From efeb865791085740073c121009e10a7935a6ef57 Mon Sep 17 00:00:00 2001 From: dex_moth Date: Fri, 20 Dec 2024 23:47:13 +0400 Subject: [PATCH 1/7] =?UTF-8?q?lab=203=20=D0=B8=204?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_3/Lab3.ipynb | 551 ++++++++++++++++++++++++++++ lab_4/Lab4.ipynb | 911 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 1462 insertions(+) create mode 100644 lab_3/Lab3.ipynb create mode 100644 lab_4/Lab4.ipynb diff --git a/lab_3/Lab3.ipynb b/lab_3/Lab3.ipynb new file mode 100644 index 0000000..dcb1c9d --- /dev/null +++ b/lab_3/Lab3.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Лабораторная 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Вариант 7. Экономика стран" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Бизнес-цели:\n", + "1) прогнозирование уровня инфляции на основе данных за года\n", + "2) определение факторов, значительно влияющих на показателль ВВП на душу населения" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Технические цели:\n", + "1) Разработать МО для прогнозирования уровня инфляции на основе исторических данных\n", + "2) Проанализировать взаимосвязь между экономическими показателями и ВВП" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n", + " 'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n", + " 'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n", + " 'tradebalance', 'USTreasury'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "df = pd.read_csv(\".//csv//EconomicData.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Подготовка данных:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stock index 0\n", + "country 0\n", + "year 0\n", + "index price 52\n", + "log_indexprice 0\n", + "inflationrate 43\n", + "oil prices 0\n", + "exchange_rate 2\n", + "gdppercent 19\n", + "percapitaincome 1\n", + "unemploymentrate 21\n", + "manufacturingoutput 91\n", + "tradebalance 4\n", + "USTreasury 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Заполним пустые значения медианами:" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "for column in df.columns:\n", + " if (column != \"stock index\" and column != \"country\"):\n", + " df[column].fillna(df[column].median())" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "ax = sns.scatterplot(x='exchange_rate', y='oil prices', hue='inflationrate', data=df)\n", + "plt.title('Уровень инфляции')\n", + "plt.xlabel('Валютный курс')\n", + "plt.ylabel('Цены на нефть')\n", + "plt.legend(title='inflationrate')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q1 = df['oil prices'].quantile(0.25)\n", + "Q3 = df['oil prices'].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "threshold = 1.5 * IQR\n", + "outliers = (df['oil prices'] < (Q1 - threshold)) | (df['oil prices'] > (Q3 + threshold))\n", + "\n", + "median_rating = df['oil prices'].median()\n", + "df.loc[outliers, 'oil prices'] = median_rating\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "ax = sns.scatterplot(x='exchange_rate', y='gdppercent', hue='inflationrate', data=df)\n", + "plt.title('Уровень инфляции')\n", + "plt.xlabel('Валютный курс')\n", + "plt.ylabel('Цены на нефть')\n", + "plt.legend(title='inflationrate')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение данных на выборки и оценка сбалансированности выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 221\n", + "Размер контрольной выборки: 74\n", + "Размер тестовой выборки: 74\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# обучающая и тестовая\n", + "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n", + "\n", + "# обучающая на обучающую и контрольную\n", + "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Конструирование признаков" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1) Кодирование категориальных признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "df = pd.get_dummies(df, columns=['country'])\n", + "print(df.head)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Дискретизация числовых признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min = 27.0\n", + "max = 65280.0\n", + "10880.0\n" + ] + } + ], + "source": [ + "print(f\"min = {df['percapitaincome'].min()}\")\n", + "print(f\"max = {df['percapitaincome'].max()}\")\n", + "print(df['percapitaincome'].max()/6)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import KBinsDiscretizer\n", + "\n", + "bins = [0, 11000, 22000, 33000, 44000, float('inf')]\n", + "labels = ['незначительный', 'низкий', 'средний', 'высокий', 'очень высокий']\n", + "\n", + "df['percapitaincome_level'] = pd.cut(df['percapitaincome'], bins=bins, labels=labels)\n", + "print(df['percapitaincome_level'].head)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3) Ручной синтез признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " pd.to_datetime(\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " pd.to_datetime(\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + " pd.to_datetime(\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", + " warnings.warn(\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\featuretools\\synthesis\\dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", + " agg_primitives: ['max', 'mean', 'min', 'std', 'sum']\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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Сгенерированные признаки:\n", + " stock index year index price log_indexprice inflationrate \\\n", + "index \n", + "0 NASDAQ 1980.0 168.61 2.23 0.14 \n", + "1 NASDAQ 1981.0 203.15 2.31 0.10 \n", + "2 NASDAQ 1982.0 188.98 2.28 0.06 \n", + "3 NASDAQ 1983.0 285.43 2.46 0.03 \n", + "4 NASDAQ 1984.0 248.89 2.40 0.04 \n", + "\n", + " oil prices exchange_rate gdppercent percapitaincome \\\n", + "index \n", + "0 21.59 1.0 0.09 12575 \n", + "1 31.77 1.0 0.12 13976 \n", + "2 28.52 1.0 0.04 14434 \n", + "3 26.19 1.0 0.09 15544 \n", + "4 25.88 1.0 0.11 17121 \n", + "\n", + " unemploymentrate ... oil prices * year percapitaincome * USTreasury \\\n", + "index ... \n", + "0 0.07 ... 42748.20 1383.25 \n", + "1 0.08 ... 62936.37 1956.64 \n", + "2 0.10 ... 56526.64 1876.42 \n", + "3 0.10 ... 51934.77 1709.84 \n", + "4 0.08 ... 51345.92 2054.52 \n", + "\n", + " percapitaincome * tradebalance percapitaincome * unemploymentrate \\\n", + "index \n", + "0 -164229.50 880.25 \n", + "1 -174979.52 1118.08 \n", + "2 -288246.98 1443.40 \n", + "3 -802692.16 1554.40 \n", + "4 -1758840.33 1369.68 \n", + "\n", + " percapitaincome * year tradebalance * USTreasury \\\n", + "index \n", + "0 24898500.0 -1.4366 \n", + "1 27686456.0 -1.7528 \n", + "2 28608188.0 -2.5961 \n", + "3 30823752.0 -5.6804 \n", + "4 33968064.0 -12.3276 \n", + "\n", + " tradebalance * unemploymentrate tradebalance * year \\\n", + "index \n", + "0 -0.9142 -25858.80 \n", + "1 -1.0016 -24802.12 \n", + "2 -1.9970 -39580.54 \n", + "3 -5.1640 -102402.12 \n", + "4 -8.2184 -203816.32 \n", + "\n", + " unemploymentrate * USTreasury unemploymentrate * year \n", + "index \n", + "0 0.0077 138.60 \n", + "1 0.0112 158.48 \n", + "2 0.0130 198.20 \n", + "3 0.0110 198.30 \n", + "4 0.0096 158.72 \n", + "\n", + "[5 rows x 207 columns]\n", + "\n", + "Описание:\n", + "[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]\n" + ] + } + ], + "source": [ + "# pip install featuretools\n", + "import featuretools as ft\n", + "\n", + "es = ft.EntitySet(id='economy_data')\n", + "es.add_dataframe(\n", + " dataframe=df,\n", + " dataframe_name='economy',\n", + " index='index',\n", + " make_index=True\n", + ")\n", + "\n", + "# Автоматическое конструирование\n", + "feature_matrix, feature_defs = ft.dfs(\n", + " entityset=es,\n", + " target_dataframe_name='economy',\n", + " agg_primitives=['mean', 'sum', 'max', 'min', 'std'],\n", + " trans_primitives=['add_numeric', 'multiply_numeric'],\n", + " max_depth=2 \n", + ")\n", + "\n", + "print(\"Сгенерированные признаки:\")\n", + "print(feature_matrix.head())\n", + "print(\"\\nОписание:\")\n", + "print(feature_defs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Масштабирование" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "scaler_minmax = MinMaxScaler()\n", + "df[['index price_scaled', 'log_indexprice_scaled']] = scaler_minmax.fit_transform(df[['index price', 'log_indexprice']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оценка качества наборов признаков:\n", + "Набор данных достаточно полный, но требует предварительной обработки (заполнение пропусков, удаление выбросов, нормализация). После обработки он может быть использован для анализа и построения моделей." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scripts", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb new file mode 100644 index 0000000..0b8116e --- /dev/null +++ b/lab_4/Lab4.ipynb @@ -0,0 +1,911 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'Gender', 'Age', 'City', 'Profession', 'Academic Pressure',\n", + " 'Work Pressure', 'CGPA', 'Study Satisfaction', 'Job Satisfaction',\n", + " 'Sleep Duration', 'Dietary Habits', 'Degree',\n", + " 'Have you ever had suicidal thoughts ?', 'Work/Study Hours',\n", + " 'Financial Stress', 'Family History of Mental Illness', 'Depression'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib.ticker import FuncFormatter\n", + "\n", + "df = pd.read_csv(\".//csv//Student Depression Dataset.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id Gender Age City Profession Academic Pressure \\\n", + "0 2 Male 33.0 Visakhapatnam Student 5.0 \n", + "1 8 Female 24.0 Bangalore Student 2.0 \n", + "2 26 Male 31.0 Srinagar Student 3.0 \n", + "3 30 Female 28.0 Varanasi Student 3.0 \n", + "4 32 Female 25.0 Jaipur Student 4.0 \n", + "\n", + " Work Pressure CGPA Study Satisfaction Job Satisfaction \\\n", + "0 0.0 8.97 2.0 0.0 \n", + "1 0.0 5.90 5.0 0.0 \n", + "2 0.0 7.03 5.0 0.0 \n", + "3 0.0 5.59 2.0 0.0 \n", + "4 0.0 8.13 3.0 0.0 \n", + "\n", + " Sleep Duration Dietary Habits Degree \\\n", + "0 5-6 hours Healthy B.Pharm \n", + "1 5-6 hours Moderate BSc \n", + "2 Less than 5 hours Healthy BA \n", + "3 7-8 hours Moderate BCA \n", + "4 5-6 hours Moderate M.Tech \n", + "\n", + " Have you ever had suicidal thoughts ? Work/Study Hours Financial Stress \\\n", + "0 Yes 3.0 1.0 \n", + "1 No 3.0 2.0 \n", + "2 No 9.0 1.0 \n", + "3 Yes 4.0 5.0 \n", + "4 Yes 1.0 1.0 \n", + "\n", + " Family History of Mental Illness Depression \n", + "0 No 1 \n", + "1 Yes 0 \n", + "2 Yes 0 \n", + "3 Yes 1 \n", + "4 No 0 \n" + ] + } + ], + "source": [ + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Бизнес-цель исследования\n", + "Разработать и внедрить систему прогнозирования уровня депрессии среди обучающихся, которая позволит выявить группы риска на ранних этапах. Результаты исследования могут быть полезны психологам, педагогам и администрации учебных заведений.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Описание набора данных для анализа\n", + "Набор данных содержит информацию о психологическом состоянии обучающихся и включает следующие поля:\n", + "- id – идентификатор, число\n", + "- Gender – пол, строка\n", + "- Age – возраст, дробное число\n", + "- City – город, строка\n", + "- Profession – профессия, строка\n", + "- Academic Pressure – академическое давление, дробное число (от 1.00 до 5.00)\n", + "- Work Pressure – рабочее давление, дробное число (от 1.00 до 5.00)\n", + "- CGPA – средний балл (GPA), дробное число\n", + "- Study Satisfaction – удовлетворенность учебой, дробное число (от 1.00 до 5.00)\n", + "- Job Satisfaction – удовлетворенность работой, дробное число (от 1.00 до 5.00)\n", + "- Sleep Duration – продолжительность сна, строка\n", + "- Dietary Habits – пищевые привычки, строка\n", + "- Degree – степень (образование), строка\n", + "- Have you ever had suicidal thoughts? – Были ли у вас когда-либо суицидальные мысли? строка (yes/no)\n", + "- Work/Study Hours – часы работы/учебы, дробное число\n", + "- Financial Stress – финансовый стресс, дробное число (от 1.00 до 5.00)\n", + "- Family History of Mental Illness – семейный анамнез психических заболеваний, строка (yes/no)\n", + "- Depression – депрессия, булевое значение (1/0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Обработка данных" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id 0\n", + "Gender 0\n", + "Age 0\n", + "City 0\n", + "Profession 0\n", + "Academic Pressure 0\n", + "Work Pressure 0\n", + "CGPA 0\n", + "Study Satisfaction 0\n", + "Job Satisfaction 0\n", + "Sleep Duration 0\n", + "Dietary Habits 0\n", + "Degree 0\n", + "Have you ever had suicidal thoughts ? 0\n", + "Work/Study Hours 0\n", + "Financial Stress 3\n", + "Family History of Mental Illness 0\n", + "Depression 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['Financial Stress'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "features = ['Age', 'Academic Pressure', 'Work Pressure', 'CGPA', 'Study Satisfaction', \n", + " 'Job Satisfaction', 'Work/Study Hours', 'Financial Stress', 'Depression']\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, feature in enumerate(features, 1):\n", + " plt.subplot(3, 3, i)\n", + " sns.boxplot(y=df[feature], color='skyblue')\n", + " plt.title(f'Boxplot of {feature}')\n", + " plt.ylabel(feature)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В Age много выбросов. Сбалансируем данные" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q1 = df['Age'].quantile(0.25)\n", + "Q3 = df['Age'].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "threshold = 1.5 * IQR\n", + "outliers = (df['Age'] < (Q1 - threshold)) | (df['Age'] > (Q3 + threshold))\n", + "\n", + "median_rating = df['Age'].median()\n", + "df.loc[outliers, 'Age'] = median_rating\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(y=df['Age'], color='skyblue')\n", + "plt.title('Boxplot of Age')\n", + "plt.ylabel('Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Конструирование признаков с помощью меток" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "le = LabelEncoder()\n", + "df['Gender'] = le.fit_transform(df['Gender'])\n", + "df['City'] = le.fit_transform(df['City'])\n", + "df['Dietary Habits'] = le.fit_transform(df['Dietary Habits'])\n", + "df['Degree'] = le.fit_transform(df['Degree'])\n", + "df['Have you ever had suicidal thoughts ?'] = le.fit_transform(df['Have you ever had suicidal thoughts ?'])\n", + "df['Sleep Duration'] = le.fit_transform(df['Sleep Duration'])\n", + "df['Profession'] = le.fit_transform(df['Profession'])\n", + "df['Study Satisfaction'] = le.fit_transform(df['Study Satisfaction'])\n", + "df['Family History of Mental Illness'] = le.fit_transform(df['Family History of Mental Illness'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "разделение на признаки и целевую переменную" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "x = df.drop('Depression', axis=1)\n", + "y = df['Depression']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Метод регрессии Лассо\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для Lasso:\n", + "{'alpha': 0.01, 'fit_intercept': False}\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import Lasso\n", + "\n", + "param_grid_lasso = {\n", + " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", + " 'fit_intercept': [True, False],\n", + "}\n", + "\n", + "# Создание объекта GridSearchCV\n", + "grid_search_lasso = GridSearchCV(\n", + " estimator=Lasso(), \n", + " param_grid=param_grid_lasso, \n", + " cv=5, \n", + " scoring='neg_mean_squared_error', \n", + " n_jobs=-1 \n", + ")\n", + "\n", + "grid_search_lasso.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для Lasso:\")\n", + "print(grid_search_lasso.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Метод градиентного бустинга" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n", + "1215 fits failed out of a total of 3645.\n", + "The score on these train-test partitions for these parameters will be set to nan.\n", + "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", + "\n", + "Below are more details about the failures:\n", + "--------------------------------------------------------------------------------\n", + "978 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", + " estimator._validate_params()\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "237 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", + " estimator._validate_params()\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "\n", + " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n", + " _data = np.array(data, dtype=dtype, copy=copy,\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.18767441 -0.15799837 -0.13080278\n", + " -0.18762913 -0.15792709 -0.13056114 -0.18792038 -0.15737146 -0.130218\n", + " -0.18725961 -0.157967 -0.13047453 -0.18766583 -0.15779565 -0.13094863\n", + " -0.18798705 -0.15693978 -0.13061215 -0.18766317 -0.15746848 -0.13072918\n", + " -0.18864158 -0.15666133 -0.13095037 -0.18817206 -0.15805489 -0.13086126\n", + " -0.18707465 -0.15864932 -0.13104947 -0.18818902 -0.15828572 -0.13063871\n", + " -0.18701628 -0.15853864 -0.13019458 -0.18740927 -0.15836397 -0.13065455\n", + " -0.18768748 -0.15828297 -0.1309458 -0.18845004 -0.15696395 -0.13023062\n", + " -0.18754854 -0.15899615 -0.13061707 -0.18831427 -0.15819939 -0.13096524\n", + " -0.18662963 -0.15815869 -0.13089186 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.1758914 -0.1442684 -0.12093344 -0.1758927 -0.14423731 -0.12084543\n", + " -0.17573339 -0.14419842 -0.12076166 -0.17512045 -0.14435454 -0.1207299\n", + " -0.17669645 -0.14397965 -0.12087019 -0.17605424 -0.1438664 -0.12091068\n", + " -0.17582192 -0.1443651 -0.12097165 -0.17588422 -0.14421003 -0.12081764\n", + " -0.17522742 -0.14424357 -0.12086484 -0.17530986 -0.14433713 -0.12091757\n", + " -0.17565647 -0.14408902 -0.12075918 -0.17561884 -0.14426355 -0.12094066\n", + " -0.17522371 -0.1439869 -0.12099023 -0.17619772 -0.14396131 -0.12079667\n", + " -0.17710789 -0.1448419 -0.12087822 -0.17608534 -0.14416684 -0.12087865\n", + " -0.1754675 -0.1442258 -0.12068226 -0.17611334 -0.14433552 -0.12093556\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.16938321 -0.13763002 -0.11703902\n", + " -0.16953091 -0.13736586 -0.11695779 -0.16881837 -0.1375676 -0.11694438\n", + " -0.16927898 -0.13748177 -0.11689982 -0.16921265 -0.13757375 -0.11682524\n", + " -0.16915872 -0.13727377 -0.11694336 -0.16939766 -0.13734972 -0.1167447\n", + " -0.16924214 -0.1373768 -0.11674816 -0.16918278 -0.13746085 -0.1169816\n", + " -0.16927003 -0.13740063 -0.1169564 -0.16916501 -0.13752074 -0.11687641\n", + " -0.16928973 -0.13751536 -0.11697948 -0.16934836 -0.13727436 -0.11693615\n", + " -0.16912453 -0.13748699 -0.11693425 -0.1692788 -0.13750784 -0.11694655\n", + " -0.16919354 -0.13747437 -0.11708782 -0.16940009 -0.13757749 -0.11700586\n", + " -0.1692801 -0.13725384 -0.11684394 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11606052 -0.1140225 -0.11403709 -0.11627212 -0.1139982 -0.11402075\n", + " -0.11613561 -0.11407941 -0.11420487 -0.11666225 -0.11462523 -0.11431901\n", + " -0.11604817 -0.11456211 -0.11392092 -0.11609343 -0.11394228 -0.11414071\n", + " -0.11611685 -0.11420178 -0.11405459 -0.11594404 -0.11408614 -0.11391662\n", + " -0.11590886 -0.11396465 -0.11389125 -0.11616694 -0.11441846 -0.11417015\n", + " -0.11617368 -0.11429765 -0.1139636 -0.11616763 -0.11433984 -0.11412121\n", + " -0.11625618 -0.11402999 -0.11419791 -0.11613603 -0.114206 -0.11423922\n", + " -0.1160801 -0.11431896 -0.11416734 -0.11608923 -0.11455498 -0.11417448\n", + " -0.11605165 -0.11427773 -0.11392205 -0.11606243 -0.11408421 -0.11395292\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11281447 -0.11245904 -0.11308822\n", + " -0.11256366 -0.11230094 -0.1130767 -0.11282651 -0.1121034 -0.11283479\n", + " -0.11260704 -0.1125136 -0.11288977 -0.11278304 -0.11242278 -0.11268564\n", + " -0.11263359 -0.11236227 -0.11329411 -0.11231603 -0.1124533 -0.11278826\n", + " -0.11291545 -0.11241223 -0.11250702 -0.11246481 -0.11228665 -0.11348916\n", + " -0.11250694 -0.11250274 -0.11298019 -0.11277323 -0.11248601 -0.11301753\n", + " -0.11259486 -0.1124685 -0.11285441 -0.11274424 -0.11232891 -0.11316456\n", + " -0.11274575 -0.11256149 -0.11252293 -0.11293524 -0.11261757 -0.11305628\n", + " -0.11253063 -0.11237109 -0.11278518 -0.1124074 -0.11276905 -0.11296684\n", + " -0.11258689 -0.11228467 -0.11331342 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11292265 -0.11395193 -0.11564599 -0.11244356 -0.11338947 -0.1148266\n", + " -0.11295702 -0.11353862 -0.11510521 -0.11244347 -0.11387967 -0.11512396\n", + " -0.11269802 -0.11364442 -0.1151339 -0.11238356 -0.11364301 -0.11496543\n", + " -0.11229193 -0.11340926 -0.11550744 -0.11215818 -0.11367944 -0.11552889\n", + " -0.11240305 -0.11352309 -0.115412 -0.1128402 -0.11338749 -0.1153551\n", + " -0.11250042 -0.11347275 -0.11548445 -0.11271132 -0.11377527 -0.11558066\n", + " -0.11318598 -0.11325792 -0.11499103 -0.11253099 -0.1129829 -0.11530949\n", + " -0.11239074 -0.11329625 -0.11544761 -0.11262484 -0.11323392 -0.1151936\n", + " -0.11253889 -0.11382403 -0.11511129 -0.11250854 -0.11339898 -0.11536332\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11542253 -0.11498664 -0.11428517\n", + " -0.11503783 -0.11473447 -0.11458687 -0.11483866 -0.1154254 -0.11479037\n", + " -0.11533015 -0.11515195 -0.11460571 -0.11563491 -0.11433835 -0.11437413\n", + " -0.11510849 -0.11472156 -0.11516494 -0.11545009 -0.115001 -0.11479743\n", + " -0.11461761 -0.11537461 -0.11497109 -0.1155148 -0.11567353 -0.11431184\n", + " -0.11546067 -0.11462564 -0.11450721 -0.11511 -0.11487988 -0.11466523\n", + " -0.11585756 -0.11462611 -0.11433121 -0.11538152 -0.11463425 -0.11527088\n", + " -0.11509145 -0.11493588 -0.11484324 -0.11528905 -0.11426327 -0.11476508\n", + " -0.11499562 -0.11451299 -0.11466765 -0.11525918 -0.11469718 -0.11476983\n", + " -0.11467865 -0.1145067 -0.11479425 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11352917 -0.1145882 -0.11643688 -0.11418115 -0.11442858 -0.11635549\n", + " -0.11408502 -0.11458383 -0.1163013 -0.1135842 -0.11453566 -0.11575264\n", + " -0.11341863 -0.11481638 -0.11635685 -0.1132144 -0.11438018 -0.11666005\n", + " -0.11311482 -0.11500883 -0.11594984 -0.11409228 -0.11464061 -0.1158012\n", + " -0.11389399 -0.11454081 -0.1157428 -0.11333869 -0.11438896 -0.11676006\n", + " -0.11382523 -0.11443669 -0.11606569 -0.11424726 -0.11464652 -0.11608159\n", + " -0.11396605 -0.11473188 -0.1167532 -0.1136805 -0.11455875 -0.11615814\n", + " -0.11372286 -0.11442829 -0.11590895 -0.1136509 -0.11368863 -0.11660073\n", + " -0.1136605 -0.1141187 -0.11613806 -0.11326355 -0.11427399 -0.11676148\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11573534 -0.11897501 -0.1226239\n", + " -0.1162633 -0.11939573 -0.12255715 -0.11636411 -0.11878021 -0.12306277\n", + " -0.11535113 -0.11813967 -0.1230085 -0.11594119 -0.11812955 -0.12217928\n", + " -0.11523023 -0.11843291 -0.12228252 -0.1159457 -0.11840108 -0.12181337\n", + " -0.11600134 -0.11790484 -0.12203724 -0.11579998 -0.11787918 -0.12317219\n", + " -0.11578704 -0.11837798 -0.12379234 -0.1155279 -0.11865384 -0.12319867\n", + " -0.11597008 -0.11886814 -0.12291788 -0.1162282 -0.11918752 -0.12363613\n", + " -0.11571473 -0.11805225 -0.12250506 -0.11640247 -0.11823175 -0.1226976\n", + " -0.11571549 -0.11813327 -0.12229009 -0.11621545 -0.11793769 -0.1229533\n", + " -0.11528287 -0.1183919 -0.12121653]\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для Gradient Boosting:\n", + "{'learning_rate': 0.1, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "param_grid_gb = {\n", + " 'n_estimators': [50, 100, 200],\n", + " 'learning_rate': [0.01, 0.1, 0.2],\n", + " 'max_depth': [3, 5, 7],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'min_samples_leaf': [1, 2, 4],\n", + " 'max_features': ['auto', 'sqrt', 'log2']\n", + "}\n", + "\n", + "grid_search_gb = GridSearchCV(\n", + " estimator=GradientBoostingRegressor(),\n", + " param_grid=param_grid_gb,\n", + " cv=5,\n", + " scoring='neg_mean_squared_error',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "grid_search_gb.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для Gradient Boosting:\")\n", + "print(grid_search_gb.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Метод k-ближайших соседей" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для k-Nearest Neighbors:\n", + "{'algorithm': 'ball_tree', 'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsRegressor\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "param_grid_knn = {\n", + " 'n_neighbors': [3, 5, 7, 10],\n", + " 'weights': ['uniform', 'distance'],\n", + " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", + " 'p': [1, 2]\n", + "}\n", + "\n", + "grid_search_knn = GridSearchCV(\n", + " estimator=KNeighborsRegressor(),\n", + " param_grid=param_grid_knn,\n", + " cv=5,\n", + " scoring='neg_mean_squared_error',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "grid_search_knn.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для k-Nearest Neighbors:\")\n", + "print(grid_search_knn.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Предсказание на тестовой выборке" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x_test)\n", + "y_pred_forest = model_forest.predict(x_test)\n", + "y_pred_lasso = model_lasso.predict(x_test)\n", + "y_pred_gb = model_gb.predict(x_test)\n", + "y_pred_neighbors = model_knn.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Оценка качества модели" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.\tMSE (Mean Squared Error)\n", + "Среднее значение квадратов разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error (MSE):\n", + "k-NN: \t\t\t0.213\n", + "Random Forest: \t\t0.118\n", + "Lasso: \t\t\t0.166\n", + "Gradient Boosting: \t0.113\n", + "k-Nearest Neighbors: \t0.326\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "import numpy as np\n", + "\n", + "mse1 = mean_squared_error(y_test, y_pred)\n", + "mse2 = mean_squared_error(y_test, y_pred_forest)\n", + "mse3 = mean_squared_error(y_test, y_pred_lasso)\n", + "mse4 = mean_squared_error(y_test, y_pred_gb)\n", + "mse5 = mean_squared_error(y_test, y_pred_neighbors)\n", + "\n", + "mse1_rounded = round(mse1, 3)\n", + "mse2_rounded = round(mse2, 3)\n", + "mse3_rounded = round(mse3, 3)\n", + "mse4_rounded = round(mse4, 3)\n", + "mse5_rounded = round(mse5, 3)\n", + "\n", + "print(\"Mean Squared Error (MSE):\")\n", + "print(f\"k-NN: \\t\\t\\t{mse1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{mse2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{mse3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{mse4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{mse5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2.\tMAE\n", + "Среднее значение абсолютных разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Absolute Error (MAE):\n", + "k-NN: \t\t\t0.213\n", + "Random Forest: \t\t0.238\n", + "Lasso: \t\t\t0.366\n", + "Gradient Boosting: \t0.246\n", + "k-Nearest Neighbors: \t0.485\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "mae1 = round(mean_absolute_error(y_test, y_pred),3)\n", + "mae2 = round(mean_absolute_error(y_test, y_pred_forest),3)\n", + "mae3 = round(mean_absolute_error(y_test, y_pred_lasso),3)\n", + "mae4 = round(mean_absolute_error(y_test, y_pred_gb),3)\n", + "mae5 = round(mean_absolute_error(y_test, y_pred_neighbors),3)\n", + "print(\"Mean Absolute Error (MAE):\")\n", + "print(f\"k-NN: \\t\\t\\t{mae1}\")\n", + "print(f\"Random Forest: \\t\\t{mae2}\")\n", + "print(f\"Lasso: \\t\\t\\t{mae3}\")\n", + "print(f\"Gradient Boosting: \\t{mae4}\")\n", + "print(f\"k-Nearest Neighbors: \\t{mae5}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3.\tR-squared\n", + "Мера, показывающая, насколько хорошо модель объясняет изменчивость данных. Значение находится в диапазоне от 0 до 1, где 1 — идеальное соответствие, а 0 — модель не объясняет данные." + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R² (R-squared): 0.127933821917115\n", + "\n", + "R² (R-squared):\n", + "k-NN: \t\t\t0.128\n", + "Random Forest: \t\t0.515\n", + "Lasso: \t\t\t0.319\n", + "Gradient Boosting: \t0.537\n", + "k-Nearest Neighbors: \t-0.337\n" + ] + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2 = r2_score(y_test, y_pred)\n", + "print(f\"R² (R-squared): {r2}\")\n", + "\n", + "r2_1 = r2_score(y_test, y_pred)\n", + "r2_2 = r2_score(y_test, y_pred_forest)\n", + "r2_3 = r2_score(y_test, y_pred_lasso)\n", + "r2_4 = r2_score(y_test, y_pred_gb)\n", + "r2_5 = r2_score(y_test, y_pred_neighbors)\n", + "\n", + "r2_1_rounded = round(r2_1, 3)\n", + "r2_2_rounded = round(r2_2, 3)\n", + "r2_3_rounded = round(r2_3, 3)\n", + "r2_4_rounded = round(r2_4, 3)\n", + "r2_5_rounded = round(r2_5, 3)\n", + "\n", + "print(\"\\nR² (R-squared):\")\n", + "print(f\"k-NN: \\t\\t\\t{r2_1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{r2_2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{r2_3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{r2_4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{r2_5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4.\tRMSE\n", + " Среднее отклонение предсказаний от реальных данных. Чем меньше модуль, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Root Mean Squared Error (RMSE):\n", + "k-NN: \t\t\t0.461\n", + "Random Forest: \t\t0.344\n", + "Lasso: \t\t\t0.407\n", + "Gradient Boosting: \t0.336\n", + "k-Nearest Neighbors: \t0.571\n" + ] + } + ], + "source": [ + "rmse1 = np.sqrt(mse1)\n", + "rmse2 = np.sqrt(mse2)\n", + "rmse3 = np.sqrt(mse3)\n", + "rmse4 = np.sqrt(mse4)\n", + "rmse5 = np.sqrt(mse5)\n", + "\n", + "rmse1_rounded = round(rmse1, 3)\n", + "rmse2_rounded = round(rmse2, 3)\n", + "rmse3_rounded = round(rmse3, 3)\n", + "rmse4_rounded = round(rmse4, 3)\n", + "rmse5_rounded = round(rmse5, 3)\n", + "\n", + "print(\"Root Mean Squared Error (RMSE):\")\n", + "print(f\"k-NN: \\t\\t\\t{rmse1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{rmse2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{rmse3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{rmse4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{rmse5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Лучший результат – градиентный бустинг и случайный лес.\n", + "Положительные результаты по всем критериям получил случайный лес. Три из четырех положительных результата у градиентного бустинга. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Значит, случайный лес – наиболее точная и устойчивая стратегия обучения модели. Итоговая модель – model_forest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Также, с помощью применение важности признаков (feature importance) на Случайном лесе, мы вывели основные факторы, вызывающие депрессию:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Feature Importance\n", + "13 Have you ever had suicidal thoughts ? 0.300542\n", + "5 Academic Pressure 0.134276\n", + "0 id 0.087970\n", + "7 CGPA 0.079078\n", + "2 Age 0.066613\n", + "15 Financial Stress 0.066330\n", + "3 City 0.059293\n", + "14 Work/Study Hours 0.052275\n", + "12 Degree 0.049539\n", + "8 Study Satisfaction 0.032944\n", + "11 Dietary Habits 0.026140\n", + "10 Sleep Duration 0.024435\n", + "16 Family History of Mental Illness 0.010547\n", + "1 Gender 0.009627\n", + "4 Profession 0.000372\n", + "9 Job Satisfaction 0.000017\n", + "6 Work Pressure 0.000003\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "model_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "model_rf.fit(x_train, y_train)\n", + "\n", + "feature_importances = model_rf.feature_importances_\n", + "\n", + "import pandas as pd\n", + "feature_importance_df = pd.DataFrame({\n", + " 'Feature': x.columns,\n", + " 'Importance': feature_importances\n", + "}).sort_values(by='Importance', ascending=False)\n", + "\n", + "print(feature_importance_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scripts", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9108fe27f63d3c3c82d983563e1a99784893ab05 Mon Sep 17 00:00:00 2001 From: dex_moth Date: Sat, 21 Dec 2024 00:17:24 +0400 Subject: [PATCH 2/7] =?UTF-8?q?=D0=A3=D0=B4=D0=B0=D0=BB=D0=B8=D1=82=D1=8C?= =?UTF-8?q?=20lab=5F4/Lab4.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_4/Lab4.ipynb | 911 ----------------------------------------------- 1 file changed, 911 deletions(-) delete mode 100644 lab_4/Lab4.ipynb diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb deleted file mode 100644 index 0b8116e..0000000 --- a/lab_4/Lab4.ipynb +++ /dev/null @@ -1,911 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['id', 'Gender', 'Age', 'City', 'Profession', 'Academic Pressure',\n", - " 'Work Pressure', 'CGPA', 'Study Satisfaction', 'Job Satisfaction',\n", - " 'Sleep Duration', 'Dietary Habits', 'Degree',\n", - " 'Have you ever had suicidal thoughts ?', 'Work/Study Hours',\n", - " 'Financial Stress', 'Family History of Mental Illness', 'Depression'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from matplotlib.ticker import FuncFormatter\n", - "\n", - "df = pd.read_csv(\".//csv//Student Depression Dataset.csv\")\n", - "print(df.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id Gender Age City Profession Academic Pressure \\\n", - "0 2 Male 33.0 Visakhapatnam Student 5.0 \n", - "1 8 Female 24.0 Bangalore Student 2.0 \n", - "2 26 Male 31.0 Srinagar Student 3.0 \n", - "3 30 Female 28.0 Varanasi Student 3.0 \n", - "4 32 Female 25.0 Jaipur Student 4.0 \n", - "\n", - " Work Pressure CGPA Study Satisfaction Job Satisfaction \\\n", - "0 0.0 8.97 2.0 0.0 \n", - "1 0.0 5.90 5.0 0.0 \n", - "2 0.0 7.03 5.0 0.0 \n", - "3 0.0 5.59 2.0 0.0 \n", - "4 0.0 8.13 3.0 0.0 \n", - "\n", - " Sleep Duration Dietary Habits Degree \\\n", - "0 5-6 hours Healthy B.Pharm \n", - "1 5-6 hours Moderate BSc \n", - "2 Less than 5 hours Healthy BA \n", - "3 7-8 hours Moderate BCA \n", - "4 5-6 hours Moderate M.Tech \n", - "\n", - " Have you ever had suicidal thoughts ? Work/Study Hours Financial Stress \\\n", - "0 Yes 3.0 1.0 \n", - "1 No 3.0 2.0 \n", - "2 No 9.0 1.0 \n", - "3 Yes 4.0 5.0 \n", - "4 Yes 1.0 1.0 \n", - "\n", - " Family History of Mental Illness Depression \n", - "0 No 1 \n", - "1 Yes 0 \n", - "2 Yes 0 \n", - "3 Yes 1 \n", - "4 No 0 \n" - ] - } - ], - "source": [ - "print(df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Бизнес-цель исследования\n", - "Разработать и внедрить систему прогнозирования уровня депрессии среди обучающихся, которая позволит выявить группы риска на ранних этапах. Результаты исследования могут быть полезны психологам, педагогам и администрации учебных заведений.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Описание набора данных для анализа\n", - "Набор данных содержит информацию о психологическом состоянии обучающихся и включает следующие поля:\n", - "- id – идентификатор, число\n", - "- Gender – пол, строка\n", - "- Age – возраст, дробное число\n", - "- City – город, строка\n", - "- Profession – профессия, строка\n", - "- Academic Pressure – академическое давление, дробное число (от 1.00 до 5.00)\n", - "- Work Pressure – рабочее давление, дробное число (от 1.00 до 5.00)\n", - "- CGPA – средний балл (GPA), дробное число\n", - "- Study Satisfaction – удовлетворенность учебой, дробное число (от 1.00 до 5.00)\n", - "- Job Satisfaction – удовлетворенность работой, дробное число (от 1.00 до 5.00)\n", - "- Sleep Duration – продолжительность сна, строка\n", - "- Dietary Habits – пищевые привычки, строка\n", - "- Degree – степень (образование), строка\n", - "- Have you ever had suicidal thoughts? – Были ли у вас когда-либо суицидальные мысли? строка (yes/no)\n", - "- Work/Study Hours – часы работы/учебы, дробное число\n", - "- Financial Stress – финансовый стресс, дробное число (от 1.00 до 5.00)\n", - "- Family History of Mental Illness – семейный анамнез психических заболеваний, строка (yes/no)\n", - "- Depression – депрессия, булевое значение (1/0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Обработка данных" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "Gender 0\n", - "Age 0\n", - "City 0\n", - "Profession 0\n", - "Academic Pressure 0\n", - "Work Pressure 0\n", - "CGPA 0\n", - "Study Satisfaction 0\n", - "Job Satisfaction 0\n", - "Sleep Duration 0\n", - "Dietary Habits 0\n", - "Degree 0\n", - "Have you ever had suicidal thoughts ? 0\n", - "Work/Study Hours 0\n", - "Financial Stress 3\n", - "Family History of Mental Illness 0\n", - "Depression 0\n", - "dtype: int64" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "df.dropna(subset=['Financial Stress'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "features = ['Age', 'Academic Pressure', 'Work Pressure', 'CGPA', 'Study Satisfaction', \n", - " 'Job Satisfaction', 'Work/Study Hours', 'Financial Stress', 'Depression']\n", - "\n", - "plt.figure(figsize=(15, 10))\n", - "for i, feature in enumerate(features, 1):\n", - " plt.subplot(3, 3, i)\n", - " sns.boxplot(y=df[feature], color='skyblue')\n", - " plt.title(f'Boxplot of {feature}')\n", - " plt.ylabel(feature)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "В Age много выбросов. Сбалансируем данные" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Q1 = df['Age'].quantile(0.25)\n", - "Q3 = df['Age'].quantile(0.75)\n", - "IQR = Q3 - Q1\n", - "\n", - "threshold = 1.5 * IQR\n", - "outliers = (df['Age'] < (Q1 - threshold)) | (df['Age'] > (Q3 + threshold))\n", - "\n", - "median_rating = df['Age'].median()\n", - "df.loc[outliers, 'Age'] = median_rating\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "sns.boxplot(y=df['Age'], color='skyblue')\n", - "plt.title('Boxplot of Age')\n", - "plt.ylabel('Age')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Конструирование признаков с помощью меток" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "le = LabelEncoder()\n", - "df['Gender'] = le.fit_transform(df['Gender'])\n", - "df['City'] = le.fit_transform(df['City'])\n", - "df['Dietary Habits'] = le.fit_transform(df['Dietary Habits'])\n", - "df['Degree'] = le.fit_transform(df['Degree'])\n", - "df['Have you ever had suicidal thoughts ?'] = le.fit_transform(df['Have you ever had suicidal thoughts ?'])\n", - "df['Sleep Duration'] = le.fit_transform(df['Sleep Duration'])\n", - "df['Profession'] = le.fit_transform(df['Profession'])\n", - "df['Study Satisfaction'] = le.fit_transform(df['Study Satisfaction'])\n", - "df['Family History of Mental Illness'] = le.fit_transform(df['Family History of Mental Illness'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "разделение на признаки и целевую переменную" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "x = df.drop('Depression', axis=1)\n", - "y = df['Depression']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Метод регрессии Лассо\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Лучшие гиперпараметры для Lasso:\n", - "{'alpha': 0.01, 'fit_intercept': False}\n" - ] - } - ], - "source": [ - "from sklearn.linear_model import Lasso\n", - "\n", - "param_grid_lasso = {\n", - " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", - " 'fit_intercept': [True, False],\n", - "}\n", - "\n", - "# Создание объекта GridSearchCV\n", - "grid_search_lasso = GridSearchCV(\n", - " estimator=Lasso(), \n", - " param_grid=param_grid_lasso, \n", - " cv=5, \n", - " scoring='neg_mean_squared_error', \n", - " n_jobs=-1 \n", - ")\n", - "\n", - "grid_search_lasso.fit(x_train, y_train)\n", - "\n", - "# Вывод лучших гиперпараметров\n", - "print(\"Лучшие гиперпараметры для Lasso:\")\n", - "print(grid_search_lasso.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Метод градиентного бустинга" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n", - "1215 fits failed out of a total of 3645.\n", - "The score on these train-test partitions for these parameters will be set to nan.\n", - "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", - "\n", - "Below are more details about the failures:\n", - "--------------------------------------------------------------------------------\n", - "978 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", - " estimator._validate_params()\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", - " validate_parameter_constraints(\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "237 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", - " estimator._validate_params()\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", - " validate_parameter_constraints(\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", - "\n", - " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n", - " _data = np.array(data, dtype=dtype, copy=copy,\n", - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.18767441 -0.15799837 -0.13080278\n", - " -0.18762913 -0.15792709 -0.13056114 -0.18792038 -0.15737146 -0.130218\n", - " -0.18725961 -0.157967 -0.13047453 -0.18766583 -0.15779565 -0.13094863\n", - " -0.18798705 -0.15693978 -0.13061215 -0.18766317 -0.15746848 -0.13072918\n", - " -0.18864158 -0.15666133 -0.13095037 -0.18817206 -0.15805489 -0.13086126\n", - " -0.18707465 -0.15864932 -0.13104947 -0.18818902 -0.15828572 -0.13063871\n", - 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" -0.1754675 -0.1442258 -0.12068226 -0.17611334 -0.14433552 -0.12093556\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.16938321 -0.13763002 -0.11703902\n", - " -0.16953091 -0.13736586 -0.11695779 -0.16881837 -0.1375676 -0.11694438\n", - " -0.16927898 -0.13748177 -0.11689982 -0.16921265 -0.13757375 -0.11682524\n", - " -0.16915872 -0.13727377 -0.11694336 -0.16939766 -0.13734972 -0.1167447\n", - " -0.16924214 -0.1373768 -0.11674816 -0.16918278 -0.13746085 -0.1169816\n", - " -0.16927003 -0.13740063 -0.1169564 -0.16916501 -0.13752074 -0.11687641\n", - " -0.16928973 -0.13751536 -0.11697948 -0.16934836 -0.13727436 -0.11693615\n", - " -0.16912453 -0.13748699 -0.11693425 -0.1692788 -0.13750784 -0.11694655\n", - " -0.16919354 -0.13747437 -0.11708782 -0.16940009 -0.13757749 -0.11700586\n", - " -0.1692801 -0.13725384 -0.11684394 nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11606052 -0.1140225 -0.11403709 -0.11627212 -0.1139982 -0.11402075\n", - " -0.11613561 -0.11407941 -0.11420487 -0.11666225 -0.11462523 -0.11431901\n", - " -0.11604817 -0.11456211 -0.11392092 -0.11609343 -0.11394228 -0.11414071\n", - " -0.11611685 -0.11420178 -0.11405459 -0.11594404 -0.11408614 -0.11391662\n", - " -0.11590886 -0.11396465 -0.11389125 -0.11616694 -0.11441846 -0.11417015\n", - " -0.11617368 -0.11429765 -0.1139636 -0.11616763 -0.11433984 -0.11412121\n", - " -0.11625618 -0.11402999 -0.11419791 -0.11613603 -0.114206 -0.11423922\n", - " -0.1160801 -0.11431896 -0.11416734 -0.11608923 -0.11455498 -0.11417448\n", - " -0.11605165 -0.11427773 -0.11392205 -0.11606243 -0.11408421 -0.11395292\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11281447 -0.11245904 -0.11308822\n", - " -0.11256366 -0.11230094 -0.1130767 -0.11282651 -0.1121034 -0.11283479\n", - " -0.11260704 -0.1125136 -0.11288977 -0.11278304 -0.11242278 -0.11268564\n", - " -0.11263359 -0.11236227 -0.11329411 -0.11231603 -0.1124533 -0.11278826\n", - " -0.11291545 -0.11241223 -0.11250702 -0.11246481 -0.11228665 -0.11348916\n", - " -0.11250694 -0.11250274 -0.11298019 -0.11277323 -0.11248601 -0.11301753\n", - " -0.11259486 -0.1124685 -0.11285441 -0.11274424 -0.11232891 -0.11316456\n", - " -0.11274575 -0.11256149 -0.11252293 -0.11293524 -0.11261757 -0.11305628\n", - " -0.11253063 -0.11237109 -0.11278518 -0.1124074 -0.11276905 -0.11296684\n", - " -0.11258689 -0.11228467 -0.11331342 nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11292265 -0.11395193 -0.11564599 -0.11244356 -0.11338947 -0.1148266\n", - " -0.11295702 -0.11353862 -0.11510521 -0.11244347 -0.11387967 -0.11512396\n", - " -0.11269802 -0.11364442 -0.1151339 -0.11238356 -0.11364301 -0.11496543\n", - " -0.11229193 -0.11340926 -0.11550744 -0.11215818 -0.11367944 -0.11552889\n", - " -0.11240305 -0.11352309 -0.115412 -0.1128402 -0.11338749 -0.1153551\n", - " -0.11250042 -0.11347275 -0.11548445 -0.11271132 -0.11377527 -0.11558066\n", - " -0.11318598 -0.11325792 -0.11499103 -0.11253099 -0.1129829 -0.11530949\n", - " -0.11239074 -0.11329625 -0.11544761 -0.11262484 -0.11323392 -0.1151936\n", - " -0.11253889 -0.11382403 -0.11511129 -0.11250854 -0.11339898 -0.11536332\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11542253 -0.11498664 -0.11428517\n", - " -0.11503783 -0.11473447 -0.11458687 -0.11483866 -0.1154254 -0.11479037\n", - " -0.11533015 -0.11515195 -0.11460571 -0.11563491 -0.11433835 -0.11437413\n", - " -0.11510849 -0.11472156 -0.11516494 -0.11545009 -0.115001 -0.11479743\n", - " -0.11461761 -0.11537461 -0.11497109 -0.1155148 -0.11567353 -0.11431184\n", - " -0.11546067 -0.11462564 -0.11450721 -0.11511 -0.11487988 -0.11466523\n", - " -0.11585756 -0.11462611 -0.11433121 -0.11538152 -0.11463425 -0.11527088\n", - " -0.11509145 -0.11493588 -0.11484324 -0.11528905 -0.11426327 -0.11476508\n", - " -0.11499562 -0.11451299 -0.11466765 -0.11525918 -0.11469718 -0.11476983\n", - " -0.11467865 -0.1145067 -0.11479425 nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11352917 -0.1145882 -0.11643688 -0.11418115 -0.11442858 -0.11635549\n", - " -0.11408502 -0.11458383 -0.1163013 -0.1135842 -0.11453566 -0.11575264\n", - " -0.11341863 -0.11481638 -0.11635685 -0.1132144 -0.11438018 -0.11666005\n", - " -0.11311482 -0.11500883 -0.11594984 -0.11409228 -0.11464061 -0.1158012\n", - " -0.11389399 -0.11454081 -0.1157428 -0.11333869 -0.11438896 -0.11676006\n", - " -0.11382523 -0.11443669 -0.11606569 -0.11424726 -0.11464652 -0.11608159\n", - " -0.11396605 -0.11473188 -0.1167532 -0.1136805 -0.11455875 -0.11615814\n", - " -0.11372286 -0.11442829 -0.11590895 -0.1136509 -0.11368863 -0.11660073\n", - " -0.1136605 -0.1141187 -0.11613806 -0.11326355 -0.11427399 -0.11676148\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11573534 -0.11897501 -0.1226239\n", - " -0.1162633 -0.11939573 -0.12255715 -0.11636411 -0.11878021 -0.12306277\n", - " -0.11535113 -0.11813967 -0.1230085 -0.11594119 -0.11812955 -0.12217928\n", - " -0.11523023 -0.11843291 -0.12228252 -0.1159457 -0.11840108 -0.12181337\n", - " -0.11600134 -0.11790484 -0.12203724 -0.11579998 -0.11787918 -0.12317219\n", - " -0.11578704 -0.11837798 -0.12379234 -0.1155279 -0.11865384 -0.12319867\n", - " -0.11597008 -0.11886814 -0.12291788 -0.1162282 -0.11918752 -0.12363613\n", - " -0.11571473 -0.11805225 -0.12250506 -0.11640247 -0.11823175 -0.1226976\n", - " -0.11571549 -0.11813327 -0.12229009 -0.11621545 -0.11793769 -0.1229533\n", - " -0.11528287 -0.1183919 -0.12121653]\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Лучшие гиперпараметры для Gradient Boosting:\n", - "{'learning_rate': 0.1, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n" - ] - } - ], - "source": [ - "\n", - "from sklearn.ensemble import GradientBoostingRegressor\n", - "\n", - "param_grid_gb = {\n", - " 'n_estimators': [50, 100, 200],\n", - " 'learning_rate': [0.01, 0.1, 0.2],\n", - " 'max_depth': [3, 5, 7],\n", - " 'min_samples_split': [2, 5, 10],\n", - " 'min_samples_leaf': [1, 2, 4],\n", - " 'max_features': ['auto', 'sqrt', 'log2']\n", - "}\n", - "\n", - "grid_search_gb = GridSearchCV(\n", - " estimator=GradientBoostingRegressor(),\n", - " param_grid=param_grid_gb,\n", - " cv=5,\n", - " scoring='neg_mean_squared_error',\n", - " n_jobs=-1\n", - ")\n", - "\n", - "grid_search_gb.fit(x_train, y_train)\n", - "\n", - "# Вывод лучших гиперпараметров\n", - "print(\"Лучшие гиперпараметры для Gradient Boosting:\")\n", - "print(grid_search_gb.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) Метод k-ближайших соседей" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Лучшие гиперпараметры для k-Nearest Neighbors:\n", - "{'algorithm': 'ball_tree', 'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsRegressor\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "param_grid_knn = {\n", - " 'n_neighbors': [3, 5, 7, 10],\n", - " 'weights': ['uniform', 'distance'],\n", - " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", - " 'p': [1, 2]\n", - "}\n", - "\n", - "grid_search_knn = GridSearchCV(\n", - " estimator=KNeighborsRegressor(),\n", - " param_grid=param_grid_knn,\n", - " cv=5,\n", - " scoring='neg_mean_squared_error',\n", - " n_jobs=-1\n", - ")\n", - "\n", - "grid_search_knn.fit(x_train, y_train)\n", - "\n", - "# Вывод лучших гиперпараметров\n", - "print(\"Лучшие гиперпараметры для k-Nearest Neighbors:\")\n", - "print(grid_search_knn.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Предсказание на тестовой выборке" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = model.predict(x_test)\n", - "y_pred_forest = model_forest.predict(x_test)\n", - "y_pred_lasso = model_lasso.predict(x_test)\n", - "y_pred_gb = model_gb.predict(x_test)\n", - "y_pred_neighbors = model_knn.predict(x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Оценка качества модели" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1.\tMSE (Mean Squared Error)\n", - "Среднее значение квадратов разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error (MSE):\n", - "k-NN: \t\t\t0.213\n", - "Random Forest: \t\t0.118\n", - "Lasso: \t\t\t0.166\n", - "Gradient Boosting: \t0.113\n", - "k-Nearest Neighbors: \t0.326\n" - ] - } - ], - "source": [ - "from sklearn.metrics import mean_squared_error\n", - "import numpy as np\n", - "\n", - "mse1 = mean_squared_error(y_test, y_pred)\n", - "mse2 = mean_squared_error(y_test, y_pred_forest)\n", - "mse3 = mean_squared_error(y_test, y_pred_lasso)\n", - "mse4 = mean_squared_error(y_test, y_pred_gb)\n", - "mse5 = mean_squared_error(y_test, y_pred_neighbors)\n", - "\n", - "mse1_rounded = round(mse1, 3)\n", - "mse2_rounded = round(mse2, 3)\n", - "mse3_rounded = round(mse3, 3)\n", - "mse4_rounded = round(mse4, 3)\n", - "mse5_rounded = round(mse5, 3)\n", - "\n", - "print(\"Mean Squared Error (MSE):\")\n", - "print(f\"k-NN: \\t\\t\\t{mse1_rounded}\")\n", - "print(f\"Random Forest: \\t\\t{mse2_rounded}\")\n", - "print(f\"Lasso: \\t\\t\\t{mse3_rounded}\")\n", - "print(f\"Gradient Boosting: \\t{mse4_rounded}\")\n", - "print(f\"k-Nearest Neighbors: \\t{mse5_rounded}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2.\tMAE\n", - "Среднее значение абсолютных разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Absolute Error (MAE):\n", - "k-NN: \t\t\t0.213\n", - "Random Forest: \t\t0.238\n", - "Lasso: \t\t\t0.366\n", - "Gradient Boosting: \t0.246\n", - "k-Nearest Neighbors: \t0.485\n" - ] - } - ], - "source": [ - "from sklearn.metrics import mean_absolute_error\n", - "\n", - "mae1 = round(mean_absolute_error(y_test, y_pred),3)\n", - "mae2 = round(mean_absolute_error(y_test, y_pred_forest),3)\n", - "mae3 = round(mean_absolute_error(y_test, y_pred_lasso),3)\n", - "mae4 = round(mean_absolute_error(y_test, y_pred_gb),3)\n", - "mae5 = round(mean_absolute_error(y_test, y_pred_neighbors),3)\n", - "print(\"Mean Absolute Error (MAE):\")\n", - "print(f\"k-NN: \\t\\t\\t{mae1}\")\n", - "print(f\"Random Forest: \\t\\t{mae2}\")\n", - "print(f\"Lasso: \\t\\t\\t{mae3}\")\n", - "print(f\"Gradient Boosting: \\t{mae4}\")\n", - "print(f\"k-Nearest Neighbors: \\t{mae5}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3.\tR-squared\n", - "Мера, показывающая, насколько хорошо модель объясняет изменчивость данных. Значение находится в диапазоне от 0 до 1, где 1 — идеальное соответствие, а 0 — модель не объясняет данные." - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R² (R-squared): 0.127933821917115\n", - "\n", - "R² (R-squared):\n", - "k-NN: \t\t\t0.128\n", - "Random Forest: \t\t0.515\n", - "Lasso: \t\t\t0.319\n", - "Gradient Boosting: \t0.537\n", - "k-Nearest Neighbors: \t-0.337\n" - ] - } - ], - "source": [ - "from sklearn.metrics import r2_score\n", - "r2 = r2_score(y_test, y_pred)\n", - "print(f\"R² (R-squared): {r2}\")\n", - "\n", - "r2_1 = r2_score(y_test, y_pred)\n", - "r2_2 = r2_score(y_test, y_pred_forest)\n", - "r2_3 = r2_score(y_test, y_pred_lasso)\n", - "r2_4 = r2_score(y_test, y_pred_gb)\n", - "r2_5 = r2_score(y_test, y_pred_neighbors)\n", - "\n", - "r2_1_rounded = round(r2_1, 3)\n", - "r2_2_rounded = round(r2_2, 3)\n", - "r2_3_rounded = round(r2_3, 3)\n", - "r2_4_rounded = round(r2_4, 3)\n", - "r2_5_rounded = round(r2_5, 3)\n", - "\n", - "print(\"\\nR² (R-squared):\")\n", - "print(f\"k-NN: \\t\\t\\t{r2_1_rounded}\")\n", - "print(f\"Random Forest: \\t\\t{r2_2_rounded}\")\n", - "print(f\"Lasso: \\t\\t\\t{r2_3_rounded}\")\n", - "print(f\"Gradient Boosting: \\t{r2_4_rounded}\")\n", - "print(f\"k-Nearest Neighbors: \\t{r2_5_rounded}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4.\tRMSE\n", - " Среднее отклонение предсказаний от реальных данных. Чем меньше модуль, тем лучше модель." - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Root Mean Squared Error (RMSE):\n", - "k-NN: \t\t\t0.461\n", - "Random Forest: \t\t0.344\n", - "Lasso: \t\t\t0.407\n", - "Gradient Boosting: \t0.336\n", - "k-Nearest Neighbors: \t0.571\n" - ] - } - ], - "source": [ - "rmse1 = np.sqrt(mse1)\n", - "rmse2 = np.sqrt(mse2)\n", - "rmse3 = np.sqrt(mse3)\n", - "rmse4 = np.sqrt(mse4)\n", - "rmse5 = np.sqrt(mse5)\n", - "\n", - "rmse1_rounded = round(rmse1, 3)\n", - "rmse2_rounded = round(rmse2, 3)\n", - "rmse3_rounded = round(rmse3, 3)\n", - "rmse4_rounded = round(rmse4, 3)\n", - "rmse5_rounded = round(rmse5, 3)\n", - "\n", - "print(\"Root Mean Squared Error (RMSE):\")\n", - "print(f\"k-NN: \\t\\t\\t{rmse1_rounded}\")\n", - "print(f\"Random Forest: \\t\\t{rmse2_rounded}\")\n", - "print(f\"Lasso: \\t\\t\\t{rmse3_rounded}\")\n", - "print(f\"Gradient Boosting: \\t{rmse4_rounded}\")\n", - "print(f\"k-Nearest Neighbors: \\t{rmse5_rounded}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Лучший результат – градиентный бустинг и случайный лес.\n", - "Положительные результаты по всем критериям получил случайный лес. Три из четырех положительных результата у градиентного бустинга. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Значит, случайный лес – наиболее точная и устойчивая стратегия обучения модели. Итоговая модель – model_forest." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Также, с помощью применение важности признаков (feature importance) на Случайном лесе, мы вывели основные факторы, вызывающие депрессию:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Feature Importance\n", - "13 Have you ever had suicidal thoughts ? 0.300542\n", - "5 Academic Pressure 0.134276\n", - "0 id 0.087970\n", - "7 CGPA 0.079078\n", - "2 Age 0.066613\n", - "15 Financial Stress 0.066330\n", - "3 City 0.059293\n", - "14 Work/Study Hours 0.052275\n", - "12 Degree 0.049539\n", - "8 Study Satisfaction 0.032944\n", - "11 Dietary Habits 0.026140\n", - "10 Sleep Duration 0.024435\n", - "16 Family History of Mental Illness 0.010547\n", - "1 Gender 0.009627\n", - "4 Profession 0.000372\n", - "9 Job Satisfaction 0.000017\n", - "6 Work Pressure 0.000003\n" - ] - } - ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "\n", - "model_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", - "model_rf.fit(x_train, y_train)\n", - "\n", - "feature_importances = model_rf.feature_importances_\n", - "\n", - "import pandas as pd\n", - "feature_importance_df = pd.DataFrame({\n", - " 'Feature': x.columns,\n", - " 'Importance': feature_importances\n", - "}).sort_values(by='Importance', ascending=False)\n", - "\n", - "print(feature_importance_df)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Scripts", - "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": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 1529fc881f7b73130e848d1d7bbc78f570edb621 Mon Sep 17 00:00:00 2001 From: dex_moth Date: Sat, 21 Dec 2024 00:19:22 +0400 Subject: [PATCH 3/7] lab_4 --- lab_4/Lab4.ipynb | 911 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 911 insertions(+) create mode 100644 lab_4/Lab4.ipynb diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb new file mode 100644 index 0000000..0b8116e --- /dev/null +++ b/lab_4/Lab4.ipynb @@ -0,0 +1,911 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'Gender', 'Age', 'City', 'Profession', 'Academic Pressure',\n", + " 'Work Pressure', 'CGPA', 'Study Satisfaction', 'Job Satisfaction',\n", + " 'Sleep Duration', 'Dietary Habits', 'Degree',\n", + " 'Have you ever had suicidal thoughts ?', 'Work/Study Hours',\n", + " 'Financial Stress', 'Family History of Mental Illness', 'Depression'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib.ticker import FuncFormatter\n", + "\n", + "df = pd.read_csv(\".//csv//Student Depression Dataset.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id Gender Age City Profession Academic Pressure \\\n", + "0 2 Male 33.0 Visakhapatnam Student 5.0 \n", + "1 8 Female 24.0 Bangalore Student 2.0 \n", + "2 26 Male 31.0 Srinagar Student 3.0 \n", + "3 30 Female 28.0 Varanasi Student 3.0 \n", + "4 32 Female 25.0 Jaipur Student 4.0 \n", + "\n", + " Work Pressure CGPA Study Satisfaction Job Satisfaction \\\n", + "0 0.0 8.97 2.0 0.0 \n", + "1 0.0 5.90 5.0 0.0 \n", + "2 0.0 7.03 5.0 0.0 \n", + "3 0.0 5.59 2.0 0.0 \n", + "4 0.0 8.13 3.0 0.0 \n", + "\n", + " Sleep Duration Dietary Habits Degree \\\n", + "0 5-6 hours Healthy B.Pharm \n", + "1 5-6 hours Moderate BSc \n", + "2 Less than 5 hours Healthy BA \n", + "3 7-8 hours Moderate BCA \n", + "4 5-6 hours Moderate M.Tech \n", + "\n", + " Have you ever had suicidal thoughts ? Work/Study Hours Financial Stress \\\n", + "0 Yes 3.0 1.0 \n", + "1 No 3.0 2.0 \n", + "2 No 9.0 1.0 \n", + "3 Yes 4.0 5.0 \n", + "4 Yes 1.0 1.0 \n", + "\n", + " Family History of Mental Illness Depression \n", + "0 No 1 \n", + "1 Yes 0 \n", + "2 Yes 0 \n", + "3 Yes 1 \n", + "4 No 0 \n" + ] + } + ], + "source": [ + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Бизнес-цель исследования\n", + "Разработать и внедрить систему прогнозирования уровня депрессии среди обучающихся, которая позволит выявить группы риска на ранних этапах. Результаты исследования могут быть полезны психологам, педагогам и администрации учебных заведений.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Описание набора данных для анализа\n", + "Набор данных содержит информацию о психологическом состоянии обучающихся и включает следующие поля:\n", + "- id – идентификатор, число\n", + "- Gender – пол, строка\n", + "- Age – возраст, дробное число\n", + "- City – город, строка\n", + "- Profession – профессия, строка\n", + "- Academic Pressure – академическое давление, дробное число (от 1.00 до 5.00)\n", + "- Work Pressure – рабочее давление, дробное число (от 1.00 до 5.00)\n", + "- CGPA – средний балл (GPA), дробное число\n", + "- Study Satisfaction – удовлетворенность учебой, дробное число (от 1.00 до 5.00)\n", + "- Job Satisfaction – удовлетворенность работой, дробное число (от 1.00 до 5.00)\n", + "- Sleep Duration – продолжительность сна, строка\n", + "- Dietary Habits – пищевые привычки, строка\n", + "- Degree – степень (образование), строка\n", + "- Have you ever had suicidal thoughts? – Были ли у вас когда-либо суицидальные мысли? строка (yes/no)\n", + "- Work/Study Hours – часы работы/учебы, дробное число\n", + "- Financial Stress – финансовый стресс, дробное число (от 1.00 до 5.00)\n", + "- Family History of Mental Illness – семейный анамнез психических заболеваний, строка (yes/no)\n", + "- Depression – депрессия, булевое значение (1/0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Обработка данных" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id 0\n", + "Gender 0\n", + "Age 0\n", + "City 0\n", + "Profession 0\n", + "Academic Pressure 0\n", + "Work Pressure 0\n", + "CGPA 0\n", + "Study Satisfaction 0\n", + "Job Satisfaction 0\n", + "Sleep Duration 0\n", + "Dietary Habits 0\n", + "Degree 0\n", + "Have you ever had suicidal thoughts ? 0\n", + "Work/Study Hours 0\n", + "Financial Stress 3\n", + "Family History of Mental Illness 0\n", + "Depression 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['Financial Stress'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "features = ['Age', 'Academic Pressure', 'Work Pressure', 'CGPA', 'Study Satisfaction', \n", + " 'Job Satisfaction', 'Work/Study Hours', 'Financial Stress', 'Depression']\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, feature in enumerate(features, 1):\n", + " plt.subplot(3, 3, i)\n", + " sns.boxplot(y=df[feature], color='skyblue')\n", + " plt.title(f'Boxplot of {feature}')\n", + " plt.ylabel(feature)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В Age много выбросов. Сбалансируем данные" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q1 = df['Age'].quantile(0.25)\n", + "Q3 = df['Age'].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "threshold = 1.5 * IQR\n", + "outliers = (df['Age'] < (Q1 - threshold)) | (df['Age'] > (Q3 + threshold))\n", + "\n", + "median_rating = df['Age'].median()\n", + "df.loc[outliers, 'Age'] = median_rating\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(y=df['Age'], color='skyblue')\n", + "plt.title('Boxplot of Age')\n", + "plt.ylabel('Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Конструирование признаков с помощью меток" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "le = LabelEncoder()\n", + "df['Gender'] = le.fit_transform(df['Gender'])\n", + "df['City'] = le.fit_transform(df['City'])\n", + "df['Dietary Habits'] = le.fit_transform(df['Dietary Habits'])\n", + "df['Degree'] = le.fit_transform(df['Degree'])\n", + "df['Have you ever had suicidal thoughts ?'] = le.fit_transform(df['Have you ever had suicidal thoughts ?'])\n", + "df['Sleep Duration'] = le.fit_transform(df['Sleep Duration'])\n", + "df['Profession'] = le.fit_transform(df['Profession'])\n", + "df['Study Satisfaction'] = le.fit_transform(df['Study Satisfaction'])\n", + "df['Family History of Mental Illness'] = le.fit_transform(df['Family History of Mental Illness'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "разделение на признаки и целевую переменную" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "x = df.drop('Depression', axis=1)\n", + "y = df['Depression']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Метод регрессии Лассо\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для Lasso:\n", + "{'alpha': 0.01, 'fit_intercept': False}\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import Lasso\n", + "\n", + "param_grid_lasso = {\n", + " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", + " 'fit_intercept': [True, False],\n", + "}\n", + "\n", + "# Создание объекта GridSearchCV\n", + "grid_search_lasso = GridSearchCV(\n", + " estimator=Lasso(), \n", + " param_grid=param_grid_lasso, \n", + " cv=5, \n", + " scoring='neg_mean_squared_error', \n", + " n_jobs=-1 \n", + ")\n", + "\n", + "grid_search_lasso.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для Lasso:\")\n", + "print(grid_search_lasso.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Метод градиентного бустинга" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n", + "1215 fits failed out of a total of 3645.\n", + "The score on these train-test partitions for these parameters will be set to nan.\n", + "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", + "\n", + "Below are more details about the failures:\n", + "--------------------------------------------------------------------------------\n", + "978 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", + " estimator._validate_params()\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "237 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", + " estimator._validate_params()\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "\n", + " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n", + " _data = np.array(data, dtype=dtype, copy=copy,\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.18767441 -0.15799837 -0.13080278\n", + " -0.18762913 -0.15792709 -0.13056114 -0.18792038 -0.15737146 -0.130218\n", + " -0.18725961 -0.157967 -0.13047453 -0.18766583 -0.15779565 -0.13094863\n", + " -0.18798705 -0.15693978 -0.13061215 -0.18766317 -0.15746848 -0.13072918\n", + " -0.18864158 -0.15666133 -0.13095037 -0.18817206 -0.15805489 -0.13086126\n", + " -0.18707465 -0.15864932 -0.13104947 -0.18818902 -0.15828572 -0.13063871\n", + " -0.18701628 -0.15853864 -0.13019458 -0.18740927 -0.15836397 -0.13065455\n", + " -0.18768748 -0.15828297 -0.1309458 -0.18845004 -0.15696395 -0.13023062\n", + " -0.18754854 -0.15899615 -0.13061707 -0.18831427 -0.15819939 -0.13096524\n", + " -0.18662963 -0.15815869 -0.13089186 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.1758914 -0.1442684 -0.12093344 -0.1758927 -0.14423731 -0.12084543\n", + " -0.17573339 -0.14419842 -0.12076166 -0.17512045 -0.14435454 -0.1207299\n", + " -0.17669645 -0.14397965 -0.12087019 -0.17605424 -0.1438664 -0.12091068\n", + " -0.17582192 -0.1443651 -0.12097165 -0.17588422 -0.14421003 -0.12081764\n", + " -0.17522742 -0.14424357 -0.12086484 -0.17530986 -0.14433713 -0.12091757\n", + " -0.17565647 -0.14408902 -0.12075918 -0.17561884 -0.14426355 -0.12094066\n", + " -0.17522371 -0.1439869 -0.12099023 -0.17619772 -0.14396131 -0.12079667\n", + " -0.17710789 -0.1448419 -0.12087822 -0.17608534 -0.14416684 -0.12087865\n", + " -0.1754675 -0.1442258 -0.12068226 -0.17611334 -0.14433552 -0.12093556\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.16938321 -0.13763002 -0.11703902\n", + " -0.16953091 -0.13736586 -0.11695779 -0.16881837 -0.1375676 -0.11694438\n", + " -0.16927898 -0.13748177 -0.11689982 -0.16921265 -0.13757375 -0.11682524\n", + " -0.16915872 -0.13727377 -0.11694336 -0.16939766 -0.13734972 -0.1167447\n", + " -0.16924214 -0.1373768 -0.11674816 -0.16918278 -0.13746085 -0.1169816\n", + " -0.16927003 -0.13740063 -0.1169564 -0.16916501 -0.13752074 -0.11687641\n", + " -0.16928973 -0.13751536 -0.11697948 -0.16934836 -0.13727436 -0.11693615\n", + " -0.16912453 -0.13748699 -0.11693425 -0.1692788 -0.13750784 -0.11694655\n", + " -0.16919354 -0.13747437 -0.11708782 -0.16940009 -0.13757749 -0.11700586\n", + " -0.1692801 -0.13725384 -0.11684394 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11606052 -0.1140225 -0.11403709 -0.11627212 -0.1139982 -0.11402075\n", + " -0.11613561 -0.11407941 -0.11420487 -0.11666225 -0.11462523 -0.11431901\n", + " -0.11604817 -0.11456211 -0.11392092 -0.11609343 -0.11394228 -0.11414071\n", + " -0.11611685 -0.11420178 -0.11405459 -0.11594404 -0.11408614 -0.11391662\n", + " -0.11590886 -0.11396465 -0.11389125 -0.11616694 -0.11441846 -0.11417015\n", + " -0.11617368 -0.11429765 -0.1139636 -0.11616763 -0.11433984 -0.11412121\n", + " -0.11625618 -0.11402999 -0.11419791 -0.11613603 -0.114206 -0.11423922\n", + " -0.1160801 -0.11431896 -0.11416734 -0.11608923 -0.11455498 -0.11417448\n", + " -0.11605165 -0.11427773 -0.11392205 -0.11606243 -0.11408421 -0.11395292\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11281447 -0.11245904 -0.11308822\n", + " -0.11256366 -0.11230094 -0.1130767 -0.11282651 -0.1121034 -0.11283479\n", + " -0.11260704 -0.1125136 -0.11288977 -0.11278304 -0.11242278 -0.11268564\n", + " -0.11263359 -0.11236227 -0.11329411 -0.11231603 -0.1124533 -0.11278826\n", + " -0.11291545 -0.11241223 -0.11250702 -0.11246481 -0.11228665 -0.11348916\n", + " -0.11250694 -0.11250274 -0.11298019 -0.11277323 -0.11248601 -0.11301753\n", + " -0.11259486 -0.1124685 -0.11285441 -0.11274424 -0.11232891 -0.11316456\n", + " -0.11274575 -0.11256149 -0.11252293 -0.11293524 -0.11261757 -0.11305628\n", + " -0.11253063 -0.11237109 -0.11278518 -0.1124074 -0.11276905 -0.11296684\n", + " -0.11258689 -0.11228467 -0.11331342 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11292265 -0.11395193 -0.11564599 -0.11244356 -0.11338947 -0.1148266\n", + " -0.11295702 -0.11353862 -0.11510521 -0.11244347 -0.11387967 -0.11512396\n", + " -0.11269802 -0.11364442 -0.1151339 -0.11238356 -0.11364301 -0.11496543\n", + " -0.11229193 -0.11340926 -0.11550744 -0.11215818 -0.11367944 -0.11552889\n", + " -0.11240305 -0.11352309 -0.115412 -0.1128402 -0.11338749 -0.1153551\n", + " -0.11250042 -0.11347275 -0.11548445 -0.11271132 -0.11377527 -0.11558066\n", + " -0.11318598 -0.11325792 -0.11499103 -0.11253099 -0.1129829 -0.11530949\n", + " -0.11239074 -0.11329625 -0.11544761 -0.11262484 -0.11323392 -0.1151936\n", + " -0.11253889 -0.11382403 -0.11511129 -0.11250854 -0.11339898 -0.11536332\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11542253 -0.11498664 -0.11428517\n", + " -0.11503783 -0.11473447 -0.11458687 -0.11483866 -0.1154254 -0.11479037\n", + " -0.11533015 -0.11515195 -0.11460571 -0.11563491 -0.11433835 -0.11437413\n", + " -0.11510849 -0.11472156 -0.11516494 -0.11545009 -0.115001 -0.11479743\n", + " -0.11461761 -0.11537461 -0.11497109 -0.1155148 -0.11567353 -0.11431184\n", + " -0.11546067 -0.11462564 -0.11450721 -0.11511 -0.11487988 -0.11466523\n", + " -0.11585756 -0.11462611 -0.11433121 -0.11538152 -0.11463425 -0.11527088\n", + " -0.11509145 -0.11493588 -0.11484324 -0.11528905 -0.11426327 -0.11476508\n", + " -0.11499562 -0.11451299 -0.11466765 -0.11525918 -0.11469718 -0.11476983\n", + " -0.11467865 -0.1145067 -0.11479425 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11352917 -0.1145882 -0.11643688 -0.11418115 -0.11442858 -0.11635549\n", + " -0.11408502 -0.11458383 -0.1163013 -0.1135842 -0.11453566 -0.11575264\n", + " -0.11341863 -0.11481638 -0.11635685 -0.1132144 -0.11438018 -0.11666005\n", + " -0.11311482 -0.11500883 -0.11594984 -0.11409228 -0.11464061 -0.1158012\n", + " -0.11389399 -0.11454081 -0.1157428 -0.11333869 -0.11438896 -0.11676006\n", + " -0.11382523 -0.11443669 -0.11606569 -0.11424726 -0.11464652 -0.11608159\n", + " -0.11396605 -0.11473188 -0.1167532 -0.1136805 -0.11455875 -0.11615814\n", + " -0.11372286 -0.11442829 -0.11590895 -0.1136509 -0.11368863 -0.11660073\n", + " -0.1136605 -0.1141187 -0.11613806 -0.11326355 -0.11427399 -0.11676148\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11573534 -0.11897501 -0.1226239\n", + " -0.1162633 -0.11939573 -0.12255715 -0.11636411 -0.11878021 -0.12306277\n", + " -0.11535113 -0.11813967 -0.1230085 -0.11594119 -0.11812955 -0.12217928\n", + " -0.11523023 -0.11843291 -0.12228252 -0.1159457 -0.11840108 -0.12181337\n", + " -0.11600134 -0.11790484 -0.12203724 -0.11579998 -0.11787918 -0.12317219\n", + " -0.11578704 -0.11837798 -0.12379234 -0.1155279 -0.11865384 -0.12319867\n", + " -0.11597008 -0.11886814 -0.12291788 -0.1162282 -0.11918752 -0.12363613\n", + " -0.11571473 -0.11805225 -0.12250506 -0.11640247 -0.11823175 -0.1226976\n", + " -0.11571549 -0.11813327 -0.12229009 -0.11621545 -0.11793769 -0.1229533\n", + " -0.11528287 -0.1183919 -0.12121653]\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для Gradient Boosting:\n", + "{'learning_rate': 0.1, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "param_grid_gb = {\n", + " 'n_estimators': [50, 100, 200],\n", + " 'learning_rate': [0.01, 0.1, 0.2],\n", + " 'max_depth': [3, 5, 7],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'min_samples_leaf': [1, 2, 4],\n", + " 'max_features': ['auto', 'sqrt', 'log2']\n", + "}\n", + "\n", + "grid_search_gb = GridSearchCV(\n", + " estimator=GradientBoostingRegressor(),\n", + " param_grid=param_grid_gb,\n", + " cv=5,\n", + " scoring='neg_mean_squared_error',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "grid_search_gb.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для Gradient Boosting:\")\n", + "print(grid_search_gb.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Метод k-ближайших соседей" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для k-Nearest Neighbors:\n", + "{'algorithm': 'ball_tree', 'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsRegressor\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "param_grid_knn = {\n", + " 'n_neighbors': [3, 5, 7, 10],\n", + " 'weights': ['uniform', 'distance'],\n", + " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", + " 'p': [1, 2]\n", + "}\n", + "\n", + "grid_search_knn = GridSearchCV(\n", + " estimator=KNeighborsRegressor(),\n", + " param_grid=param_grid_knn,\n", + " cv=5,\n", + " scoring='neg_mean_squared_error',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "grid_search_knn.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для k-Nearest Neighbors:\")\n", + "print(grid_search_knn.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Предсказание на тестовой выборке" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x_test)\n", + "y_pred_forest = model_forest.predict(x_test)\n", + "y_pred_lasso = model_lasso.predict(x_test)\n", + "y_pred_gb = model_gb.predict(x_test)\n", + "y_pred_neighbors = model_knn.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Оценка качества модели" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.\tMSE (Mean Squared Error)\n", + "Среднее значение квадратов разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error (MSE):\n", + "k-NN: \t\t\t0.213\n", + "Random Forest: \t\t0.118\n", + "Lasso: \t\t\t0.166\n", + "Gradient Boosting: \t0.113\n", + "k-Nearest Neighbors: \t0.326\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "import numpy as np\n", + "\n", + "mse1 = mean_squared_error(y_test, y_pred)\n", + "mse2 = mean_squared_error(y_test, y_pred_forest)\n", + "mse3 = mean_squared_error(y_test, y_pred_lasso)\n", + "mse4 = mean_squared_error(y_test, y_pred_gb)\n", + "mse5 = mean_squared_error(y_test, y_pred_neighbors)\n", + "\n", + "mse1_rounded = round(mse1, 3)\n", + "mse2_rounded = round(mse2, 3)\n", + "mse3_rounded = round(mse3, 3)\n", + "mse4_rounded = round(mse4, 3)\n", + "mse5_rounded = round(mse5, 3)\n", + "\n", + "print(\"Mean Squared Error (MSE):\")\n", + "print(f\"k-NN: \\t\\t\\t{mse1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{mse2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{mse3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{mse4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{mse5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2.\tMAE\n", + "Среднее значение абсолютных разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Absolute Error (MAE):\n", + "k-NN: \t\t\t0.213\n", + "Random Forest: \t\t0.238\n", + "Lasso: \t\t\t0.366\n", + "Gradient Boosting: \t0.246\n", + "k-Nearest Neighbors: \t0.485\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "mae1 = round(mean_absolute_error(y_test, y_pred),3)\n", + "mae2 = round(mean_absolute_error(y_test, y_pred_forest),3)\n", + "mae3 = round(mean_absolute_error(y_test, y_pred_lasso),3)\n", + "mae4 = round(mean_absolute_error(y_test, y_pred_gb),3)\n", + "mae5 = round(mean_absolute_error(y_test, y_pred_neighbors),3)\n", + "print(\"Mean Absolute Error (MAE):\")\n", + "print(f\"k-NN: \\t\\t\\t{mae1}\")\n", + "print(f\"Random Forest: \\t\\t{mae2}\")\n", + "print(f\"Lasso: \\t\\t\\t{mae3}\")\n", + "print(f\"Gradient Boosting: \\t{mae4}\")\n", + "print(f\"k-Nearest Neighbors: \\t{mae5}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3.\tR-squared\n", + "Мера, показывающая, насколько хорошо модель объясняет изменчивость данных. Значение находится в диапазоне от 0 до 1, где 1 — идеальное соответствие, а 0 — модель не объясняет данные." + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R² (R-squared): 0.127933821917115\n", + "\n", + "R² (R-squared):\n", + "k-NN: \t\t\t0.128\n", + "Random Forest: \t\t0.515\n", + "Lasso: \t\t\t0.319\n", + "Gradient Boosting: \t0.537\n", + "k-Nearest Neighbors: \t-0.337\n" + ] + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2 = r2_score(y_test, y_pred)\n", + "print(f\"R² (R-squared): {r2}\")\n", + "\n", + "r2_1 = r2_score(y_test, y_pred)\n", + "r2_2 = r2_score(y_test, y_pred_forest)\n", + "r2_3 = r2_score(y_test, y_pred_lasso)\n", + "r2_4 = r2_score(y_test, y_pred_gb)\n", + "r2_5 = r2_score(y_test, y_pred_neighbors)\n", + "\n", + "r2_1_rounded = round(r2_1, 3)\n", + "r2_2_rounded = round(r2_2, 3)\n", + "r2_3_rounded = round(r2_3, 3)\n", + "r2_4_rounded = round(r2_4, 3)\n", + "r2_5_rounded = round(r2_5, 3)\n", + "\n", + "print(\"\\nR² (R-squared):\")\n", + "print(f\"k-NN: \\t\\t\\t{r2_1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{r2_2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{r2_3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{r2_4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{r2_5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4.\tRMSE\n", + " Среднее отклонение предсказаний от реальных данных. Чем меньше модуль, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Root Mean Squared Error (RMSE):\n", + "k-NN: \t\t\t0.461\n", + "Random Forest: \t\t0.344\n", + "Lasso: \t\t\t0.407\n", + "Gradient Boosting: \t0.336\n", + "k-Nearest Neighbors: \t0.571\n" + ] + } + ], + "source": [ + "rmse1 = np.sqrt(mse1)\n", + "rmse2 = np.sqrt(mse2)\n", + "rmse3 = np.sqrt(mse3)\n", + "rmse4 = np.sqrt(mse4)\n", + "rmse5 = np.sqrt(mse5)\n", + "\n", + "rmse1_rounded = round(rmse1, 3)\n", + "rmse2_rounded = round(rmse2, 3)\n", + "rmse3_rounded = round(rmse3, 3)\n", + "rmse4_rounded = round(rmse4, 3)\n", + "rmse5_rounded = round(rmse5, 3)\n", + "\n", + "print(\"Root Mean Squared Error (RMSE):\")\n", + "print(f\"k-NN: \\t\\t\\t{rmse1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{rmse2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{rmse3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{rmse4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{rmse5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Лучший результат – градиентный бустинг и случайный лес.\n", + "Положительные результаты по всем критериям получил случайный лес. Три из четырех положительных результата у градиентного бустинга. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Значит, случайный лес – наиболее точная и устойчивая стратегия обучения модели. Итоговая модель – model_forest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Также, с помощью применение важности признаков (feature importance) на Случайном лесе, мы вывели основные факторы, вызывающие депрессию:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Feature Importance\n", + "13 Have you ever had suicidal thoughts ? 0.300542\n", + "5 Academic Pressure 0.134276\n", + "0 id 0.087970\n", + "7 CGPA 0.079078\n", + "2 Age 0.066613\n", + "15 Financial Stress 0.066330\n", + "3 City 0.059293\n", + "14 Work/Study Hours 0.052275\n", + "12 Degree 0.049539\n", + "8 Study Satisfaction 0.032944\n", + "11 Dietary Habits 0.026140\n", + "10 Sleep Duration 0.024435\n", + "16 Family History of Mental Illness 0.010547\n", + "1 Gender 0.009627\n", + "4 Profession 0.000372\n", + "9 Job Satisfaction 0.000017\n", + "6 Work Pressure 0.000003\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "model_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "model_rf.fit(x_train, y_train)\n", + "\n", + "feature_importances = model_rf.feature_importances_\n", + "\n", + "import pandas as pd\n", + "feature_importance_df = pd.DataFrame({\n", + " 'Feature': x.columns,\n", + " 'Importance': feature_importances\n", + "}).sort_values(by='Importance', ascending=False)\n", + "\n", + "print(feature_importance_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scripts", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 688bfe2e9beae86d59743968c3b874c54296c2be Mon Sep 17 00:00:00 2001 From: dex_moth Date: Sat, 21 Dec 2024 00:21:03 +0400 Subject: [PATCH 4/7] =?UTF-8?q?=D0=A3=D0=B4=D0=B0=D0=BB=D0=B8=D1=82=D1=8C?= =?UTF-8?q?=20lab=5F4/Lab4.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_4/Lab4.ipynb | 911 ----------------------------------------------- 1 file changed, 911 deletions(-) delete mode 100644 lab_4/Lab4.ipynb diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb deleted file mode 100644 index 0b8116e..0000000 --- a/lab_4/Lab4.ipynb +++ /dev/null @@ -1,911 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['id', 'Gender', 'Age', 'City', 'Profession', 'Academic Pressure',\n", - " 'Work Pressure', 'CGPA', 'Study Satisfaction', 'Job Satisfaction',\n", - " 'Sleep Duration', 'Dietary Habits', 'Degree',\n", - " 'Have you ever had suicidal thoughts ?', 'Work/Study Hours',\n", - " 'Financial Stress', 'Family History of Mental Illness', 'Depression'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from matplotlib.ticker import FuncFormatter\n", - "\n", - "df = pd.read_csv(\".//csv//Student Depression Dataset.csv\")\n", - "print(df.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " id Gender Age City Profession Academic Pressure \\\n", - "0 2 Male 33.0 Visakhapatnam Student 5.0 \n", - "1 8 Female 24.0 Bangalore Student 2.0 \n", - "2 26 Male 31.0 Srinagar Student 3.0 \n", - "3 30 Female 28.0 Varanasi Student 3.0 \n", - "4 32 Female 25.0 Jaipur Student 4.0 \n", - "\n", - " Work Pressure CGPA Study Satisfaction Job Satisfaction \\\n", - "0 0.0 8.97 2.0 0.0 \n", - "1 0.0 5.90 5.0 0.0 \n", - "2 0.0 7.03 5.0 0.0 \n", - "3 0.0 5.59 2.0 0.0 \n", - "4 0.0 8.13 3.0 0.0 \n", - "\n", - " Sleep Duration Dietary Habits Degree \\\n", - "0 5-6 hours Healthy B.Pharm \n", - "1 5-6 hours Moderate BSc \n", - "2 Less than 5 hours Healthy BA \n", - "3 7-8 hours Moderate BCA \n", - "4 5-6 hours Moderate M.Tech \n", - "\n", - " Have you ever had suicidal thoughts ? Work/Study Hours Financial Stress \\\n", - "0 Yes 3.0 1.0 \n", - "1 No 3.0 2.0 \n", - "2 No 9.0 1.0 \n", - "3 Yes 4.0 5.0 \n", - "4 Yes 1.0 1.0 \n", - "\n", - " Family History of Mental Illness Depression \n", - "0 No 1 \n", - "1 Yes 0 \n", - "2 Yes 0 \n", - "3 Yes 1 \n", - "4 No 0 \n" - ] - } - ], - "source": [ - "print(df.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Бизнес-цель исследования\n", - "Разработать и внедрить систему прогнозирования уровня депрессии среди обучающихся, которая позволит выявить группы риска на ранних этапах. Результаты исследования могут быть полезны психологам, педагогам и администрации учебных заведений.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Описание набора данных для анализа\n", - "Набор данных содержит информацию о психологическом состоянии обучающихся и включает следующие поля:\n", - "- id – идентификатор, число\n", - "- Gender – пол, строка\n", - "- Age – возраст, дробное число\n", - "- City – город, строка\n", - "- Profession – профессия, строка\n", - "- Academic Pressure – академическое давление, дробное число (от 1.00 до 5.00)\n", - "- Work Pressure – рабочее давление, дробное число (от 1.00 до 5.00)\n", - "- CGPA – средний балл (GPA), дробное число\n", - "- Study Satisfaction – удовлетворенность учебой, дробное число (от 1.00 до 5.00)\n", - "- Job Satisfaction – удовлетворенность работой, дробное число (от 1.00 до 5.00)\n", - "- Sleep Duration – продолжительность сна, строка\n", - "- Dietary Habits – пищевые привычки, строка\n", - "- Degree – степень (образование), строка\n", - "- Have you ever had suicidal thoughts? – Были ли у вас когда-либо суицидальные мысли? строка (yes/no)\n", - "- Work/Study Hours – часы работы/учебы, дробное число\n", - "- Financial Stress – финансовый стресс, дробное число (от 1.00 до 5.00)\n", - "- Family History of Mental Illness – семейный анамнез психических заболеваний, строка (yes/no)\n", - "- Depression – депрессия, булевое значение (1/0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Обработка данных" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id 0\n", - "Gender 0\n", - "Age 0\n", - "City 0\n", - "Profession 0\n", - "Academic Pressure 0\n", - "Work Pressure 0\n", - "CGPA 0\n", - "Study Satisfaction 0\n", - "Job Satisfaction 0\n", - "Sleep Duration 0\n", - "Dietary Habits 0\n", - "Degree 0\n", - "Have you ever had suicidal thoughts ? 0\n", - "Work/Study Hours 0\n", - "Financial Stress 3\n", - "Family History of Mental Illness 0\n", - "Depression 0\n", - "dtype: int64" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.isnull().sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "df.dropna(subset=['Financial Stress'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "features = ['Age', 'Academic Pressure', 'Work Pressure', 'CGPA', 'Study Satisfaction', \n", - " 'Job Satisfaction', 'Work/Study Hours', 'Financial Stress', 'Depression']\n", - "\n", - "plt.figure(figsize=(15, 10))\n", - "for i, feature in enumerate(features, 1):\n", - " plt.subplot(3, 3, i)\n", - " sns.boxplot(y=df[feature], color='skyblue')\n", - " plt.title(f'Boxplot of {feature}')\n", - " plt.ylabel(feature)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "В Age много выбросов. Сбалансируем данные" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Q1 = df['Age'].quantile(0.25)\n", - "Q3 = df['Age'].quantile(0.75)\n", - "IQR = Q3 - Q1\n", - "\n", - "threshold = 1.5 * IQR\n", - "outliers = (df['Age'] < (Q1 - threshold)) | (df['Age'] > (Q3 + threshold))\n", - "\n", - "median_rating = df['Age'].median()\n", - "df.loc[outliers, 'Age'] = median_rating\n", - "\n", - "plt.figure(figsize=(8, 6))\n", - "sns.boxplot(y=df['Age'], color='skyblue')\n", - "plt.title('Boxplot of Age')\n", - "plt.ylabel('Age')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Конструирование признаков с помощью меток" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import LabelEncoder\n", - "\n", - "le = LabelEncoder()\n", - "df['Gender'] = le.fit_transform(df['Gender'])\n", - "df['City'] = le.fit_transform(df['City'])\n", - "df['Dietary Habits'] = le.fit_transform(df['Dietary Habits'])\n", - "df['Degree'] = le.fit_transform(df['Degree'])\n", - "df['Have you ever had suicidal thoughts ?'] = le.fit_transform(df['Have you ever had suicidal thoughts ?'])\n", - "df['Sleep Duration'] = le.fit_transform(df['Sleep Duration'])\n", - "df['Profession'] = le.fit_transform(df['Profession'])\n", - "df['Study Satisfaction'] = le.fit_transform(df['Study Satisfaction'])\n", - "df['Family History of Mental Illness'] = le.fit_transform(df['Family History of Mental Illness'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "разделение на признаки и целевую переменную" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "x = df.drop('Depression', axis=1)\n", - "y = df['Depression']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1) Метод регрессии Лассо\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Лучшие гиперпараметры для Lasso:\n", - "{'alpha': 0.01, 'fit_intercept': False}\n" - ] - } - ], - "source": [ - "from sklearn.linear_model import Lasso\n", - "\n", - "param_grid_lasso = {\n", - " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", - " 'fit_intercept': [True, False],\n", - "}\n", - "\n", - "# Создание объекта GridSearchCV\n", - "grid_search_lasso = GridSearchCV(\n", - " estimator=Lasso(), \n", - " param_grid=param_grid_lasso, \n", - " cv=5, \n", - " scoring='neg_mean_squared_error', \n", - " n_jobs=-1 \n", - ")\n", - "\n", - "grid_search_lasso.fit(x_train, y_train)\n", - "\n", - "# Вывод лучших гиперпараметров\n", - "print(\"Лучшие гиперпараметры для Lasso:\")\n", - "print(grid_search_lasso.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2) Метод градиентного бустинга" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n", - "1215 fits failed out of a total of 3645.\n", - "The score on these train-test partitions for these parameters will be set to nan.\n", - "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", - "\n", - "Below are more details about the failures:\n", - "--------------------------------------------------------------------------------\n", - "978 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", - " estimator._validate_params()\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", - " validate_parameter_constraints(\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "237 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", - " estimator._validate_params()\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", - " validate_parameter_constraints(\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", - "\n", - " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n", - " _data = np.array(data, dtype=dtype, copy=copy,\n", - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.18767441 -0.15799837 -0.13080278\n", - " -0.18762913 -0.15792709 -0.13056114 -0.18792038 -0.15737146 -0.130218\n", - " -0.18725961 -0.157967 -0.13047453 -0.18766583 -0.15779565 -0.13094863\n", - " -0.18798705 -0.15693978 -0.13061215 -0.18766317 -0.15746848 -0.13072918\n", - " -0.18864158 -0.15666133 -0.13095037 -0.18817206 -0.15805489 -0.13086126\n", - " -0.18707465 -0.15864932 -0.13104947 -0.18818902 -0.15828572 -0.13063871\n", - 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" -0.1754675 -0.1442258 -0.12068226 -0.17611334 -0.14433552 -0.12093556\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.16938321 -0.13763002 -0.11703902\n", - " -0.16953091 -0.13736586 -0.11695779 -0.16881837 -0.1375676 -0.11694438\n", - " -0.16927898 -0.13748177 -0.11689982 -0.16921265 -0.13757375 -0.11682524\n", - " -0.16915872 -0.13727377 -0.11694336 -0.16939766 -0.13734972 -0.1167447\n", - " -0.16924214 -0.1373768 -0.11674816 -0.16918278 -0.13746085 -0.1169816\n", - " -0.16927003 -0.13740063 -0.1169564 -0.16916501 -0.13752074 -0.11687641\n", - " -0.16928973 -0.13751536 -0.11697948 -0.16934836 -0.13727436 -0.11693615\n", - " -0.16912453 -0.13748699 -0.11693425 -0.1692788 -0.13750784 -0.11694655\n", - " -0.16919354 -0.13747437 -0.11708782 -0.16940009 -0.13757749 -0.11700586\n", - " -0.1692801 -0.13725384 -0.11684394 nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11606052 -0.1140225 -0.11403709 -0.11627212 -0.1139982 -0.11402075\n", - " -0.11613561 -0.11407941 -0.11420487 -0.11666225 -0.11462523 -0.11431901\n", - " -0.11604817 -0.11456211 -0.11392092 -0.11609343 -0.11394228 -0.11414071\n", - " -0.11611685 -0.11420178 -0.11405459 -0.11594404 -0.11408614 -0.11391662\n", - " -0.11590886 -0.11396465 -0.11389125 -0.11616694 -0.11441846 -0.11417015\n", - " -0.11617368 -0.11429765 -0.1139636 -0.11616763 -0.11433984 -0.11412121\n", - " -0.11625618 -0.11402999 -0.11419791 -0.11613603 -0.114206 -0.11423922\n", - " -0.1160801 -0.11431896 -0.11416734 -0.11608923 -0.11455498 -0.11417448\n", - " -0.11605165 -0.11427773 -0.11392205 -0.11606243 -0.11408421 -0.11395292\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11281447 -0.11245904 -0.11308822\n", - " -0.11256366 -0.11230094 -0.1130767 -0.11282651 -0.1121034 -0.11283479\n", - " -0.11260704 -0.1125136 -0.11288977 -0.11278304 -0.11242278 -0.11268564\n", - " -0.11263359 -0.11236227 -0.11329411 -0.11231603 -0.1124533 -0.11278826\n", - " -0.11291545 -0.11241223 -0.11250702 -0.11246481 -0.11228665 -0.11348916\n", - " -0.11250694 -0.11250274 -0.11298019 -0.11277323 -0.11248601 -0.11301753\n", - " -0.11259486 -0.1124685 -0.11285441 -0.11274424 -0.11232891 -0.11316456\n", - " -0.11274575 -0.11256149 -0.11252293 -0.11293524 -0.11261757 -0.11305628\n", - " -0.11253063 -0.11237109 -0.11278518 -0.1124074 -0.11276905 -0.11296684\n", - " -0.11258689 -0.11228467 -0.11331342 nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11292265 -0.11395193 -0.11564599 -0.11244356 -0.11338947 -0.1148266\n", - " -0.11295702 -0.11353862 -0.11510521 -0.11244347 -0.11387967 -0.11512396\n", - " -0.11269802 -0.11364442 -0.1151339 -0.11238356 -0.11364301 -0.11496543\n", - " -0.11229193 -0.11340926 -0.11550744 -0.11215818 -0.11367944 -0.11552889\n", - " -0.11240305 -0.11352309 -0.115412 -0.1128402 -0.11338749 -0.1153551\n", - " -0.11250042 -0.11347275 -0.11548445 -0.11271132 -0.11377527 -0.11558066\n", - " -0.11318598 -0.11325792 -0.11499103 -0.11253099 -0.1129829 -0.11530949\n", - " -0.11239074 -0.11329625 -0.11544761 -0.11262484 -0.11323392 -0.1151936\n", - " -0.11253889 -0.11382403 -0.11511129 -0.11250854 -0.11339898 -0.11536332\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11542253 -0.11498664 -0.11428517\n", - " -0.11503783 -0.11473447 -0.11458687 -0.11483866 -0.1154254 -0.11479037\n", - " -0.11533015 -0.11515195 -0.11460571 -0.11563491 -0.11433835 -0.11437413\n", - " -0.11510849 -0.11472156 -0.11516494 -0.11545009 -0.115001 -0.11479743\n", - " -0.11461761 -0.11537461 -0.11497109 -0.1155148 -0.11567353 -0.11431184\n", - " -0.11546067 -0.11462564 -0.11450721 -0.11511 -0.11487988 -0.11466523\n", - " -0.11585756 -0.11462611 -0.11433121 -0.11538152 -0.11463425 -0.11527088\n", - " -0.11509145 -0.11493588 -0.11484324 -0.11528905 -0.11426327 -0.11476508\n", - " -0.11499562 -0.11451299 -0.11466765 -0.11525918 -0.11469718 -0.11476983\n", - " -0.11467865 -0.1145067 -0.11479425 nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11352917 -0.1145882 -0.11643688 -0.11418115 -0.11442858 -0.11635549\n", - " -0.11408502 -0.11458383 -0.1163013 -0.1135842 -0.11453566 -0.11575264\n", - " -0.11341863 -0.11481638 -0.11635685 -0.1132144 -0.11438018 -0.11666005\n", - " -0.11311482 -0.11500883 -0.11594984 -0.11409228 -0.11464061 -0.1158012\n", - " -0.11389399 -0.11454081 -0.1157428 -0.11333869 -0.11438896 -0.11676006\n", - " -0.11382523 -0.11443669 -0.11606569 -0.11424726 -0.11464652 -0.11608159\n", - " -0.11396605 -0.11473188 -0.1167532 -0.1136805 -0.11455875 -0.11615814\n", - " -0.11372286 -0.11442829 -0.11590895 -0.1136509 -0.11368863 -0.11660073\n", - " -0.1136605 -0.1141187 -0.11613806 -0.11326355 -0.11427399 -0.11676148\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11573534 -0.11897501 -0.1226239\n", - " -0.1162633 -0.11939573 -0.12255715 -0.11636411 -0.11878021 -0.12306277\n", - " -0.11535113 -0.11813967 -0.1230085 -0.11594119 -0.11812955 -0.12217928\n", - " -0.11523023 -0.11843291 -0.12228252 -0.1159457 -0.11840108 -0.12181337\n", - " -0.11600134 -0.11790484 -0.12203724 -0.11579998 -0.11787918 -0.12317219\n", - " -0.11578704 -0.11837798 -0.12379234 -0.1155279 -0.11865384 -0.12319867\n", - " -0.11597008 -0.11886814 -0.12291788 -0.1162282 -0.11918752 -0.12363613\n", - " -0.11571473 -0.11805225 -0.12250506 -0.11640247 -0.11823175 -0.1226976\n", - " -0.11571549 -0.11813327 -0.12229009 -0.11621545 -0.11793769 -0.1229533\n", - " -0.11528287 -0.1183919 -0.12121653]\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Лучшие гиперпараметры для Gradient Boosting:\n", - "{'learning_rate': 0.1, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n" - ] - } - ], - "source": [ - "\n", - "from sklearn.ensemble import GradientBoostingRegressor\n", - "\n", - "param_grid_gb = {\n", - " 'n_estimators': [50, 100, 200],\n", - " 'learning_rate': [0.01, 0.1, 0.2],\n", - " 'max_depth': [3, 5, 7],\n", - " 'min_samples_split': [2, 5, 10],\n", - " 'min_samples_leaf': [1, 2, 4],\n", - " 'max_features': ['auto', 'sqrt', 'log2']\n", - "}\n", - "\n", - "grid_search_gb = GridSearchCV(\n", - " estimator=GradientBoostingRegressor(),\n", - " param_grid=param_grid_gb,\n", - " cv=5,\n", - " scoring='neg_mean_squared_error',\n", - " n_jobs=-1\n", - ")\n", - "\n", - "grid_search_gb.fit(x_train, y_train)\n", - "\n", - "# Вывод лучших гиперпараметров\n", - "print(\"Лучшие гиперпараметры для Gradient Boosting:\")\n", - "print(grid_search_gb.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3) Метод k-ближайших соседей" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Лучшие гиперпараметры для k-Nearest Neighbors:\n", - "{'algorithm': 'ball_tree', 'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n" - ] - } - ], - "source": [ - "from sklearn.neighbors import KNeighborsRegressor\n", - "from sklearn.model_selection import GridSearchCV\n", - "\n", - "param_grid_knn = {\n", - " 'n_neighbors': [3, 5, 7, 10],\n", - " 'weights': ['uniform', 'distance'],\n", - " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", - " 'p': [1, 2]\n", - "}\n", - "\n", - "grid_search_knn = GridSearchCV(\n", - " estimator=KNeighborsRegressor(),\n", - " param_grid=param_grid_knn,\n", - " cv=5,\n", - " scoring='neg_mean_squared_error',\n", - " n_jobs=-1\n", - ")\n", - "\n", - "grid_search_knn.fit(x_train, y_train)\n", - "\n", - "# Вывод лучших гиперпараметров\n", - "print(\"Лучшие гиперпараметры для k-Nearest Neighbors:\")\n", - "print(grid_search_knn.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Предсказание на тестовой выборке" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = model.predict(x_test)\n", - "y_pred_forest = model_forest.predict(x_test)\n", - "y_pred_lasso = model_lasso.predict(x_test)\n", - "y_pred_gb = model_gb.predict(x_test)\n", - "y_pred_neighbors = model_knn.predict(x_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Оценка качества модели" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1.\tMSE (Mean Squared Error)\n", - "Среднее значение квадратов разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Squared Error (MSE):\n", - "k-NN: \t\t\t0.213\n", - "Random Forest: \t\t0.118\n", - "Lasso: \t\t\t0.166\n", - "Gradient Boosting: \t0.113\n", - "k-Nearest Neighbors: \t0.326\n" - ] - } - ], - "source": [ - "from sklearn.metrics import mean_squared_error\n", - "import numpy as np\n", - "\n", - "mse1 = mean_squared_error(y_test, y_pred)\n", - "mse2 = mean_squared_error(y_test, y_pred_forest)\n", - "mse3 = mean_squared_error(y_test, y_pred_lasso)\n", - "mse4 = mean_squared_error(y_test, y_pred_gb)\n", - "mse5 = mean_squared_error(y_test, y_pred_neighbors)\n", - "\n", - "mse1_rounded = round(mse1, 3)\n", - "mse2_rounded = round(mse2, 3)\n", - "mse3_rounded = round(mse3, 3)\n", - "mse4_rounded = round(mse4, 3)\n", - "mse5_rounded = round(mse5, 3)\n", - "\n", - "print(\"Mean Squared Error (MSE):\")\n", - "print(f\"k-NN: \\t\\t\\t{mse1_rounded}\")\n", - "print(f\"Random Forest: \\t\\t{mse2_rounded}\")\n", - "print(f\"Lasso: \\t\\t\\t{mse3_rounded}\")\n", - "print(f\"Gradient Boosting: \\t{mse4_rounded}\")\n", - "print(f\"k-Nearest Neighbors: \\t{mse5_rounded}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2.\tMAE\n", - "Среднее значение абсолютных разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean Absolute Error (MAE):\n", - "k-NN: \t\t\t0.213\n", - "Random Forest: \t\t0.238\n", - "Lasso: \t\t\t0.366\n", - "Gradient Boosting: \t0.246\n", - "k-Nearest Neighbors: \t0.485\n" - ] - } - ], - "source": [ - "from sklearn.metrics import mean_absolute_error\n", - "\n", - "mae1 = round(mean_absolute_error(y_test, y_pred),3)\n", - "mae2 = round(mean_absolute_error(y_test, y_pred_forest),3)\n", - "mae3 = round(mean_absolute_error(y_test, y_pred_lasso),3)\n", - "mae4 = round(mean_absolute_error(y_test, y_pred_gb),3)\n", - "mae5 = round(mean_absolute_error(y_test, y_pred_neighbors),3)\n", - "print(\"Mean Absolute Error (MAE):\")\n", - "print(f\"k-NN: \\t\\t\\t{mae1}\")\n", - "print(f\"Random Forest: \\t\\t{mae2}\")\n", - "print(f\"Lasso: \\t\\t\\t{mae3}\")\n", - "print(f\"Gradient Boosting: \\t{mae4}\")\n", - "print(f\"k-Nearest Neighbors: \\t{mae5}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "3.\tR-squared\n", - "Мера, показывающая, насколько хорошо модель объясняет изменчивость данных. Значение находится в диапазоне от 0 до 1, где 1 — идеальное соответствие, а 0 — модель не объясняет данные." - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R² (R-squared): 0.127933821917115\n", - "\n", - "R² (R-squared):\n", - "k-NN: \t\t\t0.128\n", - "Random Forest: \t\t0.515\n", - "Lasso: \t\t\t0.319\n", - "Gradient Boosting: \t0.537\n", - "k-Nearest Neighbors: \t-0.337\n" - ] - } - ], - "source": [ - "from sklearn.metrics import r2_score\n", - "r2 = r2_score(y_test, y_pred)\n", - "print(f\"R² (R-squared): {r2}\")\n", - "\n", - "r2_1 = r2_score(y_test, y_pred)\n", - "r2_2 = r2_score(y_test, y_pred_forest)\n", - "r2_3 = r2_score(y_test, y_pred_lasso)\n", - "r2_4 = r2_score(y_test, y_pred_gb)\n", - "r2_5 = r2_score(y_test, y_pred_neighbors)\n", - "\n", - "r2_1_rounded = round(r2_1, 3)\n", - "r2_2_rounded = round(r2_2, 3)\n", - "r2_3_rounded = round(r2_3, 3)\n", - "r2_4_rounded = round(r2_4, 3)\n", - "r2_5_rounded = round(r2_5, 3)\n", - "\n", - "print(\"\\nR² (R-squared):\")\n", - "print(f\"k-NN: \\t\\t\\t{r2_1_rounded}\")\n", - "print(f\"Random Forest: \\t\\t{r2_2_rounded}\")\n", - "print(f\"Lasso: \\t\\t\\t{r2_3_rounded}\")\n", - "print(f\"Gradient Boosting: \\t{r2_4_rounded}\")\n", - "print(f\"k-Nearest Neighbors: \\t{r2_5_rounded}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "4.\tRMSE\n", - " Среднее отклонение предсказаний от реальных данных. Чем меньше модуль, тем лучше модель." - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Root Mean Squared Error (RMSE):\n", - "k-NN: \t\t\t0.461\n", - "Random Forest: \t\t0.344\n", - "Lasso: \t\t\t0.407\n", - "Gradient Boosting: \t0.336\n", - "k-Nearest Neighbors: \t0.571\n" - ] - } - ], - "source": [ - "rmse1 = np.sqrt(mse1)\n", - "rmse2 = np.sqrt(mse2)\n", - "rmse3 = np.sqrt(mse3)\n", - "rmse4 = np.sqrt(mse4)\n", - "rmse5 = np.sqrt(mse5)\n", - "\n", - "rmse1_rounded = round(rmse1, 3)\n", - "rmse2_rounded = round(rmse2, 3)\n", - "rmse3_rounded = round(rmse3, 3)\n", - "rmse4_rounded = round(rmse4, 3)\n", - "rmse5_rounded = round(rmse5, 3)\n", - "\n", - "print(\"Root Mean Squared Error (RMSE):\")\n", - "print(f\"k-NN: \\t\\t\\t{rmse1_rounded}\")\n", - "print(f\"Random Forest: \\t\\t{rmse2_rounded}\")\n", - "print(f\"Lasso: \\t\\t\\t{rmse3_rounded}\")\n", - "print(f\"Gradient Boosting: \\t{rmse4_rounded}\")\n", - "print(f\"k-Nearest Neighbors: \\t{rmse5_rounded}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Лучший результат – градиентный бустинг и случайный лес.\n", - "Положительные результаты по всем критериям получил случайный лес. Три из четырех положительных результата у градиентного бустинга. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Значит, случайный лес – наиболее точная и устойчивая стратегия обучения модели. Итоговая модель – model_forest." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Также, с помощью применение важности признаков (feature importance) на Случайном лесе, мы вывели основные факторы, вызывающие депрессию:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Feature Importance\n", - "13 Have you ever had suicidal thoughts ? 0.300542\n", - "5 Academic Pressure 0.134276\n", - "0 id 0.087970\n", - "7 CGPA 0.079078\n", - "2 Age 0.066613\n", - "15 Financial Stress 0.066330\n", - "3 City 0.059293\n", - "14 Work/Study Hours 0.052275\n", - "12 Degree 0.049539\n", - "8 Study Satisfaction 0.032944\n", - "11 Dietary Habits 0.026140\n", - "10 Sleep Duration 0.024435\n", - "16 Family History of Mental Illness 0.010547\n", - "1 Gender 0.009627\n", - "4 Profession 0.000372\n", - "9 Job Satisfaction 0.000017\n", - "6 Work Pressure 0.000003\n" - ] - } - ], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "\n", - "model_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", - "model_rf.fit(x_train, y_train)\n", - "\n", - "feature_importances = model_rf.feature_importances_\n", - "\n", - "import pandas as pd\n", - "feature_importance_df = pd.DataFrame({\n", - " 'Feature': x.columns,\n", - " 'Importance': feature_importances\n", - "}).sort_values(by='Importance', ascending=False)\n", - "\n", - "print(feature_importance_df)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Scripts", - "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": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 8e79432bfa2fb82eb4a66a6812e80d5a508012e0 Mon Sep 17 00:00:00 2001 From: dex_moth Date: Sat, 21 Dec 2024 00:25:04 +0400 Subject: [PATCH 5/7] lab4 --- lab_4/Lab4.ipynb | 911 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 911 insertions(+) create mode 100644 lab_4/Lab4.ipynb diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb new file mode 100644 index 0000000..0b8116e --- /dev/null +++ b/lab_4/Lab4.ipynb @@ -0,0 +1,911 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'Gender', 'Age', 'City', 'Profession', 'Academic Pressure',\n", + " 'Work Pressure', 'CGPA', 'Study Satisfaction', 'Job Satisfaction',\n", + " 'Sleep Duration', 'Dietary Habits', 'Degree',\n", + " 'Have you ever had suicidal thoughts ?', 'Work/Study Hours',\n", + " 'Financial Stress', 'Family History of Mental Illness', 'Depression'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib.ticker import FuncFormatter\n", + "\n", + "df = pd.read_csv(\".//csv//Student Depression Dataset.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id Gender Age City Profession Academic Pressure \\\n", + "0 2 Male 33.0 Visakhapatnam Student 5.0 \n", + "1 8 Female 24.0 Bangalore Student 2.0 \n", + "2 26 Male 31.0 Srinagar Student 3.0 \n", + "3 30 Female 28.0 Varanasi Student 3.0 \n", + "4 32 Female 25.0 Jaipur Student 4.0 \n", + "\n", + " Work Pressure CGPA Study Satisfaction Job Satisfaction \\\n", + "0 0.0 8.97 2.0 0.0 \n", + "1 0.0 5.90 5.0 0.0 \n", + "2 0.0 7.03 5.0 0.0 \n", + "3 0.0 5.59 2.0 0.0 \n", + "4 0.0 8.13 3.0 0.0 \n", + "\n", + " Sleep Duration Dietary Habits Degree \\\n", + "0 5-6 hours Healthy B.Pharm \n", + "1 5-6 hours Moderate BSc \n", + "2 Less than 5 hours Healthy BA \n", + "3 7-8 hours Moderate BCA \n", + "4 5-6 hours Moderate M.Tech \n", + "\n", + " Have you ever had suicidal thoughts ? Work/Study Hours Financial Stress \\\n", + "0 Yes 3.0 1.0 \n", + "1 No 3.0 2.0 \n", + "2 No 9.0 1.0 \n", + "3 Yes 4.0 5.0 \n", + "4 Yes 1.0 1.0 \n", + "\n", + " Family History of Mental Illness Depression \n", + "0 No 1 \n", + "1 Yes 0 \n", + "2 Yes 0 \n", + "3 Yes 1 \n", + "4 No 0 \n" + ] + } + ], + "source": [ + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Бизнес-цель исследования\n", + "Разработать и внедрить систему прогнозирования уровня депрессии среди обучающихся, которая позволит выявить группы риска на ранних этапах. Результаты исследования могут быть полезны психологам, педагогам и администрации учебных заведений.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Описание набора данных для анализа\n", + "Набор данных содержит информацию о психологическом состоянии обучающихся и включает следующие поля:\n", + "- id – идентификатор, число\n", + "- Gender – пол, строка\n", + "- Age – возраст, дробное число\n", + "- City – город, строка\n", + "- Profession – профессия, строка\n", + "- Academic Pressure – академическое давление, дробное число (от 1.00 до 5.00)\n", + "- Work Pressure – рабочее давление, дробное число (от 1.00 до 5.00)\n", + "- CGPA – средний балл (GPA), дробное число\n", + "- Study Satisfaction – удовлетворенность учебой, дробное число (от 1.00 до 5.00)\n", + "- Job Satisfaction – удовлетворенность работой, дробное число (от 1.00 до 5.00)\n", + "- Sleep Duration – продолжительность сна, строка\n", + "- Dietary Habits – пищевые привычки, строка\n", + "- Degree – степень (образование), строка\n", + "- Have you ever had suicidal thoughts? – Были ли у вас когда-либо суицидальные мысли? строка (yes/no)\n", + "- Work/Study Hours – часы работы/учебы, дробное число\n", + "- Financial Stress – финансовый стресс, дробное число (от 1.00 до 5.00)\n", + "- Family History of Mental Illness – семейный анамнез психических заболеваний, строка (yes/no)\n", + "- Depression – депрессия, булевое значение (1/0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Обработка данных" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id 0\n", + "Gender 0\n", + "Age 0\n", + "City 0\n", + "Profession 0\n", + "Academic Pressure 0\n", + "Work Pressure 0\n", + "CGPA 0\n", + "Study Satisfaction 0\n", + "Job Satisfaction 0\n", + "Sleep Duration 0\n", + "Dietary Habits 0\n", + "Degree 0\n", + "Have you ever had suicidal thoughts ? 0\n", + "Work/Study Hours 0\n", + "Financial Stress 3\n", + "Family History of Mental Illness 0\n", + "Depression 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['Financial Stress'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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RqlWrlnr37q0XX3xRrVu3zna/LAMGDLD93cHBQQMGDFBGRobWrFmT4/6ZmZlavXq1unTporvuuss27ufnpyeeeEKbNm1Sampqvl+DxMRE7dmzRxEREfLy8rKN161bV+3bt9f333+f72NKV9dMLVOmTJ73lZTtImU///yzypcvb/d1+vRpu31Wr15t2xYSEqKFCxfq6aef1sSJEyVJV65c0VdffaXHHnvM9g5/u3bt5OPjo/nz59/QcwOAooZ6V3j1rlatWtqzZ4+eeuop/f7775o2bZq6dOkiX19fu2t/FIQ1a9YoIyNDAwcOtJu1Nnjw4Gz79ujRQy4uLna1btWqVTp16pTdrMSc+Pr6Kj4+Xs8//7zOnDmjDz/8UE888YR8fHz01ltv2X1Ev1SpUra/nz9/XqdOnVKzZs1kGIZ++umnG3qenp6e2r59u44dO5bv+/4zz7lz53Tq1Cm1bNlSFy5c0C+//CLp6sfvjxw5osGDB2dbC/daswFzcyM/s88++6zdY7Vs2VKZmZn6448/8v34ACBR66XCq/UFkf/ZZ5+1mwH9wgsvqGTJktfN1LdvX61cuVJt2rTRpk2b9NZbb6lly5YKCgrSli1b7Pb9Zw08c+aMUlJS1LJlS9tyaPmVVSO/++47Xb58OV/3LVGihG0JGqvVquTkZF25ckUNGza0y7N48WI5ODjkeFHSG6nJWedKgwcPlsXyfy3TZ555Ru7u7lq+fLnd/m5ubnbnRU5OTmrUqJF+++23fD82bn800YGb0KhRI4WGhio0NFRPPvmkli9fruDgYFvRl6Q//vhDFStWzNYQzvoI2T9/2XFyctKnn36qI0eO6Ny5c5ozZ06O//FbLBa7YitJd999tyRlW8sry8mTJ3XhwgXVqFEj27Z77rlHVqtVR48ezfuT//+y8ud23FOnTun8+fP5Pq67u7vOnTuXp32zXtu0tDS78erVq9tOBJ9++ukc79u4cWPFxsZqzZo12rJli06dOqXPPvvMdgKxevVqnTx5Uo0aNdLhw4d1+PBhHTlyRG3bttWXX34pq9Wa7+cGAEUN9a7w6p109Tl9/vnnOnXqlP73v/9p3LhxKlmypJ599tlcGwg3Ius5BAUF2Y2XL19eZcuWtRvz9PRUp06d7K4BMn/+fFWqVOmay85k8fPz06xZs5SYmKiDBw/q/fffV/ny5TVixAhFRUXZ9vvzzz9tzQo3NzeVL1/ets5sSkrKDT3PSZMmae/evfL391ejRo00atSoPP8yu2/fPj3yyCPy8PCQu7u7ypcvb/vlOCtPQkKCJKl27do3lO/fbuRntkqVKna3s75/eVkfFwByQq0v3Fr/TzeS/9+1283NTX5+frm+Rv/UoUMHrVq1SmfPntXGjRvVv39//fHHH3r44YftLi763XffqUmTJnJxcZGXl5fKly+vWbNm3XA9bt26tbp166bRo0erXLlyCg8P15w5c5Senp6n+8+dO1d169aVi4uLvL29Vb58eS1fvtwuT0JCgipWrGj3psfNyO1nwMnJSXfddVe2N6srV66c7ee6bNmy1ONiiiY6UIAsFovatm2rxMREHTp06IaOsWrVKknSpUuXbvgYxUHNmjX166+/2q3Req19JWnv3r12425ubrYTwX+fmGUpV66cQkNDdf/996tp06bZZpRlzcDr0aOHgoKCbF9fffWV/v77b8XFxd3AswOAoo16VzhKlCihOnXqaPjw4VqyZIkk5elTT7nNtMrpYpj50atXL/3222/asmWLzp07p2XLlqlnz552M7Pyku3uu+/WwIEDtXHjRlksFttzyszMVPv27bV8+XK9+uqrWrp0qWJjYxUdHS1JN/xGdY8ePfTbb7/pgw8+UMWKFfXOO++oVq1adjMkc3L27Fm1bt1a8fHxGjNmjL799lvFxsbaPp12O71xnts1YP45yx8Abga1vvgpXbq0WrZsqenTp+uNN97QmTNnbLXxhx9+UOfOneXi4qKZM2fq+++/V2xsrJ544okbri0ODg5atGiRtm7dqgEDBujvv/9W37591aBBg2wT4P5t3rx5ioiIUGBgoKKiorRy5UrFxsaqXbt21GOYhiY6UMCuXLki6f9mRVetWlXHjh3LNqs66yPBVatWtY3973//05gxY9SnTx/Vr19f/fr1y/FdX6vVmm1G1a+//ipJCggIyDFX+fLlVbp0aR08eDDbtl9++UUWi0X+/v6S8vexp6z8uR23XLlycnV1zfPxsnTq1EkXL17U4sWLr7tvy5Yt9f/Yu+/wKKq+jeP3poc0QklIIIQqvQhSEpAiJVJCURCw0H0sKPUBDBYIxQhKR0FQCIqIgBQb0os0pYhUaSKgQOgJQUkgmfcP3uzDkiwQIJmU7+e69jJz5szsvZvImf3tzBkfHx/NmzfvoQ6oV69e1dKlS9WhQwctWLAg1SMgIIApXQDkWox3tvu93/HOnscee0zSzUvLU9jLm3IW8uXLl23abz9bKuU13F7IOHfuXJpnTD355JMqWLCgvvjiCy1evFj//POP3Su77kWJEiXk6+trfU179uzRoUOHNHbsWA0ePFitW7dW48aNFRgYmGrb9F6SHRAQoFdffVVLlizRsWPHlD9/fo0aNeqO26xbt04XLlxQdHS0+vTpo5YtW6px48apztJPuRnZ7V/e32/m9PzNAkBmYqy33e/DGuvv59/928fu+Ph4nT592u57dDe3H2d8/fXXcnNz0/Lly9W9e3c1a9ZMjRs3TnPb9I7JtWvX1qhRo7R9+3Z98cUX2rdvn+bNm3fHbRYuXKgSJUpo0aJFeuGFFxQWFqbGjRvr2rVrNv1KliypU6dO6eLFi3fc371mtvc3kJiYqGPHjtn8jSP3oYgOPETXr1/XihUr5OLiYr2krXnz5kpKStKUKVNs+o4fP14Wi8V6x+br16+ra9euCgwM1MSJExUdHa2YmBj169cvzee6dX+GYWjKlClydnZWo0aN0uzv6Oiopk2baunSpTaXfMXExGju3LmqW7euvL29Jcl6YHD7h/G0BAQEqGrVqpo9e7ZN/71792rFihVq3rz5XfeRlpdfflkBAQEaMGCA9SDqVmfPntXIkSMl3fxGfdCgQdq7d6/eeOONNL/1vZ9vghcvXqyrV6+qV69eateuXapHy5Yt9fXXX9/z5WgAkFMw3v2v/4OOdz/99FOa84SmzHF66+XEHh4eaWZNKepu2LDB2nb16lXNnj3bpl/jxo3l7OysyZMn24yLEyZMSDObk5OTOnXqpPnz5ys6OlqVKlVS5cqV7/qafv755zQvd//ll1904cIF62tKOXvr1iyGYWjixImptr3X31VSUlKqIo2fn58CAwPvOl6nlScxMVEfffSRTb9q1aqpePHimjBhQqo8t257r5nT8zcLAJmFsf5//R90rH+Q/CmmT59uc7wwdepU3bhxw/qe27N69eo0228/znB0dJTFYrG5iu3PP//UkiVLUm1r73jkdpcuXUr1Obxq1aqSdF9j8s8//6wtW7bY9Hv66adlGIYiIyNT7eP2MfleMjdu3FguLi6aNGmSzfaffvqpYmNj1aJFi7vuAzmXk9kBgOxs2bJl1m/dz549q7lz5+rw4cN64403rINeeHi4GjZsqDfffFN//vmnqlSpohUrVmjp0qXq27ev9YPvyJEjtWvXLq1evVpeXl6qXLmy3nnnHb311ltq166dzYDt5uamH3/8UV26dFGtWrW0bNkyff/99xoyZIgKFixoN+/IkSO1cuVK1a1bV6+++qqcnJz08ccfKyEhQWPGjLH2q1q1qhwdHTV69GjFxsbK1dXVejPNtLz//vtq1qyZQkJC1KNHD/3777+aPHmyfHx8NGzYsPt6b319fbV48WI1b95cVatW1fPPP6/q1atLknbu3Kkvv/xSISEh1v5vvPGGDhw4oPfff18rVqzQ008/rSJFiujSpUvauXOnFixYID8/P7m5ud1zhi+++EL58+dXaGhomutbtWqlGTNm6Pvvv9dTTz11X68TALIDxrubMmK8Gz16tHbs2KGnnnrKWqDeuXOnPvvsM+XLl8/mpp/Vq1fX1KlTNXLkSJUqVUp+fn564okn1LRpUxUtWlQ9evTQwIED5ejoqJkzZ6pgwYI6ceKEdfuCBQvqv//9r6KiotSyZUs1b95cv/76q5YtW6YCBQqkma9z586aNGmS1q5da53W5G4+//xzffHFF2rbtq2qV68uFxcXHThwQDNnzpSbm5uGDBki6eZ0bCVLltR///tf/f333/L29tbXX3+d5lnxKccAvXv3VlhYmBwdHdWxY8dU/a5cuaIiRYqoXbt2qlKlijw9PbVq1Spt27ZNY8eOvWPu0NBQ+fr6qkuXLurdu7csFos+//zzVAUABwcHTZ06VeHh4apataq6deumgIAA/f7779q3b591+oJ7zSzd+98sAGQUxvqbMmKsf5D8KRITE9WoUSM988wzOnjwoD766CPVrVtXrVq1uuPztG7dWsWLF1d4eLhKliypq1evatWqVfr2229Vo0YNhYeHS5JatGihcePG6cknn9Szzz6rs2fP6sMPP1SpUqW0e/dum31Wr15dq1at0rhx4xQYGKjixYurVq1aqZ579uzZ+uijj9S2bVuVLFlSV65c0YwZM+Tt7X3XLyRatmypRYsWqW3btmrRooWOHTumadOmqXz58jZTwTRs2FAvvPCCJk2apMOHD+vJJ59UcnKyfvrpJzVs2NB609p7zVywYEFFREQoMjJSTz75pFq1amV9v2vUqHHXm6sjhzMApNusWbMMSTYPNzc3o2rVqsbUqVON5ORkm/5Xrlwx+vXrZwQGBhrOzs5G6dKljffff9/ab8eOHYaTk5Px+uuv22x348YNo0aNGkZgYKBx6dIlwzAMo0uXLoaHh4dx9OhRo2nTpkaePHkMf39/Y+jQoUZSUpLN9pKMoUOH2rTt3LnTCAsLMzw9PY08efIYDRs2NDZv3pzqNc6YMcMoUaKE4ej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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "features = ['Age', 'Academic Pressure', 'Work Pressure', 'CGPA', 'Study Satisfaction', \n", + " 'Job Satisfaction', 'Work/Study Hours', 'Financial Stress', 'Depression']\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, feature in enumerate(features, 1):\n", + " plt.subplot(3, 3, i)\n", + " sns.boxplot(y=df[feature], color='skyblue')\n", + " plt.title(f'Boxplot of {feature}')\n", + " plt.ylabel(feature)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В Age много выбросов. Сбалансируем данные" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q1 = df['Age'].quantile(0.25)\n", + "Q3 = df['Age'].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "threshold = 1.5 * IQR\n", + "outliers = (df['Age'] < (Q1 - threshold)) | (df['Age'] > (Q3 + threshold))\n", + "\n", + "median_rating = df['Age'].median()\n", + "df.loc[outliers, 'Age'] = median_rating\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "sns.boxplot(y=df['Age'], color='skyblue')\n", + "plt.title('Boxplot of Age')\n", + "plt.ylabel('Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Конструирование признаков с помощью меток" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "le = LabelEncoder()\n", + "df['Gender'] = le.fit_transform(df['Gender'])\n", + "df['City'] = le.fit_transform(df['City'])\n", + "df['Dietary Habits'] = le.fit_transform(df['Dietary Habits'])\n", + "df['Degree'] = le.fit_transform(df['Degree'])\n", + "df['Have you ever had suicidal thoughts ?'] = le.fit_transform(df['Have you ever had suicidal thoughts ?'])\n", + "df['Sleep Duration'] = le.fit_transform(df['Sleep Duration'])\n", + "df['Profession'] = le.fit_transform(df['Profession'])\n", + "df['Study Satisfaction'] = le.fit_transform(df['Study Satisfaction'])\n", + "df['Family History of Mental Illness'] = le.fit_transform(df['Family History of Mental Illness'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "разделение на признаки и целевую переменную" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "x = df.drop('Depression', axis=1)\n", + "y = df['Depression']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Метод регрессии Лассо\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для Lasso:\n", + "{'alpha': 0.01, 'fit_intercept': False}\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import Lasso\n", + "\n", + "param_grid_lasso = {\n", + " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", + " 'fit_intercept': [True, False],\n", + "}\n", + "\n", + "# Создание объекта GridSearchCV\n", + "grid_search_lasso = GridSearchCV(\n", + " estimator=Lasso(), \n", + " param_grid=param_grid_lasso, \n", + " cv=5, \n", + " scoring='neg_mean_squared_error', \n", + " n_jobs=-1 \n", + ")\n", + "\n", + "grid_search_lasso.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для Lasso:\")\n", + "print(grid_search_lasso.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Метод градиентного бустинга" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n", + "1215 fits failed out of a total of 3645.\n", + "The score on these train-test partitions for these parameters will be set to nan.\n", + "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", + "\n", + "Below are more details about the failures:\n", + "--------------------------------------------------------------------------------\n", + "978 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", + " estimator._validate_params()\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "237 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", + " estimator._validate_params()\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "\n", + " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n", + " _data = np.array(data, dtype=dtype, copy=copy,\n", + "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.18767441 -0.15799837 -0.13080278\n", + " -0.18762913 -0.15792709 -0.13056114 -0.18792038 -0.15737146 -0.130218\n", + " -0.18725961 -0.157967 -0.13047453 -0.18766583 -0.15779565 -0.13094863\n", + " -0.18798705 -0.15693978 -0.13061215 -0.18766317 -0.15746848 -0.13072918\n", + " -0.18864158 -0.15666133 -0.13095037 -0.18817206 -0.15805489 -0.13086126\n", + " -0.18707465 -0.15864932 -0.13104947 -0.18818902 -0.15828572 -0.13063871\n", + " -0.18701628 -0.15853864 -0.13019458 -0.18740927 -0.15836397 -0.13065455\n", + " -0.18768748 -0.15828297 -0.1309458 -0.18845004 -0.15696395 -0.13023062\n", + " -0.18754854 -0.15899615 -0.13061707 -0.18831427 -0.15819939 -0.13096524\n", + " -0.18662963 -0.15815869 -0.13089186 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.1758914 -0.1442684 -0.12093344 -0.1758927 -0.14423731 -0.12084543\n", + " -0.17573339 -0.14419842 -0.12076166 -0.17512045 -0.14435454 -0.1207299\n", + " -0.17669645 -0.14397965 -0.12087019 -0.17605424 -0.1438664 -0.12091068\n", + " -0.17582192 -0.1443651 -0.12097165 -0.17588422 -0.14421003 -0.12081764\n", + " -0.17522742 -0.14424357 -0.12086484 -0.17530986 -0.14433713 -0.12091757\n", + " -0.17565647 -0.14408902 -0.12075918 -0.17561884 -0.14426355 -0.12094066\n", + " -0.17522371 -0.1439869 -0.12099023 -0.17619772 -0.14396131 -0.12079667\n", + " -0.17710789 -0.1448419 -0.12087822 -0.17608534 -0.14416684 -0.12087865\n", + " -0.1754675 -0.1442258 -0.12068226 -0.17611334 -0.14433552 -0.12093556\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.16938321 -0.13763002 -0.11703902\n", + " -0.16953091 -0.13736586 -0.11695779 -0.16881837 -0.1375676 -0.11694438\n", + " -0.16927898 -0.13748177 -0.11689982 -0.16921265 -0.13757375 -0.11682524\n", + " -0.16915872 -0.13727377 -0.11694336 -0.16939766 -0.13734972 -0.1167447\n", + " -0.16924214 -0.1373768 -0.11674816 -0.16918278 -0.13746085 -0.1169816\n", + " -0.16927003 -0.13740063 -0.1169564 -0.16916501 -0.13752074 -0.11687641\n", + " -0.16928973 -0.13751536 -0.11697948 -0.16934836 -0.13727436 -0.11693615\n", + " -0.16912453 -0.13748699 -0.11693425 -0.1692788 -0.13750784 -0.11694655\n", + " -0.16919354 -0.13747437 -0.11708782 -0.16940009 -0.13757749 -0.11700586\n", + " -0.1692801 -0.13725384 -0.11684394 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11606052 -0.1140225 -0.11403709 -0.11627212 -0.1139982 -0.11402075\n", + " -0.11613561 -0.11407941 -0.11420487 -0.11666225 -0.11462523 -0.11431901\n", + " -0.11604817 -0.11456211 -0.11392092 -0.11609343 -0.11394228 -0.11414071\n", + " -0.11611685 -0.11420178 -0.11405459 -0.11594404 -0.11408614 -0.11391662\n", + " -0.11590886 -0.11396465 -0.11389125 -0.11616694 -0.11441846 -0.11417015\n", + " -0.11617368 -0.11429765 -0.1139636 -0.11616763 -0.11433984 -0.11412121\n", + " -0.11625618 -0.11402999 -0.11419791 -0.11613603 -0.114206 -0.11423922\n", + " -0.1160801 -0.11431896 -0.11416734 -0.11608923 -0.11455498 -0.11417448\n", + " -0.11605165 -0.11427773 -0.11392205 -0.11606243 -0.11408421 -0.11395292\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11281447 -0.11245904 -0.11308822\n", + " -0.11256366 -0.11230094 -0.1130767 -0.11282651 -0.1121034 -0.11283479\n", + " -0.11260704 -0.1125136 -0.11288977 -0.11278304 -0.11242278 -0.11268564\n", + " -0.11263359 -0.11236227 -0.11329411 -0.11231603 -0.1124533 -0.11278826\n", + " -0.11291545 -0.11241223 -0.11250702 -0.11246481 -0.11228665 -0.11348916\n", + " -0.11250694 -0.11250274 -0.11298019 -0.11277323 -0.11248601 -0.11301753\n", + " -0.11259486 -0.1124685 -0.11285441 -0.11274424 -0.11232891 -0.11316456\n", + " -0.11274575 -0.11256149 -0.11252293 -0.11293524 -0.11261757 -0.11305628\n", + " -0.11253063 -0.11237109 -0.11278518 -0.1124074 -0.11276905 -0.11296684\n", + " -0.11258689 -0.11228467 -0.11331342 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11292265 -0.11395193 -0.11564599 -0.11244356 -0.11338947 -0.1148266\n", + " -0.11295702 -0.11353862 -0.11510521 -0.11244347 -0.11387967 -0.11512396\n", + " -0.11269802 -0.11364442 -0.1151339 -0.11238356 -0.11364301 -0.11496543\n", + " -0.11229193 -0.11340926 -0.11550744 -0.11215818 -0.11367944 -0.11552889\n", + " -0.11240305 -0.11352309 -0.115412 -0.1128402 -0.11338749 -0.1153551\n", + " -0.11250042 -0.11347275 -0.11548445 -0.11271132 -0.11377527 -0.11558066\n", + " -0.11318598 -0.11325792 -0.11499103 -0.11253099 -0.1129829 -0.11530949\n", + " -0.11239074 -0.11329625 -0.11544761 -0.11262484 -0.11323392 -0.1151936\n", + " -0.11253889 -0.11382403 -0.11511129 -0.11250854 -0.11339898 -0.11536332\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11542253 -0.11498664 -0.11428517\n", + " -0.11503783 -0.11473447 -0.11458687 -0.11483866 -0.1154254 -0.11479037\n", + " -0.11533015 -0.11515195 -0.11460571 -0.11563491 -0.11433835 -0.11437413\n", + " -0.11510849 -0.11472156 -0.11516494 -0.11545009 -0.115001 -0.11479743\n", + " -0.11461761 -0.11537461 -0.11497109 -0.1155148 -0.11567353 -0.11431184\n", + " -0.11546067 -0.11462564 -0.11450721 -0.11511 -0.11487988 -0.11466523\n", + " -0.11585756 -0.11462611 -0.11433121 -0.11538152 -0.11463425 -0.11527088\n", + " -0.11509145 -0.11493588 -0.11484324 -0.11528905 -0.11426327 -0.11476508\n", + " -0.11499562 -0.11451299 -0.11466765 -0.11525918 -0.11469718 -0.11476983\n", + " -0.11467865 -0.1145067 -0.11479425 nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " -0.11352917 -0.1145882 -0.11643688 -0.11418115 -0.11442858 -0.11635549\n", + " -0.11408502 -0.11458383 -0.1163013 -0.1135842 -0.11453566 -0.11575264\n", + " -0.11341863 -0.11481638 -0.11635685 -0.1132144 -0.11438018 -0.11666005\n", + " -0.11311482 -0.11500883 -0.11594984 -0.11409228 -0.11464061 -0.1158012\n", + " -0.11389399 -0.11454081 -0.1157428 -0.11333869 -0.11438896 -0.11676006\n", + " -0.11382523 -0.11443669 -0.11606569 -0.11424726 -0.11464652 -0.11608159\n", + " -0.11396605 -0.11473188 -0.1167532 -0.1136805 -0.11455875 -0.11615814\n", + " -0.11372286 -0.11442829 -0.11590895 -0.1136509 -0.11368863 -0.11660073\n", + " -0.1136605 -0.1141187 -0.11613806 -0.11326355 -0.11427399 -0.11676148\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan nan nan nan\n", + " nan nan nan -0.11573534 -0.11897501 -0.1226239\n", + " -0.1162633 -0.11939573 -0.12255715 -0.11636411 -0.11878021 -0.12306277\n", + " -0.11535113 -0.11813967 -0.1230085 -0.11594119 -0.11812955 -0.12217928\n", + " -0.11523023 -0.11843291 -0.12228252 -0.1159457 -0.11840108 -0.12181337\n", + " -0.11600134 -0.11790484 -0.12203724 -0.11579998 -0.11787918 -0.12317219\n", + " -0.11578704 -0.11837798 -0.12379234 -0.1155279 -0.11865384 -0.12319867\n", + " -0.11597008 -0.11886814 -0.12291788 -0.1162282 -0.11918752 -0.12363613\n", + " -0.11571473 -0.11805225 -0.12250506 -0.11640247 -0.11823175 -0.1226976\n", + " -0.11571549 -0.11813327 -0.12229009 -0.11621545 -0.11793769 -0.1229533\n", + " -0.11528287 -0.1183919 -0.12121653]\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для Gradient Boosting:\n", + "{'learning_rate': 0.1, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "param_grid_gb = {\n", + " 'n_estimators': [50, 100, 200],\n", + " 'learning_rate': [0.01, 0.1, 0.2],\n", + " 'max_depth': [3, 5, 7],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'min_samples_leaf': [1, 2, 4],\n", + " 'max_features': ['auto', 'sqrt', 'log2']\n", + "}\n", + "\n", + "grid_search_gb = GridSearchCV(\n", + " estimator=GradientBoostingRegressor(),\n", + " param_grid=param_grid_gb,\n", + " cv=5,\n", + " scoring='neg_mean_squared_error',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "grid_search_gb.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для Gradient Boosting:\")\n", + "print(grid_search_gb.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) Метод k-ближайших соседей" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Лучшие гиперпараметры для k-Nearest Neighbors:\n", + "{'algorithm': 'ball_tree', 'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n" + ] + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsRegressor\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "param_grid_knn = {\n", + " 'n_neighbors': [3, 5, 7, 10],\n", + " 'weights': ['uniform', 'distance'],\n", + " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", + " 'p': [1, 2]\n", + "}\n", + "\n", + "grid_search_knn = GridSearchCV(\n", + " estimator=KNeighborsRegressor(),\n", + " param_grid=param_grid_knn,\n", + " cv=5,\n", + " scoring='neg_mean_squared_error',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "grid_search_knn.fit(x_train, y_train)\n", + "\n", + "# Вывод лучших гиперпараметров\n", + "print(\"Лучшие гиперпараметры для k-Nearest Neighbors:\")\n", + "print(grid_search_knn.best_params_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Предсказание на тестовой выборке" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x_test)\n", + "y_pred_forest = model_forest.predict(x_test)\n", + "y_pred_lasso = model_lasso.predict(x_test)\n", + "y_pred_gb = model_gb.predict(x_test)\n", + "y_pred_neighbors = model_knn.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Оценка качества модели" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.\tMSE (Mean Squared Error)\n", + "Среднее значение квадратов разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error (MSE):\n", + "k-NN: \t\t\t0.213\n", + "Random Forest: \t\t0.118\n", + "Lasso: \t\t\t0.166\n", + "Gradient Boosting: \t0.113\n", + "k-Nearest Neighbors: \t0.326\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "import numpy as np\n", + "\n", + "mse1 = mean_squared_error(y_test, y_pred)\n", + "mse2 = mean_squared_error(y_test, y_pred_forest)\n", + "mse3 = mean_squared_error(y_test, y_pred_lasso)\n", + "mse4 = mean_squared_error(y_test, y_pred_gb)\n", + "mse5 = mean_squared_error(y_test, y_pred_neighbors)\n", + "\n", + "mse1_rounded = round(mse1, 3)\n", + "mse2_rounded = round(mse2, 3)\n", + "mse3_rounded = round(mse3, 3)\n", + "mse4_rounded = round(mse4, 3)\n", + "mse5_rounded = round(mse5, 3)\n", + "\n", + "print(\"Mean Squared Error (MSE):\")\n", + "print(f\"k-NN: \\t\\t\\t{mse1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{mse2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{mse3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{mse4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{mse5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2.\tMAE\n", + "Среднее значение абсолютных разностей между предсказанными и фактическими значениями. Чем меньше значение, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Absolute Error (MAE):\n", + "k-NN: \t\t\t0.213\n", + "Random Forest: \t\t0.238\n", + "Lasso: \t\t\t0.366\n", + "Gradient Boosting: \t0.246\n", + "k-Nearest Neighbors: \t0.485\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "mae1 = round(mean_absolute_error(y_test, y_pred),3)\n", + "mae2 = round(mean_absolute_error(y_test, y_pred_forest),3)\n", + "mae3 = round(mean_absolute_error(y_test, y_pred_lasso),3)\n", + "mae4 = round(mean_absolute_error(y_test, y_pred_gb),3)\n", + "mae5 = round(mean_absolute_error(y_test, y_pred_neighbors),3)\n", + "print(\"Mean Absolute Error (MAE):\")\n", + "print(f\"k-NN: \\t\\t\\t{mae1}\")\n", + "print(f\"Random Forest: \\t\\t{mae2}\")\n", + "print(f\"Lasso: \\t\\t\\t{mae3}\")\n", + "print(f\"Gradient Boosting: \\t{mae4}\")\n", + "print(f\"k-Nearest Neighbors: \\t{mae5}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3.\tR-squared\n", + "Мера, показывающая, насколько хорошо модель объясняет изменчивость данных. Значение находится в диапазоне от 0 до 1, где 1 — идеальное соответствие, а 0 — модель не объясняет данные." + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R² (R-squared): 0.127933821917115\n", + "\n", + "R² (R-squared):\n", + "k-NN: \t\t\t0.128\n", + "Random Forest: \t\t0.515\n", + "Lasso: \t\t\t0.319\n", + "Gradient Boosting: \t0.537\n", + "k-Nearest Neighbors: \t-0.337\n" + ] + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "r2 = r2_score(y_test, y_pred)\n", + "print(f\"R² (R-squared): {r2}\")\n", + "\n", + "r2_1 = r2_score(y_test, y_pred)\n", + "r2_2 = r2_score(y_test, y_pred_forest)\n", + "r2_3 = r2_score(y_test, y_pred_lasso)\n", + "r2_4 = r2_score(y_test, y_pred_gb)\n", + "r2_5 = r2_score(y_test, y_pred_neighbors)\n", + "\n", + "r2_1_rounded = round(r2_1, 3)\n", + "r2_2_rounded = round(r2_2, 3)\n", + "r2_3_rounded = round(r2_3, 3)\n", + "r2_4_rounded = round(r2_4, 3)\n", + "r2_5_rounded = round(r2_5, 3)\n", + "\n", + "print(\"\\nR² (R-squared):\")\n", + "print(f\"k-NN: \\t\\t\\t{r2_1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{r2_2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{r2_3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{r2_4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{r2_5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4.\tRMSE\n", + " Среднее отклонение предсказаний от реальных данных. Чем меньше модуль, тем лучше модель." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Root Mean Squared Error (RMSE):\n", + "k-NN: \t\t\t0.461\n", + "Random Forest: \t\t0.344\n", + "Lasso: \t\t\t0.407\n", + "Gradient Boosting: \t0.336\n", + "k-Nearest Neighbors: \t0.571\n" + ] + } + ], + "source": [ + "rmse1 = np.sqrt(mse1)\n", + "rmse2 = np.sqrt(mse2)\n", + "rmse3 = np.sqrt(mse3)\n", + "rmse4 = np.sqrt(mse4)\n", + "rmse5 = np.sqrt(mse5)\n", + "\n", + "rmse1_rounded = round(rmse1, 3)\n", + "rmse2_rounded = round(rmse2, 3)\n", + "rmse3_rounded = round(rmse3, 3)\n", + "rmse4_rounded = round(rmse4, 3)\n", + "rmse5_rounded = round(rmse5, 3)\n", + "\n", + "print(\"Root Mean Squared Error (RMSE):\")\n", + "print(f\"k-NN: \\t\\t\\t{rmse1_rounded}\")\n", + "print(f\"Random Forest: \\t\\t{rmse2_rounded}\")\n", + "print(f\"Lasso: \\t\\t\\t{rmse3_rounded}\")\n", + "print(f\"Gradient Boosting: \\t{rmse4_rounded}\")\n", + "print(f\"k-Nearest Neighbors: \\t{rmse5_rounded}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Лучший результат – градиентный бустинг и случайный лес.\n", + "Положительные результаты по всем критериям получил случайный лес. Три из четырех положительных результата у градиентного бустинга. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Значит, случайный лес – наиболее точная и устойчивая стратегия обучения модели. Итоговая модель – model_forest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Также, с помощью применение важности признаков (feature importance) на Случайном лесе, мы вывели основные факторы, вызывающие депрессию:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Feature Importance\n", + "13 Have you ever had suicidal thoughts ? 0.300542\n", + "5 Academic Pressure 0.134276\n", + "0 id 0.087970\n", + "7 CGPA 0.079078\n", + "2 Age 0.066613\n", + "15 Financial Stress 0.066330\n", + "3 City 0.059293\n", + "14 Work/Study Hours 0.052275\n", + "12 Degree 0.049539\n", + "8 Study Satisfaction 0.032944\n", + "11 Dietary Habits 0.026140\n", + "10 Sleep Duration 0.024435\n", + "16 Family History of Mental Illness 0.010547\n", + "1 Gender 0.009627\n", + "4 Profession 0.000372\n", + "9 Job Satisfaction 0.000017\n", + "6 Work Pressure 0.000003\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "model_rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "model_rf.fit(x_train, y_train)\n", + "\n", + "feature_importances = model_rf.feature_importances_\n", + "\n", + "import pandas as pd\n", + "feature_importance_df = pd.DataFrame({\n", + " 'Feature': x.columns,\n", + " 'Importance': feature_importances\n", + "}).sort_values(by='Importance', ascending=False)\n", + "\n", + "print(feature_importance_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scripts", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2fca9fd006456c562db7b2491925dd3fe634ded8 Mon Sep 17 00:00:00 2001 From: dex_moth Date: Sat, 21 Dec 2024 01:02:44 +0400 Subject: [PATCH 6/7] lab 5 --- lab_5/Lab5.ipynb | 336 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 336 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..fabdfd0 --- /dev/null +++ b/lab_5/Lab5.ipynb @@ -0,0 +1,336 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Лабораторная 5" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n", + " 'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n", + " 'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n", + " 'tradebalance', 'USTreasury'],\n", + " dtype='object')\n", + " stock index country year index price log_indexprice \\\n", + "0 NASDAQ United States of America 1980.0 168.61 2.23 \n", + "1 NASDAQ United States of America 1981.0 203.15 2.31 \n", + "2 NASDAQ United States of America 1982.0 188.98 2.28 \n", + "3 NASDAQ United States of America 1983.0 285.43 2.46 \n", + "4 NASDAQ United States of America 1984.0 248.89 2.40 \n", + "\n", + " inflationrate oil prices exchange_rate gdppercent percapitaincome \\\n", + "0 0.14 21.59 1.0 0.09 12575.0 \n", + "1 0.10 31.77 1.0 0.12 13976.0 \n", + "2 0.06 28.52 1.0 0.04 14434.0 \n", + "3 0.03 26.19 1.0 0.09 15544.0 \n", + "4 0.04 25.88 1.0 0.11 17121.0 \n", + "\n", + " unemploymentrate manufacturingoutput tradebalance USTreasury \n", + "0 0.07 NaN -13.06 0.11 \n", + "1 0.08 NaN -12.52 0.14 \n", + "2 0.10 NaN -19.97 0.13 \n", + "3 0.10 NaN -51.64 0.11 \n", + "4 0.08 NaN -102.73 0.12 \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.cluster import KMeans\n", + "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n", + "from sklearn.metrics import silhouette_score\n", + "\n", + "df = pd.read_csv(\".//csv//EconomicData.csv\")\n", + "print(df.columns)\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Бизнес-цель: сегментировать страны на основе экономических показателей для определения схожих групп стран и последующего анализа каждой группы." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Данные содержат текстовые значения.\n", + "Исходный размер датасета: 369\n", + "Очищенный размер датасета: 219\n" + ] + } + ], + "source": [ + "df = df.copy()\n", + "df_clean = df.dropna()\n", + "\n", + "if not np.issubdtype(df_clean.dtypes.iloc[1], np.number):\n", + " print(\"Данные содержат текстовые значения.\")\n", + " cleaned_data = df_clean.select_dtypes(include=[np.number])\n", + "\n", + "print(f\"Исходный размер датасета: {df.shape[0]}\")\n", + "print(f\"Очищенный размер датасета: {df_clean.shape[0]}\")\n", + "\n", + "df = pd.get_dummies(df_clean, columns=['country'], drop_first=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Выбор признаков для кластеризации\n", + "features = ['index price', 'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent', \n", + " 'percapitaincome', 'unemploymentrate', 'manufacturingoutput', 'tradebalance', 'USTreasury']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Стандартизируем, чтобы устранить влияние масштаба.\n", + "А также понизим размерность с помощью РСА для уменьшения количества признаков для визуализации данных." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Предобработка данных: стандартизация\n", + "scaler = StandardScaler()\n", + "scaled_data = scaler.fit_transform(df[features])\n", + "\n", + "# Понижение размерности с помощью PCA\n", + "pca = PCA(n_components=2)\n", + "pca_data = pca.fit_transform(scaled_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Используем метод главных компонент (PCA) для уменьшения размерности данных до 2D для визуализации." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Неиерархическая\n", + "kmeans = KMeans(n_clusters=3, random_state=42)\n", + "kmeans_labels = kmeans.fit_predict(scaled_data)\n", + "\n", + "# Визуализация кластеров K-Means\n", + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(x=pca_data[:, 0], y=pca_data[:, 1], hue=kmeans_labels, palette='inferno', s=100)\n", + "plt.title('K-Means Clustering')\n", + "plt.xlabel('Количество кластеров')\n", + "plt.ylabel('Инерция')\n", + "plt.show()\n", + "\n", + "#оценка неиерархического\n", + "silhouette_avg_kmeans = silhouette_score(scaled_data, kmeans_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Для иерархической кластеризации потребуется предварительно определить количество кластеров, так как она не возвращает метки кластеров." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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N6d/jjz/u1UIDAAAAQHIehZutW7dq+PDh2rFjhzZu3Kj4+Hjdd999unHjhtN8gwcP1t9//23+e+2117xaaAAAAABIzqOevBs2bHB6/sEHHyg8PFx79uzRXXfdZf49NDRUpUqV8k4JAQAAAMANmepzc+XKFUlS0aJFnf6+bNkyFS9eXHXq1NGECRMUFRWV6jJiY2N19epVp38AAAAA4KkMj8GamJioUaNGqXnz5qpTp4759169eqlChQoqU6aM9u/fr2effVaHDh3S6tWrXS5n6tSpmjRpUkaLAQAAAACSMhFuhg8froMHD2rbtm1Ofx8yZIj5+NZbb1Xp0qV177336tixY6pcuXKK5UyYMEFjxowxn1+9elUREREZLRZgWYZhWOKu1lFxCS4f52Yhgf6y2Ww5XQwAAPK8DIWbESNGaN26dfr2229Vrly5NOdt3LixJOno0aMuw01wcLCCg4MzUgwgzzAMQ93mbdeevy7ldFG86vbJX+d0Ebzi9gpFtOLxpgQcAABymEd9bgzD0IgRI7RmzRpt3rxZFStWTPc1+/btkySVLl06QwUEIEXH37RcsLGS3X9dskStGgAAuZ1HNTfDhw/X8uXL9dlnn6lAgQI6c+aMJKlQoUIKCQnRsWPHtHz5cnXo0EHFihXT/v37NXr0aN11112qW7dulrwBIK/ZPbG1QoP8c7oYkBQVd1O3T96U08UAAAD/z6NwM3fuXElJN+p0tGjRIg0YMEBBQUHatGmTZs2apRs3bigiIkIPPfSQJk6c6LUCA3ldaJC/QoMy3F0OAADAsjy6QjIMI83pERER2rp1a6YKBAAAAAAZkan73AAAAACAryDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALCEgJwuAAAAuYlhGIqOv5nTxUAuExWX4PIx4K6QQH/ZbLacLobPI9wAAOAmwzDUbd527fnrUk4XBbnY7ZO/zukiIBe6vUIRrXi8KQEnHTRLAwDATdHxNwk2AHLE7r8uUWvsBmpuAADIgN0TWys0yD+niwHA4qLibur2yZtyuhi5BuEGAIAMCA3yV2gQp1EA8CU0SwMAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCYQbAAAAAJZAuAEAAABgCR6Fm6lTp6pRo0YqUKCAwsPD1aVLFx06dMhpnpiYGA0fPlzFihVT/vz59dBDD+ns2bNeLTQAAAAAJOdRuNm6dauGDx+uHTt2aOPGjYqPj9d9992nGzdumPOMHj1a//3vf7VixQpt3bpVp0+f1oMPPuj1ggMAAACAowBPZt6wYYPT8w8++EDh4eHas2eP7rrrLl25ckXvvfeeli9frnvuuUeStGjRItWsWVM7duxQkyZNvFdyAAAAAHCQqT43V65ckSQVLVpUkrRnzx7Fx8erdevW5jw1atRQ+fLltX37dpfLiI2N1dWrV53+AQAAAICnMhxuEhMTNWrUKDVv3lx16tSRJJ05c0ZBQUEqXLiw07wlS5bUmTNnXC5n6tSpKlSokPkvIiIio0UCAAAAkIdlONwMHz5cBw8e1Mcff5ypAkyYMEFXrlwx/0VGRmZqeQAAAADyJo/63NiNGDFC69at07fffqty5cqZfy9VqpTi4uJ0+fJlp9qbs2fPqlSpUi6XFRwcrODg4IwUAwAAAABMHtXcGIahESNGaM2aNdq8ebMqVqzoNL1hw4YKDAzU119/bf7t0KFDOnHihJo2beqdEgMAAACACx7V3AwfPlzLly/XZ599pgIFCpj9aAoVKqSQkBAVKlRIjz32mMaMGaOiRYuqYMGCevLJJ9W0aVNGSgMAAACQpTwKN3PnzpUktWrVyunvixYt0oABAyRJM2fOlJ+fnx566CHFxsaqbdu2mjNnjlcKCwAAAACp8SjcGIaR7jz58uXTO++8o3feeSfDhQIAAAAAT2XqPjcAAAAA4CsINwAAAAAsgXADAAAAwBIINwAAAAAsIUM38QQAICMMw1B0/M2cLkaGRcUluHycG4UE+stms+V0MQDAqwg3AIBsYRiGus3brj1/XcrponjF7ZO/Tn8mH3Z7hSJa8XhTAg4AS6FZGgAgW0TH37RMsLGC3X9dytW1aADgCjU3AIBst3tia4UG+ed0MfKkqLibun3yppwuBgBkCcINACDbhQb5KzSIUxAAwLtolgYAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACwhIKcLAOQIw5Dio3K6FO6Lu+nwOEqSf44VxWOBoZLNltOlAAAAeQDhBnmPYUjvt5Uid+Z0SdxnBEtalPT49SqSLTZHi+ORiCbSwA0EHAAAkOUIN8h74qNyV7CRFGqL1Z/5euV0MTImckfSNg8Ky+mSAAAAiyPcIG8be1QKCs3pUlhTXJQ0o0pOlwIAAOQhhBvkbUGh1CgAAABYBKOlAQAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAASyDcAAAAALAEwg0AAAAAS/A43Hz77be6//77VaZMGdlsNq1du9Zp+oABA2Sz2Zz+tWvXzlvlBQAAAACXPA43N27cUL169fTOO++kOk+7du30999/m/8++uijTBUSAAAAANIT4OkL2rdvr/bt26c5T3BwsEqVKpXhQgEAAACAp7Kkz82WLVsUHh6u6tWra9iwYbpw4UKq88bGxurq1atO/wAAAADAU14PN+3atdOSJUv09ddfa/r06dq6davat2+vmzdvupx/6tSpKlSokPkvIiLC20UCAAAAkAd43CwtPT179jQf33rrrapbt64qV66sLVu26N57700x/4QJEzRmzBjz+dWrVwk4AAAAADyW5UNBV6pUScWLF9fRo0ddTg8ODlbBggWd/gEAAACAp7I83Jw8eVIXLlxQ6dKls3pVAAAAAPIwj5ulXb9+3akW5vjx49q3b5+KFi2qokWLatKkSXrooYdUqlQpHTt2TOPGjVOVKlXUtm1brxYcAAAAABx5HG52796tu+++23xu7y/Tv39/zZ07V/v379fixYt1+fJllSlTRvfdd59eeeUVBQcHe6/UAAAAAJCMx+GmVatWMgwj1elffvllpgoEAAAAeJNhGEqIjc3pYmRIfNz/RhyOj4lVfGJCDpYm4wKCg2Wz2bJ+PVm+BgAAACCHGIahj18Yp9OHf8vpomRIvC1AumWwJGnukN4KNHJnuClTvZZ6Tpqe5QGHcAMAAADLSoiNzbXBRpICjQQ9eXxuThcj004f+lUJsbEKzJcvS9dDuAEAAECeMGz+UgUGZ+3FNZzFx8Zo7pA+2bY+wg0AAADyhMDgfFlec4CcleX3uQEAAACA7EC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJhBsAAAAAlkC4AQAAAGAJATldAABAzjAMQ0Z0dLatLzHu5v8eR0UrMcE/W9ZrCwmRzWbLlnUBAHIW4QYA8iDDMPRXr96K/umnbFtnjH+QdP+rkqQjzVso3824bFlvSIMGqrBsKQEHAPIAwg0A5EFGdHS2BhtJynczTl+sHZut65Sk6L17ZURHyxYamu3rBgBkL8INAORxVb/fJr+QkJwuhtclRkfrSPMWOV0MAEA2ItwAQB7nFxIiP2o1AAAWwGhpAAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgg3AAAAACyBcAMAAADAEgJyugAAACBjDMNQQlyiR6+Jj7v5v8exNxVv2Nx+bUCQn2w29+cHgOxGuAEAIBcyDEOrX9+rM39c8eh1cTKkwkmP339mm4LkflgpXbmQuo5tQMAB4LMINwAA5EIJcYkeBxtJCpJNz1wOydA6/z52RQlxiQoM9s/Q6wEgqxFuAADI5R59rUWWBo742JtaNG5bli0fALyFcAMAQC4XGOxPbQoAiNHSAAAAAFgE4QYAAACAJRBuAAAAAFgC4QYAAACAJRBuAAAAAFgC4QYAAACAJTAUNLKHYUjxUTldiiRxUa4f57TAUIm7fgMAAGQY4QZZzzCk99tKkTtzuiQpzaiS0yX4n4gm0sANBBwAAIAMolkasl58lG8GG18TucN3arcAAAByIWpukL3GHpWCQnO6FL4lLsq3apAAAAByKcINsldQqBQUltOlAAAAgAXRLA0AAACAJXgcbr799lvdf//9KlOmjGw2m9auXes03TAMvfDCCypdurRCQkLUunVrHTlyxFvlBQAAAACXPA43N27cUL169fTOO++4nP7aa6/prbfe0rx587Rz506FhYWpbdu2iomJyXRhAQAAACA1Hve5ad++vdq3b+9ymmEYmjVrliZOnKjOnTtLkpYsWaKSJUtq7dq16tmzZ+ZKCwAAAACp8OqAAsePH9eZM2fUunVr82+FChVS48aNtX37dpfhJjY2VrGxsebzq1everNIAAAAyADDMJTgcI2WW8XHxrh8nJsFBAfLxn3xXPJquDlz5owkqWTJkk5/L1mypDktualTp2rSpEneLAYAAAAywTAMffzCOJ0+/FtOF8Wr5g7pk9NF8Ioy1Wup56TpBBwXcny0tAkTJujKlSvmv8jIyJwuEgAAQJ6WEBtruWBjJacP/WqJWrWs4NWam1KlSkmSzp49q9KlS5t/P3v2rOrXr+/yNcHBwQoODvZmMQAAAOAlw+YvVWBwvpwuBpTUrM4qtU9ZxavhpmLFiipVqpS+/vprM8xcvXpVO3fu1LBhw7y5KgAAAGSDwOB8CsxHuEHu4HG4uX79uo4ePWo+P378uPbt26eiRYuqfPnyGjVqlCZPnqyqVauqYsWKev7551WmTBl16dLFm+UGAAAAACceh5vdu3fr7rvvNp+PGTNGktS/f3998MEHGjdunG7cuKEhQ4bo8uXLatGihTZs2KB8JH4AAAAAWcjjcNOqVSsZhpHqdJvNppdfflkvv/xypgoGAAAAAJ7I8dHSAAAAAMAbCDcAAAAALMGro6UBAAAAyDsMw0jznjvxsTEuHycXEBzslZuSEm4AAAAAeMwwDH38wji3b/ia1j16ylSvpZ6Tpmc64BBuAEBJB+johGiPXhMVf9PhcbRk8/fo9SEBIV75lQoAgJyQEBvrdrBJz+lDvyohNjbT91Qi3ADI8wzDUL8v+mnfP/s8e11ioKRXJEmtPm0pm1+8R6+/Lfw2LW63mIADAMj1hs1fqsBgz4NJfGxMmjU6niLcAMjzohOiPQ42kmTzi1eBmuMzvN6fzv2k6IRohQaGZngZAAD4gsDgfJmudfEGwg0AONjSY4tCAkKydB3RCdFq9WmrLF0HAAB5EeEGAByEBIRQkwIAQC7FfW4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWEJAThcAAAAAzgzDUEJsbI6tPz42xuXjnBIQHCybzZbTxUAuQLgBAADwIYZh6OMXxun04d9yuiiSpLlD+uR0EVSmei31nDSdgIN0EW4AwKIMw5ARHe1yWqLD3xNTmcfOFhLCBQWQjRJiY30m2PiK04d+VUJsrALz5cvposDHEW4AwIIMw9BfvXor+qef0p33SPMWaU4PadBAFZYtJeAAOWDY/KUKDM67F/TxsTE+UXOE3INwAwAWZERHuxVs3BG9d6+M6GjZQkO9sjwA7gsMzkdtBeABwg0AWFzV77fJLyTE49clRkenW6sDAIAvIdwAgMX5hYTIj1oXAEAewH1uAAAAAFgC4QYAAACAJRBuAAAAAFgC4QYAAACAJTCgQF5lGFJ8VPasKy7K9eOsFBgqcU8OAACAPIVwkxcZhvR+WylyZ/ave0aV7FlPRBNp4AYCDgAAQB5Cs7S8KD4qZ4JNdorckX01UwAAAPAJ1NzkdWOPSkEWuv9FXFT21Q4BAADApxBu8rqgUCkoLKdLAQAAAGQazdIAAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWAKjpQFALmcYhozoaKe/JTo8T0w2zRYSIhs3uAUAWBDhBgByMcMw9Fev3or+6adU5znSvIXT85AGDVRh2dJcE3BchTd3pBXwPEEYBIDcg3ADALmYER2dZrBxJXrvXhnR0bKF+v4NfN0Jb+5IHvA8kdvCIADfZhiGEmJjM/Ta+NgYl48zIiA42JLHNcINAFhE1e+3yS8kJNXpidHRmbrIzwkZCW/elpvCIADfZhiGPn5hnE4f/i3Ty5o7pE+mXl+mei31nDTdcgGHcAMAFuEXEiI/C1+ApxfevC03hkEAvi0hNtYrwcYbTh/6VQmxsQrMly+ni+JVhBsAQK5g9fAGIG8ZNn+pAoOzP1jEx8ZkutbHlxFuAAAAgGwWGJzPcrUmvoD73AAAAACwBMINAAAAAEsg3AAAAACwBPrc+BrDkOKjsnYdcVGuH2eVwFDJYsMMAgAAwPcQbnyJYUjvt5Uid2bfOmdUyfp1RDSRBm4g4AAAACBL0SzNl8RHZW+wyS6RO7K+NgoAAAB5HjU3vmrsUSkol9/PIS4qe2qGAAAAABFufFdQqBQUltOlAAAAAHINmqUBAAAAsATCDQAAAABLINwAAAAAsATCDQAAAABLYEABwJsychNWb9xUlRulAgAAEG4Ar/HGTVgzOnQ2N0oFAACgWRrgNTl5E1ZulAoAAEDNDZAlsusmrNwoFQAAwES4AbICN2EFAADIdjRLAwAAAGAJ1NwAAOCDDMNQQlxiqtPjY2+6fOxKQJCfbAw4AiAPINwAAOBjDMPQ6tf36swfV9yaf9G4bWlOL125kLqObUDAyQTDMJQQG5st64qPjXH5OKsFBAezjyDXI9wAyDMMw1B0QnSKvzv+zdV0SQoJCOGkj2yTEJfodrBxx9/HrighLlGBwf5eW2ZeYhiGPn5hnE4f/i3b1z13SJ9sW1eZ6rXUc9J0jnXI1Qg3APIEwzDU74t+2vfPvjTna/VpK5d/vy38Ni1ut5iTPrLdo6+1yHAoiY+9mW6tDtKXEBubI8Emu50+9KsSYmMVmC9fThcFyDDCDYA8ITohOt1gk5afzv2k6IRohQZmwxDfgIPAYH9qXHzIsPlLFRhsrYv/+NiYbK0hArIS4QZAnrOlxxaFBIS4NW90QnSqtTkA8p7A4HzUbAA+jHADIM8JCQihBgYAAAviPjcAAAAALIFwAwAAAMASaJYGAAAAIF3J7/eU3j2ZcuLeSYQbAAAAAGlK735Prkbcy4l7JxFuAAAAADclr73wRHo1He7KiRqRjNzvKSfuneT1cPPSSy9p0qRJTn+rXr26fv/9d2+vCgAAAMg26dVeeCIz9xbKiRoRR+nd7ykn752UJTU3tWvX1qZNm/63kgAqiAAAAJC7ZaT2IivkRI2II1++31OWpI6AgACVKlUqKxbtOwxDio/y7jLjolw/9pbAUCmHEj4AAICVpFd7kRVyskYkt8iScHPkyBGVKVNG+fLlU9OmTTV16lSVL1/e5byxsbGKdWi3ePXq1awokncZhvR+WylyZ9atY0YV7y8zook0cAMBBwAAIJN8ufYiL/P6fW4aN26sDz74QBs2bNDcuXN1/Phx3Xnnnbp27ZrL+adOnapChQqZ/yIiIrxdJO+Lj8raYJNVInd4v7YJAAAA8BFer7lp3769+bhu3bpq3LixKlSooE8//VSPPfZYivknTJigMWPGmM+vXr2aOwKO3dijUlBoTpcibXFRWVMTBAAAAPiQLO/pX7hwYVWrVk1Hjx51OT04OFjBwcFZXYysExQqBYXldCkAAACAPM/rzdKSu379uo4dO6bSpUtn9aoAAAAA5GFeDzdjx47V1q1b9eeff+qHH35Q165d5e/vr0ceecTbqwIAAAAAk9ebpZ08eVKPPPKILly4oBIlSqhFixbasWOHSpQo4e1VAQAAAIDJ6+Hm448/9vYis05G71XjrfvRcN8ZAAAAwGuyfEABn+Wte9VkZhQy7jsDAAAAeE2WDyjgs3zhXjXcdwYAAADwmrxbc+Mou+9Vw31nAORhhmHIiI52a95Eh/kS3XyNLSRENmrEASBPItxI3KsGALKJYRj6q1dvRf/0k8evPdK8hVvzhTRooArLlhJwACAPyrvN0gAA2c6Ijs5QsPFE9N69btcMAQCshZobAECOqPr9NvmFhHhteYnR0W7X7gAArIlwAwDIEX4hIfILzcb+jgAAy6NZGgAAAABLINwAAAAAsATCDQAAAABLoM8NAAAAcoxhGEqIjXU5LT42xuVjRwHBwQz9DhPhBgAAADnCMAx9/MI4nT78W7rzzh3Sx+Xfy1SvpZ6TphNwIIlwA8BThiHFR6U/X1yU68dpCQyVODkBQJ6REBvrVrBJy+lDvyohNlaB+fJ5qVTIzQg3ANxnGNL7baXInZ69bkYV9+aLaCIN3EDAAYA8aNj8pQoMdj+gxMfGpFqbg7yLcAOkh5qK/4mP8jzYeCJyR9I6gsKybh0AAJ8UGJyP2hdkGuEGSAs1Fakbe1QK8tINGOOi3N9mAAAAqSDcAGmhpiJ1QaG5s9wAAMCyCDdwj7tNsxxlpJmWI19rskVNBQAAgE8j3CB9GW2a5SgjF/K+1mSLmgoAAACfRrhB+rK6aVZqcnOTrdwutZo6d2vjfK3WDQAA5AmEG3jGm02zUkOTrZzlbk1dWp+Rr9W6AQCAPIFwA8/QNMv6vFFTR60bgBxmGIYSYmO9sqz42BiXjzMrIDhYNn4EAryKcAMgdZ7W1FHrBsAHGIahj18Yl+k737vizZtGlqleSz0nTSfgAF5EuAGQOmrqAORCCbGxWRJsvO30oV+VEBvLjSsBLyLcAAAAyxo2f6kCg30rPMTHxni1BgjA/xBuAABZyjAMGdHRkqTE//8/+WNJsoWE0DwHXhcYnI+aESAPIdwAALKMYRj6q1dvRf/0U4ppR5q3cHoe0qCBKixbSsABAGSYX04XAABgXUZ0tMtg40r03r1mDQ8AABlBzQ0AZDHDMBSd8L+L9tQehwRYu1lW1e+3yS8kJMXfE6OjU9TiAACQEYQbAMhChmGo3xf9tO+ffS6nt/q0lfn4tvDbtLjdYssGHL+QEPmFZvFNgHMpwzCUEJdoPo+PvenysSQFBPlZdh8BgMwi3ABAFopOiE412CT307mfFJ0QrdBAAkBeYhiGVr++V2f+uOJy+qJx25yel65cSF3HNiDgAIALhBukzjCS7jIfF/W/v9kfB4ZKnFgBj2zpsUUhASmbZUUnRDvV4CBvSYhLTDXYuPL3sStKiEtUYLB/FpYKAHInwg1cMwzp/bZS5E7nv9vvPh/RRBq4wXcCTlpBTCKMwSeEBIRQK4M0Pfpai1RDS3zszRS1OIA7DMNQQmxsqtPjY2NcPnYlIDiYWkP4NMINXIuPShlsHEXuSJrHF+5en14Qk1IPY/ZQlJrUwlJy2RGeXJXVnfIR7IBcIzDYnxoZeJVhGPr4hXE6ffg3t+ZP7+aiZarXUs9J0wk48FnWCzfpXazauXvR6iivXiSOPSoF/f+vzXFRzqHBF6QXxCTXYSy1UJSatN53VtdkuVPW1Mrna7VseYR9hLS8OjIacjdPBjiQGOTAlyXExrodbNxx+tCvSoiN5cao8FnWCjeeXqzauXuxnlcvEoNCfaOGxh2OQUxKO4y5E4rcldU1WZkpqy/VsuURqY2Q5o2R0QzDcLoXTGIqjyXJFkKAguc8HeBAYpCD3GLY/KUKDM5YKImPjUm3VgfwBdYKN968WHWFi0Tfl9EgljwUuSsnarLcLasv1rLlEe6MkJaRkdEMw9BfvXqnelPM5PeKCWnQQBWWLeWCEx7xdIADiUEOcovA4HzUuMDyrBVuHGX0YtUVLhKtLzfVTuWmsiLFCGmZGRnNiI5ONdi4Er13r4zoaNm4twwyKK0BDiQGOQDge6wbbrgABOADsmqEtKrfb5NfSMphpaWk5mnJa3GAjGCAAwD20fZcjarni6PnWTfcAICF+YWEyI8aGQCAGxyHA/ckpKQ22p69/5Uvjp5HuAFgWfYRyySlOmqZxMhlAADrSms48PRCSnqj7fni6HmEGwCWlNqIZZJS9HnJ6MhlAAD4OneGA3cnpDiOtufLo+cRbgBYkjsjltllZOQyAABym+TDgXsSUnLLaHuEG+RuhpHyhqz2m62mNQ15SvIRy+wyM3KZtzg2nZOSykQzOetzvElmWjfI5OaYALwptwSUzCDcIPdyddPWGVWSbrb66BfSonaup+XFG7HmcVk1YllmuWo61+rTVjSTy0GON0lN7Qapmb05alo3yUw+rDI3x4TVOHZsl1x3brfzxZG44PsIN8i9Urtpa+QOKepC6tO4ESt8RMzNGJdN52gm5x7HICKlHkYk9wJJWjdJdRxaO7M3R/XkJpncHBNWklbHdkkpmkf54khc8H2EG1jD2KNJ/7u62Wpa0wAfsaXHFkkpBzuAa2kFEUkp7vPjTiBx9yap3rw5amo3yeTmmLAidzq2O/LFkbjgPWndP0fKeM0d4QbWEJTGRUZa0wAf4apPEFLnbhCx8zSQuLpJalbcHJWbZCKvSt6x3ZEvj8QF70jv/jnS/2ruPJV7w41hJDUvcpS883hydCYHAMtxFUTsMhpIuEkqkLXyQsd2pM6T4ak9lTvDjauO5Mm5aoJEZ3LflDyophVSCagAkiGIZI7jyG2Sb43elrzzubvS6qTuCTq0Z57jZ+jqc/GVbezuvpbRfctX3qcvyszw1K7kznCTWkfy9NCZ3PekF1STh1QCKrKJfYjm5MM02zFcM6wgrZHbpJwdvS29zufuysxFEh3aMyetz9D+ufjCNs7ovubJvuUL79NXebsWL3eGG0djj6bfpyIuis7kvsrToEpARTZwNUSz5NzZn+GaYQWejNwmZe/obZ52Ps8K3urQnleHP/ak6VFONlHLjn3NF95nXpH7w01QKBe6VpFWUCWgIhtFJ0S7HKLZkVWHa86O+7zAN6U2cpuU86O3pdX5PCt4s0M7wx8n8XbTo6zi7X3NG+8zvaZ9krVCcWbl/nDjq1wNeGCX3sAHUt7sW0JQRSbZm5JJ8lpzsi09tjiNZBadEG3Z4Zqz6z4veZFjvxZf6tPiyJdHbsvNnc8Z/jhJbvkMfa2c7jTtk6wbijOCcJMV3BnwwC612gj6lsAxILsKxN4MwNm5riySWlMyKXPNyUICQixXO5OanLjPS16QVr+W1Pq0wJoY/hiecjccWzUUZwThJitkdMADR/QtyVqGkfIi3t0L+PSCgJT5MJBWQLYHYm8F4OxcVxZypymZZN3mZN6WXfd5yQs86ddi79Nide6MTOXJqFS5pUmOr9UKIHdxFY4JxSkRbrKaOwMeOKJvSdZzdTE/o4p7F/DuBAEp82HAnYDsrQCcnevKJsmbkknWbk6WFRheOWuk1q8lp/u0ZKeMjEyV3sUbTXKQFxCO3UO4SYv9F/rMNNOhH4nviY92fTHvzgW8u7Vy3gwDyQNyVgbg7FxXFspLTcnyEisMduDL/VqyS1aMTEWTHAB2hJvUpPYLfS5qpgM3jD2a9H9GLuBd1cplRRjIzoBMGIePYrADa8rsyFS+2iTHndGtpNzTnA7ITXJXuPFGTYq70vuFPpc100EqPGky6Oq1fP5IxnHENul/I7Vx08/MYbADa7JiMxt3R7eSaE4H67EH++ShPjuDfO4JNzlZk+L4C30ubaYDIOu5GrHN3s/HPkpbRpdrREenaIrl602wsgqDHcCXedLsjuZ0sJLUgv3cIX2yNcjnnnCTkzUp/EIPwA1pjdhmH6XNU6k1xzrSvIVPN8FKLZBJme8X4+3BDrKyrDnFfl8dV/fUycl76eQ1qTW7y+rmdDSLQ05IK9hnZ5DPPeHGkS/WpFjgPiGwiNSab7IPuiV5szLDMDK0HPuIbZkdpS2t5li+2gQrrUAm+Va/mNxUVneldl8d+2hs9nvpZNd7Sj7sc3pDPFvpgjsnmt3RLC51qQ1Bnpf2yexiD/Y50S8ud4YbX6tJsch9QmABaTXfZB9Ml6tmZUM2DdHS9p5f3GbFiG325li+3gQrvf4xvhTKsqqs9poTSS5rTyTPalAMw0ixnNRen959dez30smOUdvSG/bZ1UVPXrvg9jZfaRaXtM/mXL8LV+VxZwhy9knvyMn+dLkz3PgaC94nBLlUWvsi+2C6XDUr2//Pfp+56WduvPeMY/8YV6HMMAyn5mAZrSnzhvTK6q7Uak4kOd3Lxt0aFFfLWzRuW4rXu2qK1ndyU+XLHyibzZYj99LJyLDP9EPxnpxsFpc8SGR3v4vkMjMEOftk7kK48TaL3CcEFmDfFy28Dzo2IfP2CGVfPPiF2q9un/lC5nFpBTJXTcJODHxMt3z8UY5c/HgrPKZXc2Lnbg1KastzfH1qgerDidvNEJTT0hv22VeHdc7NMvPruSfNCZPXyKQWJHwlJLg7BLkV9klXNWiStZvaEW6SS2+46fT4WpM55F0W3xddNSGzSz5CWUYO4CEBIenP5IOS14Q4doz3Na6ahMX8/LPPNFnzhkdfa5EivGSmBuXR15Jqk5K/Pq1AZQ9BOc2Kwz5blafNCdOqkRk2f6nL1+SkvLIvplaDJv3vM7Miwo0jd4ab7rMq+8uVneiMDk+lN5iGlCX7T1ojk9nZRyjzhSZl2cFVTYhjx/iIBfPTfG1OjhZWedNGHWvdJsvXk90Cg/292rfFnWXZA1VONEPLapmpTbAiVx3kvbFNPG3ClVaNTGZu0orMcWf0Misi3Dhya7hpz4dyTZc7F4dS1gcMdzqje2Md8am8x+x+v57w1g1ks/NGtO6WJzNlcWcwDSnLBzOwj0xml9kRynKrdEdWi0k5CpCUfijKjtHCkt+3Bhnn7UDlK7xZm5AVZUtv6GVvhy13Osintk08kVYTrsw023LsX+fpYAOp3ShSyhuhNiNycvSy7Ea4SU12DTft7sWh5NkFYkYCkzud0TMjrfcqZe79ZiVv3UA2J29E6255kpclPe4MpiGlPphBWmFPcjtkZcXIZLmdJyOr5cbhppF5nozA5gu8WZvgTe4OveztsJWZwRo8kRVNuAzD0IrJE83nngw2kNaNIqWcH9nMW7WLqQW4jIa3vNIUTyLcpC67+iu4e3EouT/alTcCU1Z0RvfkvUq+M7qXt24gm5M3ovW0PBkJs8kH05DS3n/SC3uS7wTcXCijneNzy3DTyBx3R2DzVVlVm5AR7oaMrAxbuW2whoTYWJ05esjpb+5un/S2d04OWuCt2sW0AlxOh7fcgHDjS1xdHEqeBwxvBKasDnepvVcp5ft11XQqp36lT69Gz3EY27T6KvnajWi9EWY93WcYQt0nZWbEMMMwZERH55oBDTzhqk9SdvVHygrujMDmy7KqNiGzTctchYzsCBa5+Vf5QbMXauGTg5z+5m6TNcft7QsBzlu1i+70lcmtn3d2INz4kqwIFN4KTBmRVv8aV1wFgdSaTuXU4A5pfUaGIS3p/L/nad0409dGMsvp8jCEeq7nqt+O5P6ABr7MMAydcNEnKbv6I2W11EZgy0u81bQsN4eMnJI8DHrSZM2Xt7e3ahfzUl8Zb/H9cMPoXe5JrY+NXXZvL0/710iu+3nER7teRlYN7pAZ8VHSqd3Of8tIzUNqNVVW3t9zOlx5geM9d+zP85K0+u1IaQ9o4OuMmBiX781X+iOl1nfGXb5eS5MdfKFpGZJkpsmaL/FW8MqpAOdpnx9X99MJCA7OlrIm59vhJjOjd6XXod5KF4vu9LHJ6r4LrmppPOlfI6Xfz2Ps0aT/s+sX/cwE65E/S2/Wy9g6U6upou9JhiUPHtEJ0V69j42re+4M2TRES9sv9do6chN7vx1Jluu7U/X7pNoNV+/JMAwlxCVmayf9tPrOdHoyA8cg5FjTMl/g7U7smeWqyZqnsuM9+dp2yyx3+vykN7993ocmTMqWMjvy7XCT0Q7PvnCxn10MQ7pxPmf7LqRXS5Nc2UZSv7X/2/buNkFKrY9OVsjssNiBGdzOadZU0fckI1wFj1afttJt4bdp7r1zvbIOV/fc2f/PfqdAlZdkpt+Or0ttyGpXIUNK2Uk/eS1LZmv40us7k1O8PVRv8l+Fs7Jm1JebOmUlX+zEntl75GTHe/JkHbnle+Hp/XES4lzPf/rQr0qIy/576fh2uHHkSYfnvNJRObULcMfwEHdDmlE16e9Z1bzJ01HQTu1KKoMvb/usHhbbHZ7WVPnSwAvpSV7WLLxQibkZ4/Jmnz+d+0kxN73fTOqLB79Q+9Xtvb7crJK8o3xaJ0fHabm9Q31qgx9k9D2lFjKk/wWNgCC/FAHoP2/u00PjGnr+Blzwlb4z3h6q19XyVkx5Xr1eed0r+5+r5jRS9t4vJbUmPY7rz+obmFqxE3t2vCd315Hbvhd2nvb5GTY/qcVCWvO6s79nRu4JNxltk+/LHZXducBL6yaLqV2A28NDYKi0+IH//d2bN+NMjSejoOUWWTEstjs8qanK7oEX3Gmyl9r+7aqsS7pIgzYmvTYLg8+WHlskKUtv8OnN5m5ZzdUgACcGPqZbPv7I5bwnHh1oPs/NHerTGvzA/p48XZ5jbUzfyU2VL3+gEuISnYKGqwB09vhVr9Ww+ErfGW8P1etqeWeO/J5iGWn9Ki65vuBPrTmNlH33S0mrSY99/dl9A1MrdmLPjveU1jpy6nvhqeTBQ5JHfWfSq21zZ3/PrNwTbtyV/MJIcq+2Irs7cad3gZfaPI7N6hwvWl39yp9WB3dX5fHGr/4W6Bie6j7kC1L7nFILulkx8IK7TfZS279dlfXUrqS/B4am/73IhNwUPKTUhx/22vJdDAIQ8/PPMqJT7jNGdLRi9u93+puvdKj3lDs3LXV7WYahNclqYz6cuD3dPi99JzfVhxO3u1/oXMrbQ/Wm1gcjvV/FJdcX/N74Zd/dYYtTk2aTnv9ff3bfwNSdpnlp1XhlpYxu7+xobujuOtL6XmSkY743+ibZ153VfWdS25ft+2xAcHCK92/Is/O/tcJNRjtjp/c6d9Zr524oSusCzx4O0m0a5XACTu9X/rQ6uGfmV3+rjezla0NPO3K3bO40Z8tMmHW3yV5q+7ej5PulO98LuR4gQEoKL7mtFiE1rmoX7DULroZU9qR5mSuVN23UsdZtvD6vr8vsTUsz2uclIMg3almyQvJ9z5vNTVL7Vdidi//0Lvgz8su+J8MWu8OdJj2+cAPT9Gq8sqoTeVrb29vr8bQW0BOphaCMhgtX+0NGQlJ2951Jvr+n9v4L31LZo+VaK9xktDN2mr96p9O3wpN7m6TGnZG1Mts0Kq0O7hkdbtndMJmbhvP25aGn3S1bekHXmwHOnf0yrf07rf0yldelNkCAJN0WfpsWt1vsZuE9l53DPRvRaQw/nGxIZU+al6UmtY7ymZ3XG1zVYEnySp8fbw5+4Ct9XrzJ0xHgsuviM/k6HS/iHn/3QwXl+98+6u4Ff0Z+2ff2sMXuXKRK2dsfyJV0a7yyqBN5WtvbWzJaC+gN3goX3qiBcSdoZ1by/T2193/myO8eLdda4cZRRocN9vR13ri3iasLvOS/rAeGZE9zL0/evzthMrOjjuWk7B562hOZKZs3m7C50wwxoyPHuXidYRi6GHPR5QABUtIgAVk1QllODvec1vDDkmfNy3Kb1GqwJDn1j/HWwACZ4St9XrzFnRHgksuOi8/kZUx+Ebd2xhSvd6p2pymUt5oGJV9vVvdPsK8no/coyan+OVm1vaOvXnG7FtBVE6rUtpunzekyEy7SbPrlZkjK7Eh1mZWZ9+/+Xb5ym6DQjA0dnNHXSUm/NHuDPRA4Xrgu6ZKlI0qZMvr+xx793wW3I18YdSyjMrMvuJK8+WJmPk9vlS21zy01rpqzZcd+qf+Fi+SDAdQtUVffdP8my9efk8M9+4WEuF1bUnnTxiwuTfZKr39MYlSU/urV2yn4HWneQn/17pOlNWtWZxiGoq/FpzsCXFoGzV7o9rriY2JStrN34/NLq1O1uexMDpXrqjbq4xefTbEsd2pdPF1/ev1xvMEeoBwvIlN7j67Ya7yy+2LYnY7rnuxTrraDXamqNfTkByvMC+7U5k9tu7m7DyV/f97YpsPmL3Uqd26Rmffvu+EmBy+iMiy1Ghg7d99DWv0OfJU7F9tjj0r/Ou3ZxbRVuGq++H67nN+nPQlJrkJ3Nr4PV+FCSgoY2f0L/RcPfpGt6/NEdjcZs0s+TLQ3gkXy5mhVtn2n6nv3mDVZkmTEpNF8zwI1VznBXmOTvHldyYoFNWB6c7eX487FZ1x0tD6aOFZv9e+W4Ytru+RhytXF54opz3seMDJYG+Wt9dtl1UWqr92jxBsyEtjSamp35sjvstlsTvu0J9stu2s0HXkrJOUmvhtuPuyaYxdRXuONC1pv1QblhNRGHcvOm3H6Ck9GrvNVPtQfaUuPLeawzjkht426ltVcDROd2ZoTe3M0xxqZk8NHyJZGLVbV77c5BR9kTGqDJJw9ftWrzaA+fmGcZg/orr+TXfTZeXrxl6L9fjq1Ohnhbm1UVqw/Oy5Sc+uv/MllNrANm79UTy1e6fa28GS7ebIPIWN8N9wkvxCUfKNTtye8cUGb0f4KOS2Hf+X3abk5sNp52pzNy0ICQggYPiStYaIzs0xP+xG503zP1eAENF9L3aOvtTAHSvCm1C4+JWnQ7Pe8vj5XF5SeNluSMt4PIbdc0LoToLzR1C87uRM8kr+ngKBgj5raeRI881otSk7w3XBj582LqJxs6maFC1pP+NCv/D4ntwZWR97ujwTLyIo+P95apqvaIPrnpC0w2D/LB0pIfvEZmAX3SUl+QZnZfiaZXb+3pdZvKT7Gvb5LnqzHm03tskN6wSM3viekzfdHS/PWBVRO37/EChe0GeXLo44B8Kqs6PPjrWWm2T8n2fDayD458Uu2N0aT8hXpDV2c1TdgzGxTv5xmxfeU1/l+zY23UJOQc/iVH4CPoX8O7HJ7P5P0bmCaVYEttzS184QV31Ne5Ps1N1mBmgQAyNNyalQ5+B4r9YGw33NGcv/mpRllpe1mZ8X3lBdlWc3NO++8o1tuuUX58uVT48aN9eOPP2bVqjxHTQIAALCA5H1DAoI96wwPWE2WhJtPPvlEY8aM0Ysvvqi9e/eqXr16atu2rc6dO5cVqwMAAMhzMnJzSMDqsiTcvPHGGxo8eLAeffRR1apVS/PmzVNoaKjef//9rFgdAABAnpOTN4cEfJXX+9zExcVpz549mjBhgvk3Pz8/tW7dWtu3b08xf2xsrGIdvoRXriTdOOxq7P//6nD16v/PaH9+zeFxdkzL6fVT7twxLafXb/1yR9lsuhl98/+fJs1rf37t6rVcMc2zea/p+k3naebza9k8LafX78vlvnpV0XE3nKY5Ps/oNG8tJ7unyYhXTHy8+dzxsSSfnZbT6/dGufu9/raWPDPCZ8pm9e3ta2XLreV2Z96Y+ARJ7t9TyWZ4ue7y9OnTKlu2rH744Qc1bdrU/Pu4ceO0detW7dzpPGLZSy+9pEmTvDNEIQAAAADriYyMVLly5dKdL8dHS5swYYLGjBljPk9MTNTFixdVrFgx2Wy2HCwZAAAAgJxkGIauXbumMmXKuDW/18NN8eLF5e/vr7Nnzzr9/ezZsypVqlSK+YODgxWc7G7EhQsX9naxAAAAAORChQoVcnterw8oEBQUpIYNG+rrr782/5aYmKivv/7aqZkaAAAAAHhTljRLGzNmjPr376/bb79dd9xxh2bNmqUbN27o0UcfzYrVAQAAAEDWhJuHH35Y//zzj1544QWdOXNG9evX14YNG1SyZMmsWB0AAAAAeH+0NAAAAADICVlyE08AAAAAyG6EGwAAAACWQLgBAAAAYAmEGwAAAACWQLgBAACA5Xz77bf6448/1KdPH/Xo0UPffvutOe3zzz/X559/rvXr16tr1676/PPPU13Oxo0bNXjwYO3bt0+SNH/+/KwuOjIhV4+W9ssvv6hkyZKaOnWqbty4oaeeekq1atWSJHMnNQxDCxcu1ODBg9WhQwdJSTt7uXLl9MILL+j8+fPq1auXbr/9dr377ruqWrWqDh06pG7dumnSpEkqVaqUXn/9dZUtW1Z33nmnXnjhBbVp00YxMTFatWqVTp06pRo1auj222/X7t27de+99+q9997T3XffrVtvvVWSdObMGR07dkwnT55UuXLlZLPZdPLkSdWoUUNVqlTR0aNHVadOHa1bt06NGjVS6dKlJUmxsbHavXu3+brmzZub7/2TTz7Rpk2bNHz4cNWvX1/z58/XkCFD0n3vyX333XeaNWuWYmJidPnyZRUvXlxPPfWU3n33XRUoUEAjR47U1KlTJUk9evRQ165dJUlHjhxR1apVJUlxcXEKCgrSli1bdO3aNbVr106BgYEp1vXjjz/qypUr+vTTT12W2+7y5cuKiYlRqVKlUv3s169fr7Vr12r48OGqU6eOZs2apVGjRmn79u2KjY1VpUqV9MILLygmJkZ9+vRRp06dtH37dp09e1Zbt25Vt27dNGvWLPXt21cPPvigJGn//v3673//qwEDBqhs2bKaNGmSDh8+LJvNJkkaNGiQWrVq5bI8Cxcu1KBBgyQlHQTTe4+S834YFxen22+/XXXq1HHrc3OU1mdYrFgxXb16NcV7OHjwoK5fv64mTZpIkjZs2KAmTZro6NGjqlatmpYsWaIHH3xQX375pa5fv65evXqpWLFiunz5sq5fv67AwEAVK1ZM27dvV6NGjZQvXz7zM77jjjvMssXGxio4ONh8vm/fPp04cUL33nuvnnnmGT3++OOqW7euJOn777839/WGDRs6fb8eeOABcxnufrfj4uI0YsQI3XXXXS6325YtW7RgwQK3Pl9HyT/fJ598UoMHD9bUqVN15coVPfzww9q/f79u3LihDh06qEqVKpKkF154QVeuXFFQUJCKFi2qW2+9VQcOHNCAAQM0cOBAtW3b1uUJMzo6Wm+++aZsNpvKlSunrl276tKlSypbtqxiYmK0cOFCRUVFpTgOJd++ly9fNj/fzz77TE2bNtXly5dVrVo1rV27VvXr1zfn/fnnn1WrVi0tXbrU/Pz//vtvp31Gkl599VVNmDDB3IZnz55V0aJFtXTpUp06dUq33HKLunbtqvfee0+tWrVSsWLFVLZsWUVHR5vlLleunDp06KDPP//c6fO276M1atRwKne7du1UokQJbdiwQS1btjT3k3LlyqlTp04qVKiQy2PR7t27FRQUpDp16mj+/Pnq3LmzeayVpB9++ME8Ltu3Q/L9WUo679i3fWJiYorjvs1m0/Xr13Xt2jW1bt1aNpstxfmicePG5nZy/H7ZP2/H9xQaGqrg4GCX+7vjZ5r8Mzx9+rTKlCnjVO7atWtrw4YNuu2221Ks3/751q9f31x/iRIlVKxYMfP95cuXT4MHDzY/b0efffaZrl27Zpb7wQcf1E8//ZRimyb/Dl2+fFnPPvuseT654447tHXrVsXExKT4nrz00ktq1KiR/v77b0nSqVOn9Pzzz6tNmzaKiopyOpcWLFhQderU0dSpU3Xy5En17t1bVapU0bvvvquaNWuqfPnyLrep47HccbtJzseXf/75R8OHD9fWrVtTfNcffPBBjR071lzO+vXrFRER4fJcOmLECNlsNpfXHKtWrdJDDz2UYlunx5PjoLvnq/SkdVwePHiwrl27Jn9/f0VGRiokJET/+te/NGvWLN15552Kjo7WgAEDFBUVpf3795vv2d1ri+Q+/PBDlStXTnPmzNG+fftUokQJrV27VoULF1bZsmX16quvqlevXmrVqpUefvhhlShRQsuXL1fp0qWVmJioDh06aOnSperYsaOGDRsmSXrkkUdUvXp1Xb9+XR06dNCSJUu0YMECLV26VOfOnVPDhg11xx136LPPPlO+fPnUrVs3p+9J8nOuJH3xxReqW7euypYtKylpf5ZkPnf8DjueB06fPq1jx47pypUrKl68uEqXLq1Lly6pXbt2WrdunSpWrKjXXnst3e2UfD+pUqWKIiMjXZ6THPfhtM5zzz77rAoWLKghQ4ak+HyT79PPPPOMTp8+7fE52B0+F25WrVqlJUuW6OLFi7p48aLKlCmjPn36aN26ddq+fbtefPFF9erVS2FhYRo2bJgSExM1YsQIbd26VatWrVLx4sUVEBCgH374QU8++aRKlCihUaNGqWvXrpo9e7b5utjYWE2bNk1NmjRRvnz59PDDD2vDhg26fPmytm/frm7duqlIkSK6efOmChcurNGjR+vBBx/UiBEjtHHjRv3zzz8aOHCgfv31V9WrV0+vvvqqJk+erPnz52vixImaPXu2li1bJsMw1KhRI40YMULly5c3b2hao0YNRUREaPXq1erTp4927NihuLg4HThwQAMHDtSkSZNUu3ZtDR8+XOXLl9fRo0f1yy+/6M0335RhGKpXr552796tyZMnq0OHDnr11VdVvHhx+fv7a8OGDWrXrp3uu+++dL+wTZo00ffff6/nn39ef/75pyTp5MmT+vbbb1WnTh21adNGM2bMkL+/v+666y7dcsst6tChgx577DFVrVpVo0aN0k8//aSSJUuqcOHCKlKkiL7++mstXLhQktS9e3cdOnRI1atX1/79+xUdHa39+/eb5Z40aZIqVKhgPm7cuLFiYmJUpEgRnT59WiEhIfL399eePXtUpEgR3XfffZo0aZKqVq2qXbt2afLkyfrpp5904cIF1apVS7fddpvef/99NW3aVNOmTdNTTz2lffv2qUGDBrrtttv09ttv6+eff1a3bt20YcMGtWrVSpUqVVKHDh00ZswYDRgwQGfOnNHo0aPVvXt3/fbbb7LZbFq5cqWefvpplS9fXsWLF1doaKh5ANqxY4cOHDigWbNmad26dTp48KC2b9+uyZMn68svv1SxYsW0fv16hYWFpXnQLV26tCZMmGAeaHv37q1+/fpJkpYvX67p06eb4adkyZL6448/dPHiRZ08eVJLlizRF198keIzrF69un7//XfZbDZzv+nfv79Onz6tNWvWyGazqUGDBtq/f7+uX7+uZ555Rps3b9bNmzd14sQJTZ8+XUWKFNGIESM0atQorV+/Xnv27FHRokVVuXJl3XbbbZozZ44aNWokwzD09ddfKywszNyHBw8erDfffFNS0knPfrExf/58nTp1SteuXdOQIUN07NgxNW3aVOXLl9eJEyc0Z84cvfjii9q2bZvq1aunjz/+WBMmTNCcOXO0efNmtWzZUp07d9by5ct1/PhxPfPMM+rVq5fGjh1rfrcLFy6se+65R8WKFXN5PPntt9904MABc9sMHjzY3G+Tb++QkBDFx8erQ4cOevrppzVmzBj9/fff6tChg0aNGqV7771XM2bMMI9Fn3zyicLDw1W5cmU9+eSTypcvn+bMmaNhw4YpLi5OEyZMUL169XTkyBHdfffdKlu2rP766y8VLVpUTZo0UYsWLRQeHi5J+te//qVLly7pnXfe0YkTJzRq1Ch16NBBcXFxMgxDf/zxh2rUqJHiOOS4fQcOHKiRI0eqZcuW2rx5s/r27Wu+j82bN+v69ev6+++/dffdd+utt95SgwYN9MADD6hZs2Zavny5Dh06pLvuukvh4eF688031aJFCxmGof/+978qWrSouY433nhD+fPnV7NmzfT6668rMTFR/v7+mjx5snr37q3OnTsrLi5O169f14ULFzR9+nSdOHFCTz/9tF5++WVt27ZNhw8f1vXr13XHHXcoPDxc8+bN08iRI7V582YdO3ZMN27cUKNGjbR//37FxsZqzpw55n4zbtw49erVS4ULF9aNGze0Y8cOvfLKKzIMQ+3bt9cTTzyhHTt2aMeOHfL399eUKVP08MMP6/nnn1fJkiX166+/aufOnbLZbKpVq5b279+vihUr6o033pCUdOH7ww8/6JZbblGRIkX0zTff6KWXXtL8+fNVunRpHTx4UK1bt1Z4eLhef/11NWvWTA0aNNAff/yh+++/39yfZ8+erY4dO6pZs2YqUqSInn32WZUpU0b+/v7m+adfv346ceKE+vfvr2effdblcaF27doaPHiwy8+wT58+euGFFyRJ/fv319mzZ9W4cWPt379fhmGoR48eLj/fJUuW6KWXXtK2bdu0bds2hYeHKzY2VpMnT1bHjh115513qkGDBho4cKD69eun8PBwGYahr776SnXr1tXcuXPNz6J///769ddfde7cOZ08eVITJkzQ0qVLdfbsWW3cuFGTJ0/WgQMHVKlSJc2dO1eGYahWrVo6ePCgnn/++RTfk/r16+ubb77Rvn375O/vr6pVq5rn5L/++kv333+/fv31V/Xo0UMTJ05UmzZtNGLECN1///0KCQlRz549tWHDBp04cUKjR49WiRIl9NJLL6lWrVqqXbu2DMPQunXrtGrVKklJgW3jxo0qVqyYAgICdOrUKVWpUkXTpk3TxIkTtXnzZq1ZsybFd3327Nlq1KiR/P391aRJEx0/flwFCxbUjBkzNGHCBH311Ve67777ZBiGFixYoD///NPlNcfo0aPN83ry435a10fJj4N33XWXpk6dqjlz5iggIEB33XWXeQ1Qv359bdmyxTwnr1y5UnPmzJEkzZs3T/Pnz9e4ceO0dOlShYeHKyoqSv7+/jpx4oSio6MVEhKi4sWL6/fff9djjz3mcpsuWrRI58+flyR16dJFxYsX159//qkNGzaoWrVqWr16td566y19/fXX8vf31wsvvKCHH35YEydOVLFixcxri9GjR+uVV15Rr169tHr1avNcGhAQoJiYGOXLl0/t2rXT5MmTVbhwYe3atUt33XWXIiMjdfz4cUlSzZo1NW3aNC1fvlz58+eXJN13333auXOnTp48qU8++USdOnXS+vXrVblyZTVs2FBS0o8j/v7+OnLkiMaPH69FixZp8ODBatasmaZPn67o6GiVKlVKffv21eOPP67mzZurQYMGOnPmjKpWrarTp0+nOH5+9dVXKlmypFq3bq233npLt956q9q2bau4uLgU32HH88D48eMVHh6u4sWLa9q0aapWrZqOHj2q1q1ba9OmTWrSpIkqVqyodu3a6emnn1bDhg21evXqdK9BqlevrqNHj7o8Jznuw2md5958800NHTpUu3btSvH5Goahffv2qU2bNjIMQ++//77Onz9vnoNbt26t/Pnzu9y/3Q1sdllyE8/M2Lx5sz777DONGzdOFy5cUFxcnD788ENt2rRJNWvWVHh4uAYOHKjChQvr4MGDKlu2rG699VYtWLBAly5d0jfffCNJeuKJJ3Tt2jVFRESoXLly6ty5s/m6H3/8UWXKlFF4eLi+//57tW3bVm3atNGVK1cUExOjwoULq2/fvvrhhx8UHBysmTNn6oknnlBUVJRGjhypkSNHqkuXLtq1a5dCQ0M1cOBAvf766+rZs6cWLFigYcOGyWazafjw4ZKkv//+W0888YSkpF83goKCZLPZ9PLLL2vlypUaP368OnTooIiICFWsWFFt2rTR2LFj5efnZ76uevXqCggIMJd5/vx5FS5cWDNmzND48eP1888/659//pEkRUVFqWXLlnrggQdUu3Ztpy9sWFiYZsyYoW+++UaGYeiXX37RmTNndOnSJcXExMgwDCUkJOjkyZNKSEgwP5MiRYroxIkT2rp1qzp16qR+/fopISFBYWFhWrdunQoVKqS9e/dKkl588UX16NFDkvT777/r3Llz2r9/v0aPHq0bN244lfvAgQPasmWLOnXqpFatWmnLli367bffJEm1atXSr7/+KkkaOnSoAgICzG0TGBhoLqdu3boKDg5WwYIF9cwzz2jWrFk6e/aswsPDFRoaav4S8Mwzz+jDDz80P9+AgACdPXtW3333nTp16qT7779fFy9e1IIFC/TEE0/o8uXL+vXXXxUREaFVq1apVatWKlmypKZNm6ZKlSpp69atkqQTJ06ofPny5n5aq1Yts2wbN27UyZMnzX3v0qVLKl++vFasWGEedO0XsV26dDH32dq1a2vdunVat26dpP8dWJctWyZJ5q+Z48aNU2RkpGbNmqXw8PAUn2FsbKz5HuzPjxw5onnz5pm/YC9fvlzjxo3T9evXNWrUKB09elQ2m01nz55Vu3bt5Ofnp5iYGG3dulUff/yxBg4cqKCgIHObrl27Vvfff7/69u1rlmvbtm0aO3asVqxYoZs3b8r+G8qlS5fM78k999wjSerYsaMeffRRRUdHa+XKlZKkuXPnqnv37vruu+80cOBAzZo1S+vXr9eKFSsUFRWlu+++W/ny5VPt2rVVvnx587jw7bffqlKlSuY2vXTpkrZt2+byeFKtWjVz20RGRmrTpk3mfpt8e1epUkVHjhxRp06d1LFjR3Mbjh8/XufOnTO/JwcOHFBUVJRZa/vggw8qNjZW999/vz766CMNHjxYkydPVoECBSRJO3fuVEJCggICAlShQgV99NFH+v7779W5c2c99dRTMgxDkZGR8vf3V506dVSnTh1zHz906JC6dOmi5s2byzCMFMchx+07ZcoU7du3T2vWrNHRo0fVvXt3vfTSS+bnnZCQoDp16mj48OEaNmyYbt68qZs3b6pdu3b673//q6ioKP373/+WJH355Zdq1KiRRo0apYYNG2r//v3atWuXpkyZoq+//lrdu3dXu3bttGrVKgUHB+vQoUNq1qyZQkNDNWvWLP3+++/q0qWLWrRo4fSe7J/3Lbfcom+//dZc3+LFi81ydunSRe+//765z9p/NZSkOnXq6Nlnn9XVq1f1r3/9Sw0bNlRQUJBWrFghSYqJiTGPtV27dpVhGAoLC1OfPn106NAhHTx4UKNHj1b//v311VdfacWKFRo9erS+/fZbrVy5UoZh6LvvvtOff/6pHTt2SJJq1Khhbu+CBQsqISHBLPeSJUv0n//8R7t27VL//v0VFRWlMmXKmPuzffv6+fnpwoULZnOZIUOG6MaNG+a2cTwuHD9+3DwuGIahkydPpvgMn3jiCQ0bNsw8FhmGoTJlyujMmTPmdktISEj1812yZIn5WdSvX1/58uXT77//rmbNmqlMmTLme5oyZYquX7+ujh07atSoUWrQoIG+++4787MYN26cnnnmGfO436xZM3344Ydav369atasaR4ja9eurVOnTunq1auKjIxUTEyMeU5K/j2JjY1VXFyceU7y8/Mzz8kPPPCAzp07p7i4OPXq1UsTJkzQpUuXdOutt2r79u1q166deZ7/+uuvNX36dEVERCgoKMi8mJek999/3/y8P//8c/311186c+aMpKRf1AsUKKDw8HCdOnVKhmG4/K6vXr1aX3zxhaSkGulPPvlErVu31ubNm7Vv3z5VqVLFPJd/9tlnqV5z/Pbbb+av+cmP+2ldHyU/Dt64ccM8fvbo0UNvvPGGeQ1w/Phxp3Pyxx9/bAaR7du3Kzg42PzcHM/J9vKHhYVp2rRp6t27t7mfJt+mq1evNn/5j4+PV2JionkODg0NVf369bVgwQLdeuutCg8PN7+Xhw8fVseOHfWvf/1LkjRlyhTzPf7yyy+qW7eu+f2uXbu2fvnlF7Vu3VoPP/ywVqxYoQULFujee+/Vjz/+aJalQoUK6ty5szp37qzIyEi99957KlasmEJDQ1WsWDHZbDY9/vjjkqR8+fLp008/NT+nDRs2SJKmTZum7du3m9+hNWvWKDAwUH5+furevbtefvllp+/+V199pT/++ENSUiuJ5MfPQ4cOmWHTfmxP/h12PA+EhYXp0qVLCgsLk5+fn27evKnly5crMTFR27Zt09mzZ7Vjxw61bt1aXbt2VWRkpFvXIGmdkxz34bTOc6tXr9bUqVO1ePHiFJ9vtWrVVLJkSXPfWblypdM5+NSpU/r9999d7t/27eMunws358+f1/fff6/o6GhdvHhRV69elWEY2rZtm+Li4px2yieffFL333+/JKlgwYIKDQ3VggULVKRIEcXFxWnSpEnatGmTgoKCnF73/PPPa+DAgZKk0qVLm82PYmNj1atXL0lJv3QlJCSoaNGi8vf31zvvvGMe4KSkg8wvv/xiXjhXqlRJvXv3Vs+ePfX666/roYce0quvvirDMHTx4kX17dtXJUqU0KZNm1SsWDE9/PDDkqQCBQqoV69eatiwoT755BO1atVKLVq00M2bNzV//nzzdUFBQXrsscc0atQoSZKf3/+6S02bNk3ffPONefCIjIzUrbfeqmLFiiksLMzpC/vZZ5+pU6dO5q/pnTt31vTp0zV27FhduXJFn3zyiWbOnKnp06frjTfe0OHDh7V69WpFRESoZcuW5nI+//xzhYaGqnv37goODta///1vtWjRQqVKlVLx4sXNA4KUtOOPHDlSV65cMavi7eXetm2buczJkyfr+vXrkpJ+5Y+LizPf0x9//KGIiAi1aNFC8fHxeu+998zldOjQQWXLljUvTBs3bmxup/DwcDVu3Nhs0mdv+tC/f39JUuXKlc3120OY/fOWpDVr1ujkyZM6ePCgJk+erE2bNsnPz0+33XabKlSoICmp6dUdd9yhdevWadu2bSpSpIhZtmbNmqlWrVp68sknFRkZqd69e5v7afKDbqdOndS1a1dt2rRJoaGhKliwoFmTMGDAALN8J0+e1PXr1/X9998rKipKw4YN0/Dhw/Xaa6+Zzf7sn+GMGTPM92AP+hcuXJAkPfroo1q3bp1Gjx6tCxcuqEGDBpKkmTNn6scff9TGjRtVo0YN1apVS3FxceYvX+Hh4fr777/NX1EaNGigqlWrauTIkYqOjpYktWjRQgkJCZo3b57ef/998z2ePXvW/J689tpr6t69uxo1aqSBAwdq165dGjNmjM6fP6/SpUvrwQcfNJsMlipVSufPnze/3477d3R0tPn9XrFihVO76cKFC6d6PKlWrZrTtrH/ip18e0dGRiogIMDcTxITE7V582ZzH7bXFq9evVqlSpVSz549zfXfeeedGjx4sD744APVqFFD06dP19NPPy1Jevnll7Vv3z4tX75cw4cP1zPPPCNJat68uZ577jmNHj1aklStWjXNmjXLXG6JEiUkJf3g0bJlSy1evFgLFiyQ5Hwccty+jRo10uTJk83PV5JKlixpPn/88cfNX+5GjhxphqCaNWvq8uXLqlSpkvmeypcvr27dumn06NHmDyD2dWzcuFHz589XzZo15efnp+LFi6t///7q3LmzAgKSTjc1atTQY489phdeeEHXrl2TzWZT9erVzc97+fLlTk1PQkNDJSWd9AMCAvTjjz+a+8kDDzyghx9+2GzW0K5dO+3bt888hvbu3VsjRoyQlHSRaW+CvGLFCt15553q3r27unfvrt69e2vw4MGqXbu2Ro0apUOHDmnUqFG6fPmy+vTpY34Wn3/+udOFQEhIiHr37q1HHnlEzz33nIoWLWpOsx+jGzVqpIceekg///yz2cTt7rvvNrdvzZo1VaJECfNYV7hwYa1YscL8vLt06aKePXtq06ZNCgsLczq2Nm3a1Nw2Q4YMUeHChVWjRg2NGjVKn3zyifmrryT9+9//NrfbiBEjNGvWLJefb0BAgPlZ/PXXX1qzZo2GDh2qzp07q2DBgk6f9/Xr1/XPP/9o9OjRKlCggNNnUbp0aXObxsTEqESJEmYQrVy5srm+TZs2afDgwRo3bpwiIiL0xhtvpPo9mTZtmlq3bm2ek+wXvZJUsWJF/fPPP2rYsKEee+wxVaxY0bw+KF26tEaMGGGe50NDQ83rgxYtWpjHcinphzT7douPj9fWrVvN4079+vX17LPPSkpqWmb/QU9y/q7ba/WlpO/zjh07tGDBAq1evVpVqlTRiy++aH7/Jk2aJMn1Nce2bduczuv2435610fJj4M1atQwj58XLlxQy5YtzXNLu3btzPmmTZumrVu3mvuY/YLSsXm0vTwHDhxQgQIFVLFiRfn5+algwYLmNm3evLnTNl2zZo3279+vAwcOqGHDhho/frz5vW7ZsqUkyd/fX3feeafy589vfi8XLlyolStXqkWLFipTpoyKFClivsennnpK3377rfnZ3Lhxw7zAb9u2rTZv3qyLFy+qatWqGjdunFmWiRMnmo8jIiL00ksvSZJat26t33//XZLM/eb555835+3cubPuvPNO8/mrr75qfocCAgI0adIkde7cWVJS4JOSvie33HKLU9PsChUqmMdP+/VO9erVNWrUKPPHNPtx+NNPPzX3xWrVqmnmzJl6+OGHde3aNRUrVsyctnz5cl26dEkrV67UokWLVKlSJXNb/PPPPypQoIA++eSTdK9Bhg8f7nROGjt2rMt9OK3znP1YYr/Gcvx8ly5dqpUrV5r7xtKlS7VmzRqzKWu1atXM65rk+7c9cLvL55ql/fbbb9qwYYP69OmjmJgYff3113rggQe0aNEihYeHq2/fvilec/DgQV27dk1nz57VoUOHFBERofbt2+uLL77QyZMnlZiYqPHjx5vzJ2///MADD+jAgQO6ceOG2R7SsX1k8raSBw8e1JIlS/T333+rY8eOWrp0qQoUKKCAgAB17NhRb7zxhho1aqSOHTtqwYIFql+/vho0aKArV65o1qxZatmype655x4tXLhQ9evX1+233y4pqcZj2LBh+uabbxQXF6fatWurV69eGjZsmOLj4zVs2DCtW7fO/GXKvu7r16+rS5cu2rp1q06dOqUCBQqod+/eWr16ta5fv65GjRqZZT9+/LhOnDhh7nAXL15U0aJF03y/9m1jP3FKSSf5AwcOqE6dOpo8ebLatGmj6tWra9myZerWrZsZHj///HNdu3ZNYWFhevPNNzV8+HDduHHD3PbVqlUzl7l+/XodOHBAt956qxYuXKj7779fQUFBOnXqlP7880+1adNGwcHBWrhwoTp16qTg4GCdOnVK58+fV7NmzRQUFKSFCxeqa9eueuSRR7R06VLt27dP/fr108yZMxUXF6dBgwapdu3a5snn22+/NdsiG4ahVatWqVu3bpKc27/+9ttv+vjjjzVixAiVKFFCf/75pwIDA1W2bFlFRkZqy5Yt6tixoxYtWqR27dqZbbTtYcf+3DAMffbZZzp06JDZLj0kJESSc9tuKekX8rZt20pKqo1bsmSJLl++rIiICNWsWVNbt25V3759Vbx4ca1cuVI3b95UuXLlZBiGebBw7KclJfX/cuzLNH36dI0bN05//PGHVq5caZ647dPsz+1tx0eMGKF//vlHzz33nObPn6/69etr+PDhiouL08CBA7Vp0yZ99dVXmj17turXr6/+/fsrKChII0aMUL169VK057avY+PGjZowYYJmz56t22+/XYsWLXKab9q0aapRo4YOHTqkokWLqmbNmqpbt67Wrl2rIkWKmCej5P1KKlWqpJ07d6pv374pjieOn9OGDRtUvXp1VaxY0eX2tjd5tK/jk08+0SOPPKLPPvtMbdu2VXx8vMqWLSvDMLR3716zKYPjPmQYhvbs2WN+35NPc3zdc889pz/++EPt2rXTjBkzlJCQoGeffVbr16/XuXPnVKZMGbPJQYMGDbRmzRqFhYWpefPmGjBggHr16qX77rvPfBwWFqbp06dr3759ZrvsH374QS+99JLLeVu0aKH+/fvrkUceUdu2bV1O69mzp9599101bNjQbBoSHBysfv36ac6cOYqMjFTTpk116tQp+fv7Kzg4WAMGDNCcOXO0ZcsW3XfffZo/f77CwsK0ZMkSlStXTnPnzlVkZKSaNGmiU6dOmcvs37+/ucwmTZooMjJSNptN+fPnV79+/TR37lz5+/urTJkyevzxx/XEE0/op59+0sCBA/XSSy8pLCxMW7duVbly5fTiiy/qt99+09ixY7V+/XrFxcWpefPmatq0qd566y2dOnVKQ4cO1cqVK3X58mU1btxYrVu31pw5c3Tx4kW1bNlShw8fVlxcnCpWrKjff/9dFy9edOpfVrx4cd1+++368ccf05x24cIFs4/c6dOnderUKbM9vb3/3MiRI3X69GlFRkYqODhYjz/+uHnM+OCDD7RmzRqX62jYsKF27drlNO3mzZsqWrSoypcvr4sXL6pdu3b697//rYSEBI0fP17r1q3TuXPnVLp0abVv397c9xyn2fc9x+dt2rTR22+/rdjYWE2cOFHLli1TxYoVzb6w+fLl07fffmt+p1etWqXAwECnvkqSnM7B9u+l48W34/Pk01599VX98ccfGjFihCIiIrRixQrzF/jkr6tbt67ZV2rhwoW65557zL5S+/bt0w8//KAOHTqYTXgqVaqkiIgINW7cWL/88otZ7kKFCilfvnzmsfb7779XZGSkIiIiJMmpv+y2bdvMH1Icj8vJ++Q6vu62227T6tWrzdfVrFlTW7ZscXk8c7w+2rhxo9nfS3I+7wQFBTntQ6tWrdKDDz5oznv8+HHzOPj7778rPDzcDO1ffvml/vnnH508eVJ+fn5KTEzUoEGDVLx4cS1dulS9e/dO0R8r+eeU1rRXX31Vx48fN/v/jBo1SlevXtWQIUPUsGFDPffcc+aPao7vKSIiQo0aNdKRI0fUpEmTFMf3rDBv3jx9//33ZreA0NBQjR492rweDAwMNKeFhISY0woWLKiAgAC1b98+xescuwzYt4djgHc8Jzs+Ti4yMlIHDx5U48aN9cILL2jYsGEur0FKlixpBqDk1yOS8/VC8muHjE77z3/+o+joaJffi6JFi2rDhg3q27evoqOjUz1fu8Pnam4KFSqkypUrKzQ0VPPmzVOhQoUUHBysgIAAs0rcfmD56aefVKdOHZ0+fVpFihTR5s2b1a5dO+XPn18TJ05Uq1atdOrUKdWqVUtt27bVvffeqxo1aujLL780p33zzTd6/fXXzfbdQ4cOVb9+/XT69Gnt3r1bcXFxatasWYppGzduVJMmTczq2sqVK+vo0aPq1KmTeaFyxx13KCoqSosXL1ZYWJhKlCihI0eOqEqVKjp//nyKaWfPntXixYu1cuVKFS5cWLVr19Zff/2ljz76SBMnTtS//vUv7dixQ4ULF9att96q3bt3q1mzZqpRo4bGjh2rDz/8UMuWLdPixYt17Ngx8/3OnDlTS5cu1dKlS3X69Gk99thjWrRoka5fv67Dhw/rlltuSfP9/v333ypRooQOHz6su+++W6dOndK7776rwYMH6/z58/r1119VoUIFVa5cWTdv3lRiYqKWLVumU6dOacGCBRo6dKhKlCghPz8/vf322xo6dKhOnTql4sWL68svv1SzZs2c5rVvm8TERNlsNtlsNm3atEmVK1dWiRIlFBUVpQ8++EATJkxQ/fr19eCDD2rLli0aMWKEoqKitGzZMh0+fFjNmjVTZGSkevbsqe+//94MZw888IDZpvXjjz82w82gQYPMJg9vvfWWWrdu7dT+9Z9//jF/NR8zZozKly9vTtu+fbvZj+E///mPeYAvUqSIJk+erI8++kiS9Ouvv6pLly7m440bN6pKlSoyDEPPPPOMZsyYYX4XPvjgAzPchIaG6s477zS/3Hv27FGJEiX03nvvaePGjerUqZOqVKmi8ePHy2azady4cfr555/1wQcfmLV0UlJNkv3ievv27bp8+bL27NkjwzC0YcMG7dmzJ9VpkZGRmjx5svbu3atbb71VS5cu1cWLF/XVV1+Z/Z/27t2ratWqmdN++OEHc9qFCxf02muvadOmTSnWsWPHDgUEBGjVqlWKjo52ms8eCj799FPdc889uvvuu9W1a1e99tpr6tu3r5YtW6bSpUurWrVqat26tbp06aKXX35Zffv21ezZszV27FgFBwdr1apVqly5sgICAlSiRAkNGTLEDBb79+9XzZo1NXHiRFWrVk3Lly9X5cqVzQ6iBQoU0O7du53W0adPH/Xt21fNmjVTp06dFBcXp+joaO3Zs0ctWrRIsQ9FR0dr7969at68uctpjq9bvXq1fvvtN7Vu3VqNGzdWUFCQli5dajY/2bp1q9nkwLHZY/Hixc1mGxcvXtSlS5fMaX///bf+85//mO2ya9Wqleq8xYoVU3h4uB577LFUpw0aNEi//PKL9u7dazYNqV+/vhm27b+s2n/drl+/vtk0ZvDgwfr7779dNpVw9brixYunO02S6tatq8uXL+ujjz7S+PHjtWPHDnMd//zzjwoXLqyZM2fqueee03PPPWceT2vXrq2ff/7ZnPavf/1LO3fuVKFChVSnTh3FxsZqxYoVeuKJJ/TJJ5/om2++MV935MgRs1nntWvXVLp0aU2bNk01a9bUoUOH0pz27LPPaurUqerdu7c6duwowzD066+/6j//+Y9eeuklTZ06VVWrVtVbb71l9j1p166dOnTooHLlymn79u1mM25P1u/YRt++fzk2b7LvX2lNS/68SpUq5iAF69evd+oXeccdd2jNmjXq3bu33nvvPf3nP//RoEGDdP/996t06dJ677331KZNG5UoUUIPPvig2Sdh//79unTpklq1aiXDMLR+/XoVK1ZMTZo0STHtyy+/1D333KOlS5eqQ4cOeuWVV7R582aXr7tx44ZmzJhhrv/99983+0rNnz/fPJcvWLBAjz32mN544w0tXLhQQ4cONctdqFAh/fXXX3r55Zf1888/a8iQIXriiSdUoUKFFMfhIUOGmP1nd+3aZR6XDcPQPffcY/bJTf66+fPn66GHHtKTTz6phQsXavfu3Ro9erRsNps2btyo/v37y2az6emnn9bGjRtlGIZsNpueffZZlStXzuwbdejQIXXu3NnsjL5x40bzOmru3LlavHixOe/Ro0d1yy23yGazKTIyUtWrVzfPH35+fmaAOXv2rIoUKaJly5bp+vXrWrBggT799FM1aNBAX375pSIiIszPsF69emZtUKtWrcx+Wsk/ww0bNujee+81zx/r168396GoqCidO3dO8fHxTtcyly5d0oULF5SQkKA2bdpo6dKlCgoKSnNAIm/YvHmz2dy/Zs2aKlq0qMvrwRo1aqhIkSIup9WsWTPFNHvXCvv2sI/OlvycvG/fvlTDTfHixXXhwgUtWLBADRs21M6dO7Vu3TqVK1dO7du31/Xr180+L6NHj9Ydd9yhEydOaOjQoZo3b56kpB/4/vWvf+ntt9+WYRjm48xMe+qpp/T777+bXSkcvxc///yz9u3bp5YtW+r9999XpUqVFBgYqIULF6pGjRoeB1WfCzdPPvmkunfvrvvvv1+JiYmKiYnR2rVrNXnyZPXo0cM8IE2ePFlvvvmmgoODNW/ePA0fPlyBgYHmRfO3336rd955R9999532798vSS6nJW/fvWTJErM9/bBhw9Kc5tg2MywszHx+7733OvUJWLNmjdm+t1+/fipatKjLaYZhmO15Jen69es6e/asSpQood9++003b940pwUGBjq12R46dKhefvllNWvWTCVKlNBXX32l119/3Zw2adIkNWvWTBs2bNB9991n/hL+9ttv6+677073/UpJVdsPPfSQ6tevr6pVq+qVV17R+PHjU7ynn376yQyPY8aM0QcffKAxY8aodu3aioqKcupLMXPmTLVq1UonT55MMW9ay7GPWCIljcB11113meuPj483t820adMUFxdnbrd8+fI5tWm9ePGi2YbYPprLyJEjU7R/7dmzp3nhYT8or169OsU0KalJ06hRo2QYhs6dO6eDBw+ar3MMMN27d5efn59ZU7R161azrbck83WSdPToUc2bN898bbdu3fTOO++ofv362rZtm/bv369Ro0bpyy+/lL+/vzp16iRJZgdV+zIvXrxonmQGDRqkwMBAzZ07V1JSR8vUpjn2I6pfv76uXbumXbt2afz48bp8+bJb08aPH282m0u+jiFDhsjf39/lfFJSk5Nt27bp5ZdfVuHChXX48GGVL19e3bt31/jx481phQoVSnXamTNnZBiGSpcurb59+5qDfYwaNUrjxo3Tn3/+meq899xzj8aMGeNyHS+//HKKPjCu9iFPpkVHR5vNCuxNacLCwrRt2zZdvXrVqclB/vz5zSYHXbt2NZtt9O3bV1FRUeY0+wAj9nbZsbGxqc6b1nIcpyVvGiLJbP5y6tQp86In+bS//vpL5cuX18KFC1M0lUjrdWlNK1KkiM6dO2ceM4OCglS7dm3NmzdPkZGRZh+fEiVK6Pfff3c6njoea+3T7D9k5M+f31yHfdQm++sSExPNZkIXLlww+2z6+fkpISEh3WlRUVHy8/PT3r171bdvX9WvX1+ff/65unXrpkqVKsnPz09BQUHq2LGjJGnGjBnKnz+/OnbsqBMnTuirr77K0Pod2+intX+lNS3587i4OF24cME8JwYHB5vf/erVqyshIcE8d8+cOTNFXyX7sc2xT9e4ceNUt25dnT9/XqNGjVLHjh3Vpk0bl9MaNGigtWvXSpLGjx+voKAgcznJX3ft2rU0+0rZz+VPPvmkJJnltjcPXbBggcqVK6dSpUqZx9r58+ebfQmSH4fnz5+fav9Zxz65yV/3xhtvqH379ub6O3bsqHXr1qlBgwZauXKlKlWqZIYSx0BTu3Ztffjhh2bfqE8//dQcCGLnzp3q37+/ucy4uDinflSO86a1jsjISJUuXdr8gfXtt982l2PvO7t3716NGzfObAorJTVXsn9m6X2Gyc8fX3zxRarXMg899JAefPBBszxPP/20PvjgA2UVx+b+9r7Urq4HPZnm2MdHSvucnFYflGHDhqlHjx5m7ZfNZtPMmTN14sQJNW7cWG+++aY5kMR///tfvf/++6pTp4569eqlgIAAGYah1atXmx39JZlNtjMzzd5X0dX3QpLq1aunNm3a6NSpU9qwYYPy5cunKlWqmDXYjj/Upsfnwk2RIkXMg0eVKlUUGBho/gpfoEABc1qzZs0UHx9v9h+4efOmSpcubXasnTNnjtlueNu2berQoYOOHTuWYlry9t1Xrlwxl/nXX3/pxo0bLqeNHDlS06ZNM5vD2Nsm2p+/9tprio+PV2hoqFP73kKFCpmPk0+7++67NXjwYHN9w4cPN2sVXnnlFbOzpiT16tVLP/30k9knomzZsuYFfaFChdSvXz+X07744gvz1z97R3F33q8kc8Q1KanDqL3/UPL35Bhghg0bpoMHD5od9sLCwpz6UhQsWDDVedNaTqNGjdSjRw+zZmfs2LHmtPbt22vWrFmqUaOG+euznX3oV3ub1gYNGqhevXoyDENHjhxRYGCgOc2x/euvv/6qkydPmsOZXrlyJdVp+fPnN9vCNmzYUFWrVjV/WXYMMBEREbp48aJefPFFSdKhQ4ec2sgvWrTInPfdd9+VzWYzl+PYkfruu+/Wu+++qzFjxmj79u2SZLatb9SokVOfF8e24M8995wKFSpkPrcfhFxNc6wanzRpkk6cOCEpqbmY4z6S1rRp06aZQ0YnX4d9FCZX80lJbXrtHacrVKigyMhI82Ikf/78bk0rWbKkQkJClJiYaO5X9rbP9uZBqc3r2Pk++TrstXTVq1dXjRo19MUXX2jhwoUp9iFPpv33v/9V4cKFtXLlSs2cOVNlypTRww8/rEWLFmnx4sWKi4vTypUrzSYHUlL78VmzZpnbbOjQoWrRooU57auvvtKBAwfMdtn2tuau5k1rOY7T3nzzTbOJQ1hYmH744Qd99dVXOnTokB588EH16NFDf/75Z4ppTZs2NZsKR0REaOvWreZy0npdWtPszdvs+07v3r3NQVYiIiLMi1Yp5fE0rWPtxIkT5efnp0OHDumhhx5yGlJ31KhR2rlzp3lbgeXLl+upp56SlHTidmealHScsH+f3333XQ0aNMicZr9Qsdls2r17t6ZMmWIONtCkSZMMrd+xjX5a+1da05I/nz59uvr162f+wvrII4+Y2+nOO+9UvXr1tHbtWvMHOHvfsIkTJzr1VXLs03XhwgXNnDlTx48f1+jRo1WsWLFUpxUuXNhcxrRp01S2bFl16tTJ5evq1KmT6vqrVasmKelc/tlnn+mpp55y+uHQ/rpZs2bJZrOZx9pbbrnF7COb/DjsOC15/1nHPrnJX1e8eHGnax7HAR0+/fRTM/hOmTJFly9f1t69e7Vr1y4NHjxY3bp108qVK9WoUSPdvHlTCxcu1K5du/Txxx9r5cqVstlsatasWYp+VI7zprWORx99VAkJCXr55Zfl5+fn1M9ky5Yt5gAdjucAKamvp/0zS+8zTH7+cOzEn/xaxt/f3+kH3w8//FBZaeTIkZKS9pOqVavq7NmzZnN/x+tBT6Y59vGR0j4nT5kyJdWyBQUFmceTSpUqKSAgwDxmjB071pzWv39/Pf/882Zfubvvvtu8XihbtqyKFStm9jc8efJkpqe98cYbWr58ucvvxfnz5xUWFmaem6tVqyZ/f39J0sCBA80fA9xm+JjJkycbvXr1MubPn29Ur17dqFWrljF//nzj/vvvN+666y5z2v33329MmDDBfN3EiRON2rVrGx9++KFhGIYxa9Yst6YZhmFMmzbN5WNPpuW0xx9/3OjWrZtRtWpVo3PnzsaKFStcTmvRooXRpEkTl/MZRtrvt2fPnkb37t2NHj16GA8//LDx0UcfuSzLxIkTja5du6a6vR3dc889qc7ryXLS+izSmtahQwen54sXLzYfT5kyxXz8xhtvOD13nC+9aY7rf+SRR1JdX/JyduzY0Wk5jvM+/vjj5mfRsmVLo2XLlsYPP/xgfP7550azZs2MH374wYiLizNGjhxpDBo0yNi3b59hGIbx7rvvprYpfNprr71mGIZhxMXFGYZhGM8++6w5rX379m5Ne+2114xnn302xXyJiYnG0aNH05y3VatWqa4j+efmuC84Ps7MNFjb8uXL3Tq2Ll++3Lj11lvTnc/XOJ7Xk5+7DcP1ecf+vXT1PK1pyaX1urTWn165v/rqK6Nhw4ZOx9pHH3001eOw47QWLVqYx+S0Xte5c2en9Tdp0sRc/7PPPmtcu3bNfD569Gin9+Q4rWXLlk7vqUePHql+Fo7zprWObdu2GV26dEnzWsLdz8XVc1e2bduW6rXMK6+8kuo1UF7jeDxp0qSJ0bhxY/OYMWLEiFSPNY899ph5vfDVV1+l2E+9MS2170VcXJzRoUMH85pv4sSJRt26dd26/nPF52punnvuOfPx4MGDNX36dA0ePNh8bG9jOHjwYNWtW1dHjx6VlFTlFRsbqz59+kiS3nvvPX3//ffpTvO034F9mpFOm8esZh8ZzLEsR44cMae9/PLL+vTTT1NMk5w7otWtW9es9nTn/SZfjiuvvPKK0/OYmJhUy33ixAmnZTrO68lyHD8LT6YdPnzYaZm///67OcyqY3tX+3LsNRgzZswwh2l2Z5p9/fXq1UtzfY7b+9ChQ+a8o0ePdtret9xyi1k9bb+52KpVq7R3717VqFHD7Lvi2Gb5woUL5vvJbewjiQUGBprb7Y8//jBr3NyZ9swzz2j69OlmTe3nn39uDs+Z3rz2Tq+u1uHqmLFv375U20y7M81xf4L1PfLII041HY7f9eTHrOjoaH3yyScp5vNlyc/rjufu9M47GT0nO243T87zjstIq9zJ+wk6HmvTOg7b+yV++OGHunDhQpqvK1OmjDmksv0ayG7atGlO53L76Gv29/7777+rRo0a5gAzjp+F43VV8usox3nTWoe9M/iaNWskpX0tkdY52JPrqubNmzsNxpC8g729ZYP9eV7l6njiuJ1mz54tKemzePXVV7V69WqXfWvT2k8zOi2170V0dLQqVKjgNIR4/vz5zet2x+s/d/jcaGmuDkjt27c3N3z79u0lJX0JvvrqK7N5UI8ePbRnzx41bNjQo2mu+h3Y77OS1jQpqbmAfVp2GzRokDmUoySnm03t2rVLAQEB5sWa47Tk29TdbZF8Oa7Cjl1awSutcrsKae4ux/Gz8GRaWtvN8f1ndP9KPs1xH05rfel9bo7Lcby5WP369RUYGGj2eXnvvffMpjnjx4/X119/rV27dqX4zHyd476Q1v6d0X0/vXnd/dwyejxx1Z46p44tyH7uHuuSH09SOwb7muTvz5PzTkbPyY7bLaPfvbTKbe8nOHfu3BTH2rSOw55Mmz9/vlq3bm2u3/E45MkxKiuOg55cS6R1DvbkusqT68Pc8t3ICu5up+T7ieN9jLy1D7s7Lfn+nrzcHn+eHtXzZIPHHnvM6fHjjz9uPq9Ro4bTvL169XKa98KFCx5P++OPP5ym7d27161phmE4Tctuf/zxh9PzPn36OE1z3G6O05JvU3e3RfLlGIbhtBxHjp9h8vnSKnfyeT1ZjmO5PZmW1nZzfP8Z3b+ST3Pch9NaX/KypfVdWLt2rTlt7dq1xltvvWVOGzRokNMyHaflJo77Qlr7d0b3/fTmdfdzy+jxJPm0nDy2IPu5e6xLfjxJ7Rjsa5K/P0/OOxk9Jztut4x+99Iq99q1a53mdTzWpnUc9mSaYzM0w3A+DnlyjMqK46An1xJpnYM9ua7y5Powt3w3soK72yn5fmJvAmYY3tuH3Z1mGM77e/Jye/p5+ly4cfeAZBgZv6C14oWDu+8/MxdR7m5HT7Z3Tn9u3nhPnkzz5ECe0e+CFXlj+6e377O9kVOy4tjqSyi3d9afFUEvo8fBnPhBhmO0e3LrdvLWtaphGIbPNUsDAAAAgIzwy+kCAAAAAIA3EG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAlEG4AAAAAWALhBgAAAIAl/B9w+axgY8a5VgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Иерархическая кластеризация\n", + "linked = linkage(scaled_data, 'ward')\n", + "\n", + "# Визуализация\n", + "plt.figure(figsize=(10, 7))\n", + "dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)\n", + "plt.title('Иерархическая')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# Определение меток\n", + "n_clusters = 3\n", + "hierarchical_labels = fcluster(linked, n_clusters, criterion='maxclust')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + " # Визуализация кластеров\n", + "plt.figure(figsize=(10, 6))\n", + "sns.scatterplot(x=pca_data[:, 0], y=pca_data[:, 1], hue=hierarchical_labels, palette='inferno', s=100)\n", + "plt.title('Иерархическая')\n", + "plt.xlabel('PC1')\n", + "plt.ylabel('PC2')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " year index price log_indexprice inflationrate oil prices \\\n", + "Cluster \n", + "0 1998.054545 13563.522364 3.929091 0.054182 34.765091 \n", + "1 2005.619048 7237.508776 3.713401 0.020680 48.031361 \n", + "2 2009.294118 3554.822941 3.494118 0.022941 59.845294 \n", + "\n", + " exchange_rate gdppercent percapitaincome unemploymentrate \\\n", + "Cluster \n", + "0 85.857273 0.045818 7502.927273 0.061818 \n", + "1 6.610340 0.029320 27037.510204 0.077823 \n", + "2 1.000000 0.025294 49157.352941 0.058235 \n", + "\n", + " manufacturingoutput tradebalance USTreasury \n", + "Cluster \n", + "0 132.100000 -6.739455 0.063636 \n", + "1 473.491633 34.495510 0.042993 \n", + "2 251.887059 -555.851765 0.035294 \n" + ] + } + ], + "source": [ + "# Добавление меток кластеров в исходный датафрейм\n", + "df['Cluster'] = kmeans_labels\n", + "\n", + "# Удаление нечисловых столбцов перед вычислением среднего\n", + "numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns\n", + "cluster_analysis = df.groupby('Cluster')[numeric_columns].mean()\n", + "\n", + "# Вывод результата\n", + "print(cluster_analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оценка для неиерархического: 0.20251\n", + "Оценка для иерархического: 0.20251\n" + ] + } + ], + "source": [ + "# Оценка\n", + "print(f\"Оценка для неиерархического: {round(silhouette_avg_kmeans,5)}\")\n", + "\n", + "silhouette_avg = silhouette_score(scaled_data, kmeans_labels)\n", + "print(f\"Оценка для иерархического: {round(silhouette_avg,5)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оценки совпадают, потому что, вероятно, для расхождения в оценке нужно большее число различных данных." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Scripts", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3863672121382f4b167c410aef759d36b4d10b11 Mon Sep 17 00:00:00 2001 From: dex_moth Date: Sat, 21 Dec 2024 12:33:06 +0400 Subject: [PATCH 7/7] correct lab4 --- lab_4/Lab4.ipynb | 256 +++++++++++++---------------------------------- 1 file changed, 70 insertions(+), 186 deletions(-) diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb index 0b8116e..62032e7 100644 --- a/lab_4/Lab4.ipynb +++ b/lab_4/Lab4.ipynb @@ -23,6 +23,13 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from matplotlib.ticker import FuncFormatter\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.neighbors import KNeighborsRegressor\n", "\n", "df = pd.read_csv(\".//csv//Student Depression Dataset.csv\")\n", "print(df.columns)" @@ -293,6 +300,50 @@ "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Создание конвейера\n", + "\n", + "# Обработаем данные\n", + "# Определим категориальные и числовые признаки\n", + "categorical_features = ['Gender', 'City', 'Dietary Habits', 'Degree', 'Have you ever had suicidal thoughts ?', 'Profession', 'Family History of Mental Illness', 'Sleep Duration']\n", + "numerical_features = ['Age', 'Academic Pressure', 'Work Pressure', 'CGPA', 'Study Satisfaction', 'Job Satisfaction', 'Work/Study Hours', 'Financial Stress']\n", + "\n", + "categorical_transformer = Pipeline(steps=[\n", + " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n", + "])\n", + "\n", + "numerical_transformer = Pipeline(steps=[\n", + " ('scaler', StandardScaler())\n", + "])\n", + "\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('num', numerical_transformer, numerical_features),\n", + " ('cat', categorical_transformer, categorical_features)\n", + " ])\n", + "\n", + "# Построим модели\n", + "pipeline_lasso = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', Lasso())\n", + "])\n", + "\n", + "pipeline_gb = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', GradientBoostingRegressor())\n", + "])\n", + "\n", + "pipeline_knn = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', KNeighborsRegressor())\n", + "])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -302,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -310,7 +361,7 @@ "output_type": "stream", "text": [ "Лучшие гиперпараметры для Lasso:\n", - "{'alpha': 0.01, 'fit_intercept': False}\n" + "{'model__alpha': 0.01, 'model__fit_intercept': False}\n" ] } ], @@ -318,8 +369,8 @@ "from sklearn.linear_model import Lasso\n", "\n", "param_grid_lasso = {\n", - " 'alpha': [0.01, 0.1, 1.0, 10.0],\n", - " 'fit_intercept': [True, False],\n", + " 'model__alpha': [0.01, 0.1, 1.0, 10.0],\n", + " 'model__fit_intercept': [True, False],\n", "}\n", "\n", "# Создание объекта GridSearchCV\n", @@ -347,193 +398,28 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n", - "1215 fits failed out of a total of 3645.\n", - "The score on these train-test partitions for these parameters will be set to nan.\n", - "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", - "\n", - "Below are more details about the failures:\n", - "--------------------------------------------------------------------------------\n", - "978 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", - " estimator._validate_params()\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", - " validate_parameter_constraints(\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "237 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 1466, in wrapper\n", - " estimator._validate_params()\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\base.py\", line 666, in _validate_params\n", - " validate_parameter_constraints(\n", - " File \"e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n", - " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of GradientBoostingRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", - "\n", - " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n", - " _data = np.array(data, dtype=dtype, copy=copy,\n", - "e:\\AIM1.5\\Scripts\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.18767441 -0.15799837 -0.13080278\n", - " -0.18762913 -0.15792709 -0.13056114 -0.18792038 -0.15737146 -0.130218\n", - " -0.18725961 -0.157967 -0.13047453 -0.18766583 -0.15779565 -0.13094863\n", - " -0.18798705 -0.15693978 -0.13061215 -0.18766317 -0.15746848 -0.13072918\n", - " -0.18864158 -0.15666133 -0.13095037 -0.18817206 -0.15805489 -0.13086126\n", - " -0.18707465 -0.15864932 -0.13104947 -0.18818902 -0.15828572 -0.13063871\n", - 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" nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " -0.11606052 -0.1140225 -0.11403709 -0.11627212 -0.1139982 -0.11402075\n", - " -0.11613561 -0.11407941 -0.11420487 -0.11666225 -0.11462523 -0.11431901\n", - " -0.11604817 -0.11456211 -0.11392092 -0.11609343 -0.11394228 -0.11414071\n", - " -0.11611685 -0.11420178 -0.11405459 -0.11594404 -0.11408614 -0.11391662\n", - " -0.11590886 -0.11396465 -0.11389125 -0.11616694 -0.11441846 -0.11417015\n", - " -0.11617368 -0.11429765 -0.1139636 -0.11616763 -0.11433984 -0.11412121\n", - " -0.11625618 -0.11402999 -0.11419791 -0.11613603 -0.114206 -0.11423922\n", - " -0.1160801 -0.11431896 -0.11416734 -0.11608923 -0.11455498 -0.11417448\n", - " -0.11605165 -0.11427773 -0.11392205 -0.11606243 -0.11408421 -0.11395292\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11281447 -0.11245904 -0.11308822\n", - " -0.11256366 -0.11230094 -0.1130767 -0.11282651 -0.1121034 -0.11283479\n", - 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" nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan nan nan nan\n", - " nan nan nan -0.11573534 -0.11897501 -0.1226239\n", - " -0.1162633 -0.11939573 -0.12255715 -0.11636411 -0.11878021 -0.12306277\n", - " -0.11535113 -0.11813967 -0.1230085 -0.11594119 -0.11812955 -0.12217928\n", - " -0.11523023 -0.11843291 -0.12228252 -0.1159457 -0.11840108 -0.12181337\n", - " -0.11600134 -0.11790484 -0.12203724 -0.11579998 -0.11787918 -0.12317219\n", - " -0.11578704 -0.11837798 -0.12379234 -0.1155279 -0.11865384 -0.12319867\n", - " -0.11597008 -0.11886814 -0.12291788 -0.1162282 -0.11918752 -0.12363613\n", - " -0.11571473 -0.11805225 -0.12250506 -0.11640247 -0.11823175 -0.1226976\n", - " -0.11571549 -0.11813327 -0.12229009 -0.11621545 -0.11793769 -0.1229533\n", - " -0.11528287 -0.1183919 -0.12121653]\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "Лучшие гиперпараметры для Gradient Boosting:\n", - "{'learning_rate': 0.1, 'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 100}\n" + "{'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__max_features': 'sqrt', 'model__min_samples_leaf': 2, 'model__min_samples_split': 5, 'model__n_estimators': 100}\n" ] } ], "source": [ - "\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "\n", "param_grid_gb = {\n", - " 'n_estimators': [50, 100, 200],\n", - " 'learning_rate': [0.01, 0.1, 0.2],\n", - " 'max_depth': [3, 5, 7],\n", - " 'min_samples_split': [2, 5, 10],\n", - " 'min_samples_leaf': [1, 2, 4],\n", - " 'max_features': ['auto', 'sqrt', 'log2']\n", + " 'model__n_estimators': [50, 100, 200],\n", + " 'model__learning_rate': [0.01, 0.1, 0.2],\n", + " 'model__max_depth': [3, 5, 7],\n", + " 'model__min_samples_split': [2, 5, 10],\n", + " 'model__min_samples_leaf': [1, 2, 4],\n", + " 'model__max_features': ['auto', 'sqrt', 'log2']\n", "}\n", "\n", "grid_search_gb = GridSearchCV(\n", @@ -577,10 +463,10 @@ "from sklearn.model_selection import GridSearchCV\n", "\n", "param_grid_knn = {\n", - " 'n_neighbors': [3, 5, 7, 10],\n", - " 'weights': ['uniform', 'distance'],\n", - " 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", - " 'p': [1, 2]\n", + " 'model__n_neighbors': [3, 5, 7, 10],\n", + " 'model__weights': ['uniform', 'distance'],\n", + " 'model__algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n", + " 'model__p': [1, 2]\n", "}\n", "\n", "grid_search_knn = GridSearchCV(\n", @@ -611,11 +497,9 @@ "metadata": {}, "outputs": [], "source": [ - "y_pred = model.predict(x_test)\n", - "y_pred_forest = model_forest.predict(x_test)\n", - "y_pred_lasso = model_lasso.predict(x_test)\n", - "y_pred_gb = model_gb.predict(x_test)\n", - "y_pred_neighbors = model_knn.predict(x_test)" + "y_pred_lasso = grid_search_lasso.predict(x_test)\n", + "y_pred_forest = grid_search_gb.predict(x_test)\n", + "y_pred_neighbors = grid_search_knn.predict(x_test)" ] }, {