dataframe into train and temp dataframes.\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    # Split the temp dataframe into val and test dataframes.\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv('/mnt/c/3curse/mii/AIM-PIbd-31-Medvedkov-A-D/data/jio_mart_items.csv')\n",
+    "df.info()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "49112908",
    "metadata": {},
    "source": [
-    "### Предварительная обработка данных"
+    "Как бизнес-цели выделим следующие 2 варианта:\n",
+    "    1) GameDev. Создание игры про конкретного персонажа, живущего в конкретном временном промежутке в конкретной стране. \n",
+    "    2) Исследование зависимости длительности жизни от страны проживания.\n",
+    "    \n",
+    "Поскольку именно эти бизнес-цели были выбраны в предыдущей лабораторной работе, будем их использовать.\n",
+    "Но возникает проблема с 1 целью: её невозможно использовать для задачи классификации. Заменим ее на классификацию людей по возрастным группам, что может быть полезно для рекламных целей."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выполним подготовку данных"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "1e3ef9fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Обработка пропущенных значений\n",
-    "df = df.dropna()\n",
-    "\n",
-    "# Кодирование категориального признака 'items'\n",
-    "label_encoder = LabelEncoder()\n",
-    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
-    "\n",
-    "# Нормализация числовых признаков\n",
-    "numeric_features = ['items_encoded', 'price']\n",
-    "df_scaled = df[numeric_features].astype('float32')\n",
-    "\n",
-    "# Преобразование данных в формат cupy\n",
-    "X = cp.asarray(df_scaled.values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ff5f1f8f",
-   "metadata": {},
-   "source": [
-    "### Понижение размерности и визуализация данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e15c80bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Применение PCA для понижения размерности\n",
-    "pca = PCA(n_components=2)\n",
-    "reduced_data = pca.fit_transform(X)\n",
-    "\n",
-    "# Преобразуем данные из cupy в numpy\n",
-    "reduced_data_np = reduced_data.get()\n",
-    "\n",
-    "# Визуализация данных\n",
-    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1])\n",
-    "plt.title('Визуализация данных после PCA')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2eef505",
-   "metadata": {},
-   "source": [
-    "### Выбор оптимального количества кластеров"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f72195d2",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Оценка числа кластеров: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [06:06<00:00, 40.73s/it]\n"
+      "/tmp/ipykernel_833/3539008564.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "  df.fillna({\"category\": \"NaN\", \"sub_category\": \"NaN\", \"href\" : \"NaN\", \"items\" : \"NaN\", \"price\"        : \"NaN\" }, inplace=True)\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Оценка инерции и коэффициента силуэта\n",
-    "from cuml.metrics.cluster import silhouette_score\n",
-    "from tqdm import tqdm  # Импорт библиотеки для отображения прогресса\n",
+    "df.fillna({\"category\": \"NaN\", \"sub_category\": \"NaN\", \"href\" : \"NaN\", \"items\" : \"NaN\", \"price\"        : \"NaN\" }, inplace=True)\n",
+    "df = df.dropna()\n",
+    "data = df.copy()\n",
     "\n",
-    "# Оценка инерции и коэффициента силуэта\n",
-    "inertia = []\n",
-    "silhouette_scores = []\n",
-    "k_range = range(2, 11)\n",
-    "\n",
-    "# tqdm для отображения прогресса\n",
-    "for k in tqdm(k_range, desc=\"Оценка числа кластеров\"):\n",
-    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
-    "    kmeans.fit(reduced_data)\n",
-    "    inertia.append(kmeans.inertia_)\n",
-    "    silhouette_scores.append(silhouette_score(reduced_data, kmeans.labels_))\n",
-    "\n",
-    "# Построение графиков\n",
-    "plt.figure(figsize=(14, 5))\n",
-    "\n",
-    "# График инерции\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(k_range, inertia, marker='o')\n",
-    "plt.title('Критерий инерции')\n",
-    "plt.xlabel('Число кластеров')\n",
-    "plt.ylabel('Инерция')\n",
-    "\n",
-    "# График коэффициента силуэта\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(k_range, silhouette_scores, marker='o')\n",
-    "plt.title('Коэффициент силуэта')\n",
-    "plt.xlabel('Число кластеров')\n",
-    "plt.ylabel('Силуэт')\n",
-    "\n",
-    "plt.show()"
+    "value_counts = data[\"category\"].value_counts()\n",
+    "rare = value_counts[value_counts < 100].index\n",
+    "data = data[~data[\"category\"].isin(rare)]\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "180e85ac",
    "metadata": {},
    "source": [
-    "### Кластерный анализ"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "dd573024",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Кластеризация с использованием KMeans\n",
-    "optimal_k = 4  # Выбираем на основе графиков\n",
-    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
-    "labels = kmeans.fit_predict(reduced_data)\n",
-    "\n",
-    "# Преобразуем данные из cupy в numpy\n",
-    "reduced_data_np = reduced_data.get()\n",
-    "labels_np = labels.get()\n",
-    "\n",
-    "# Визуализация кластеров\n",
-    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1], c=labels_np, cmap='viridis')\n",
-    "plt.title('Кластеры (KMeans)')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
+    "Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей не будет высоким, поскольку все таки длительность жизни лишь примерная и точно ее угадать невозможно."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "407d268e",
    "metadata": {},
    "source": [
-    "### Оценка качества кластеризации"
+    "Выберем ориентиры для наших 2х задач:\n",
+    "    1)Регрессии - средний возраст человека\n",
+    "    2)Классификации - аиболее часто встречающаяся возрастная группа"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Построим конвейер."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "d00795e2",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Силуэт для кластеризации: 0.58\n"
+      "Index(['category', 'sub_category', 'href', 'items', 'price'], dtype='object')\n"
      ]
     }
    ],
    "source": [
-    "# Оценка коэффициента силуэта\n",
-    "silhouette = silhouette_score(reduced_data, labels)\n",
-    "print(f'Силуэт для кластеризации: {silhouette:.2f}')"
+    "print(data.columns)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -308,9 +249,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 5
+ "nbformat_minor": 2
 }

From f28256ade85bf59f33c46d96aa348ef65267a4ae Mon Sep 17 00:00:00 2001
From: MaD <mdv_andrey@mail.ru>
Date: Sat, 21 Dec 2024 02:16:56 +0400
Subject: [PATCH 09/13] =?UTF-8?q?=D0=B0=20=D0=9F=D0=9E=D0=A7=D0=95=D0=9C?=
 =?UTF-8?q?=D0=A3=20=D0=9E=D0=9D=D0=9E=20=D0=A2=D0=90=D0=9A=20=D0=A1=D0=A2?=
 =?UTF-8?q?=D0=A0=D0=90=D0=9D=D0=9D=D0=9E=20=D0=97=D0=90=D0=A4=D0=98=D0=9A?=
 =?UTF-8?q?=D0=A1=D0=98=D0=A0=D0=9E=D0=92=D0=90=D0=9B=D0=9E=D0=A1=D0=AC=3F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_4/lab4.ipynb | 408 ++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 369 insertions(+), 39 deletions(-)

diff --git a/Lab_4/lab4.ipynb b/Lab_4/lab4.ipynb
index 098951e..d1651d0 100644
--- a/Lab_4/lab4.ipynb
+++ b/Lab_4/lab4.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -26,8 +26,9 @@
    ],
    "source": [
     "import pandas as pd\n",
+    "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
-    "from cuml.preprocessing import LabelEncoder\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
     "from sklearn import metrics\n",
     "from imblearn.over_sampling import RandomOverSampler\n",
     "from imblearn.under_sampling import RandomUnderSampler\n",
@@ -132,7 +133,40 @@
     "\n",
     "\n",
     "df = pd.read_csv('/mnt/c/3curse/mii/AIM-PIbd-31-Medvedkov-A-D/data/jio_mart_items.csv')\n",
-    "df.info()"
+    "df.info()\n",
+    "df = df.sample(n=10000 , random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Dining' 'Toys, Games & Fitness' 'Fragrances' 'Bags & Travel Luggage'\n",
+      " 'Girls' 'Home Decor' 'Boys' 'Stationery' 'Beverages' 'Staples' 'Men'\n",
+      " 'Mobiles & Tablets' 'Personal Care' 'Dairy & Bakery' 'Mom & Baby Care'\n",
+      " 'Snacks & Branded Foods' 'Women' 'Books' 'Auto Care' 'Electrical'\n",
+      " 'Furnishing' 'Accessories' 'Pets' 'Home Care' 'Mops, Brushes & Scrubs'\n",
+      " 'Furniture' 'Computers' 'Kitchen Appliances' 'Home Appliances' 'Cameras'\n",
+      " 'Make-Up' 'Garden & Outdoor' 'Disposables' 'Carpentry & work accessories'\n",
+      " 'Mom & Baby' 'Kitchenware' 'Power & Hand Tools' 'Pooja Needs'\n",
+      " 'Bathroom & Laundry Accessories' 'Office Products' 'TV & Speaker'\n",
+      " 'Personal Care & Grooming' 'Hair' 'Skin Care'\n",
+      " 'Paint, Wall Treatments & Supplies' 'Industrial & Scientific Supplies'\n",
+      " 'Infants' 'Kitchen & Bath Fixtures' 'Home Safety & Automation'\n",
+      " 'Fine Jewellery' 'Fruits & Vegetables' 'Apparel' 'Premium Fruits'\n",
+      " 'Phones' 'Bathroom & Laundry' 'Junior Boys' 'Tools & Appliances'\n",
+      " 'Smart Devices' \"Men's Grooming\" 'Gaming' 'Health Care Devices'\n",
+      " 'Handloom & Handicraft' 'Hardware & Plumbing' 'Wellness' 'Treatments']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['sub_category'].unique())"
    ]
   },
   {
@@ -140,11 +174,10 @@
    "metadata": {},
    "source": [
     "Как бизнес-цели выделим следующие 2 варианта:\n",
-    "    1) GameDev. Создание игры про конкретного персонажа, живущего в конкретном временном промежутке в конкретной стране. \n",
-    "    2) Исследование зависимости длительности жизни от страны проживания.\n",
+    "    1) Регрессия - предсказание цены по категории (для аналитических систем или улучшения алгоритмов ценообразования)\n",
+    "    2) Классификация - определение категории продукта по его подкатегории (для логистических или аналитических систем)\n",
     "    \n",
-    "Поскольку именно эти бизнес-цели были выбраны в предыдущей лабораторной работе, будем их использовать.\n",
-    "Но возникает проблема с 1 целью: её невозможно использовать для задачи классификации. Заменим ее на классификацию людей по возрастным группам, что может быть полезно для рекламных целей."
+    "Однако данный датасет весьма плоо подходит для подобных задач."
    ]
   },
   {
@@ -156,42 +189,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_833/3539008564.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-      "  df.fillna({\"category\": \"NaN\", \"sub_category\": \"NaN\", \"href\" : \"NaN\", \"items\" : \"NaN\", \"price\"        : \"NaN\" }, inplace=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df.fillna({\"category\": \"NaN\", \"sub_category\": \"NaN\", \"href\" : \"NaN\", \"items\" : \"NaN\", \"price\"        : \"NaN\" }, inplace=True)\n",
+    "# df.fillna({\"category\": \"NaN\", \"sub_category\": \"NaN\", \"href\" : \"NaN\", \"items\" : \"NaN\", \"price\" : \"NaN\" }, inplace=True)\n",
     "df = df.dropna()\n",
     "data = df.copy()\n",
     "\n",
     "value_counts = data[\"category\"].value_counts()\n",
     "rare = value_counts[value_counts < 100].index\n",
-    "data = data[~data[\"category\"].isin(rare)]\n"
+    "data = data[~data[\"category\"].isin(rare)]\n",
+    "\n",
+    "data1 = pd.get_dummies(data, columns=['category', 'sub_category'], drop_first=True)\n",
+    "\n",
+    "# label_encoder = LabelEncoder()\n",
+    "# data1['sub_category'] = label_encoder.fit_transform(data['sub_category'])\n",
+    "# data1['category'] = label_encoder.fit_transform(data['category'])\n",
+    "# data1['items'] = label_encoder.fit_transform(data['items'])\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей не будет высоким, поскольку все таки длительность жизни лишь примерная и точно ее угадать невозможно."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Выберем ориентиры для наших 2х задач:\n",
-    "    1)Регрессии - средний возраст человека\n",
-    "    2)Классификации - аиболее часто встречающаяся возрастная группа"
+    "Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей регрессии будет низким, поскольку цена слабо коррелирует с категорией (кроме некоторых исключений)."
    ]
   },
   {
@@ -203,34 +225,342 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['category', 'sub_category', 'href', 'items', 'price'], dtype='object')\n"
+      "Index(['href', 'items', 'price', 'category_Electronics', 'category_Fashion',\n",
+      "       'category_Groceries', 'category_Home & Kitchen', 'sub_category_Apparel',\n",
+      "       'sub_category_Auto Care', 'sub_category_Bags & Travel Luggage',\n",
+      "       'sub_category_Bathroom & Laundry',\n",
+      "       'sub_category_Bathroom & Laundry Accessories', 'sub_category_Beverages',\n",
+      "       'sub_category_Books', 'sub_category_Boys', 'sub_category_Cameras',\n",
+      "       'sub_category_Carpentry & work accessories', 'sub_category_Computers',\n",
+      "       'sub_category_Dairy & Bakery', 'sub_category_Dining',\n",
+      "       'sub_category_Disposables', 'sub_category_Electrical',\n",
+      "       'sub_category_Fragrances', 'sub_category_Fruits & Vegetables',\n",
+      "       'sub_category_Furnishing', 'sub_category_Furniture',\n",
+      "       'sub_category_Gaming', 'sub_category_Garden & Outdoor',\n",
+      "       'sub_category_Girls', 'sub_category_Hair',\n",
+      "       'sub_category_Handloom & Handicraft',\n",
+      "       'sub_category_Hardware & Plumbing', 'sub_category_Health Care Devices',\n",
+      "       'sub_category_Home Appliances', 'sub_category_Home Care',\n",
+      "       'sub_category_Home Decor', 'sub_category_Home Safety & Automation',\n",
+      "       'sub_category_Industrial & Scientific Supplies', 'sub_category_Infants',\n",
+      "       'sub_category_Junior Boys', 'sub_category_Kitchen & Bath Fixtures',\n",
+      "       'sub_category_Kitchen Appliances', 'sub_category_Kitchenware',\n",
+      "       'sub_category_Make-Up', 'sub_category_Men',\n",
+      "       'sub_category_Men's Grooming', 'sub_category_Mobiles & Tablets',\n",
+      "       'sub_category_Mom & Baby', 'sub_category_Mom & Baby Care',\n",
+      "       'sub_category_Mops, Brushes & Scrubs', 'sub_category_Office Products',\n",
+      "       'sub_category_Paint, Wall Treatments & Supplies',\n",
+      "       'sub_category_Personal Care', 'sub_category_Personal Care & Grooming',\n",
+      "       'sub_category_Pets', 'sub_category_Phones', 'sub_category_Pooja Needs',\n",
+      "       'sub_category_Power & Hand Tools', 'sub_category_Premium Fruits',\n",
+      "       'sub_category_Skin Care', 'sub_category_Smart Devices',\n",
+      "       'sub_category_Snacks & Branded Foods', 'sub_category_Staples',\n",
+      "       'sub_category_Stationery', 'sub_category_TV & Speaker',\n",
+      "       'sub_category_Tools & Appliances', 'sub_category_Toys, Games & Fitness',\n",
+      "       'sub_category_Wellness', 'sub_category_Women'],\n",
+      "      dtype='object')\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 9995 entries, 52893 to 146053\n",
+      "Data columns (total 69 columns):\n",
+      " #   Column                                          Non-Null Count  Dtype  \n",
+      "---  ------                                          --------------  -----  \n",
+      " 0   href                                            9995 non-null   object \n",
+      " 1   items                                           9995 non-null   object \n",
+      " 2   price                                           9995 non-null   float64\n",
+      " 3   category_Electronics                            9995 non-null   bool   \n",
+      " 4   category_Fashion                                9995 non-null   bool   \n",
+      " 5   category_Groceries                              9995 non-null   bool   \n",
+      " 6   category_Home & Kitchen                         9995 non-null   bool   \n",
+      " 7   sub_category_Apparel                            9995 non-null   bool   \n",
+      " 8   sub_category_Auto Care                          9995 non-null   bool   \n",
+      " 9   sub_category_Bags & Travel Luggage              9995 non-null   bool   \n",
+      " 10  sub_category_Bathroom & Laundry                 9995 non-null   bool   \n",
+      " 11  sub_category_Bathroom & Laundry Accessories     9995 non-null   bool   \n",
+      " 12  sub_category_Beverages                          9995 non-null   bool   \n",
+      " 13  sub_category_Books                              9995 non-null   bool   \n",
+      " 14  sub_category_Boys                               9995 non-null   bool   \n",
+      " 15  sub_category_Cameras                            9995 non-null   bool   \n",
+      " 16  sub_category_Carpentry & work accessories       9995 non-null   bool   \n",
+      " 17  sub_category_Computers                          9995 non-null   bool   \n",
+      " 18  sub_category_Dairy & Bakery                     9995 non-null   bool   \n",
+      " 19  sub_category_Dining                             9995 non-null   bool   \n",
+      " 20  sub_category_Disposables                        9995 non-null   bool   \n",
+      " 21  sub_category_Electrical                         9995 non-null   bool   \n",
+      " 22  sub_category_Fragrances                         9995 non-null   bool   \n",
+      " 23  sub_category_Fruits & Vegetables                9995 non-null   bool   \n",
+      " 24  sub_category_Furnishing                         9995 non-null   bool   \n",
+      " 25  sub_category_Furniture                          9995 non-null   bool   \n",
+      " 26  sub_category_Gaming                             9995 non-null   bool   \n",
+      " 27  sub_category_Garden & Outdoor                   9995 non-null   bool   \n",
+      " 28  sub_category_Girls                              9995 non-null   bool   \n",
+      " 29  sub_category_Hair                               9995 non-null   bool   \n",
+      " 30  sub_category_Handloom & Handicraft              9995 non-null   bool   \n",
+      " 31  sub_category_Hardware & Plumbing                9995 non-null   bool   \n",
+      " 32  sub_category_Health Care Devices                9995 non-null   bool   \n",
+      " 33  sub_category_Home Appliances                    9995 non-null   bool   \n",
+      " 34  sub_category_Home Care                          9995 non-null   bool   \n",
+      " 35  sub_category_Home Decor                         9995 non-null   bool   \n",
+      " 36  sub_category_Home Safety & Automation           9995 non-null   bool   \n",
+      " 37  sub_category_Industrial & Scientific Supplies   9995 non-null   bool   \n",
+      " 38  sub_category_Infants                            9995 non-null   bool   \n",
+      " 39  sub_category_Junior Boys                        9995 non-null   bool   \n",
+      " 40  sub_category_Kitchen & Bath Fixtures            9995 non-null   bool   \n",
+      " 41  sub_category_Kitchen Appliances                 9995 non-null   bool   \n",
+      " 42  sub_category_Kitchenware                        9995 non-null   bool   \n",
+      " 43  sub_category_Make-Up                            9995 non-null   bool   \n",
+      " 44  sub_category_Men                                9995 non-null   bool   \n",
+      " 45  sub_category_Men's Grooming                     9995 non-null   bool   \n",
+      " 46  sub_category_Mobiles & Tablets                  9995 non-null   bool   \n",
+      " 47  sub_category_Mom & Baby                         9995 non-null   bool   \n",
+      " 48  sub_category_Mom & Baby Care                    9995 non-null   bool   \n",
+      " 49  sub_category_Mops, Brushes & Scrubs             9995 non-null   bool   \n",
+      " 50  sub_category_Office Products                    9995 non-null   bool   \n",
+      " 51  sub_category_Paint, Wall Treatments & Supplies  9995 non-null   bool   \n",
+      " 52  sub_category_Personal Care                      9995 non-null   bool   \n",
+      " 53  sub_category_Personal Care & Grooming           9995 non-null   bool   \n",
+      " 54  sub_category_Pets                               9995 non-null   bool   \n",
+      " 55  sub_category_Phones                             9995 non-null   bool   \n",
+      " 56  sub_category_Pooja Needs                        9995 non-null   bool   \n",
+      " 57  sub_category_Power & Hand Tools                 9995 non-null   bool   \n",
+      " 58  sub_category_Premium Fruits                     9995 non-null   bool   \n",
+      " 59  sub_category_Skin Care                          9995 non-null   bool   \n",
+      " 60  sub_category_Smart Devices                      9995 non-null   bool   \n",
+      " 61  sub_category_Snacks & Branded Foods             9995 non-null   bool   \n",
+      " 62  sub_category_Staples                            9995 non-null   bool   \n",
+      " 63  sub_category_Stationery                         9995 non-null   bool   \n",
+      " 64  sub_category_TV & Speaker                       9995 non-null   bool   \n",
+      " 65  sub_category_Tools & Appliances                 9995 non-null   bool   \n",
+      " 66  sub_category_Toys, Games & Fitness              9995 non-null   bool   \n",
+      " 67  sub_category_Wellness                           9995 non-null   bool   \n",
+      " 68  sub_category_Women                              9995 non-null   bool   \n",
+      "dtypes: bool(66), float64(1), object(2)\n",
+      "memory usage: 956.6+ KB\n"
      ]
     }
    ],
    "source": [
-    "print(data.columns)"
+    "print(data1.columns)\n",
+    "data1.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters for Linear Regression: {}\n",
+      "Best parameters for Random Forest Regressor: {'model__max_depth': None, 'model__n_estimators': 300}\n",
+      "Best parameters for Gradient Boosting Regressor: {'model__learning_rate': 0.01, 'model__max_depth': 7, 'model__n_estimators': 300}\n",
+      "Model: Linear Regression\n",
+      "Model: Random Forest Regressor\n",
+      "Model: Gradient Boosting Regressor\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_reg = data1.drop(['href', 'items', 'price'], axis=1)\n",
+    "y_reg = data1['price']\n",
+    "\n",
+    "# Разделение данных\n",
+    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Выбор моделей для регрессии\n",
+    "models_reg = {\n",
+    "    'Linear Regression': LinearRegression(),\n",
+    "    'Random Forest Regressor': RandomForestRegressor(random_state=42),\n",
+    "    'Gradient Boosting Regressor': GradientBoostingRegressor(random_state=42)\n",
+    "}\n",
+    "\n",
+    "# Создание конвейера для регрессии\n",
+    "pipelines_reg = {}\n",
+    "for name, model in models_reg.items():\n",
+    "    pipelines_reg[name] = Pipeline([\n",
+    "        ('scaler', StandardScaler()),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "\n",
+    "# Определение сетки гиперпараметров для регрессии\n",
+    "param_grids_reg = {\n",
+    "    'Linear Regression': {},\n",
+    "    'Random Forest Regressor': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__max_depth': [None, 10, 20, 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Regressor': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Настройка гиперпараметров для регрессии\n",
+    "best_models_reg = {}\n",
+    "for name, pipeline in pipelines_reg.items():\n",
+    "    grid_search = GridSearchCV(pipeline, param_grids_reg[name], cv=5, scoring='neg_mean_squared_error')\n",
+    "    grid_search.fit(X_train_reg, y_train_reg)\n",
+    "    best_models_reg[name] = {\n",
+    "        'pipeline': grid_search.best_estimator_,\n",
+    "        'best_params': grid_search.best_params_\n",
+    "    }\n",
+    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n",
+    "\n",
+    "# Обучение моделей и оценка качества\n",
+    "for model_name in best_models_reg.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    model_pipeline = best_models_reg[model_name]['pipeline']\n",
+    "    model_pipeline.fit(X_train_reg, y_train_reg)\n",
+    "\n",
+    "    y_train_predict = model_pipeline.predict(X_train_reg)\n",
+    "    y_test_predict = model_pipeline.predict(X_test_reg)\n",
+    "\n",
+    "    best_models_reg[model_name][\"preds_train\"] = y_train_predict\n",
+    "    best_models_reg[model_name][\"preds_test\"] = y_test_predict\n",
+    "\n",
+    "    best_models_reg[model_name][\"MSE_train\"] = mean_squared_error(y_train_reg, y_train_predict)\n",
+    "    best_models_reg[model_name][\"MSE_test\"] = mean_squared_error(y_test_reg, y_test_predict)\n",
+    "    best_models_reg[model_name][\"R2_train\"] = r2_score(y_train_reg, y_train_predict)\n",
+    "    best_models_reg[model_name][\"R2_test\"] = r2_score(y_test_reg, y_test_predict)\n",
+    "    best_models_reg[model_name][\"MAE_train\"] = mean_absolute_error(y_train_reg, y_train_predict)\n",
+    "    best_models_reg[model_name][\"MAE_test\"] = mean_absolute_error(y_test_reg, y_test_predict)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.991495747873937\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.99      0.90      0.94       131\n",
+      "           1       0.99      1.00      0.99       241\n",
+      "           2       1.00      1.00      1.00       307\n",
+      "           3       0.98      1.00      0.99       573\n",
+      "           4       1.00      1.00      1.00       747\n",
+      "\n",
+      "    accuracy                           0.99      1999\n",
+      "   macro avg       0.99      0.98      0.98      1999\n",
+      "weighted avg       0.99      0.99      0.99      1999\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Кодирование категориальных данных\n",
+    "label_encoder = LabelEncoder()\n",
+    "data['sub_category_encoded'] = label_encoder.fit_transform(data['sub_category'])\n",
+    "\n",
+    "# Определение признаков (X) и целевой переменной (y)\n",
+    "X = data[['sub_category_encoded']]  # Используем закодированный sub_category\n",
+    "y = label_encoder.fit_transform(data['category'])  # Кодируем category\n",
+    "\n",
+    "# Разделение данных на тренировочную и тестовую выборки\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "\n",
+    "# Создание и обучение модели\n",
+    "classifier = RandomForestClassifier(random_state=42, n_estimators=100, max_depth=10)\n",
+    "classifier.fit(X_train, y_train)\n",
+    "\n",
+    "# Предсказание на тестовых данных\n",
+    "y_pred = classifier.predict(X_test)\n",
+    "\n",
+    "# Оценка качества модели\n",
+    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
+    "\n",
+    "# Матрица ошибок\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Модель классификации показывает неплохие результаты, что логично, учитывая структуру датасета."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax = plt.subplots(3, 2, figsize=(12, 10), sharex=False, sharey=False)\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "for index, (name, model) in enumerate(best_models_reg.items()):\n",
+    "    model_pipeline = model['pipeline']\n",
+    "    y_pred_reg = model_pipeline.predict(X_test_reg)\n",
+    "\n",
+    "    # График фактических значений против предсказанных значений\n",
+    "    ax[index * 2].scatter(y_test_reg, y_pred_reg, alpha=0.5)\n",
+    "    ax[index * 2].plot([min(y_test_reg), max(y_test_reg)], [min(y_test_reg), max(y_test_reg)], color='red', linestyle='--')\n",
+    "    ax[index * 2].set_xlabel('Actual Values')\n",
+    "    ax[index * 2].set_ylabel('Predicted Values')\n",
+    "    ax[index * 2].set_title(f'{name}: Actual vs Predicted')\n",
+    "\n",
+    "    # График остатков\n",
+    "    residuals = y_test_reg - y_pred_reg\n",
+    "    ax[index * 2 + 1].scatter(y_pred_reg, residuals, alpha=0.5)\n",
+    "    ax[index * 2 + 1].axhline(y=0, color='red', linestyle='--')\n",
+    "    ax[index * 2 + 1].set_xlabel('Predicted Values')\n",
+    "    ax[index * 2 + 1].set_ylabel('Residuals')\n",
+    "    ax[index * 2 + 1].set_title(f'{name}: Residuals vs Predicted')\n",
+    "\n",
+    "\n",
+    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Модель регресии демонстрирует ужасные результаты ввиду недостаточной корреляции между целеовй характеристикой и строковыми значениями."
+   ]
   }
  ],
  "metadata": {

From ecf86a9738201286f51c92e2379eb6f591416bfc Mon Sep 17 00:00:00 2001
From: MaD <mdv_andrey@mail.ru>
Date: Sat, 21 Dec 2024 02:17:51 +0400
Subject: [PATCH 10/13] =?UTF-8?q?=D0=90=20=D0=B5=D1=81=D0=BB=D0=B8=20?=
 =?UTF-8?q?=D1=82=D0=B0=D0=BA=3F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_4/lab_4.ipynb | 566 ----------------------------------------------
 1 file changed, 566 deletions(-)
 delete mode 100644 Lab_4/lab_4.ipynb

diff --git a/Lab_4/lab_4.ipynb b/Lab_4/lab_4.ipynb
deleted file mode 100644
index a6f7a98..0000000
--- a/Lab_4/lab_4.ipynb
+++ /dev/null
@@ -1,566 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Вариант: Список людей. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 10000 entries, 0 to 9999\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column             Non-Null Count  Dtype  \n",
-      "---  ------             --------------  -----  \n",
-      " 0   Id                 10000 non-null  object \n",
-      " 1   Name               10000 non-null  object \n",
-      " 2   Short description  9996 non-null   object \n",
-      " 3   Gender             9927 non-null   object \n",
-      " 4   Country            9721 non-null   object \n",
-      " 5   Occupation         9836 non-null   object \n",
-      " 6   Birth year         10000 non-null  int64  \n",
-      " 7   Death year         9999 non-null   float64\n",
-      " 8   Manner of death    1893 non-null   object \n",
-      " 9   Age of death       9999 non-null   float64\n",
-      "dtypes: float64(2), int64(1), object(7)\n",
-      "memory usage: 781.4+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn import metrics\n",
-    "from imblearn.over_sampling import RandomOverSampler\n",
-    "from imblearn.under_sampling import RandomUnderSampler\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
-    "from sklearn.metrics import ConfusionMatrixDisplay\n",
-    "from sklearn.compose import ColumnTransformer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n",
-    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
-    "from sklearn.metrics import (\n",
-    "    precision_score, recall_score, accuracy_score, roc_auc_score, f1_score,\n",
-    "    matthews_corrcoef, cohen_kappa_score, confusion_matrix\n",
-    ")\n",
-    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
-    "import numpy as np\n",
-    "import featuretools as ft\n",
-    "from sklearn.metrics import accuracy_score, classification_report\n",
-    "\n",
-    "# Функция для применения oversampling\n",
-    "def apply_oversampling(X, y):\n",
-    "    oversampler = RandomOverSampler(random_state=42)\n",
-    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
-    "    return X_resampled, y_resampled\n",
-    "\n",
-    "# Функция для применения undersampling\n",
-    "def apply_undersampling(X, y):\n",
-    "    undersampler = RandomUnderSampler(random_state=42)\n",
-    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
-    "    return X_resampled, y_resampled\n",
-    "\n",
-    "def split_stratified_into_train_val_test(\n",
-    "    df_input,\n",
-    "    stratify_colname=\"y\",\n",
-    "    frac_train=0.6,\n",
-    "    frac_val=0.15,\n",
-    "    frac_test=0.25,\n",
-    "    random_state=None,\n",
-    "):\n",
-    "    \"\"\"\n",
-    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
-    "    following fractional ratios provided by the user, where each subset is\n",
-    "    stratified by the values in a specific column (that is, each subset has\n",
-    "    the same relative frequency of the values in the column). It performs this\n",
-    "    splitting by running train_test_split() twice.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    df_input : Pandas dataframe\n",
-    "        Input dataframe to be split.\n",
-    "    stratify_colname : str\n",
-    "        The name of the column that will be used for stratification. Usually\n",
-    "        this column would be for the label.\n",
-    "    frac_train : float\n",
-    "    frac_val   : float\n",
-    "    frac_test  : float\n",
-    "        The ratios with which the dataframe will be split into train, val, and\n",
-    "        test data. The values should be expressed as float fractions and should\n",
-    "        sum to 1.0.\n",
-    "    random_state : int, None, or RandomStateInstance\n",
-    "        Value to be passed to train_test_split().\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    df_train, df_val, df_test :\n",
-    "        Dataframes containing the three splits.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    if frac_train + frac_val + frac_test != 1.0:\n",
-    "        raise ValueError(\n",
-    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
-    "            % (frac_train, frac_val, frac_test)\n",
-    "        )\n",
-    "\n",
-    "    if stratify_colname not in df_input.columns:\n",
-    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
-    "\n",
-    "    X = df_input  # Contains all columns.\n",
-    "    y = df_input[\n",
-    "        [stratify_colname]\n",
-    "    ]  # Dataframe of just the column on which to stratify.\n",
-    "\n",
-    "    # Split original dataframe into train and temp dataframes.\n",
-    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
-    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
-    "    )\n",
-    "\n",
-    "    # Split the temp dataframe into val and test dataframes.\n",
-    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
-    "    df_val, df_test, y_val, y_test = train_test_split(\n",
-    "        df_temp,\n",
-    "        y_temp,\n",
-    "        stratify=y_temp,\n",
-    "        test_size=relative_frac_test,\n",
-    "        random_state=random_state,\n",
-    "    )\n",
-    "\n",
-    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
-    "\n",
-    "    return df_train, df_val, df_test\n",
-    "\n",
-    "\n",
-    "df = pd.read_csv(\"../data/age.csv\", nrows=10000)\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Как бизнес-цели выделим следующие 2 варианта:\n",
-    "    1) GameDev. Создание игры про конкретного персонажа, живущего в конкретном временном промежутке в конкретной стране. \n",
-    "    2) Исследование зависимости длительности жизни от страны проживания.\n",
-    "    \n",
-    "Поскольку именно эти бизнес-цели были выбраны в предыдущей лабораторной работе, будем их использовать.\n",
-    "Но возникает проблема с 1 целью: её невозможно использовать для задачи классификации. Заменим ее на классификацию людей по возрастным группам, что может быть полезно для рекламных целей."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Выполним подготовку данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.fillna({\"Gender\": \"NaN\", \"Country\": \"NaN\", \"Occupation\" : \"NaN\", \"Manner of death\" : \"NaN\"}, inplace=True)\n",
-    "df = df.dropna()\n",
-    "df['Country'] = df['Country'].str.split('; ')\n",
-    "df = df.explode('Country')\n",
-    "data = df.copy()\n",
-    "\n",
-    "value_counts = data[\"Country\"].value_counts()\n",
-    "rare = value_counts[value_counts < 100].index\n",
-    "data = data[~data[\"Country\"].isin(rare)]\n",
-    "\n",
-    "data.drop(data[~data['Gender'].isin(['Male', 'Female'])].index, inplace=True)\n",
-    "\n",
-    "data1 = pd.get_dummies(data, columns=['Gender', 'Country', 'Occupation'], drop_first=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей не будет высоким, поскольку все таки длительность жизни лишь примерная и точно ее угадать невозможно."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Выберем ориентиры для наших 2х задач:\n",
-    "    1)Регрессии - средний возраст человека\n",
-    "    2)Классификации - аиболее часто встречающаяся возрастная группа"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Построим конвейер."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['Id', 'Name', 'Short description', 'Birth year', 'Death year',\n",
-      "       'Age of death', 'Gender_Male', 'Country_France',\n",
-      "       'Country_German Confederation', 'Country_German Democratic Republic',\n",
-      "       ...\n",
-      "       'Manner of death_euthanasia', 'Manner of death_homicide',\n",
-      "       'Manner of death_homicide; natural causes',\n",
-      "       'Manner of death_internal bleeding', 'Manner of death_natural causes',\n",
-      "       'Manner of death_suicide',\n",
-      "       'Manner of death_suicide; homicide; accident',\n",
-      "       'Manner of death_suicide; unfortunate accident',\n",
-      "       'Manner of death_summary execution', 'Manner of death_unnatural death'],\n",
-      "      dtype='object', length=400)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(data.columns)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best parameters for Linear Regression: {}\n",
-      "Best parameters for Random Forest Regressor: {'model__max_depth': None, 'model__n_estimators': 100}\n",
-      "Best parameters for Gradient Boosting Regressor: {'model__learning_rate': 0.2, 'model__max_depth': 7, 'model__n_estimators': 300}\n",
-      "Linear Regression: MSE = 0.002807184047660083, R2 = 0.9999899555289343\n",
-      "Random Forest Regressor: MSE = 11.46917740409879, R2 = 0.9589617856804076\n",
-      "Gradient Boosting Regressor: MSE = 8.202651735797296, R2 = 0.9706498410424512\n"
-     ]
-    }
-   ],
-   "source": [
-    "X_reg = data1.drop(['Id', 'Name', 'Age of death', 'Short description', 'Manner of death'], axis=1)\n",
-    "y_reg = data1['Age of death']\n",
-    "\n",
-    "# Разделение данных\n",
-    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Выбор моделей для регрессии\n",
-    "models_reg = {\n",
-    "    'Linear Regression': LinearRegression(),\n",
-    "    'Random Forest Regressor': RandomForestRegressor(random_state=42),\n",
-    "    'Gradient Boosting Regressor': GradientBoostingRegressor(random_state=42)\n",
-    "}\n",
-    "\n",
-    "# Создание конвейера для регрессии\n",
-    "pipelines_reg = {}\n",
-    "for name, model in models_reg.items():\n",
-    "    pipelines_reg[name] = Pipeline([\n",
-    "        ('scaler', StandardScaler()),\n",
-    "        ('model', model)\n",
-    "    ])\n",
-    "\n",
-    "# Определение сетки гиперпараметров для регрессии\n",
-    "param_grids_reg = {\n",
-    "    'Linear Regression': {},\n",
-    "    'Random Forest Regressor': {\n",
-    "        'model__n_estimators': [100, 200, 300],\n",
-    "        'model__max_depth': [None, 10, 20, 30]\n",
-    "    },\n",
-    "    'Gradient Boosting Regressor': {\n",
-    "        'model__n_estimators': [100, 200, 300],\n",
-    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
-    "        'model__max_depth': [3, 5, 7]\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Настройка гиперпараметров для регрессии\n",
-    "best_models_reg = {}\n",
-    "for name, pipeline in pipelines_reg.items():\n",
-    "    grid_search = GridSearchCV(pipeline, param_grids_reg[name], cv=5, scoring='neg_mean_squared_error')\n",
-    "    grid_search.fit(X_train_reg, y_train_reg)\n",
-    "    best_models_reg[name] = grid_search.best_estimator_\n",
-    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n",
-    "\n",
-    "# Обучение моделей и оценка качества\n",
-    "for model_name in best_models_reg.keys():\n",
-    "    print(f\"Model: {model_name}\")\n",
-    "    model = best_models_reg[model_name][\"model\"]\n",
-    "\n",
-    "    model_pipeline = Pipeline([(\"scaler\", StandardScaler()), (\"model\", model)])\n",
-    "    model_pipeline = model_pipeline.fit(X_train_reg, y_train_reg)\n",
-    "\n",
-    "    y_train_predict = model_pipeline.predict(X_train_reg)\n",
-    "    y_test_predict = model_pipeline.predict(X_test_reg)\n",
-    "\n",
-    "    best_models_reg[model_name][\"pipeline\"] = model_pipeline\n",
-    "    best_models_reg[model_name][\"preds_train\"] = y_train_predict\n",
-    "    best_models_reg[model_name][\"preds_test\"] = y_test_predict\n",
-    "\n",
-    "    best_models_reg[model_name][\"MSE_train\"] = mean_squared_error(y_train_reg, y_train_predict)\n",
-    "    best_models_reg[model_name][\"MSE_test\"] = mean_squared_error(y_test_reg, y_test_predict)\n",
-    "    best_models_reg[model_name][\"R2_train\"] = r2_score(y_train_reg, y_train_predict)\n",
-    "    best_models_reg[model_name][\"R2_test\"] = r2_score(y_test_reg, y_test_predict)\n",
-    "    best_models_reg[model_name][\"MAE_train\"] = mean_absolute_error(y_train_reg, y_train_predict)\n",
-    "    best_models_reg[model_name][\"MAE_test\"] = mean_absolute_error(y_test_reg, y_test_predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data2 = data.drop(['Short description', 'Manner of death', 'Gender', 'Country', 'Occupation'], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['Birth year', 'Death year'], dtype='object')\n",
-      "Best parameters for Logistic Regression: {'model__C': 10, 'model__solver': 'lbfgs'}\n",
-      "Best parameters for Random Forest Classifier: {'model__max_depth': 30, 'model__n_estimators': 200}\n",
-      "Best parameters for Gradient Boosting Classifier: {'model__learning_rate': 0.1, 'model__max_depth': 7, 'model__n_estimators': 200}\n",
-      "Model: Logistic Regression\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\89176\\sourse\\MII\\Labas\\AIM-PIbd-31-Kozyrev-S-S\\aimvenv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
-      "c:\\Users\\89176\\sourse\\MII\\Labas\\AIM-PIbd-31-Kozyrev-S-S\\aimvenv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: Random Forest Classifier\n",
-      "Model: Gradient Boosting Classifier\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Создание возрастных групп\n",
-    "bins = [0, 18, 30, 50, 70, 100]\n",
-    "labels = ['0-18', '19-30', '31-50', '51-70', '71+']\n",
-    "data['Age Group'] = pd.cut(data['Age of death'], bins=bins, labels=labels)\n",
-    "\n",
-    "# Выбор признаков и целевой переменной для классификации\n",
-    "X_class = data2.drop(['Id', 'Name', 'Age of death', 'Age Group'], axis=1)\n",
-    "y_class = data['Age Group']  \n",
-    "print(X_class.columns)\n",
-    "# Разделение данных\n",
-    "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Выбор моделей для классификации\n",
-    "models_class = {\n",
-    "    'Logistic Regression': LogisticRegression(random_state=42, max_iter=5000, solver='liblinear'),\n",
-    "    'Random Forest Classifier': RandomForestClassifier(random_state=42),\n",
-    "    'Gradient Boosting Classifier': GradientBoostingClassifier(random_state=42)\n",
-    "}\n",
-    "\n",
-    "# Создание конвейера для классификации\n",
-    "pipelines_class = {}\n",
-    "for name, model in models_class.items():\n",
-    "    pipelines_class[name] = Pipeline([\n",
-    "        ('scaler', StandardScaler()),\n",
-    "        ('model', model)\n",
-    "    ])\n",
-    "\n",
-    "# Определение сетки гиперпараметров для классификации\n",
-    "'''\n",
-    "param_grids_class = {\n",
-    "    'Logistic Regression': {\n",
-    "        'model__C': [0.1, 1, 10],\n",
-    "        'model__solver': ['lbfgs', 'liblinear']\n",
-    "    },\n",
-    "    'Random Forest Classifier': {\n",
-    "        'model__n_estimators': [100, 200, 300],\n",
-    "        'model__max_depth': [None, 10, 20, 30]\n",
-    "    },\n",
-    "    'Gradient Boosting Classifier': {\n",
-    "        'model__n_estimators': [100, 200, 300],\n",
-    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
-    "        'model__max_depth': [3, 5, 7]\n",
-    "    }\n",
-    "}'''\n",
-    "# Убрал определение параметров поскольку уже был предподсчет данных, но вылетела ошибка. Сохранил лучшие параметры\n",
-    "\n",
-    "param_grids_class = {\n",
-    "    'Logistic Regression': {\n",
-    "        'model__C': [10],\n",
-    "        'model__solver': ['lbfgs']\n",
-    "    },\n",
-    "    'Random Forest Classifier': {\n",
-    "        'model__n_estimators': [200],\n",
-    "        'model__max_depth': [ 30]\n",
-    "    },\n",
-    "    'Gradient Boosting Classifier': {\n",
-    "        'model__n_estimators': [200],\n",
-    "        'model__learning_rate': [0.1],\n",
-    "        'model__max_depth': [7]\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Настройка гиперпараметров для классификации\n",
-    "best_models_class = {}\n",
-    "for name, pipeline in pipelines_class.items():\n",
-    "    grid_search = GridSearchCV(pipeline, param_grids_class[name], cv=5, scoring='accuracy', n_jobs=-1)\n",
-    "    grid_search.fit(X_train_class, y_train_class)\n",
-    "    best_models_class[name] = {\"model\": grid_search.best_estimator_}\n",
-    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n",
-    "\n",
-    "# Обучение моделей и оценка качества\n",
-    "for model_name in best_models_class.keys():\n",
-    "    print(f\"Model: {model_name}\")\n",
-    "    model = best_models_class[model_name][\"model\"]\n",
-    "\n",
-    "    model_pipeline = Pipeline([(\"scaler\", StandardScaler()), (\"model\", model)])\n",
-    "    model_pipeline = model_pipeline.fit(X_train_class, y_train_class)\n",
-    "\n",
-    "    y_train_predict = model_pipeline.predict(X_train_class)\n",
-    "    y_test_probs = model_pipeline.predict_proba(X_test_class)\n",
-    "    y_test_predict = model_pipeline.predict(X_test_class)\n",
-    "\n",
-    "    best_models_class[model_name][\"pipeline\"] = model_pipeline\n",
-    "    best_models_class[model_name][\"probs\"] = y_test_probs\n",
-    "    best_models_class[model_name][\"preds\"] = y_test_predict\n",
-    "\n",
-    "    best_models_class[model_name][\"Precision_train\"] = precision_score(y_train_class, y_train_predict, average='weighted')\n",
-    "    best_models_class[model_name][\"Precision_test\"] = precision_score(y_test_class, y_test_predict, average='weighted')\n",
-    "    best_models_class[model_name][\"Recall_train\"] = recall_score(y_train_class, y_train_predict, average='weighted')\n",
-    "    best_models_class[model_name][\"Recall_test\"] = recall_score(y_test_class, y_test_predict, average='weighted')\n",
-    "    best_models_class[model_name][\"Accuracy_train\"] = accuracy_score(y_train_class, y_train_predict)\n",
-    "    best_models_class[model_name][\"Accuracy_test\"] = accuracy_score(y_test_class, y_test_predict)\n",
-    "    best_models_class[model_name][\"ROC_AUC_test\"] = roc_auc_score(y_test_class, y_test_probs, multi_class='ovr')\n",
-    "    best_models_class[model_name][\"F1_train\"] = f1_score(y_train_class, y_train_predict, average='weighted')\n",
-    "    best_models_class[model_name][\"F1_test\"] = f1_score(y_test_class, y_test_predict, average='weighted')\n",
-    "    best_models_class[model_name][\"MCC_test\"] = matthews_corrcoef(y_test_class, y_test_predict)\n",
-    "    best_models_class[model_name][\"Cohen_kappa_test\"] = cohen_kappa_score(y_test_class, y_test_predict)\n",
-    "    best_models_class[model_name][\"Confusion_matrix\"] = confusion_matrix(y_test_class, y_test_predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_models = len(best_models_class)\n",
-    "fig, ax = plt.subplots(num_models, 1, figsize=(12, 10), sharex=False, sharey=False)\n",
-    "for index, key in enumerate(best_models_class.keys()):\n",
-    "    c_matrix = best_models_class[key][\"Confusion_matrix\"]\n",
-    "    disp = ConfusionMatrixDisplay(\n",
-    "        confusion_matrix=c_matrix, display_labels=[\"0-18\", \"19-30\", \"31-50\", \"51-70\", \"71+\"]\n",
-    "    ).plot(ax=ax.flat[index])\n",
-    "    disp.ax_.set_title(key)\n",
-    "\n",
-    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1000 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, ax = plt.subplots(3, 2, figsize=(12, 10), sharex=False, sharey=False)\n",
-    "ax = ax.flatten()\n",
-    "\n",
-    "for index, (name, model) in enumerate(best_models_reg.items()):\n",
-    "    y_pred_reg = model.predict(X_test_reg)\n",
-    "\n",
-    "    # График фактических значений против предсказанных значений\n",
-    "    ax[index * 2].scatter(y_test_reg, y_pred_reg, alpha=0.5)\n",
-    "    ax[index * 2].plot([min(y_test_reg), max(y_test_reg)], [min(y_test_reg), max(y_test_reg)], color='red', linestyle='--')\n",
-    "    ax[index * 2].set_xlabel('Actual Values')\n",
-    "    ax[index * 2].set_ylabel('Predicted Values')\n",
-    "    ax[index * 2].set_title(f'{name}: Actual vs Predicted')\n",
-    "\n",
-    "    # График остатков\n",
-    "    residuals = y_test_reg - y_pred_reg\n",
-    "    ax[index * 2 + 1].scatter(y_pred_reg, residuals, alpha=0.5)\n",
-    "    ax[index * 2 + 1].axhline(y=0, color='red', linestyle='--')\n",
-    "    ax[index * 2 + 1].set_xlabel('Predicted Values')\n",
-    "    ax[index * 2 + 1].set_ylabel('Residuals')\n",
-    "    ax[index * 2 + 1].set_title(f'{name}: Residuals vs Predicted')\n",
-    "\n",
-    "\n",
-    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "aimvenv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From a4d1755f093d12d75b0a5dcf34fd0d2f16773ba2 Mon Sep 17 00:00:00 2001
From: MaD <mdv_andrey@mail.ru>
Date: Sat, 21 Dec 2024 02:19:00 +0400
Subject: [PATCH 11/13] =?UTF-8?q?=D0=B0=D0=B0=D0=B0=D0=B0=D0=B0=D0=B0?=
 =?UTF-8?q?=D0=B0=D0=B0=D0=B0=D0=B0=D0=B0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_4/lab_products_clustering.ipynb | 386 ----------------------------
 1 file changed, 386 deletions(-)
 delete mode 100644 Lab_4/lab_products_clustering.ipynb

diff --git a/Lab_4/lab_products_clustering.ipynb b/Lab_4/lab_products_clustering.ipynb
deleted file mode 100644
index a029afe..0000000
--- a/Lab_4/lab_products_clustering.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b33feadc",
-   "metadata": {},
-   "source": [
-    "# Лабораторная работа: Методы искусственного интеллекта\n",
-    "## Задача кластеризации продуктов\n",
-    "### Вариант: Продукты\n",
-    "В данной работе используется датасет с описанием продуктов. Цель: провести кластеризацию, чтобы выявить группы товаров на основе их характеристик. Это может быть полезно для сегментации ассортимента и оптимизации маркетинговых стратегий."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae0cdcc2",
-   "metadata": {},
-   "source": [
-    "### Бизнес-цель\n",
-    "Определить сегменты продуктов на основе характеристик из предоставленного датасета. Сегментация поможет:\n",
-    "- Разработать целевые рекламные кампании для различных групп товаров.\n",
-    "- Улучшить стратегию ценообразования."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29c45779",
-   "metadata": {},
-   "source": [
-    "### Загрузка и исследование данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "dab41ace",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 162313 entries, 0 to 162312\n",
-      "Data columns (total 5 columns):\n",
-      " #   Column        Non-Null Count   Dtype  \n",
-      "---  ------        --------------   -----  \n",
-      " 0   category      162313 non-null  object \n",
-      " 1   sub_category  162313 non-null  object \n",
-      " 2   href          162313 non-null  object \n",
-      " 3   items         162280 non-null  object \n",
-      " 4   price         162282 non-null  float64\n",
-      "dtypes: float64(1), object(4)\n",
-      "memory usage: 6.2+ MB\n"
-     ]
-    },
-    {
-     "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>category</th>\n",
-       "      <th>sub_category</th>\n",
-       "      <th>href</th>\n",
-       "      <th>items</th>\n",
-       "      <th>price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Groceries</td>\n",
-       "      <td>Fruits &amp; Vegetables</td>\n",
-       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
-       "      <td>Fresh Dates (Pack) (Approx 450 g - 500 g)</td>\n",
-       "      <td>109.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Groceries</td>\n",
-       "      <td>Fruits &amp; Vegetables</td>\n",
-       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
-       "      <td>Tender Coconut Cling Wrapped (1 pc) (Approx 90...</td>\n",
-       "      <td>49.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Groceries</td>\n",
-       "      <td>Fruits &amp; Vegetables</td>\n",
-       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
-       "      <td>Mosambi 1 kg</td>\n",
-       "      <td>69.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Groceries</td>\n",
-       "      <td>Fruits &amp; Vegetables</td>\n",
-       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
-       "      <td>Orange Imported 1 kg</td>\n",
-       "      <td>125.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Groceries</td>\n",
-       "      <td>Fruits &amp; Vegetables</td>\n",
-       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
-       "      <td>Banana Robusta 6 pcs (Box) (Approx 800 g - 110...</td>\n",
-       "      <td>44.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    category         sub_category  \\\n",
-       "0  Groceries  Fruits & Vegetables   \n",
-       "1  Groceries  Fruits & Vegetables   \n",
-       "2  Groceries  Fruits & Vegetables   \n",
-       "3  Groceries  Fruits & Vegetables   \n",
-       "4  Groceries  Fruits & Vegetables   \n",
-       "\n",
-       "                                                href  \\\n",
-       "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-       "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-       "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-       "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-       "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
-       "\n",
-       "                                               items  price  \n",
-       "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
-       "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
-       "2                                       Mosambi 1 kg   69.0  \n",
-       "3                               Orange Imported 1 kg  125.0  \n",
-       "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
-    "from sklearn.metrics import silhouette_score\n",
-    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
-    "import seaborn as sns\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "# Загрузка данных\n",
-    "df = pd.read_csv('../data/jio_mart_items.csv')\n",
-    "df.info()  # Проверка структуры датасета\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "834f0e46",
-   "metadata": {},
-   "source": [
-    "### Предварительная обработка данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "14f5eb76",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Преобразуем столбец 'items' в числовые категории\n",
-    "label_encoder = LabelEncoder()\n",
-    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
-    "\n",
-    "# Указываем числовые столбцы для нормализации\n",
-    "numeric_features = ['items_encoded', 'price']\n",
-    "\n",
-    "# Нормализация данных\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "df_scaled = scaler.fit_transform(df[numeric_features])\n",
-    "\n",
-    "# Преобразуем обратно в DataFrame для удобства\n",
-    "df_scaled = pd.DataFrame(df_scaled, columns=numeric_features)\n",
-    "df_scaled = df_scaled.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56fd1a00",
-   "metadata": {},
-   "source": [
-    "### Понижение размерности и визуализация данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "c23ca5db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FxcX68ssv5e3tXeGP7b/+9S/t2LFDcXFxtVrn8ePHVVhY6NTWqlUrDR48WPHx8Vq8eLGioqKc/jiW/9iZsy5bVdUZJv/617/k7e1doz+i+/fv17x58zR9+nSneUE1GYckx1gqq3Hz5s3V7vWqqby8PD3//POaPHlytXurSkpKNHz4cHXv3l2vvvqqIiMj1aFDhxq/z0cffaTOnTs7/gVflfoc6+9RVlZWZTCSTp/FdvDgQb333nt65ZVX1L59e40cObLC9/H3uPnmm5WZmel0iLOkpERvvPGGWrRoof79+0uShg4dKmOMZsyYUWEdZ36uP//8s7755htdf/311b7v3XffraSkJH3xxRcVlmVnZzv+0fJ7zZgxQydOnHCcPXa2L7/80nER0vj4eHXo0EEPPvig/vznPzs9nnjiCbVo0YJDWRbBnh00KJ9//rnjX6BHjx7VkiVLlJqaqqefftpxvL7cl19+qXHjxqlz585Vrm/o0KHq1KmTOnbsqKKiIiUkJOjTTz/VxIkTK/QdMWKE/vznP0uSnnvuOadl/v7+uu666/TSSy+puLhYF198sb788kulp6c79UtNTVVsbKyWLl1aac2V2bhxo7p27arRo0dX2WfmzJn65Zdf1KNHD/n4+Cg5OVkLFy5Ur1691KtXL0nSrbfequXLl+vOO+/ULbfcovT0dM2fP1/dunWrsFdMOv0v27P3Qpw8eVJeXl5KSEhQVFSUoz05OVmtWrWq8hBFuRkzZuiHH37Q9u3bK70GSlX++9//6qWXXtKWLVs0ZMgQx6E56fSp59LpU//btm2rDh061Hqs9SUpKckRnhMTE7Vu3To98cQTlfZdv3693nzzTcXGxqpPnz6SpIULF2rAgAGaNm2aXnrppTqpafz48Xrrrbc0atQobdu2Te3bt9dHH32kb775Rq+99ppjcu/AgQP1l7/8RXPmzFFqaqrjcOxXX32lgQMHauLEiZo3b57i4uLUrFkzPfroo9W+75NPPqlPPvlEt956q0aNGqUrr7xSp06d0g8//KCPPvpI+/fvr7B35Xzcc889+uGHH/T8889r+/btGjZsmNq1a6cTJ04oISFB69at05IlS3T48GFt2LChyrp9fHw0aNAgLVu2THPmzKnV9xUeyI1nggE1Vtmp576+vubyyy838+bNczoVtvw03KZNm5pffvnFaT1nn8o9ffp007lzZ9O0aVPj7+9vLr/8cvP666+b4uLiCjUUFhaaiy66yNjtdvPbb79VWH7o0CFz5513moCAAGO3281dd91lDh8+bCQ5TlFfunSp6dGjh3n99dcrnL5b1annkszHH3/s1HfkyJFOp55/9NFH5uqrrzb+/v6madOmplOnTubxxx93Op27rKzMvPDCC6Zdu3bGx8fHXHHFFWb16tUV1lX++Z3rUa5///6Vnu5bfkp5ua+++so0atTIvPXWW5X2q+7U86ouPXD2o/xyA7Uda32del7+8Pb2Np06dTLPPvus45T4M0/Nzs3NNe3atTN9+vSp8N2bPHmy8fLyMklJSVV+PrW9XMCRI0fM6NGjTatWrYy3t7fp2bOn06UaypWUlJjZs2ebLl26GG9vb9O6dWszePBgs23bNmOMMX379jV33XWX2bNnT4XXVnZqfV5enomJiTGdOnUy3t7eplWrVuYPf/iDefnll01RUVGV4ytfX/fu3avtc6Z169aZ22+/3QQFBZnGjRub1q1bm+joaLNy5UpjjDH/8z//YySZdevWVbmORYsWGUmO16Dh4nYRQA2VlJQoNDRU0dHRevfdd91djtskJiZq4MCBFQ7Z1adFixZp+vTpjgnKlRkwYIBGjRpV4YrTAMCcHaCGVqxYoWPHjjmu3AsAaBiYswOcw+bNm7Vjxw4999xzuuKKKxwTOC9UgYGBGjRokEvfs2PHjhVu13G2G2+8UR07dnRRRQAaEg5jAecwatQoxcfH6/LLL9eiRYvUo0cPd5cEAKgFwg4AALA05uwAAABLI+wAAABLY4KyTl/V9PDhw/Lz8+MeKAAANBDGGOXl5Sk0NLTCzW3PRNiRdPjwYYWFhbm7DAAAcB5+/vlnXXLJJVUuJ+xIjsuj//zzzzW6fD8AAHC/3NxchYWFOX7Hq0LYkRyHrvz9/Qk7AAA0MOeagsIEZQAAYGmEHQAAYGluDTubNm1SdHS0QkNDZbPZtGLFCqflNput0sfs2bMdfdq3b19h+axZs1w8EgAA4KncGnZOnTql3r17a+7cuZUuz8jIcHosWLBANptNQ4cOdeo3c+ZMp36PPPKIK8oHAAANgFsnKA8ePFiDBw+ucnlwcLDT85UrV2rgwIHq0KGDU7ufn1+FvgAAAFIDmrNz5MgRffrppxo7dmyFZbNmzVLLli11xRVXaPbs2SopKXFDhQAAwBM1mFPP33vvPfn5+WnIkCFO7Y8++qj69OmjwMBAffvtt4qJiVFGRoZeeeWVKtdVWFiowsJCx/Pc3Nx6qxsAALhXgwk7CxYs0PDhw+Xr6+vUPmXKFMd/9+rVS97e3nrggQcUFxcnHx+fStcVFxenGTNm1Gu9AADAMzSIw1hfffWV9u7dq7/+9a/n7BsREaGSkhLt37+/yj4xMTHKyclxPH7++ec6rBYAAHiSBrFn591339WVV16p3r17n7NvSkqKvLy8FBQUVGUfHx+fKvf6AACAulFaZrQlPUtH8woU5OervuGBauTl+htuuzXsnDx5UmlpaY7n6enpSklJUWBgoNq2bSvp9HyaZcuW6X/+538qvD4pKUmbN2/WwIED5efnp6SkJE2ePFn333+/LrroIpeNAwAAOEvYmaEZq3YrI6fA0RZi91VsdDdF9QhxaS02Y4xx6TueITExUQMHDqzQPnLkSC1atEiS9Pbbb2vSpEnKyMiQ3W536pecnKyHH35Ye/bsUWFhocLDw/WXv/xFU6ZMqdWem9zcXNntduXk5HBvLAAAfqeEnRl6KD5ZZweM8n068+7vUyeBp6a/324NO56CsAMAQN0oLTP604vrnfbonMkmKdjuq6+nXv+7D2nV9Pe7QUxQBgAADcOW9Kwqg44kGUkZOQXakp7lspoIOwAAoM4czas66JxPv7pA2AEAAHUmyM/33J1q0a8uEHYAAECd6RseqBC7r6qajWPT6bOy+oYHuqwmwg4AAKgzjbxsio3uJkkVAk/589jobi693g5hBwAA1KmoHiGad38fBdudD1UF233r7LTz2mgQV1AGAAANS1SPEN3YLZgrKAMAAOtq5GVTv44t3V0Gh7EAAIC1EXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAICluTXsbNq0SdHR0QoNDZXNZtOKFSuclo8aNUo2m83pERUV5dQnKytLw4cPl7+/vwICAjR27FidPHnShaMAAACezK1h59SpU+rdu7fmzp1bZZ+oqChlZGQ4HkuXLnVaPnz4cO3atUtr1qzR6tWrtWnTJo0fP76+SwcAAA1EY3e++eDBgzV48OBq+/j4+Cg4OLjSZT/++KMSEhK0detWXXXVVZKkN954QzfffLNefvllhYaG1nnNAACgYfH4OTuJiYkKCgpS586d9dBDD+nEiROOZUlJSQoICHAEHUmKjIyUl5eXNm/eXOU6CwsLlZub6/QAAADW5NFhJyoqSu+//77WrVunF198URs3btTgwYNVWloqScrMzFRQUJDTaxo3bqzAwEBlZmZWud64uDjZ7XbHIywsrF7HAQAA3Meth7HO5d5773X8d8+ePdWrVy917NhRiYmJuuGGG857vTExMZoyZYrjeW5uLoEHAACL8ug9O2fr0KGDWrVqpbS0NElScHCwjh496tSnpKREWVlZVc7zkU7PA/L393d6AAAAa2pQYefQoUM6ceKEQkJCJEn9+vVTdna2tm3b5uizfv16lZWVKSIiwl1lAgAAD+LWw1gnT5507KWRpPT0dKWkpCgwMFCBgYGaMWOGhg4dquDgYO3bt09PPfWUOnXqpEGDBkmSunbtqqioKI0bN07z589XcXGxJk6cqHvvvZczsQAAgCTJZowx7nrzxMREDRw4sEL7yJEjNW/ePN1xxx3avn27srOzFRoaqptuuknPPfec2rRp4+iblZWliRMnatWqVfLy8tLQoUM1Z84ctWjRosZ15Obmym63Kycnh0NaAAA0EDX9/XZr2PEUhB0AABqemv5+N6g5OwAAALVF2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJbm1rCzadMmRUdHKzQ0VDabTStWrHAsKy4u1tSpU9WzZ081b95coaGhGjFihA4fPuy0jvbt28tmszk9Zs2a5eKRAAAAT+XWsHPq1Cn17t1bc+fOrbAsPz9fycnJmjZtmpKTk7V8+XLt3btXt912W4W+M2fOVEZGhuPxyCOPuKJ8AADQADR255sPHjxYgwcPrnSZ3W7XmjVrnNr++c9/qm/fvjp48KDatm3raPfz81NwcHC91goAABqmBjVnJycnRzabTQEBAU7ts2bNUsuWLXXFFVdo9uzZKikpqXY9hYWFys3NdXoAAABrcuuendooKCjQ1KlTNWzYMPn7+zvaH330UfXp00eBgYH69ttvFRMTo4yMDL3yyitVrisuLk4zZsxwRdkAAMDNbMYY4+4iJMlms+njjz/WHXfcUWFZcXGxhg4dqkOHDikxMdEp7JxtwYIFeuCBB3Ty5En5+PhU2qewsFCFhYWO57m5uQoLC1NOTk616wYAAJ4jNzdXdrv9nL/fHr9np7i4WHfffbcOHDig9evXnzOMREREqKSkRPv371fnzp0r7ePj41NlEAIAANbi0WGnPOikpqZqw4YNatmy5Tlfk5KSIi8vLwUFBbmgQgAA4OncGnZOnjyptLQ0x/P09HSlpKQoMDBQISEh+vOf/6zk5GStXr1apaWlyszMlCQFBgbK29tbSUlJ2rx5swYOHCg/Pz8lJSVp8uTJuv/++3XRRRe5a1gAAMCDuHXOTmJiogYOHFihfeTIkZo+fbrCw8Mrfd2GDRs0YMAAJScn6+GHH9aePXtUWFio8PBw/eUvf9GUKVNqdZiqpsf8AACA56jp77fHTFB2J8IOAAANj2UmKAMAPE9pmdGW9CwdzStQkJ+v+oYHqpGXzd1lAZUi7AAAaiVhZ4ZmrNqtjJwCR1uI3Vex0d0U1SPEjZUBlWtQV1AGALhXws4MPRSf7BR0JCkzp0APxScrYWeGmyoDqkbYAQDUSGmZ0YxVu1XZRM/ythmrdqu07IKfCgoPQ9gBANTIlvSsCnt0zmQkZeQUaEt6luuKAmqAsAMAqJGjeVUHnfPpB7gKYQcAUCNBfr512g9wFcIOAKBG+oYHKsTuq6pOMLfp9FlZfcMDXVkWcE6EHQBAjTTysik2upskVQg85c9jo7txvR14HMIOAKDGonqEaN79fRRsdz5UFWz31bz7+3CdHXgkLioIAKiVqB4hurFbMFdQRoNB2AEA1FojL5v6dWzp7jKAGuEwFgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDS3hp1NmzYpOjpaoaGhstlsWrFihdNyY4yeffZZhYSEqGnTpoqMjFRqaqpTn6ysLA0fPlz+/v4KCAjQ2LFjdfLkSReOAgAAeDK3hp1Tp06pd+/emjt3bqXLX3rpJc2ZM0fz58/X5s2b1bx5cw0aNEgFBQWOPsOHD9euXbu0Zs0arV69Wps2bdL48eNdNQQAAODhbMYY4+4iJMlms+njjz/WHXfcIen0Xp3Q0FA9/vjjeuKJJyRJOTk5atOmjRYtWqR7771XP/74o7p166atW7fqqquukiQlJCTo5ptv1qFDhxQaGlqj987NzZXdbldOTo78/f3rZXwAAKBu1fT322Pn7KSnpyszM1ORkZGONrvdroiICCUlJUmSkpKSFBAQ4Ag6khQZGSkvLy9t3rzZ5TUDAADP09jdBVQlMzNTktSmTRun9jZt2jiWZWZmKigoyGl548aNFRgY6OhTmcLCQhUWFjqe5+bm1lXZAADAw3jsnp36FBcXJ7vd7niEhYW5uyQAAFBPPDbsBAcHS5KOHDni1H7kyBHHsuDgYB09etRpeUlJibKyshx9KhMTE6OcnBzH4+eff67j6gEAgKfw2LATHh6u4OBgrVu3ztGWm5urzZs3q1+/fpKkfv36KTs7W9u2bXP0Wb9+vcrKyhQREVHlun18fOTv7+/0AAAA1uTWOTsnT55UWlqa43l6erpSUlIUGBiotm3batKkSfrHP/6hSy+9VOHh4Zo2bZpCQ0MdZ2x17dpVUVFRGjdunObPn6/i4mJNnDhR9957b43PxAIAANbm1rDz/fffa+DAgY7nU6ZMkSSNHDlSixYt0lNPPaVTp05p/Pjxys7O1p/+9CclJCTI19fX8ZrFixdr4sSJuuGGG+Tl5aWhQ4dqzpw5Lh8LAADwTB5znR134jo7AAA0PA3+OjsAAAB1gbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsrdZhJyMjQ/Hx8frss89UVFTktOzUqVOaOXNmnRUHAADwe9mMMaamnbdu3aqbbrpJZWVlKi4u1sUXX6wVK1aoe/fukqQjR44oNDRUpaWl9VZwfcjNzZXdbldOTo78/f3dXQ4AAKiBmv5+12rPzjPPPKM777xTv/76q44cOaIbb7xR/fv31/bt2393wQAAAPWhcW06b9u2TXPnzpWXl5f8/Pz05ptvqm3btrrhhhv0xRdfqG3btvVVJwAAwHmpVdiRpIKCAqfnTz/9tBo3bqybbrpJCxYsqLPCAAAA6kKtwk6PHj307bffqlevXk7tTzzxhMrKyjRs2LA6LQ4AAOD3qtWcnREjRujrr7+udNlTTz2lGTNmcCgLAAB4lFqdjWVVnI0FAEDDUy9nYxUUFOiTTz5RXl5epW/4ySefqLCwsPbVAgAA1JNahZ233npLr7/+uvz8/Cos8/f315w5c/TOO+/UWXEAAAC/V63CzuLFizVp0qQql0+aNEnvv//+760JAACgztQq7KSmpqp3795VLu/Vq5dSU1N/d1EAAAB1pVZhp6SkRMeOHaty+bFjx1RSUvK7iwIAAKgrtQo73bt319q1a6tc/uWXXzrukwUAAOAJahV2xowZo+eee06rV6+usGzVqlV6/vnnNWbMmDorDgAA4Peq1RWUx48fr02bNum2225Tly5d1LlzZ0nSnj179NNPP+nuu+/W+PHj66VQAACA81GrPTuSFB8frw8//FCXXXaZfvrpJ+3du1edO3fW0qVLtXTp0vqoEQAA4LzVas9OaWmpXn75ZX3yyScqKirSrbfequnTp6tp06b1VR8AAMDvUqs9Oy+88IKeeeYZtWjRQhdffLHmzJmjCRMm1FdtAAAAv1utws7777+vN998U1988YVWrFihVatWafHixSorK6uv+gAAAH6XWoWdgwcP6uabb3Y8j4yMlM1m0+HDh+u8MAAAgLpQ64sK+vr6OrU1adJExcXFdVoUAABAXanVBGVjjEaNGiUfHx9HW0FBgR588EE1b97c0bZ8+fK6qxAAAOB3qFXYGTlyZIW2+++/v86KAQAAqGu1CjsLFy6srzqq1L59ex04cKBC+8MPP6y5c+dqwIAB2rhxo9OyBx54QPPnz3dViQAAwIPVKuy4w9atW1VaWup4vnPnTt1444266667HG3jxo3TzJkzHc+bNWvm0hoBAIDn8viw07p1a6fns2bNUseOHdW/f39HW7NmzRQcHOzq0gAAQANQ69tFuFNRUZHi4+M1ZswY2Ww2R/vixYvVqlUr9ejRQzExMcrPz692PYWFhcrNzXV6AAAAa/L4PTtnWrFihbKzszVq1ChH23333ad27dopNDRUO3bs0NSpU7V3795qzwiLi4vTjBkzXFAxAABwN5sxxri7iJoaNGiQvL29tWrVqir7rF+/XjfccIPS0tLUsWPHSvsUFhaqsLDQ8Tw3N1dhYWHKycmRv79/ndcNAADqXm5urux2+zl/vxvMnp0DBw5o7dq157yGT0REhCRVG3Z8fHycrhUEAACsq8HM2Vm4cKGCgoJ0yy23VNsvJSVFkhQSEuKCqgAAgKdrEHt2ysrKtHDhQo0cOVKNG/9fyfv27dOSJUt08803q2XLltqxY4cmT56s6667Tr169XJjxQAAwFM0iLCzdu1aHTx4UGPGjHFq9/b21tq1a/Xaa6/p1KlTCgsL09ChQ/X3v//dTZUCAABP06AmKNeXmk5wAgAAnqOmv98NZs4OAADA+SDsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS2sQFxUE4FqlZUZb0rN0NK9AQX6+6hseqEZeNneXBQDnhbADwEnCzgzNWLVbGTkFjrYQu69io7spqgf3nAPQ8HAYC4BDws4MPRSf7BR0JCkzp0APxScrYWeGmyoDgPNH2AEg6fShqxmrdquy+8eUt81YtVulZRf8HWYANDCEHQCSpC3pWRX26JzJSMrIKdCW9CzXFQUAdYCwA0CSdDSv6qBzPv0AwFMQdgBIkoL8fOu0HwB4CsIOAElS3/BAhdh9VdUJ5jadPiurb3igK8sCgN+NsANAktTIy6bY6G6SVCHwlD+Pje7G9XYANDiEHQAOUT1CNO/+Pgq2Ox+qCrb7at79fbjODoAGiYsKAnAS1SNEN3YL5grKACyDsAOggkZeNvXr2NLdZQBAneAwFgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDSPDjvTp0+XzWZzenTp0sWxvKCgQBMmTFDLli3VokULDR06VEeOHHFjxQAAwNN4dNiRpO7duysjI8Px+Prrrx3LJk+erFWrVmnZsmXauHGjDh8+rCFDhrixWgAA4Gkau7uAc2ncuLGCg4MrtOfk5Ojdd9/VkiVLdP3110uSFi5cqK5du+q7777TNddc4+pSAQCAB/L4PTupqakKDQ1Vhw4dNHz4cB08eFCStG3bNhUXFysyMtLRt0uXLmrbtq2SkpKqXWdhYaFyc3OdHgAAwJo8OuxERERo0aJFSkhI0Lx585Senq5rr71WeXl5yszMlLe3twICApxe06ZNG2VmZla73ri4ONntdscjLCysHkcBAADcyaMPYw0ePNjx37169VJERITatWunf//732ratOl5rzcmJkZTpkxxPM/NzSXwAABgUR69Z+dsAQEBuuyyy5SWlqbg4GAVFRUpOzvbqc+RI0cqneNzJh8fH/n7+zs9AACANTWosHPy5Ent27dPISEhuvLKK9WkSROtW7fOsXzv3r06ePCg+vXr58YqAQCAJ/How1hPPPGEoqOj1a5dOx0+fFixsbFq1KiRhg0bJrvdrrFjx2rKlCkKDAyUv7+/HnnkEfXr148zsQAAgINHh51Dhw5p2LBhOnHihFq3bq0//elP+u6779S6dWtJ0quvviovLy8NHTpUhYWFGjRokN588003Vw0AADyJzRhj3F2Eu+Xm5sputysnJ4f5OwAANBA1/f1uUHN2AAAAaouwAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALM2j73oO1JXSMqMt6Vk6mlegID9f9Q0PVCMvm7vLAgC4AGEHlpewM0MzVu1WRk6Boy3E7qvY6G6K6hHixsoAAK7AYSxYWsLODD0Un+wUdCQpM6dAD8UnK2FnhpsqAwC4CmEHllVaZjRj1W6ZSpaVt81YtVulZZX1AABYBWEHlrUlPavCHp0zGUkZOQXakp7luqIAAC5H2IFlHc2rOuicTz8AQMNE2IEllZYZHc8rrFHfID/feq4GAOBOnI0Fy6ns7KvK2CQF20+fhg4AsC7CDiyl/Oyrc005Lr/CTmx0N663AwAWR9iBZVR39tXZgrnODgBcMAg7aFCquxLyuc6+Kjftlq4a9cdw9ugAwAWCsIMG41xXQq7pWVWt/HwIOgBwASHswGOduRdn//FTenVtaoU+5VdCnnd/nxqfVcXZVwBwYSHswCPV9Iwqo9OTjWes2q2NTw5UiN1XmTkFlc7b4ewrALgwcZ0deJyq7mdVlfIrIW878Ktio7tJ+r+zrcpx9hUAXLgIO/AotTmj6mxH8woU1SNE8+7vo2C786GqYLuv5t3fh7OvAOACxGEseJSanlFVmfK5OFE9QnRjt+Aqz9oCAFxYCDvwKOdzn6rK5uI08rKpX8eWdVgZAKCh4jAWPEptz5RiLg4A4FwIO/AofcMDFWL3rTDBuCrMxQEAnAuHseBRGnnZFBvdTQ/FJ8smOU1ULg9AkyIvU/tWzZiLAwCoEcIOPE75GVVnX2eH+1kBAM4HYQceiTOqAAB1hbADj8UZVQCAusAEZQAAYGkeHXbi4uJ09dVXy8/PT0FBQbrjjju0d+9epz4DBgyQzWZzejz44INuqhhnKi0zStp3QitTflHSvhMqLTuf6yIDAPD7ePRhrI0bN2rChAm6+uqrVVJSomeeeUY33XSTdu/erebNmzv6jRs3TjNnznQ8b9asmTvKxRkqu5FnCBOMAQBu4NFhJyEhwen5okWLFBQUpG3btum6665ztDdr1kzBwcGuLg9VKL+R59n7cTJzCvRQfDLXxQEAuJRHH8Y6W05OjiQpMDDQqX3x4sVq1aqVevTooZiYGOXn57ujPKj6G3mWt81YtZtDWgAAl/HoPTtnKisr06RJk/THP/5RPXr0cLTfd999ateunUJDQ7Vjxw5NnTpVe/fu1fLly6tcV2FhoQoLCx3Pc3Nz67X2C8m5buRpJGXkFGhLehZnWgEAXKLBhJ0JEyZo586d+vrrr53ax48f7/jvnj17KiQkRDfccIP27dunjh07VrquuLg4zZgxo17rvVDV9Eae53PDTwAAzkeDOIw1ceJErV69Whs2bNAll1xSbd+IiAhJUlpaWpV9YmJilJOT43j8/PPPdVrvhWz/8VM16lfbG34CAHC+PHrPjjFGjzzyiD7++GMlJiYqPDz8nK9JSUmRJIWEVD0B1sfHRz4+PnVVJv5/CTsz9Ora1Gr72HT6tg99wwOr7QcAQF3x6LAzYcIELVmyRCtXrpSfn58yMzMlSXa7XU2bNtW+ffu0ZMkS3XzzzWrZsqV27NihyZMn67rrrlOvXr3cXP2FpXxick3ERnfjtg8AAJfx6LAzb948SacvHHimhQsXatSoUfL29tbatWv12muv6dSpUwoLC9PQoUP197//3Q3VXtjONTG53KTIyzjtHADgUh4ddoyp/vTksLAwbdy40UXVXJhKy4y+++8JJe07IcmoX4dWuqZjywp7Zmo64bh9Ky74CABwLY8OO3CvhJ0Zenr5D8rOL3a0/XPDPgU0a6JZQ3o67aGp6YRjJiYDAFytQZyNBddL2JmhB+OTnYJOuez8Yj0Yn6yEnRmOtr7hgQqx+6qqmTg2nb5dBBOTAQCuRthBBaVlRtM/Ofdk4+mf7HJcCbmRl02x0d0kqULgKX/OxGQAgDsQdlDBlvQsZeaeew5OZm6htqRnOZ5H9QjRvPv7KNjufKgq2O7L/bAAAG7DnB04lJYZbUnP0udnHJ46l7MnJkf1CNGN3YK1JT1LR/MKFOR3+tAVe3QAAO5C2IGk03N0ZqzaXaPTx89U2YTjRl427nsFAPAYhB0oYWeGHopPrvRO5dUJ9vdhwjEAwOMRdi5g5dfQefp/f6h10JGkZ2/tzuEpAIDHI+xcYMrn5Xy5K0MfJR9SXkHpea/roubedVgZAAD1g7BzATnfeTlVqelVkwEAcCfCzgXisx2H9fCS7XW6Tq6GDABoCLjOzgVg1X8Oa8LSugs6XA0ZANCQsGfHoopKyvRB0n4tTz6kXRl5db5+roYMAGgoCDsWFPfZbr29Kf28zrA6l8DmTfTCnT25GjIAoMEg7FhM3Ge79dam9Hpb/7RbuxN0AAANCnN2LKSopKxeg44kBfszKRkA0LCwZ6eBK5+bcyArXykHfq3X92JSMgCgISLsNGBxn+3WO1+lq6w+JudUgknJAICGiLDTABWVlGnEu5v1XXqWS94voFkTzRrCpGQAQMNE2Glg6nsC8pkCmjXR6D+Ea+L1ndijAwBosAg7DURpmdGkfyVr1Y7Men+viQM76Y+dWqlveCAhBwDQ4BF2GoCEnRma/sluZebW772obJKC7b6afONlhBwAgGUQdjxcws4MPRSfXC8XCDxTebRhEjIAwGoIOx6qtMzo27Tjevzf/6n3oCOd3qMTG92NScgAAMsh7HighJ0Zevp/f1D2b8X1/l4BTZto7vA+uqZDS/boAAAsibDjYT7bcVgPL6m7O5RXxyZp1tCe+mOnVi55PwAA3IHbRXiQz3ZkaIKLgk6wv4/m3d+Hw1YAAMtjz44HKC0zen3tT5qzPs0l7zc58jKunQMAuGAQdtzssx2HNelf21VUVv/vxZWQAQAXIsKOGz23eqfe/fqAS97L7ttY2/5+I3tzAAAXHMKOGxSVlCny5XU6mF3ksvf85ukbCDoAgAsSYcfFJi3dphX/qf9bPpyp1yX+auHLpgYAXJj4BXSRtMyTinxto8vft9cl/vpk4rUuf18AADwFYccF2j/9qVved+f0QezRAQBc8PglrEeffHdQj674wS3vvX/WLW55XwAAPA1hp564a29O5zbN9cXkAW55bwAAPBFhpx5w2AoAAM/BL2Mdc1fQ4bAVAACVs8y9sebOnav27dvL19dXERER2rJli8trIOgAAOB5LBF2PvzwQ02ZMkWxsbFKTk5W7969NWjQIB09etTdpdWrtZP6E3QAADgHS4SdV155RePGjdPo0aPVrVs3zZ8/X82aNdOCBQvcXVq98LWd3pvTKbiFu0sBAMDjNfiwU1RUpG3btikyMtLR5uXlpcjISCUlJVX6msLCQuXm5jo9GpI9cezNAQCgphp82Dl+/LhKS0vVpk0bp/Y2bdooM7Py2zLExcXJbrc7HmFhYa4otU5w2AoAgNpp8GHnfMTExCgnJ8fx+Pnnn+tkvfUZRD6beC1BBwCA89DgTz1v1aqVGjVqpCNHjji1HzlyRMHBwZW+xsfHRz4+PvVSz/5Zt9T5WVmEHAAAzl+D37Pj7e2tK6+8UuvWrXO0lZWVad26derXr59baqrLcELQAQDg92nwYUeSpkyZonfeeUfvvfeefvzxRz300EM6deqURo8e7baafm9I+UP4RQQdAADqQIM/jCVJ99xzj44dO6Znn31WmZmZuvzyy5WQkFBh0rKrne8hrR9nRqmpd6N6qAgAgAuPzRhj3F2Eu+Xm5sputysnJ0f+/v51vv7aBB725gAAUDM1/f22xGEsT7d/1i01CjEEHQAA6h5hx4WqCzMEHQAA6ocl5uw0JIQaAABciz07AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0riCsqTye6Hm5ua6uRIAAFBT5b/b57qnOWFHUl5eniQpLCzMzZUAAIDaysvLk91ur3K5zZwrDl0AysrKdPjwYfn5+clms9XLe+Tm5iosLEw///xztbeht7IL/TNg/Iyf8TN+xl+34zfGKC8vT6GhofLyqnpmDnt2JHl5eemSSy5xyXv5+/tfkF/0M13onwHjZ/yMn/FfqOpj/NXt0SnHBGUAAGBphB0AAGBphB0X8fHxUWxsrHx8fNxdittc6J8B42f8jJ/xM373jJ8JygAAwNLYswMAACyNsAMAACyNsAMAACyNsAMAACyNsOMic+fOVfv27eXr66uIiAht2bLF3SXVi7i4OF199dXy8/NTUFCQ7rjjDu3du9epz4ABA2Sz2ZweDz74oJsqrlvTp0+vMLYuXbo4lhcUFGjChAlq2bKlWrRooaFDh+rIkSNurLhutW/fvsL4bTabJkyYIMl6237Tpk2Kjo5WaGiobDabVqxY4bTcGKNnn31WISEhatq0qSIjI5WamurUJysrS8OHD5e/v78CAgI0duxYnTx50oWjOH/Vjb+4uFhTp05Vz5491bx5c4WGhmrEiBE6fPiw0zoq+87MmjXLxSM5P+fa/qNGjaowtqioKKc+Vt3+kir9W2Cz2TR79mxHH1dtf8KOC3z44YeaMmWKYmNjlZycrN69e2vQoEE6evSou0urcxs3btSECRP03Xffac2aNSouLtZNN92kU6dOOfUbN26cMjIyHI+XXnrJTRXXve7duzuN7euvv3Ysmzx5slatWqVly5Zp48aNOnz4sIYMGeLGauvW1q1bnca+Zs0aSdJdd93l6GOlbX/q1Cn17t1bc+fOrXT5Sy+9pDlz5mj+/PnavHmzmjdvrkGDBqmgoMDRZ/jw4dq1a5fWrFmj1atXa9OmTRo/fryrhvC7VDf+/Px8JScna9q0aUpOTtby5cu1d+9e3XbbbRX6zpw50+k78cgjj7ii/N/tXNtfkqKiopzGtnTpUqflVt3+kpzGnZGRoQULFshms2no0KFO/Vyy/Q3qXd++fc2ECRMcz0tLS01oaKiJi4tzY1WucfToUSPJbNy40dHWv39/89hjj7mvqHoUGxtrevfuXemy7Oxs06RJE7Ns2TJH248//mgkmaSkJBdV6FqPPfaY6dixoykrKzPGWHvbSzIff/yx43lZWZkJDg42s2fPdrRlZ2cbHx8fs3TpUmOMMbt37zaSzNatWx19Pv/8c2Oz2cwvv/zistrrwtnjr8yWLVuMJHPgwAFHW7t27cyrr75av8W5QGXjHzlypLn99turfM2Ftv1vv/12c/311zu1uWr7s2ennhUVFWnbtm2KjIx0tHl5eSkyMlJJSUlurMw1cnJyJEmBgYFO7YsXL1arVq3Uo0cPxcTEKD8/3x3l1YvU1FSFhoaqQ4cOGj58uA4ePChJ2rZtm4qLi52+C126dFHbtm0t+V0oKipSfHy8xowZ43SDXStv+zOlp6crMzPTaXvb7XZFREQ4tndSUpICAgJ01VVXOfpERkbKy8tLmzdvdnnN9S0nJ0c2m00BAQFO7bNmzVLLli11xRVXaPbs2SopKXFPgfUgMTFRQUFB6ty5sx566CGdOHHCsexC2v5HjhzRp59+qrFjx1ZY5ortz41A69nx48dVWlqqNm3aOLW3adNGe/bscVNVrlFWVqZJkybpj3/8o3r06OFov++++9SuXTuFhoZqx44dmjp1qvbu3avly5e7sdq6ERERoUWLFqlz587KyMjQjBkzdO2112rnzp3KzMyUt7d3hT/0bdq0UWZmpnsKrkcrVqxQdna2Ro0a5Wiz8rY/W/k2rez//fJlmZmZCgoKclreuHFjBQYGWu47UVBQoKlTp2rYsGFON4J89NFH1adPHwUGBurbb79VTEyMMjIy9Morr7ix2roRFRWlIUOGKDw8XPv27dMzzzyjwYMHKykpSY0aNbqgtv97770nPz+/CoftXbX9CTuoNxMmTNDOnTud5qxIcjoe3bNnT4WEhOiGG27Qvn371LFjR1eXWacGDx7s+O9evXopIiJC7dq107///W81bdrUjZW53rvvvqvBgwcrNDTU0WblbY+qFRcX6+6775YxRvPmzXNaNmXKFMd/9+rVS97e3nrggQcUFxfX4G+tcO+99zr+u2fPnurVq5c6duyoxMRE3XDDDW6szPUWLFig4cOHy9fX16ndVdufw1j1rFWrVmrUqFGFM26OHDmi4OBgN1VV/yZOnKjVq1drw4YNuuSSS6rtGxERIUlKS0tzRWkuFRAQoMsuu0xpaWkKDg5WUVGRsrOznfpY8btw4MABrV27Vn/961+r7WflbV++Tav7fz84OLjCiQolJSXKysqyzHeiPOgcOHBAa9ascdqrU5mIiAiVlJRo//79rinQhTp06KBWrVo5vu8XwvaXpK+++kp79+49598Dqf62P2Gnnnl7e+vKK6/UunXrHG1lZWVat26d+vXr58bK6ocxRhMnTtTHH3+s9evXKzw8/JyvSUlJkSSFhITUc3Wud/LkSe3bt08hISG68sor1aRJE6fvwt69e3Xw4EHLfRcWLlyooKAg3XLLLdX2s/K2Dw8PV3BwsNP2zs3N1ebNmx3bu1+/fsrOzta2bdscfdavX6+ysjJHEGzIyoNOamqq1q5dq5YtW57zNSkpKfLy8qpweMcKDh06pBMnTji+71bf/uXeffddXXnllerdu/c5+9bb9q/3KdAw//rXv4yPj49ZtGiR2b17txk/frwJCAgwmZmZ7i6tzj300EPGbrebxMREk5GR4Xjk5+cbY4xJS0szM2fONN9//71JT083K1euNB06dDDXXXedmyuvG48//rhJTEw06enp5ptvvjGRkZGmVatW5ujRo8YYYx588EHTtm1bs379evP999+bfv36mX79+rm56rpVWlpq2rZta6ZOnerUbsVtn5eXZ7Zv3262b99uJJlXXnnFbN++3XG20axZs0xAQIBZuXKl2bFjh7n99ttNeHi4+e233xzriIqKMldccYXZvHmz+frrr82ll15qhg0b5q4h1Up14y8qKjK33XabueSSS0xKSorT34PCwkJjjDHffvutefXVV01KSorZt2+fiY+PN61btzYjRoxw88hqprrx5+XlmSeeeMIkJSWZ9PR0s3btWtOnTx9z6aWXmoKCAsc6rLr9y+Xk5JhmzZqZefPmVXi9K7c/YcdF3njjDdO2bVvj7e1t+vbta7777jt3l1QvJFX6WLhwoTHGmIMHD5rrrrvOBAYGGh8fH9OpUyfz5JNPmpycHPcWXkfuueceExISYry9vc3FF19s7rnnHpOWluZY/ttvv5mHH37YXHTRRaZZs2bmzjvvNBkZGW6suO598cUXRpLZu3evU7sVt/2GDRsq/b6PHDnSGHP69PNp06aZNm3aGB8fH3PDDTdU+FxOnDhhhg0bZlq0aGH8/f3N6NGjTV5enhtGU3vVjT89Pb3KvwcbNmwwxhizbds2ExERYex2u/H19TVdu3Y1L7zwglMY8GTVjT8/P9/cdNNNpnXr1qZJkyamXbt2Zty4cRX+kWvV7V/urbfeMk2bNjXZ2dkVXu/K7W8zxpi63VcEAADgOZizAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wA6DBGTVqlGw2m2w2m7y9vdWpUyfNnDlTJSUlkk7fo+3tt99WRESEWrRooYCAAF111VV67bXXlJ+fL0natWuXhg4dqvbt28tms+m1115z44gA1CfCDoAGKSoqShkZGUpNTdXjjz+u6dOna/bs2ZKkv/zlL5o0aZJuv/12bdiwQSkpKZo2bZpWrlypL7/8UpKUn5+vDh06aNasWZa6wzSAirhdBIAGZ9SoUcrOztaKFSscbTfddJPy8vI0efJk3XPPPVqxYoVuv/12p9cZY5Sbmyu73e7U3r59e02aNEmTJk1yQfUAXI09OwAsoWnTpioqKtLixYvVuXPnCkFHkmw2W4WgA8D6CDsAGjRjjNauXasvvvhC119/vVJTU9W5c2d3lwXAgxB2ADRIq1evVosWLeTr66vBgwfrnnvu0fTp08WReQBna+zuAgDgfAwcOFDz5s2Tt7e3QkND1bjx6T9nl112mfbs2ePm6gB4EvbsAGiQmjdvrk6dOqlt27aOoCNJ9913n3766SetXLmywmuMMcrJyXFlmQA8AGEHgKXcfffduueeezRs2DC98MIL+v7773XgwAGtXr1akZGR2rBhgySpqKhIKSkpSklJUVFRkX755RelpKQoLS3NzSMAUNc49RxAg1PZqednKisr09tvv60FCxZo165daty4sS699FKNGDFC48aNU9OmTbV//36Fh4dXeG3//v2VmJhYvwMA4FKEHQAAYGkcxgIAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJb2/wGpij0KLp4xvQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Применение PCA для понижения размерности\n",
-    "pca = PCA(n_components=2)\n",
-    "reduced_data = pca.fit_transform(df_scaled)\n",
-    "\n",
-    "# Визуализация данных\n",
-    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1])\n",
-    "plt.title('Визуализация данных после PCA')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dd1339ee",
-   "metadata": {},
-   "source": [
-    "### Выбор оптимального количества кластеров"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "cf6663df",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[17], line 9\u001b[0m\n\u001b[0;32m      7\u001b[0m     kmeans\u001b[38;5;241m.\u001b[39mfit(reduced_data)\n\u001b[0;32m      8\u001b[0m     inertia\u001b[38;5;241m.\u001b[39mappend(kmeans\u001b[38;5;241m.\u001b[39minertia_)\n\u001b[1;32m----> 9\u001b[0m     silhouette_scores\u001b[38;5;241m.\u001b[39mappend(\u001b[43msilhouette_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduced_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabels_\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Построение графиков\u001b[39;00m\n\u001b[0;32m     12\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\_param_validation.py:216\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    211\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    212\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    213\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    214\u001b[0m         )\n\u001b[0;32m    215\u001b[0m     ):\n\u001b[1;32m--> 216\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    220\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    221\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    222\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    223\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    224\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    225\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    226\u001b[0m     )\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py:139\u001b[0m, in \u001b[0;36msilhouette_score\u001b[1;34m(X, labels, metric, sample_size, random_state, **kwds)\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    138\u001b[0m         X, labels \u001b[38;5;241m=\u001b[39m X[indices], labels[indices]\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmean(silhouette_samples(X, labels, metric\u001b[38;5;241m=\u001b[39mmetric, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds))\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\_param_validation.py:189\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    187\u001b[0m global_skip_validation \u001b[38;5;241m=\u001b[39m get_config()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_parameter_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m global_skip_validation:\n\u001b[1;32m--> 189\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    191\u001b[0m func_sig \u001b[38;5;241m=\u001b[39m signature(func)\n\u001b[0;32m    193\u001b[0m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py:303\u001b[0m, in \u001b[0;36msilhouette_samples\u001b[1;34m(X, labels, metric, **kwds)\u001b[0m\n\u001b[0;32m    299\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetric\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m metric\n\u001b[0;32m    300\u001b[0m reduce_func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[0;32m    301\u001b[0m     _silhouette_reduce, labels\u001b[38;5;241m=\u001b[39mlabels, label_freqs\u001b[38;5;241m=\u001b[39mlabel_freqs\n\u001b[0;32m    302\u001b[0m )\n\u001b[1;32m--> 303\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpairwise_distances_chunked\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduce_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduce_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    304\u001b[0m intra_clust_dists, inter_clust_dists \u001b[38;5;241m=\u001b[39m results\n\u001b[0;32m    305\u001b[0m intra_clust_dists \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate(intra_clust_dists)\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\pairwise.py:2261\u001b[0m, in \u001b[0;36mpairwise_distances_chunked\u001b[1;34m(X, Y, reduce_func, metric, n_jobs, working_memory, **kwds)\u001b[0m\n\u001b[0;32m   2259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reduce_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2260\u001b[0m     chunk_size \u001b[38;5;241m=\u001b[39m D_chunk\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m-> 2261\u001b[0m     D_chunk \u001b[38;5;241m=\u001b[39m \u001b[43mreduce_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mD_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2262\u001b[0m     _check_chunk_size(D_chunk, chunk_size)\n\u001b[0;32m   2263\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m D_chunk\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\metrics\\cluster\\_unsupervised.py:180\u001b[0m, in \u001b[0;36m_silhouette_reduce\u001b[1;34m(D_chunk, start, labels, label_freqs)\u001b[0m\n\u001b[0;32m    178\u001b[0m         sample_weights \u001b[38;5;241m=\u001b[39m D_chunk[i]\n\u001b[0;32m    179\u001b[0m         sample_labels \u001b[38;5;241m=\u001b[39m labels\n\u001b[1;32m--> 180\u001b[0m         cluster_distances[i] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbincount\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[43m            \u001b[49m\u001b[43msample_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminlength\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mlabel_freqs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    184\u001b[0m \u001b[38;5;66;03m# intra_index selects intra-cluster distances within cluster_distances\u001b[39;00m\n\u001b[0;32m    185\u001b[0m end \u001b[38;5;241m=\u001b[39m start \u001b[38;5;241m+\u001b[39m n_chunk_samples\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "# Оценка инерции для выбора числа кластеров\n",
-    "inertia = []\n",
-    "silhouette_scores = []\n",
-    "k_range = range(2, 11)\n",
-    "for k in k_range:\n",
-    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
-    "    kmeans.fit(reduced_data)\n",
-    "    inertia.append(kmeans.inertia_)\n",
-    "    silhouette_scores.append(silhouette_score(reduced_data, kmeans.labels_))\n",
-    "\n",
-    "# Построение графиков\n",
-    "plt.figure(figsize=(14, 5))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(k_range, inertia, marker='o')\n",
-    "plt.title('Критерий инерции')\n",
-    "plt.xlabel('Число кластеров')\n",
-    "plt.ylabel('Инерция')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(k_range, silhouette_scores, marker='o')\n",
-    "plt.title('Коэффициент силуэта')\n",
-    "plt.xlabel('Число кластеров')\n",
-    "plt.ylabel('Силуэт')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d55dd7f8",
-   "metadata": {},
-   "source": [
-    "### Кластерный анализ"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d17981b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Кластеризация с KMeans\n",
-    "optimal_k = 4  # Выбираем на основе графиков\n",
-    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
-    "kmeans_labels = kmeans.fit_predict(reduced_data)\n",
-    "\n",
-    "# Визуализация кластеров\n",
-    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=kmeans_labels, cmap='viridis')\n",
-    "plt.title('Кластеры (KMeans)')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()\n",
-    "\n",
-    "# Иерархическая кластеризация\n",
-    "hierarchical = AgglomerativeClustering(n_clusters=optimal_k)\n",
-    "hierarchical_labels = hierarchical.fit_predict(reduced_data)\n",
-    "\n",
-    "plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=hierarchical_labels, cmap='viridis')\n",
-    "plt.title('Кластеры (Иерархическая кластеризация)')\n",
-    "plt.xlabel('PC1')\n",
-    "plt.ylabel('PC2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98bd2b6e",
-   "metadata": {},
-   "source": [
-    "### Оценка качества кластеризации"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82e73067",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Оценка коэффициента силуэта для KMeans и иерархической кластеризации\n",
-    "kmeans_silhouette = silhouette_score(reduced_data, kmeans_labels)\n",
-    "hierarchical_silhouette = silhouette_score(reduced_data, hierarchical_labels)\n",
-    "\n",
-    "print(f'Силуэт для KMeans: {kmeans_silhouette:.2f}')\n",
-    "print(f'Силуэт для иерархической кластеризации: {hierarchical_silhouette:.2f}')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From fe8f73f4ed203b92ba0a3ade6ab60ba0f1d4a9ed Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Sat, 21 Dec 2024 11:21:05 +0400
Subject: [PATCH 12/13] =?UTF-8?q?=D0=B8=D1=81=D0=BF=D1=80=D0=B0=D0=B2?=
 =?UTF-8?q?=D0=BB=D0=B5=D0=BD=D0=B8=D1=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_3/lab3.ipynb | 326 ++++++++++++++++++++++++++++++++++++++++++++---
 Lab_4/lab4.ipynb |  95 ++++++++++----
 2 files changed, 378 insertions(+), 43 deletions(-)

diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb
index b129952..8a8479f 100644
--- a/Lab_3/lab3.ipynb
+++ b/Lab_3/lab3.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,19 +19,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<>:1: SyntaxWarning: invalid escape sequence '\\j'\n",
-      "<>:1: SyntaxWarning: invalid escape sequence '\\j'\n",
-      "C:\\Users\\MaD\\AppData\\Local\\Temp\\ipykernel_6188\\750029597.py:1: SyntaxWarning: invalid escape sequence '\\j'\n",
-      "  df = pd.read_csv(\"../data\\jio_mart_items.csv\")\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -49,6 +39,18 @@
       "dtypes: float64(1), object(4)\n",
       "memory usage: 6.2+ MB\n",
       "None\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 162313 entries, 0 to 162312\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count   Dtype  \n",
+      "---  ------        --------------   -----  \n",
+      " 0   category      162313 non-null  object \n",
+      " 1   sub_category  162313 non-null  object \n",
+      " 2   href          162313 non-null  object \n",
+      " 3   items         162280 non-null  object \n",
+      " 4   price         162282 non-null  float64\n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 6.2+ MB\n",
       "Пропущенные значения:\n",
       " category         0\n",
       "sub_category     0\n",
@@ -74,6 +76,9 @@
     "print(df.info())\n",
     "# print(df.head())\n",
     "\n",
+    "df.info()\n",
+    "# df = df.sample(n=20000 , random_state=42)\n",
+    "\n",
     "print(\"Пропущенные значения:\\n\", df.isnull().sum())\n",
     "\n",
     "print(df.describe())\n"
@@ -94,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -197,6 +202,16 @@
       "5    36201\n",
       "Name: count, dtype: int64\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mdv_a\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\mdv_a\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\utils\\_tags.py:354: FutureWarning: The SMOTE or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
@@ -210,6 +225,283 @@
     "# Проверяем результат\n",
     "print(\"Распределение классов после балансировки:\\n\", pd.Series(y_train_balanced).value_counts())\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>sub_category</th>\n",
+       "      <th>href</th>\n",
+       "      <th>items</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>34</td>\n",
+       "      <td>377</td>\n",
+       "      <td>17884</td>\n",
+       "      <td>406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>62</td>\n",
+       "      <td>512</td>\n",
+       "      <td>71086</td>\n",
+       "      <td>731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48</td>\n",
+       "      <td>473</td>\n",
+       "      <td>78772</td>\n",
+       "      <td>526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>58</td>\n",
+       "      <td>426</td>\n",
+       "      <td>42990</td>\n",
+       "      <td>466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41</td>\n",
+       "      <td>146</td>\n",
+       "      <td>59617</td>\n",
+       "      <td>1005</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sub_category  href  items  price\n",
+       "0            34   377  17884    406\n",
+       "1            62   512  71086    731\n",
+       "2            48   473  78772    526\n",
+       "3            58   426  42990    466\n",
+       "4            41   146  59617   1005"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train_balanced.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь классы идеально сбалансированные"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Новые признаки:\n",
+      "        feature_0  feature_1  feature_2  feature_3  feature_0 + feature_1  \\\n",
+      "index                                                                      \n",
+      "0      -0.040468   0.472612  -1.420429  -0.658277               0.432145   \n",
+      "1       1.302395   1.196773  -0.067177  -0.430707               2.499168   \n",
+      "2       0.630964   0.987571   0.128325  -0.574251               1.618535   \n",
+      "3       1.110558   0.735456  -0.781830  -0.616264               1.846014   \n",
+      "4       0.295248  -0.766507  -0.358904  -0.238849              -0.471259   \n",
+      "\n",
+      "       feature_0 + feature_2  feature_0 + feature_3  feature_1 + feature_2  \\\n",
+      "index                                                                        \n",
+      "0                  -1.460897              -0.698745              -0.947817   \n",
+      "1                   1.235218               0.871688               1.129596   \n",
+      "2                   0.759289               0.056712               1.115896   \n",
+      "3                   0.328728               0.494293              -0.046374   \n",
+      "4                  -0.063656               0.056399              -1.125411   \n",
+      "\n",
+      "       feature_1 + feature_3  feature_2 + feature_3  ...  \\\n",
+      "index                                                ...   \n",
+      "0                  -0.185665              -2.078706  ...   \n",
+      "1                   0.766066              -0.497884  ...   \n",
+      "2                   0.413320              -0.445926  ...   \n",
+      "3                   0.119192              -1.398094  ...   \n",
+      "4                  -1.005356              -0.597752  ...   \n",
+      "\n",
+      "       feature_0 / feature_3  feature_1 / feature_0  feature_1 / feature_2  \\\n",
+      "index                                                                        \n",
+      "0                   0.061475             -11.678788              -0.332725   \n",
+      "1                  -3.023851               0.918902             -17.815253   \n",
+      "2                  -1.098759               1.565179               7.695854   \n",
+      "3                  -1.802080               0.662240              -0.940685   \n",
+      "4                  -1.236131              -2.596146               2.135690   \n",
+      "\n",
+      "       feature_1 / feature_3  feature_2 / feature_0  feature_2 / feature_1  \\\n",
+      "index                                                                        \n",
+      "0                  -0.717954              35.100411              -3.005484   \n",
+      "1                  -2.778623              -0.051579              -0.056132   \n",
+      "2                  -1.719754               0.203379               0.129940   \n",
+      "3                  -1.193410              -0.703998              -1.063055   \n",
+      "4                   3.209176              -1.215600               0.468233   \n",
+      "\n",
+      "       feature_2 / feature_3  feature_3 / feature_0  feature_3 / feature_1  \\\n",
+      "index                                                                        \n",
+      "0                   2.157798              16.266772              -1.392848   \n",
+      "1                   0.155969              -0.330704              -0.359891   \n",
+      "2                  -0.223465              -0.910118              -0.581478   \n",
+      "3                   1.268660              -0.554914              -0.837935   \n",
+      "4                   1.502641              -0.808976               0.311607   \n",
+      "\n",
+      "       feature_3 / feature_2  \n",
+      "index                         \n",
+      "0                   0.463435  \n",
+      "1                   6.411541  \n",
+      "2                  -4.474973  \n",
+      "3                   0.788233  \n",
+      "4                   0.665495  \n",
+      "\n",
+      "[5 rows x 22 columns]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\mdv_a\\AppData\\Roaming\\Python\\Python310\\site-packages\\featuretools\\synthesis\\dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
+      "  agg_primitives: ['max', 'mean']\n",
+      "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
+      "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Предполагаем, что X_train_balanced — это DataFrame или NumPy массив\n",
+    "if isinstance(X_train_balanced, pd.DataFrame):\n",
+    "    data = X_train_balanced\n",
+    "else:\n",
+    "    column_names = [f\"feature_{i}\" for i in range(X_train_balanced.shape[1])]\n",
+    "    data = pd.DataFrame(X_train_balanced, columns=column_names)\n",
+    "\n",
+    "# Масштабирование данных\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(data)\n",
+    "\n",
+    "# Уменьшаем размер данных для Featuretools\n",
+    "X_train_scaled_sample = X_train_scaled[:1000, :10]\n",
+    "dataframe_sample = pd.DataFrame(\n",
+    "    X_train_scaled_sample,\n",
+    "    columns=[f\"feature_{i}\" for i in range(X_train_scaled_sample.shape[1])]\n",
+    ")\n",
+    "dataframe_sample[\"index\"] = range(len(dataframe_sample))\n",
+    "\n",
+    "# Создаём EntitySet\n",
+    "es = ft.EntitySet(id=\"products\")\n",
+    "es = es.add_dataframe(\n",
+    "    dataframe_name=\"products\",\n",
+    "    dataframe=dataframe_sample,\n",
+    "    index=\"index\"\n",
+    ")\n",
+    "\n",
+    "# Генерация новых признаков с Featuretools\n",
+    "feature_matrix, feature_defs = ft.dfs(\n",
+    "    entityset=es,\n",
+    "    target_dataframe_name=\"products\",\n",
+    "    agg_primitives=[\"mean\", \"max\"],  # Агрегирующие примитивы\n",
+    "    trans_primitives=[\"add_numeric\", \"divide_numeric\"],  # Трансформационные примитивы\n",
+    "    max_depth=1,  # Ограничиваем глубину\n",
+    ")\n",
+    "\n",
+    "# Вывод первых строк сгенерированных данных\n",
+    "print(\"Новые признаки:\\n\", feature_matrix.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Предсказательная способность (classification): 0.9993554476186091\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Цельность данных проверена: дублирующихся строк нет, пропусков нет.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Предсказательная способность\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# Пример для классификации\n",
+    "clf = RandomForestClassifier(random_state=42)\n",
+    "scores = cross_val_score(clf, X_train_balanced, y_train_balanced, cv=5)\n",
+    "print(\"Предсказательная способность (classification):\", scores.mean())\n",
+    "\n",
+    "# Оценка корреляции\n",
+    "correlation_matrix = pd.DataFrame(X_train_scaled).corr()\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n",
+    "plt.title(\"Корреляция признаков\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Цельность\n",
+    "print(\"Цельность данных проверена: дублирующихся строк нет, пропусков нет.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сконструированные признаки демонстрируют слабую корелляцию, что не удивительно для наших исходных данных"
+   ]
   }
  ],
  "metadata": {
@@ -228,7 +520,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
diff --git a/Lab_4/lab4.ipynb b/Lab_4/lab4.ipynb
index d1651d0..7ac0417 100644
--- a/Lab_4/lab4.ipynb
+++ b/Lab_4/lab4.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -132,14 +132,14 @@
     "    return df_train, df_val, df_test\n",
     "\n",
     "\n",
-    "df = pd.read_csv('/mnt/c/3curse/mii/AIM-PIbd-31-Medvedkov-A-D/data/jio_mart_items.csv')\n",
+    "df = pd.read_csv('../data/jio_mart_items.csv')\n",
     "df.info()\n",
     "df = df.sample(n=10000 , random_state=42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -446,17 +446,17 @@
      "text": [
       "Accuracy: 0.991495747873937\n",
       "Classification Report:\n",
-      "               precision    recall  f1-score   support\n",
+      "                 precision    recall  f1-score   support\n",
       "\n",
-      "           0       0.99      0.90      0.94       131\n",
-      "           1       0.99      1.00      0.99       241\n",
-      "           2       1.00      1.00      1.00       307\n",
-      "           3       0.98      1.00      0.99       573\n",
-      "           4       1.00      1.00      1.00       747\n",
+      "        Beauty       0.99      0.90      0.94       131\n",
+      "   Electronics       0.99      1.00      0.99       241\n",
+      "       Fashion       1.00      1.00      1.00       307\n",
+      "     Groceries       0.98      1.00      0.99       573\n",
+      "Home & Kitchen       1.00      1.00      1.00       747\n",
       "\n",
-      "    accuracy                           0.99      1999\n",
-      "   macro avg       0.99      0.98      0.98      1999\n",
-      "weighted avg       0.99      0.99      0.99      1999\n",
+      "      accuracy                           0.99      1999\n",
+      "     macro avg       0.99      0.98      0.98      1999\n",
+      "  weighted avg       0.99      0.99      0.99      1999\n",
       "\n"
      ]
     },
@@ -472,7 +472,7 @@
     }
    ],
    "source": [
-    "# Кодирование категориальных данных\n",
+    "# Кодирование категориальных данных через LabelEncoder\n",
     "label_encoder = LabelEncoder()\n",
     "data['sub_category_encoded'] = label_encoder.fit_transform(data['sub_category'])\n",
     "\n",
@@ -483,16 +483,20 @@
     "# Разделение данных на тренировочную и тестовую выборки\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
     "\n",
-    "# Создание и обучение модели\n",
-    "classifier = RandomForestClassifier(random_state=42, n_estimators=100, max_depth=10)\n",
-    "classifier.fit(X_train, y_train)\n",
+    "# Создание конвейера для классификатора\n",
+    "pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),  # Масштабирование данных (хотя для категориальных признаков это не всегда нужно)\n",
+    "    ('classifier', RandomForestClassifier(random_state=42, n_estimators=100, max_depth=10))\n",
+    "])\n",
+    "\n",
+    "# Обучение модели\n",
+    "pipeline.fit(X_train, y_train)\n",
     "\n",
     "# Предсказание на тестовых данных\n",
-    "y_pred = classifier.predict(X_test)\n",
+    "y_pred = pipeline.predict(X_test)\n",
     "\n",
-    "# Оценка качества модели\n",
     "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
-    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
+    "print(\"Classification Report:\\n\", classification_report(y_test, y_pred, target_names=label_encoder.inverse_transform(np.unique(y_test))))\n",
     "\n",
     "# Матрица ошибок\n",
     "cm = confusion_matrix(y_test, y_pred)\n",
@@ -513,12 +517,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 9995 entries, 52893 to 146053\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   category      9995 non-null   object \n",
+      " 1   sub_category  9995 non-null   object \n",
+      " 2   href          9995 non-null   object \n",
+      " 3   items         9995 non-null   object \n",
+      " 4   price         9995 non-null   float64\n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 468.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'best_models_reg' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m _, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m10\u001b[39m), sharex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, sharey\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      2\u001b[0m ax \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, (name, model) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[43mbest_models_reg\u001b[49m\u001b[38;5;241m.\u001b[39mitems()):\n\u001b[0;32m      5\u001b[0m     model_pipeline \u001b[38;5;241m=\u001b[39m model[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpipeline\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      6\u001b[0m     y_pred_reg \u001b[38;5;241m=\u001b[39m model_pipeline\u001b[38;5;241m.\u001b[39mpredict(X_test_reg)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'best_models_reg' is not defined"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x1000 with 6 Axes>"
       ]
@@ -565,7 +608,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -579,7 +622,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

From a2a177418f7d78f53dbaf85ea733094402ef0793 Mon Sep 17 00:00:00 2001
From: MaDerniszator <mdv_andrei@mail.ru>
Date: Sat, 21 Dec 2024 11:39:09 +0400
Subject: [PATCH 13/13] =?UTF-8?q?=D0=BD=D0=B0=D0=B4=D0=B5=D1=8E=D1=81?=
 =?UTF-8?q?=D1=8C=20=D1=8D=D1=82=D0=BE=20=D0=BA=D0=BE=D0=BD=D0=B5=D1=86?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Lab_5/lab5.ipynb | 320 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 320 insertions(+)
 create mode 100644 Lab_5/lab5.ipynb

diff --git a/Lab_5/lab5.ipynb b/Lab_5/lab5.ipynb
new file mode 100644
index 0000000..8368036
--- /dev/null
+++ b/Lab_5/lab5.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7893b9e",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа: Методы искусственного интеллекта\n",
+    "## Задача кластеризации продуктов с использованием cuML\n",
+    "### Вариант: Продукты\n",
+    "В данной работе используется библиотека cuML для GPU-ускоренного анализа данных. Цель: провести кластеризацию продуктов на основе их характеристик."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3834005",
+   "metadata": {},
+   "source": [
+    "### Загрузка и исследование данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5530d138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'cudf.core.dataframe.DataFrame'>\n",
+      "RangeIndex: 162313 entries, 0 to 162312\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column        Non-Null Count   Dtype\n",
+      "---  ------        --------------   -----\n",
+      " 0   category      162313 non-null  object\n",
+      " 1   sub_category  162313 non-null  object\n",
+      " 2   href          162313 non-null  object\n",
+      " 3   items         162280 non-null  object\n",
+      " 4   price         162282 non-null  float64\n",
+      "dtypes: float64(1), object(4)\n",
+      "memory usage: 28.9+ MB\n",
+      "None\n",
+      "    category         sub_category  \\\n",
+      "0  Groceries  Fruits & Vegetables   \n",
+      "1  Groceries  Fruits & Vegetables   \n",
+      "2  Groceries  Fruits & Vegetables   \n",
+      "3  Groceries  Fruits & Vegetables   \n",
+      "4  Groceries  Fruits & Vegetables   \n",
+      "\n",
+      "                                                href  \\\n",
+      "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
+      "\n",
+      "                                               items  price  \n",
+      "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
+      "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
+      "2                                       Mosambi 1 kg   69.0  \n",
+      "3                               Orange Imported 1 kg  125.0  \n",
+      "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import cudf\n",
+    "import cuml\n",
+    "from cuml.preprocessing import LabelEncoder\n",
+    "from cuml.decomposition import PCA\n",
+    "from cuml.cluster import KMeans\n",
+    "import cupy as cp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = cudf.read_csv('/mnt/c/3curse/mii/AIM-PIbd-31-Medvedkov-A-D/data/jio_mart_items.csv')\n",
+    "print(df.info())\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5ea4ef3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49112908",
+   "metadata": {},
+   "source": [
+    "### Предварительная обработка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1e3ef9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Обработка пропущенных значений\n",
+    "df = df.dropna()\n",
+    "\n",
+    "# Кодирование категориального признака 'items'\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['items_encoded'] = label_encoder.fit_transform(df['items'])\n",
+    "\n",
+    "# Нормализация числовых признаков\n",
+    "numeric_features = ['items_encoded', 'price']\n",
+    "df_scaled = df[numeric_features].astype('float32')\n",
+    "\n",
+    "# Преобразование данных в формат cupy\n",
+    "X = cp.asarray(df_scaled.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff5f1f8f",
+   "metadata": {},
+   "source": [
+    "### Понижение размерности и визуализация данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e15c80bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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582bMmzfPYMvoxo0bkZ+fj0WLFqlbf0qdP38en332Gf744w88/fTTlVZnspwq3S1lyNixY5GYmIh169bh1KlTeOONN9C3b1/1l9iWLVvQuHFjbN26FYGBgWjUqBHeffddttxUkqKiIuzcuRPOzs5aH66l/0YxMTEm7fPu3bsoKCjQ2FarVi3069cPcXFxWL16Nfr27avxYVj65SYeWyJK3wyQdevWwdnZ2agPzWvXrmHRokWYNm2axrgeY44DgPpYdNXxzz//LLdVy1jZ2dmYNWsWPvzww3Jbo4qLizF48GC0bt0a33zzDUJDQ9G4cWOj/86GDRvQokUL9S90fSrzWJ+ESqXSG4SAR7PMUlNTsXLlSvz73/9Go0aNMGTIEK3X45N48cUXkZ6ertFlWVxcjPnz58Pd3R09e/YEALz22msQQmD69Ola+yh7Xm/cuIE//vgDzz33XLl/980330RiYiISEhK0Hnvw4IH6R8qTmj59Ou7du6ee3fW4nTt3qhf9jIuLQ+PGjTFq1Ci8/vrrGrd//etfcHd3Z9eUhFTplpvypKamYsWKFUhNTVVPy/zXv/6F+Ph4rFixArNnz8aVK1dw/fp1rF+/Hj/++CNKSkrw4Ycf4vXXX8eePXusfAT2b8eOHepfmJmZmVizZg0uXryIyZMnq/vbS+3cuRMjR45Ut6Dp8tprr6Fp06Zo0qQJCgsLER8fj23btmHs2LFaZSMjI/H6668DAGbOnKnxmKenJ5599ll8+eWXKCoqQr169bBz505cvXpVo9zFixcRHR2NtWvX6qyzLr///jtatWqFYcOG6S0zY8YM3Lp1C8HBwZDL5Thx4gRWrFiBtm3bom3btgCAl156CRs3bsQrr7yC/v374+rVq1i8eDGCgoK0Wr2AR79cH29lyMnJgYODA+Lj49G3b1/19hMnTqBWrVp6uxxKTZ8+HadPn8bJkyd1rkGiz5UrV/Dll1/iyJEjePXVV9VdbcCjqeDAo6n4DRo0QOPGjU0+1sqSmJioDsv79u3D7t278a9//Utn2T179mDhwoWIjo5Gx44dAQArVqxAr169MGXKFHz55ZdmqdN7772HJUuWYOjQoTh+/DgaNWqEDRs24I8//sC3336rHozbu3dvvPPOO/juu+9w8eJFdffqgQMH0Lt3b4wdOxaLFi1CTEwM3Nzc8MEHH5T7dydNmoRff/0VL730EoYOHYpOnTohNzcXp0+fxoYNG3Dt2jWt1pOKGDRoEE6fPo1Zs2bh5MmTiIiIQMOGDXHv3j3Ex8dj9+7dWLNmDW7fvo29e/fqrbdcLkdYWBjWr1+P7777zqTXK9ko603Usi0AxC+//KK+v3XrVgFAVK9eXePm5OQk3nzzTSGEECNHjhQAxPnz59XPO378uAAgzp07Z+lDkAxdU8FdXFxE+/btxaJFizSmppZOi3V1dRW3bt3S2M/jU6unTZsmWrRoIVxdXYWnp6do3769mDdvnigqKtKqQ0FBgfD29hZeXl7i4cOHWo/fvHlTvPLKK6JGjRrCy8tLvPHGG+L27dsCgHrK+Nq1a0VwcLCYN2+e1nRafVPBH38dCiHEkCFDNKaCb9iwQXTp0kV4enoKV1dX0bRpUzFx4kSN6dUqlUrMnj1bNGzYUMjlctGhQwexdetWrX2Vnj9Dt1I9e/bUOf22dIp3qQMHDghHR0exZMkSneXKmwqubymAx2+l0/9NPdbKmgpeenN2dhZNmzYVU6dOVU9RLztVWqlUioYNG4qOHTtqvfY+/PBD4eDgIBITE/WeH1On72dkZIhhw4aJWrVqCWdnZ9GmTRuNpRNKFRcXi6+++kq0bNlSODs7i9q1a4t+/fqJ48ePCyGE6Nq1q3jjjTd0frbpmuqenZ0toqKiRNOmTYWzs7OoVauW6NGjh/j6669FYWGh3uMr3V/r1q3LLVPW7t27xcCBA4Wvr69wcnIStWvXFgMGDBCbN28WQggxd+5cAUDs3r1b7z5iY2MFAPVzyL7x8gv/U7oybXh4OIBH038HDx6MlJQUraZld3d3+Pv7Izo6GrNnz0ZRUZH6sYcPH8LNzQ07d+5Enz59LHkIZEbFxcWoW7cuBgwYgP/85z/Wro7V7Nu3D71799bqgqtMsbGxmDZtmnpAsS69evXC0KFDtVZ0JiIC2C2lV4cOHVBSUoLMzEyt2RKlnnrqKRQXF+Py5cto0qQJAODChQsA/h6oR/Zp06ZNuHPnjnplXCIish9VOtzk5OTg0qVL6vtXr15FUlISfHx80Lx5cwwePBiRkZGYO3cuOnTogDt37mD37t1o27Yt+vfvj9DQUHTs2BHDhw/Ht99+C5VKhTFjxqBPnz5GTXUk2/Pnn3/i1KlTmDlzJjp06KAecFlV+fj4ICwszKJ/s0mTJlqXv3hcnz591D8oiIgeV6W7pUqb3B83ZMgQxMbGoqioCJ9//jl+/PFH3Lp1C7Vq1UL37t0xffp0tGnTBgBw+/Zt/POf/8TOnTtRvXp19OvXD3PnzjVqMSyyPUOHDkVcXBzat2+P2NhYBAcHW7tKRERkoiodboiIiEh6uM4NERERSQrDDREREUlKlRtQrFKpcPv2bXh4ePAaIkRERHZCCIHs7GzUrVtX62Kwj6ty4eb27dsICAiwdjWIiIioAm7cuIH69euXW6bKhZvS5cZv3Lhh1HL4REREZH1KpRIBAQHq7/HyVLlwU9oV5enpyXBDRERkZ4wZUsIBxURERCQpNhNu5syZA5lMhvHjx5dbbv369WjZsiVcXFzQpk0bbN++3TIVJCIiIrtgE+Hm6NGjWLJkCdq2bVtuuUOHDiEiIgIjRozAyZMnER4ejvDwcCQnJ1uopkRERGTrrB5ucnJyMHjwYCxbtgze3t7llp03bx769u2LSZMmoVWrVpg5cyY6duyI77//3kK1JSIiIltn9XAzZswY9UUoDUlMTNQqFxYWhsTERL3PKSgogFKp1LgRERGRdFl1ttS6detw4sQJHD161Kjy6enp8PPz09jm5+eH9PR0vc+JiYnB9OnTn6ieREREZD+s1nJz48YNjBs3DqtXr4aLi0ul/Z2oqCgoFAr17caNG5X2t4iIiMj6rNZyc/z4cWRmZqJjx47qbSUlJdi/fz++//57FBQUwNHRUeM5/v7+yMjI0NiWkZEBf39/vX9HLpdDLpebt/JERERks6zWcvP888/j9OnTSEpKUt86d+6MwYMHIykpSSvYAEBISAh2796tsW3Xrl0ICQmxVLWJiIjIxlmt5cbDwwPBwcEa26pXr46aNWuqt0dGRqJevXqIiYkBAIwbNw49e/bE3Llz0b9/f6xbtw7Hjh3D0qVLLV5/IqraSlQCR65mITM7H74eLuga6ANHB16Ml8gW2PTlF1JTUzWu/NmjRw+sWbMGn332GT755BM0a9YMmzZt0gpJRESVKT45DdO3nEGaIl+9rY6XC6IHBKFvcB0r1oyIAEAmhBDWroQlKZVKeHl5QaFQ8NpSRGSy+OQ0jI47gcc/OEvbbBa93ZEBh6gSmPL9bfV1boiI7EWJSmD6ljNawQaAetv0LWdQoqpSvxmJbA7DDRGRkY5czdLoinqcAJCmyMeRq1mWqxQRaWG4ISIyUma2/mBTkXJEVDkYboiIjOTrYdyCo8aWI6LKwXBDRGSkroE+qOPlAn0TvmV4NGuqa6CPJatFRI9huCEiMpKjgwzRA4IAQCvglN6PHhDE9W6IrIzhhojIBH2D62DR2x3h76XZ9eTv5cJp4EQ2wqYX8SMiskV9g+ugT5A/VygmslEMN0REFeDoIENIk5rWrgYR6cBuKSIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFKuGm0WLFqFt27bw9PSEp6cnQkJCsGPHDr3lY2NjIZPJNG4uLi4WrDERERHZOidr/vH69etjzpw5aNasGYQQWLlyJQYOHIiTJ0+idevWOp/j6emJ8+fPq+/LZDJLVZeIiIjsgFXDzYABAzTuz5o1C4sWLcLhw4f1hhuZTAZ/f39LVI+IiIjskM2MuSkpKcG6deuQm5uLkJAQveVycnLQsGFDBAQEYODAgUhJSSl3vwUFBVAqlRo3IiIiki6rh5vTp0/D3d0dcrkco0aNwi+//IKgoCCdZVu0aIHly5dj8+bNiIuLg0qlQo8ePXDz5k29+4+JiYGXl5f6FhAQUFmHQkRERDZAJoQQ1qxAYWEhUlNToVAosGHDBvzwww/4/fff9QacsoqKitCqVStERERg5syZOssUFBSgoKBAfV+pVCIgIAAKhQKenp5mOw4iIiKqPEqlEl5eXkZ9f1t1zA0AODs7o2nTpgCATp064ejRo5g3bx6WLFli8LnVqlVDhw4dcOnSJb1l5HI55HK52epLREREts3q3VKPU6lUGi0t5SkpKcHp06dRp06dSq4VERER2QurttxERUWhX79+aNCgAbKzs7FmzRrs27cPCQkJAIDIyEjUq1cPMTExAIAZM2age/fuaNq0KR48eICvvvoK169fx7vvvmvNwyAiIiIbYtVwk5mZicjISKSlpcHLywtt27ZFQkIC+vTpAwBITU2Fg8PfjUv379/HyJEjkZ6eDm9vb3Tq1AmHDh0yanwOERERVQ1WH1BsaaYMSCIiIiLbYMr3t82NuSEiIiJ6Egw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJClWDTeLFi1C27Zt4enpCU9PT4SEhGDHjh3lPmf9+vVo2bIlXFxc0KZNG2zfvt1CtSUiIiJ7YNVwU79+fcyZMwfHjx/HsWPH8Nxzz2HgwIFISUnRWf7QoUOIiIjAiBEjcPLkSYSHhyM8PBzJyckWrjkRERHZKpkQQli7EmX5+Pjgq6++wogRI7QeGzRoEHJzc7F161b1tu7du6N9+/ZYvHixUftXKpXw8vKCQqGAp6en2epNRERElceU72+bGXNTUlKCdevWITc3FyEhITrLJCYmIjQ0VGNbWFgYEhMT9e63oKAASqVS40ZERETSZfVwc/r0abi7u0Mul2PUqFH45ZdfEBQUpLNseno6/Pz8NLb5+fkhPT1d7/5jYmLg5eWlvgUEBJi1/kRERGRbrB5uWrRogaSkJPz5558YPXo0hgwZgjNnzpht/1FRUVAoFOrbjRs3zLZvIiIisj1O1q6As7MzmjZtCgDo1KkTjh49innz5mHJkiVaZf39/ZGRkaGxLSMjA/7+/nr3L5fLIZfLzVtpIiIisllWb7l5nEqlQkFBgc7HQkJCsHv3bo1tu3bt0jtGh4iIiKoeq7bcREVFoV+/fmjQoAGys7OxZs0a7Nu3DwkJCQCAyMhI1KtXDzExMQCAcePGoWfPnpg7dy769++PdevW4dixY1i6dKk1D4OIiIhsiFXDTWZmJiIjI5GWlgYvLy+0bdsWCQkJ6NOnDwAgNTUVDg5/Ny716NEDa9aswWeffYZPPvkEzZo1w6ZNmxAcHGytQyAiIiIbY3Pr3FQ2rnNDRERkf+xynRsiIiIic2C4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklxsnYFiIiqkhKVwJGrWcjMzoevhwu6BvrA0UFm7WoRSQrDDRGRhcQnp2H6ljNIU+Srt9XxckH0gCD0Da5jxZoRSQu7pYiILCA+OQ2j405oBBsASFfkY3TcCcQnp1mpZkTSw3BDRFTJSlQC07ecgdDxWOm26VvOoESlqwQRmYrhhoiokh25mqXVYlOWAJCmyMeRq1mWqxSRhDHcEBFVssxs/cGmIuWIqHwMN0RElczXw8Ws5YiofAw3RESVrGugD+p4uUDfhG8ZHs2a6hroY8lqEUkWww0RUSVzdJAhekAQAGgFnNL70QOCuN4NkZkw3BARWUDf4DpY9HZH+Htpdj35e7lg0dsduc4NkRlxET8iIgvpG1wHfYL8uUIxUSVjuCEisiBHBxlCmtS0djWIJI3dUkRERCQpDDdEREQkKQw3REREJCkMN0RERCQpDDdEREQkKQw3REREJClWDTcxMTHo0qULPDw84Ovri/DwcJw/f77c58TGxkImk2ncXFx4PRYiIiJ6xKrh5vfff8eYMWNw+PBh7Nq1C0VFRXjhhReQm5tb7vM8PT2Rlpamvl2/ft1CNSYiIiJbZ9VF/OLj4zXux8bGwtfXF8ePH8ezzz6r93kymQz+/v6VXT0iIiKyQzY15kahUAAAfHzKvzJuTk4OGjZsiICAAAwcOBApKSl6yxYUFECpVGrciIiISLpsJtyoVCqMHz8eTz31FIKDg/WWa9GiBZYvX47NmzcjLi4OKpUKPXr0wM2bN3WWj4mJgZeXl/oWEBBQWYdARERENkAmhBDWrgQAjB49Gjt27MDBgwdRv359o59XVFSEVq1aISIiAjNnztR6vKCgAAUFBer7SqUSAQEBUCgU8PT0NEvdiYiIqHIplUp4eXkZ9f1tExfOHDt2LLZu3Yr9+/ebFGwAoFq1aujQoQMuXbqk83G5XA65XG6OahIREZEdsGq3lBACY8eOxS+//II9e/YgMDDQ5H2UlJTg9OnTqFOnTiXUkIiIiOyNVVtuxowZgzVr1mDz5s3w8PBAeno6AMDLywuurq4AgMjISNSrVw8xMTEAgBkzZqB79+5o2rQpHjx4gK+++grXr1/Hu+++a7XjICIiItth1XCzaNEiAECvXr00tq9YsQJDhw4FAKSmpsLB4e8Gpvv372PkyJFIT0+Ht7c3OnXqhEOHDiEoKMhS1SYiIiIbZjMDii3FlAFJREREZBtM+f62mangRERERObAcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSYnK4SUtLQ1xcHLZv347CwkKNx3JzczFjxgyj9xUTE4MuXbrAw8MDvr6+CA8Px/nz5w0+b/369WjZsiVcXFzQpk0bbN++3dTDICIiIokyKdwcPXoUQUFBGDNmDF5//XW0bt0aKSkp6sdzcnIwffp0o/f3+++/Y8yYMTh8+DB27dqFoqIivPDCC8jNzdX7nEOHDiEiIgIjRozAyZMnER4ejvDwcCQnJ5tyKERERCRRMiGEMLZwnz59EBAQgB9++AG5ubn4+OOP8d///he7du1Chw4dkJGRgbp166KkpKRClblz5w58fX3x+++/49lnn9VZZtCgQcjNzcXWrVvV27p374727dtj8eLFBv+GUqmEl5cXFAoFPD09K1RPIiIisixTvr+dTNnx8ePHsWDBAjg4OMDDwwMLFy5EgwYN8PzzzyMhIQENGjR4ooorFAoAgI+Pj94yiYmJmDBhgsa2sLAwbNq0SWf5goICFBQUqO8rlconqiMRERHZNpPCDQDk5+dr3J88eTKcnJzwwgsvYPny5RWuiEqlwvjx4/HUU08hODhYb7n09HT4+flpbPPz80N6errO8jExMSZ1lREREZF9MyncBAcH49ChQ2jbtq3G9n/9619QqVSIiIiocEXGjBmD5ORkHDx4sML70CUqKkqjpUepVCIgIMCsf4OIiIhsh0kDiiMjI/WGj48++gjTp0+vUNfU2LFjsXXrVuzduxf169cvt6y/vz8yMjI0tmVkZMDf319neblcDk9PT40bERERSZdJA4rNTQiBf/7zn/jll1+wb98+NGvWzOBzBg0ahLy8PGzZskW9rUePHmjbti0HFBMREUmUKd/fJrXc5Ofn49dff0V2drbOP/rrr79qDN41ZMyYMYiLi8OaNWvg4eGB9PR0pKen4+HDh+oykZGRiIqKUt8fN24c4uPjMXfuXJw7dw7Tpk3DsWPHMHbsWFMOhYiIiCTKpHCzZMkSzJs3Dx4eHlqPeXp64rvvvsOyZcuM3t+iRYugUCjQq1cv1KlTR3376aef1GVSU1ORlpamvt+jRw+sWbMGS5cuRbt27bBhwwZs2rSp3EHIREREVHWY1C3VtWtXTJkyBQMGDND5+NatWzFjxgwcOXLEbBU0N3ZLERER2Z9K65a6ePEi2rVrp/fxtm3b4uLFi6bskoiIiMisTAo3xcXFuHPnjt7H79y5g+Li4ieuFBEREVFFmRRuWrdujd9++03v4zt37kTr1q2fuFJEREREFWVSuBk+fDhmzpypcV2nUlu2bMGsWbMwfPhws1WOiIiIyFQmrVD83nvvYf/+/Xj55ZfRsmVLtGjRAgBw7tw5XLhwAW+++Sbee++9SqkoERERkTFMarkBgLi4OPz0009o3rw5Lly4gPPnz6NFixZYu3Yt1q5dWxl1JCIiIjKaSS03JSUl+Prrr/Hrr7+isLAQL730EqZNmwZXV9fKqh8RERGRSUxquZk9ezY++eQTuLu7o169evjuu+8wZsyYyqobERERkclMCjc//vgjFi5ciISEBGzatAlbtmzB6tWroVKpKqt+RERERCYxKdykpqbixRdfVN8PDQ2FTCbD7du3zV4xIiIioooweRE/FxcXjW3VqlVDUVGRWStFREREVFEmDSgWQmDo0KGQy+Xqbfn5+Rg1ahSqV6+u3rZx40bz1ZCIiIjIBCaFmyFDhmhte/vtt81WGSIiIqInZVK4WbFiRWXVg4iIiMgsTF7Ej4iIiMiWMdwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaRYNdzs378fAwYMQN26dSGTybBp06Zyy+/btw8ymUzrlp6ebpkKE5FBJSqBxMv3sDnpFhIv30OJSli7SkRUxThZ84/n5uaiXbt2GD58OF599VWjn3f+/Hl4enqq7/v6+lZG9YjIRPHJaZi+5QzSFPnqbXW8XBA9IAh9g+tYsWZEVJVYNdz069cP/fr1M/l5vr6+qFGjhvkrREQVFp+chtFxJ/B4O026Ih+j405g0dsdGXCIyCLscsxN+/btUadOHfTp0wd//PGHtatDVOWVqASmbzmjFWwAqLdN33KGXVREZBF2FW7q1KmDxYsX4+eff8bPP/+MgIAA9OrVCydOnND7nIKCAiiVSo0bEZnXkatZGl1RjxMA0hT5OHI1y3KVIqIqy6rdUqZq0aIFWrRoob7fo0cPXL58Gd988w1WrVql8zkxMTGYPn26papIVCVlZusPNhUpR0T0JOyq5UaXrl274tKlS3ofj4qKgkKhUN9u3LhhwdoRVQ2+Hi5mLUdE9CTsquVGl6SkJNSpo3+Qolwuh1wut2CNiKqeroE+qOPlgnRFvs5xNzIA/l4u6BroY+mqEVEVZNVwk5OTo9HqcvXqVSQlJcHHxwcNGjRAVFQUbt26hR9//BEA8O233yIwMBCtW7dGfn4+fvjhB+zZswc7d+601iEQEQBHBxmiBwRhdNwJyACNgCP733+jBwTB0UGm49lEROZl1XBz7Ngx9O7dW31/woQJAIAhQ4YgNjYWaWlpSE1NVT9eWFiIiRMn4tatW3Bzc0Pbtm3x22+/aeyDiKyjb3AdLHq7o9Y6N/5c54aILEwmhKhSczOVSiW8vLygUCg0FgIkIvMoUQkcuZqFzOx8+Ho86opiiw0RPSlTvr/tfswNEdkWRwcZQprUtHY1iKgKs/vZUkRERERlMdwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkWDXc7N+/HwMGDEDdunUhk8mwadMmg8/Zt28fOnbsCLlcjqZNmyI2NrbS60lERET2w6rhJjc3F+3atcOCBQuMKn/16lX0798fvXv3RlJSEsaPH493330XCQkJlVxTIiIishdO1vzj/fr1Q79+/Ywuv3jxYgQGBmLu3LkAgFatWuHgwYP45ptvEBYWVlnVJCIiIjtiV2NuEhMTERoaqrEtLCwMiYmJVqoRERER2RqrttyYKj09HX5+fhrb/Pz8oFQq8fDhQ7i6umo9p6CgAAUFBer7SqWy0utJRERE1mNXLTcVERMTAy8vL/UtICDA2lUiIiKiSmRX4cbf3x8ZGRka2zIyMuDp6amz1QYAoqKioFAo1LcbN25YoqpERERkJXbVLRUSEoLt27drbNu1axdCQkL0Pkcul0Mul1d21YiIiMhGWLXlJicnB0lJSUhKSgLwaKp3UlISUlNTATxqdYmMjFSXHzVqFK5cuYKPPvoI586dw8KFC/Hf//4XH374oTWqT0RERDbIquHm2LFj6NChAzp06AAAmDBhAjp06ICpU6cCANLS0tRBBwACAwOxbds27Nq1C+3atcPcuXPxww8/cBo4ERERqcmEEMLalbAkpVIJLy8vKBQKeHp6Wrs6REREZARTvr/takAxERERkSEMN0RERCQpdjVbiohIakpUAkeuZiEzOx++Hi7oGugDRweZtatFZNcYboiIrCQ+OQ3Tt5xBmiJfva2OlwuiBwShb3AdK9aMyL6xW4qIyArik9MwOu6ERrABgHRFPkbHnUB8cpqVakZk/xhuiIgsrEQlMH3LGeiaqlq6bfqWMyhRVanJrERmw3BDJFElKoHEy/ewOekWEi/f4xelDTlyNUurxaYsASBNkY8jV7MsVykiCeGYGyIJ4lgO25aZrT/YVKQcEWliyw2RxHAsh+3z9XAxazki0sRwQyQhHMthH7oG+qCOlwv0TfiW4VFLW9dAH0tWi0gyGG6IJIRjOeyDo4MM0QOCAEAr4JTejx4QxPVuiCqI4YZIQjiWw370Da6DRW93hL+XZteTv5cLFr3dkWOjiJ4ABxQTSQjHctiXvsF10CfInysUl8EVm8kcGG6IJKR0LEe6Il/nuBsZHrUMcCyH7XB0kCGkSU1rV8MmcJYfmQu7pYgkhGM5yF5xlh+ZE8MNkcRwLAfZG87yqzgu1qkbu6WIJIhjOciemDLLj114f2M3nn4MN0QSxbEcZC84y890pd14j7fTlHbjVfVWWnZLERGRVXGWn2nYjWcYww0REVkVV2w2DRfrNIzhhoiIrIqz/EzDbjzDGG6IiMjqOMvPeOzGM4wDiomIyCZwlp9xuFinYQw3RERkMzjLz7DSbrzRcScgAzQCDrvxHmG3FBERkZ1hN1752HJDRERkh9iNpx/DDRERkZ1iN55u7JYiIiIiSWHLDRERVQklKsEunCqC4YaIiCSPF5msWtgtRUREklZ6kcnHL1lQepHJ+OQ0K9WMKgvDDRFZVYlKIPHyPWxOuoXEy/eq9MX+yPx4kcmqySbCzYIFC9CoUSO4uLigW7duOHLkiN6ysbGxkMlkGjcXl6q7xDSRPYtPTsPTX+xBxLLDGLcuCRHLDuPpL/bwlzSZDS8yWTVZPdz89NNPmDBhAqKjo3HixAm0a9cOYWFhyMzM1PscT09PpKWlqW/Xr1+3YI2JyBzYVUCWwItMVk1WDzf//ve/MXLkSAwbNgxBQUFYvHgx3NzcsHz5cr3Pkclk8Pf3V9/8/PwsWGMielLsKiBL4UUmqyarhpvCwkIcP34coaGh6m0ODg4IDQ1FYmKi3ufl5OSgYcOGCAgIwMCBA5GSkmKJ6hKRmbCrgCyl9CKT+iZ8y/Bo1lRVvsikFFk13Ny9exclJSVaLS9+fn5IT0/X+ZwWLVpg+fLl2Lx5M+Li4qBSqdCjRw/cvHlTZ/mCggIolUqNGxFZF7sKyFJKLzIJQCvg8CKT0mX1bilThYSEIDIyEu3bt0fPnj2xceNG1K5dG0uWLNFZPiYmBl5eXupbQECAhWtMRI9jVwFZEi8yWfVYdRG/WrVqwdHRERkZGRrbMzIy4O/vb9Q+qlWrhg4dOuDSpUs6H4+KisKECRPU95VKJQMOkZWVdhWkK/J1jruR4dEXD7sKyFzKXmQyXZmPrJwC+FR3hperM0pUQjItN1yF+RGrhhtnZ2d06tQJu3fvRnh4OABApVJh9+7dGDt2rFH7KCkpwenTp/Hiiy/qfFwul0Mul5urykRkBqVdBaPjTkAGaAQcdhVQZXF0kEHxsBBfxp+T5ErFXIX5b1bvlpowYQKWLVuGlStX4uzZsxg9ejRyc3MxbNgwAEBkZCSioqLU5WfMmIGdO3fiypUrOHHiBN5++21cv34d7777rrUOgcgq7H3xO3YVkKVJefkBKR9bRVj92lKDBg3CnTt3MHXqVKSnp6N9+/aIj49XDzJOTU2Fg8PfGez+/fsYOXIk0tPT4e3tjU6dOuHQoUMICgqy1iEQWZxUfqGV7Sqo6s3oVLkMLT8gw6PlB/oE+dvd60/Kx1ZRMiGEff3ce0JKpRJeXl5QKBTw9PS0dnWITFb6C+3xN27pRxZbPYi0JV6+h4hlhw2WWzuyO0Ka1LRAjcxHysdWlinf31bvliIi43HxO6qKzNEFK+XlB6R8bBVl9W4psj0cbW+7TFn8zp5/oRGVMlcXrJSXH5DysVUUww1pkMpYDqniLzSqSvR1wZYOkjWlC1bKyw9I+dgqit1SpPYko+3tfeaOveAvNKoqDHXBCpjWBSvllYqlfGwVxXBDAJ5sLEd8chqe/mIPIpYdxrh1SYhYdhhPf7Gnyk09tAReJ4eqCkNdsIDp1x+T8vID+o7Nu3o1LPi/DnZ9bBXBbikCUPGxHOZsNibDuPgdVRXGdq3uOpNu0vgyKS8/0De4DlQq4LPNycjKLQQAZOUWYea2s3BwkFWpz2K23BCAio3l4Mwd65Dyr0+iUrWqG7ey/Kak2yZ/xjg6yNA10Ae+Hi7IzH70o00Kn1PxyWkYs+aEOtiUqooL+bHlhgBUbCwHZ+5YjxR/fXKWnqYqfz6MPNSs3EKTP2Pik9Mw7dczSFf+/fnl7+mCaS/b78QJLuSnieGGAFRstL2xrT2/mdhsTMZxdJBJ5rxWhVl6poSVR1++KUhXFqi3+XvKMe3l1pI5H4bczSkwXOh/TJkdGJ+chlFxJ7S2pyvzMSruBBbbaesnf2xqYrcUASh/tD3w6I3x+FgOY1t7fkm6JYkmX6ocVeGaOPHJaXhqjuag+6fm6B50X/rlWzbYAEC6sgCjJHI+jGHKjD9jy5aoBCZvPF1umaiNp+3y84rLRGhiuCG10rEcXm7VtB6roWNb10Af+FR3NrjfrNwik2Y0UNVRFcZt/R1WHgtv/2spKBtWjPnynfDfv+z6fBira6AP/D0Nj7sxZXbg4Sv38CCvqNwy9/OKcPjKPaP2Z0u4TIQmhhvSotDx5lfkFWn9inZ0kCG8fV2j9llVfi2QaUxpSrdHprYUHL5s+Ms3r7AE83dfNFsdbZWjgwzTXm5tsJwpswMTLxsXWr5OOG9UOVtSOrTAkD3nMixQG+tjuCG1ivyK7hPkb9S+rf1rgYsM2iZTxm3ZI1NbChKv3DVqvz8cvFIlXsN9g+tg8dsddbYce7tVq8D4GOPOWdKNBygsVpmwX+tzdJDh034tDZb7z8GrdndsFcEBxaRWkQFppb8WynteDbdqVl1UjoMzbZcp47Y+6W9/6/ccvHjH6HJPNa0FY6cI5RSUVJmBoaUzAw9euINlB65AkV+EdvW98Gn/1nB1djRpXyGNa+H7vZcNlhMAViVew4hnGlew1taRkW14ELZKmH5shcUqrEq8hutZeWjo44Z3QhrB2cm220Zsu3akpbBYhf8cuIKpm5PxnwNXzJrAKzIgrexAZH0e5BVhl5V+eXNwpm2T+ritUzcVRpXbdy4TAEwKK1Wpq/fL+LMYtvIoDl6+h9O3lIj78wZaR8cjZvsZk/bTvUlNODsaFyCvZ+VVpKpWZWydTTm2mO1n0HLKDszcdhY/Jl7HzG1n0XLKDpPPvaUx3NiRmO1n0PwzzRdZ88/M9yKr6IC0PkH+OpuNS5Wur2DpZnRjxjtMttOZEVLh6CDDwHbGtZ6lKx5Wcm3Mz83IloULmTkoUQl0b1wT1eXGPcfaXb2WErP9DJbsv4rH36YqASzZf9Wkzz9HBxlebmfcOMGGPm6mVNMmGFtnY8uZ89xbGsONnSh9kelirhdZRa9bdORqVrnjCqw1KNSYwZkP8opw2MhBhlQ56nsb90H7+Kqr9qBroHEtMSXi0fgcRwcZvnqtrcHy1u7qtZTCYhWWHdD9uVdq2QHTxpDMftXw+QWAd0IaGb1PW/FOSCMY6rl1kBl3bJVx7i2J4cYOFBar9AabUkv2P/mLrKJXlrXV9RWMHZxpbDmqHD7uxi2zb2w5WzKkRyOjy5bO5HmxbV28/2xguWWt2dVrSasSr2m1GjyudAyJsRwdZJAbMV7ktzP2N6vI2ckBAT6u5ZYJ8HE1arxMZZx7S2K4sQM/7Dc8AA4AOs3YYfK+H59F1CfI3+TrFtnu+grGDj61r0GqUuPvadzrYvfZR1829jTzzdnJAW3rexpVtrCkWP3/H/VtVW5XL2D/6/8Yw9ixIQv3XjJ6n4cv30OBET8EP/r5lN2d34eFJbh+r/zu2+v3HuJhYYnBfVXG+B1L4mwpO2BsMs4ufLQeja5F+HQpb8n7gx8/Z/RS8Z0aekMmA0Q5nwMy2aNylhTSpCa+N+JDryrMOLFlXQN94OfhjIzs8rudtp9Ow5a/bmP29rN2dZmG2ka2OF3OzFX/v6GuXqBqLKVv7NiQe3lF2HTsBsI7Bxgsa2xLbU5BMQ5fufe/WWz2YbaRwxNmbz+DmeFtyr0kiLHnPq+g2HAhK2DLjR24k2P8WINnvvgNOfmGX2yGlrzf9b/rQQ1sXw8hTWqWOwX36LWscoMN8Cj4HL1m2TE33RvXNPjrV+7kgO6NpfvlYA8cHWR4plltg+VUAvjn2pMVvkyDsS0+5m4ZSntg3EDoc2l/z6wy9jnGlrNXpox7Gb/hlJGzH41vqd1/PtPosrbg2j3jWlGu3ctDfHIanv5C85IgT3/x9yVB3glpZNSZ+sNGW08ZbuxAsQmvG2WBCsHTEvDy9wf0ljH3kvd/XDLul5Cx5czF0UGGOa+2KbdMQbEKX8aftVCNSB9X54o3IhvzmjX0QW6o3PZTaRUOPHeNHAh9S1Go3u9xI38IGFvOXjk7OcBTbvzX1EQjLk1hSktXgp2Na2pU08iWrpwCg9dzc3ZywEttDS/SaqsriDPcSNSpm0q9AcfcS97fNvLX47FK+CA29Cv7uZZ+kBn4+WFrI/4fFpZgyqbTeOc/f2LKptNG9Y8/zp7GpQCAMNT0Z+j50P+aNfbCnPrKpSny8Y81JwwGI33cTAhuBy88WvTvXIbSqPLGlrNrht7AZeQWluCQgR9RprTUZj80LpjayvvtkxfLX3Os1Nm0bKN+3IYauQK9La65xDE3EnbqphI5+cVwd9H8Zzb37KZ6NcofnV/qrxsPUKISZltltrwxQ6XjL1YlXjPYZVaRFTsry8gfj2LXmb+bwg9cBFYdTkVoq9r4YUhXo/ZhzHkpq7x+d0t5/DVaUY+/Zg21UpauwfRcSz+95XRJ+18w0jfIvqx6NVyN7i5YeuAKerb0BQfD/82tmiOU+cYH/J9P3MQzzfV3czo6yODp4gBlvuEfNE4Ohn//m/p+q0yuzo7o2MALJ1LLXzyyvNd52R8KtaobN17M2HKWxJYbifvwp5Na28w9u6mHkQPuCkqE2a62a+yvcXsa8f94sCnrt7N3EDJ7t8EWpvJaH3StyKyrG6bLrF3Yfur2kx2Micz1Ff34a9bYVspVidfKLafvucZ03773rPGhWfHw0SDihj7GfVkYW86e1TXyx1MpZZ7hf8fa7sZ9tskMtBoZ+zlkSeYaQ5iZnW/XGZvhxg64mHb5FA2pOr602wfUgIcRO72fW2hUF0n3xjXhYuR1Roy9Km95DP0aL/ulY+4VO8ur05M0Sz8sLNEbbEqlKfPLXZG6sFiFT35JLvdXWdkVmfV9MGflFuEfa06affXR8s7RvSds1ta3wKSxrY8VDbfGdN8+3ay2wYXVSvl7PgorBy8b14W7I8W4a1fZs3/0bmZS+QsZOQbLGNvVm55dWO7Ac1PGLlqq6+r2A/N0EdWqLsfdHMPXqgJgdDlLYreUHXBwcABKKjYmxM3ZQaPbYdeZDGw/nWZwcSYA+PC/SRrrQZR2kfQJ8sWyyC7q7Y4OMvRsUQsJKYZnFqjEk49tOXzlnsFf2aVfOu+ENMKs7WfLPd7HV+w0tZvGHM3Sxk7hBKBe0DGqTP96fHIaojaexn0jVmT+eMNfeKpZbczcmlJuEFqy/yra1ffGi22fvGnd0Dnac+HJvqQFdC8waWzr45OEW0OXhXB0kOG5lrXx21nDx6hSqZB4+R6UD42bXvuwSIXCYpXGomzlvX5toQvSVLmFpk01vqkwPE7mfp7xM1APXrjzv65CTaaMXVQ8LLRY11U9b9NauvSSAa5O9nspEIYbCyj9QLmRlYufjt5AVs5D3MsrhpuzE+rVcMXyoV3LXZvGQVbxhJ96NwdPzdmtdeFIY+hb6GrXmUyM/PGoOuDEJ6fh4AXjZkJ5yo1bg0ef+OQ0nV1tutzIysOpm/dR20OOjHKOv1+wP3Ykp8HXwwX3cwsxY+sZpCv//hDy93TBtJd1fwiVtn48/i+U/r9uoJ7Na+PZZrXwTkgjlKgEZm8/g2v38tCophs+eTFIfVVjY8dklFqy/yomvtASzk4O6ouDGmvDiVvYcOKWUWWnbE5GWLC/0V+Auq4evOdchs76pZcZt2LMomqGLNx3SevfqPSSIumKfJ1BToZHC1S+E9IIPxy8qrdceYy5LIS7ka/7PRfuYc8F01o3lx24jDH/a92IT07DZxtP4W7e34GglpsTPv/fJQem/Wr8a7siFHlFGBb7J67dy4Obkwy1PFzwsEiggY8rvhnUoUJjqyryxWlobN+jx4z7l/57HJQmY1sFd51Jx4o/run8jDB23JYpejSphQVGXPnckHdXHsXDovLfl6Xvn66BPjYXnGXiSacp2BmlUgkvLy8oFAp4ehq3cuiT2H7qNj7bnIys3PJ/UTf0ccXvHz2n87EX/r0PF8os8GUrzs7oi98vZOr8ctdnQBt/zB/cSedjht4cW/66jX+uNS7YVIbFj30IlagEnv5ij8ljNcp6vqUv/jO0Cz775TTi/kw16blR/Vri3Wcao9Pnuwwu+PYk1o7srnP67OP/XnvOZeCHA1c1XgsyANWcHPSOFSr9cPT3cMbJm08+82fE040w5aXWGttKAyig++ts4f91wLPNffF/yxJx6pbpdfhmUHu80qFeuWW+3HEOC39/8i8cXZwdgQuz+psccst6/LVdET2/2mNwddy29T3x69hnDO6rbEgO8HbDrO2mLdcwY0AQIp/SfwmLVxccxIkbxl2xXd9nc+Lle4hYdtjg873dqultUS19/R/8+LknCgJl34u1qssx+D9/Vnhfplr8dkcAQPTmFGRk//0j0l3uiJhX22KAkRcqNYYp398MN2aSk1+MD386idT7D9HA+9GvlPl7Lhi8JlRZtd2dcfSzPlrbs3IK0fHzXWarq7k81aQmrtzNNenLXQbg6pz+WtsNdVvM2paCZQeumaHWFVdd7ohT0WHqDyFjP9wMaVjTFTNeCsaQlUdNel5gTTf8X7eGJn/wm0rXl3d8cppWK8CT+M/gThix+rhZ9pU8LQw/HU3Vaj2auikZmToWxHRyAJ6k4Wj1iG54qpn2oPqyXzhHr2aZHF5N8dfUF9Bh5k6jupt18XarhkOTn8eaP69rnDdD1yAqUQkcvnwPo1cfh9KIxUMBwwEnZvsZLDugfSVqU3m4OMLF0QFDejTCez2bahyLIq8I7WbsNGo/Xi4O+GtaP63tJSphth8Wj/+A0NUCqu/fQtdnpyV5ujiV+2//+DCGJ8FwU47KCDcvf38Ap8zwqxMA/v1aG7zapYHWr2JzfInaCg9nGbo3qa1upv71xE188N+/rF0to8x9vS2mbE7GwyIVnGRAkZnePYHecly9b3uD8gDg0xdbYWSZGT9P0kKgz7y32mPcuiSz7rOshj6uuJ5VOav5hjT2QceG3ugc4I3d5zNxPSsPDjLgfHp2hbqDreXxjhoHGTDymUCNsV2lFHlFeGXhQVy5W7GB2MnTwuDu4qTVQnMiNQs7kivngpXebtXQuaE3vhnUAa7OjmjyyXajn3tNxw+yEpUwaR/lmfdWewxs/+gHRMz2M1i6X7sF9L1ntf8t9HWL25qRzzTCp/1bGy5oAMNNOcwdbswZbEqFt6+LLX/dRkkV+JdxlKFKHKe9q+8uQ21vDzjKHHAs9YHZ9792ZHdJBXgqX59Wvqjv44oVf1y3dlWMUs/LBdkFxXhYWAwDw1AqpGcLb+w/f99gSGlVxx2T+7ZCmuIhFvx2FjeUpi/waS0XPu9n1NXIy2PK97dNTAVfsGABGjVqBBcXF3Tr1g1Hjhwpt/z69evRsmVLuLi4oE2bNti+3Tzp2VQ5+cVmDzYAsCmpagQbgMHGXtzMETh5Q1kpwQYAg00Vs+tspt0EGwC4pciHMr9ygg0A/G5EsAGAs2k5GLLiKCZvTLarYAMAc7anWPTvWT3c/PTTT5gwYQKio6Nx4sQJtGvXDmFhYcjM1D2t+NChQ4iIiMCIESNw8uRJhIeHIzw8HMnJyRauOfDBmmMW/5tERET2ZvmhyhtzpovVu6W6deuGLl264PvvvwfwaJ2HgIAA/POf/8TkyZO1yg8aNAi5ubnYunWrelv37t3Rvn17LF682ODfM2e3VNvoeCgL7Cs9ExERWcPl2S8+0awwu+mWKiwsxPHjxxEaGqre5uDggNDQUCQmJup8TmJiokZ5AAgLC9NbvjIx2BARERmn12eWG0Ji1UX87t69i5KSEvj5+Wls9/Pzw7lz53Q+Jz09XWf59HTdl6YvKChAQcHfMxaUyipwFV0iIiIbc6OSxizpYvUxN5UtJiYGXl5e6ltAQIC1q0RERESVyKrhplatWnB0dERGhua6BhkZGfD399f5HH9/f5PKR0VFQaFQqG83btwwT+WJiIjIJlk13Dg7O6NTp07YvXu3eptKpcLu3bsREhKi8zkhISEa5QFg165desvL5XJ4enpq3Myl+RNcrZuIiKgqaW3B62tavVtqwoQJWLZsGVauXImzZ89i9OjRyM3NxbBhwwAAkZGRiIqKUpcfN24c4uPjMXfuXJw7dw7Tpk3DsWPHMHbsWIvXfecs7VUriYiISNu2aZb7zrT6VcEHDRqEO3fuYOrUqUhPT0f79u0RHx+vHjScmpoKB4e/M1iPHj2wZs0afPbZZ/jkk0/QrFkzbNq0CcHBwVap/7U5/dFo8jar/G0iIiJ7oOsSFpXJ6uvcWFplXTjzhU+34QJnhhMREam1djFfi40p399Wb7mRCnZRERER2Qarj7khIiIiMieGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSlCq3QnHp1SaUSqWVa0JERETGKv3eNuaqUVUu3GRnZwMAAgICrFwTIiIiMlV2dja8vLzKLVPlLpypUqlw+/ZteHh4QCaTWbs6ZqNUKhEQEIAbN26Y9YKg9oTn4BGeB54DgOcA4DkoJZXzIIRAdnY26tatCweH8kfVVLmWGwcHB9SvX9/a1ag0np6edv3iNQeeg0d4HngOAJ4DgOeglBTOg6EWm1IcUExERESSwnBDREREksJwIxFyuRzR0dGQy+XWrorV8Bw8wvPAcwDwHAA8B6Wq4nmocgOKiYiISNrYckNERESSwnBDREREksJwQ0RERJLCcENERESSwnBjg7Zt24Zu3brB1dUV3t7eCA8P13g8NTUV/fv3h5ubG3x9fTFp0iQUFxdrlNm3bx86duwIuVyOpk2bIjY2VuvvLFiwAI0aNYKLiwu6deuGI0eOaDyen5+PMWPGoGbNmnB3d8drr72GjIwMcx+uXgUFBWjfvj1kMhmSkpI0Hjt16hSeeeYZuLi4ICAgAF9++aXW89evX4+WLVvCxcUFbdq0wfbt2zUeF0Jg6tSpqFOnDlxdXREaGoqLFy9qlMnKysLgwYPh6emJGjVqYMSIEcjJyTH7sZZ17do1jBgxAoGBgXB1dUWTJk0QHR2NwsJCjXJSPgdPwtDr2lbFxMSgS5cu8PDwgK+vL8LDw3H+/HmNMsa8Jy31+WAJc+bMgUwmw/jx49Xbqso5uHXrFt5++23UrFkTrq6uaNOmDY4dO6Z+3FzvXXN8jtgkQTZlw4YNwtvbWyxatEicP39epKSkiJ9++kn9eHFxsQgODhahoaHi5MmTYvv27aJWrVoiKipKXebKlSvCzc1NTJgwQZw5c0bMnz9fODo6ivj4eHWZdevWCWdnZ7F8+XKRkpIiRo4cKWrUqCEyMjLUZUaNGiUCAgLE7t27xbFjx0T37t1Fjx49LHMihBAffPCB6NevnwAgTp48qd6uUCiEn5+fGDx4sEhOThZr164Vrq6uYsmSJeoyf/zxh3B0dBRffvmlOHPmjPjss89EtWrVxOnTp9Vl5syZI7y8vMSmTZvEX3/9JV5++WURGBgoHj58qC7Tt29f0a5dO3H48GFx4MAB0bRpUxEREVGpx71jxw4xdOhQkZCQIC5fviw2b94sfH19xcSJE6vMOagoY17XtiosLEysWLFCJCcni6SkJPHiiy+KBg0aiJycHHUZQ+9JS34+VLYjR46IRo0aibZt24px48apt1eFc5CVlSUaNmwohg4dKv78809x5coVkZCQIC5duqQuY473rrk+R2wRw40NKSoqEvXq1RM//PCD3jLbt28XDg4OIj09Xb1t0aJFwtPTUxQUFAghhPjoo49E69atNZ43aNAgERYWpr7ftWtXMWbMGPX9kpISUbduXRETEyOEEOLBgweiWrVqYv369eoyZ8+eFQBEYmLikx2oEbZv3y5atmwpUlJStMLNwoULhbe3t/p4hRDi448/Fi1atFDff/PNN0X//v019tmtWzfx/vvvCyGEUKlUwt/fX3z11Vfqxx88eCDkcrlYu3atEEKIM2fOCADi6NGj6jI7duwQMplM3Lp1y6zHa8iXX34pAgMD1fer4jkwhqHXtT3JzMwUAMTvv/8uhDDuPWmpz4fKlp2dLZo1ayZ27dolevbsqQ43VeUcfPzxx+Lpp5/W+7i53rvm+ByxVeyWsiEnTpzArVu34ODggA4dOqBOnTro168fkpOT1WUSExPRpk0b+Pn5qbeFhYVBqVQiJSVFXSY0NFRj32FhYUhMTAQAFBYW4vjx4xplHBwcEBoaqi5z/PhxFBUVaZRp2bIlGjRooC5TWTIyMjBy5EisWrUKbm5uWo8nJibi2WefhbOzs3pbWFgYzp8/j/v376vLlHcOrl69ivT0dI0yXl5e6Natm7pMYmIiatSogc6dO6vLhIaGwsHBAX/++af5DtgICoUCPj4+6vtV8RwYYszr2p4oFAoAUP+7G/OetNTnQ2UbM2YM+vfvr1XPqnIOfv31V3Tu3BlvvPEGfH190aFDByxbtkz9uLneu+b4HLFVDDc25MqVKwCAadOm4bPPPsPWrVvh7e2NXr16ISsrCwCQnp6u8aYFoL6fnp5ebhmlUomHDx/i7t27KCkp0Vmm7D6cnZ1Ro0YNvWUqgxACQ4cOxahRozTelGU9yTko+3jZ5+kr4+vrq/G4k5MTfHx8KvUcPO7SpUuYP38+3n//ffW2qnYOjGHM69peqFQqjB8/Hk899RSCg4MBGPeetNTnQ2Vat24dTpw4gZiYGK3Hqso5uHLlChYtWoRmzZohISEBo0ePxgcffICVK1dqHMeTvnfN8TliqxhuLGDy5MmQyWTl3s6dOweVSgUA+PTTT/Haa6+hU6dOWLFiBWQyGdavX2/lo3gyxp6D+fPnIzs7G1FRUdaustkZew7KunXrFvr27Ys33ngDI0eOtFLNydLGjBmD5ORkrFu3ztpVsagbN25g3LhxWL16NVxcXKxdHatRqVTo2LEjZs+ejQ4dOuC9997DyJEjsXjxYmtXzW44WbsCVcHEiRMxdOjQcss0btwYaWlpAICgoCD1drlcjsaNGyM1NRUA4O/vrzViv3SmgL+/v/q/j88eyMjIgKenJ1xdXeHo6AhHR0edZcruo7CwEA8ePND4lVS2jCmMPQd79uxBYmKi1jVQOnfujMGDB2PlypV6j6+03qX/NXR8pdvq1KmjUaZ9+/bqMpmZmRr7KC4uRlZWVqWeg1K3b99G79690aNHDyxdulSjnL2eg8pUq1Ytg69rezB27Fhs3boV+/fvR/369dXbjXlPWurzobIcP34cmZmZ6Nixo3pbSUkJ9u/fj++//x4JCQmSPwcAUKdOHY3vAQBo1aoVfv75ZwDme++a43PEZll70A/9TaFQCLlcrjGguLCwUPj6+qpHr5cOlis7Yn/JkiXC09NT5OfnCyEeDZYLDg7W2HdERITWYLmxY8eq75eUlIh69eppDSjesGGDusy5c+cqfUDx9evXxenTp9W3hIQEAUBs2LBB3LhxQwjx9yC4wsJC9fOioqK0BsG99NJLGvsOCQnRGkz79ddfqx8vPf+PD8g7duyYukxCQoJFBtPevHlTNGvWTLz11luiuLhY6/GqcA4qwtDr2papVCoxZswYUbduXXHhwgWtx415T1rq86GyKJVKjff/6dOnRefOncXbb78tTp8+XSXOQWldHh9QPH78eBESEiKEMN971xyfI7aK4cbGjBs3TtSrV08kJCSIc+fOiREjRghfX1+RlZUlhPh7muMLL7wgkpKSRHx8vKhdu7bOaY6TJk0SZ8+eFQsWLNA5zVEul4vY2Fhx5swZ8d5774kaNWpozDAYNWqUaNCggdizZ484duyYCAkJUb+5LOXq1atas6UePHgg/Pz8xDvvvCOSk5PFunXrhJubm9b0RScnJ/H111+Ls2fPiujoaJ3ToGvUqCE2b94sTp06JQYOHKhzKmWHDh3En3/+KQ4ePCiaNWtW6dOgb968KZo2bSqef/55cfPmTZGWlqa+VZVzUFHGvK5t1ejRo4WXl5fYt2+fxr95Xl6euoyh96QlPx8spexsKSGqxjk4cuSIcHJyErNmzRIXL14Uq1evFm5ubiIuLk5dxhzvXXN9jtgihhsbU1hYKCZOnCh8fX2Fh4eHCA0NFcnJyRplrl27Jvr16ydcXV1FrVq1xMSJE0VRUZFGmb1794r27dsLZ2dn0bhxY7FixQqtvzV//nzRoEED4ezsLLp27SoOHz6s8fjDhw/FP/7xD+Ht7S3c3NzEK6+8ovEFawm6wo0QQvz111/i6aefFnK5XNSrV0/MmTNH67n//e9/RfPmzYWzs7No3bq12LZtm8bjKpVKTJkyRfj5+Qm5XC6ef/55cf78eY0y9+7dExEREcLd3V14enqKYcOGiezsbLMfZ1krVqwQAHTeypLyOXgShl7Xtkrfv3nZ964x70lLfT5YyuPhpqqcgy1btojg4GAhl8tFy5YtxdKlSzUeN9d71xyfI7ZIJoQQFu0HIyIiIqpEnC1FREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENENm3o0KHqq6Y7OzujadOmmDFjBoqLiwEAQggsXboU3bp1g7u7O2rUqIHOnTvj22+/RV5eHgAgJSUFr732Gho1agSZTIZvv/3WikdERJWN4YaIbF7fvn2RlpaGixcvYuLEiZg2bRq++uorAMA777yD8ePHY+DAgdi7dy+SkpIwZcoUbN68GTt37gQA5OXloXHjxpgzZ47tX82YiJ4YL79ARDZt6NChePDgATZt2qTe9sILLyA7OxsffvghBg0ahE2bNmHgwIEazxNCQKlUwsvLS2N7o0aNMH78eIwfP94CtScia2DLDRHZHVdXVxQWFmL16tVo0aKFVrABAJlMphVsiKhqYLghIrshhMBvv/2GhIQEPPfcc7h48SJatGhh7WoRkY1huCEim7d161a4u7vDxcUF/fr1w6BBgzBt2jSwV52IdHGydgWIiAzp3bs3Fi1aBGdnZ9StWxdOTo8+upo3b45z585ZuXZEZGvYckNENq969epo2rQpGjRooA42APB///d/uHDhAjZv3qz1HCEEFAqFJatJRDaC4YaI7Nabb76JQYMGISIiArNnz8axY8dw/fp1bN26FaGhodi7dy8AoLCwEElJSUhKSkJhYSFu3bqFpKQkXLp0ycpHQESVgVPBicim6ZoKXpZKpcLSpUuxfPlypKSkwMnJCc2aNUNkZCRGjhwJV1dXXLt2DYGBgVrP7dmzJ/bt21e5B0BEFsdwQ0RERJLCbikiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpKU/wdYlnREkSkSHgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Применение PCA для понижения размерности\n",
+    "pca = PCA(n_components=2)\n",
+    "reduced_data = pca.fit_transform(X)\n",
+    "\n",
+    "# Преобразуем данные из cupy в numpy\n",
+    "reduced_data_np = reduced_data.get()\n",
+    "\n",
+    "# Визуализация данных\n",
+    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1])\n",
+    "plt.title('Визуализация данных после PCA')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2eef505",
+   "metadata": {},
+   "source": [
+    "### Выбор оптимального количества кластеров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f72195d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Оценка числа кластеров: 100%|█████████████████████████████████████████████████████████████| 9/9 [01:08<00:00,  7.67s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Оценка инерции и коэффициента силуэта\n",
+    "from cuml.metrics.cluster import silhouette_score\n",
+    "from tqdm import tqdm  # Импорт библиотеки для отображения прогресса\n",
+    "\n",
+    "# Оценка инерции и коэффициента силуэта\n",
+    "inertia = []\n",
+    "silhouette_scores = []\n",
+    "k_range = range(2, 11)\n",
+    "\n",
+    "# tqdm для отображения прогресса\n",
+    "for k in tqdm(k_range, desc=\"Оценка числа кластеров\"):\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(reduced_data)\n",
+    "    inertia.append(kmeans.inertia_)\n",
+    "    silhouette_scores.append(silhouette_score(reduced_data, kmeans.labels_))\n",
+    "\n",
+    "# Построение графиков\n",
+    "plt.figure(figsize=(14, 5))\n",
+    "\n",
+    "# График инерции\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(k_range, inertia, marker='o')\n",
+    "plt.title('Критерий инерции')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Инерция')\n",
+    "\n",
+    "# График коэффициента силуэта\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(k_range, silhouette_scores, marker='o')\n",
+    "plt.title('Коэффициент силуэта')\n",
+    "plt.xlabel('Число кластеров')\n",
+    "plt.ylabel('Силуэт')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "180e85ac",
+   "metadata": {},
+   "source": [
+    "### Кластерный анализ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dd573024",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Кластеризация с использованием KMeans\n",
+    "optimal_k = 4  # Выбираем на основе графиков\n",
+    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
+    "labels = kmeans.fit_predict(reduced_data)\n",
+    "\n",
+    "# Преобразуем данные из cupy в numpy\n",
+    "reduced_data_np = reduced_data.get()\n",
+    "labels_np = labels.get()\n",
+    "\n",
+    "# Визуализация кластеров\n",
+    "plt.scatter(reduced_data_np[:, 0], reduced_data_np[:, 1], c=labels_np, cmap='viridis')\n",
+    "plt.title('Кластеры (KMeans)')\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "407d268e",
+   "metadata": {},
+   "source": [
+    "### Оценка качества кластеризации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d00795e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Силуэт для кластеризации: 0.58\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Оценка коэффициента силуэта\n",
+    "silhouette = silhouette_score(reduced_data, labels)\n",
+    "print(f'Силуэт для кластеризации: {silhouette:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b4aa1da",
+   "metadata": {},
+   "source": [
+    "Получился вплоне неплохой силуэт кластеризации, кластеры хорошо различимы, хоть и имеют пересечение."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}