{ "cells": [ { "cell_type": "markdown", "id": "837693ee", "metadata": {}, "source": [ "# Поиск аномалий и снижение размерности для датасета стран мира\n", "\n", "В ноутбуке выполняются:\n", "- загрузка и предобработка данных;\n", "- отбор содержательных непрерывных числовых признаков;\n", "- поиск аномалий методом Isolation Forest;\n", "- оценка качества найденного разбиения внутренними метриками;\n", "- снижение размерности до двух новых признаков;\n", "- повторный поиск аномалий тем же методом на новых признаках;\n", "- сравнение результатов до и после снижения размерности.\n", "\n", "Финальный вывод намеренно не приводится." ] }, { "cell_type": "code", "execution_count": 1, "id": "5a6bdd71", "metadata": {}, "outputs": [], "source": [ "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import re\n", "import json\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.ensemble import IsolationForest\n", "from sklearn.decomposition import PCA\n", "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, jaccard_score, confusion_matrix\n", "from sklearn.neighbors import LocalOutlierFactor\n", "\n", "plt.rcParams[\"figure.figsize\"] = (10, 6)\n", "pd.set_option(\"display.max_columns\", 200)\n", "pd.set_option(\"display.width\", 160)\n", "RANDOM_STATE = 42" ] }, { "cell_type": "markdown", "id": "2d038537", "metadata": {}, "source": [ "## 1. Загрузка данных и первичный просмотр" ] }, { "cell_type": "code", "execution_count": 2, "id": "8909528e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размер исходного датафрейма: (195, 35)\n" ] }, { "data": { "text/html": [ "
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CountryDensity\\n(P/Km2)AbbreviationAgricultural Land( %)Land Area(Km2)Armed Forces sizeBirth RateCalling CodeCapital/Major CityCo2-EmissionsCPICPI Change (%)Currency-CodeFertility RateForested Area (%)Gasoline PriceGDPGross primary education enrollment (%)Gross tertiary education enrollment (%)Infant mortalityLargest cityLife expectancyMaternal mortality ratioMinimum wageOfficial languageOut of pocket health expenditurePhysicians per thousandPopulationPopulation: Labor force participation (%)Tax revenue (%)Total tax rateUnemployment rateUrban_populationLatitudeLongitude
0Afghanistan60AF58.10%652,230323,00032.4993.0Kabul8,672149.92.30%AFN4.472.10%$0.70$19,101,353,833104.00%9.70%47.9Kabul64.5638.0$0.43Pashto78.40%0.2838,041,75448.90%9.30%71.40%11.12%9,797,27333.93911067.709953
1Albania105AL43.10%28,7489,00011.78355.0Tirana4,536119.051.40%ALL1.6228.10%$1.36$15,278,077,447107.00%55.00%7.8Tirana78.515.0$1.12Albanian56.90%1.202,854,19155.70%18.60%36.60%12.33%1,747,59341.15333220.168331
2Algeria18DZ17.40%2,381,741317,00024.28213.0Algiers150,006151.362.00%DZD3.020.80%$0.28$169,988,236,398109.90%51.40%20.1Algiers76.7112.0$0.95Arabic28.10%1.7243,053,05441.20%37.20%66.10%11.70%31,510,10028.0338861.659626
3Andorra164AD40.00%468NaN7.20376.0Andorra la Vella469NaNNaNEUR1.2734.00%$1.51$3,154,057,987106.40%NaN2.7Andorra la VellaNaNNaN$6.63Catalan36.40%3.3377,142NaNNaNNaNNaN67,87342.5062851.521801
4Angola26AO47.50%1,246,700117,00040.73244.0Luanda34,693261.7317.10%AOA5.5246.30%$0.97$94,635,415,870113.50%9.30%51.6Luanda60.8241.0$0.71Portuguese33.40%0.2131,825,29577.50%9.20%49.10%6.89%21,061,025-11.20269217.873887
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" ], "text/plain": [ " Country Density\\n(P/Km2) Abbreviation Agricultural Land( %) Land Area(Km2) Armed Forces size Birth Rate Calling Code Capital/Major City \\\n", "0 Afghanistan 60 AF 58.10% 652,230 323,000 32.49 93.0 Kabul \n", "1 Albania 105 AL 43.10% 28,748 9,000 11.78 355.0 Tirana \n", "2 Algeria 18 DZ 17.40% 2,381,741 317,000 24.28 213.0 Algiers \n", "3 Andorra 164 AD 40.00% 468 NaN 7.20 376.0 Andorra la Vella \n", "4 Angola 26 AO 47.50% 1,246,700 117,000 40.73 244.0 Luanda \n", "\n", " Co2-Emissions CPI CPI Change (%) Currency-Code Fertility Rate Forested Area (%) Gasoline Price GDP \\\n", "0 8,672 149.9 2.30% AFN 4.47 2.10% $0.70 $19,101,353,833 \n", "1 4,536 119.05 1.40% ALL 1.62 28.10% $1.36 $15,278,077,447 \n", "2 150,006 151.36 2.00% DZD 3.02 0.80% $0.28 $169,988,236,398 \n", "3 469 NaN NaN EUR 1.27 34.00% $1.51 $3,154,057,987 \n", "4 34,693 261.73 17.10% AOA 5.52 46.30% $0.97 $94,635,415,870 \n", "\n", " Gross primary education enrollment (%) Gross tertiary education enrollment (%) Infant mortality Largest city Life expectancy \\\n", "0 104.00% 9.70% 47.9 Kabul 64.5 \n", "1 107.00% 55.00% 7.8 Tirana 78.5 \n", "2 109.90% 51.40% 20.1 Algiers 76.7 \n", "3 106.40% NaN 2.7 Andorra la Vella NaN \n", "4 113.50% 9.30% 51.6 Luanda 60.8 \n", "\n", " Maternal mortality ratio Minimum wage Official language Out of pocket health expenditure Physicians per thousand Population \\\n", "0 638.0 $0.43 Pashto 78.40% 0.28 38,041,754 \n", "1 15.0 $1.12 Albanian 56.90% 1.20 2,854,191 \n", "2 112.0 $0.95 Arabic 28.10% 1.72 43,053,054 \n", "3 NaN $6.63 Catalan 36.40% 3.33 77,142 \n", "4 241.0 $0.71 Portuguese 33.40% 0.21 31,825,295 \n", "\n", " Population: Labor force participation (%) Tax revenue (%) Total tax rate Unemployment rate Urban_population Latitude Longitude \n", "0 48.90% 9.30% 71.40% 11.12% 9,797,273 33.939110 67.709953 \n", "1 55.70% 18.60% 36.60% 12.33% 1,747,593 41.153332 20.168331 \n", "2 41.20% 37.20% 66.10% 11.70% 31,510,100 28.033886 1.659626 \n", "3 NaN NaN NaN NaN 67,873 42.506285 1.521801 \n", "4 77.50% 9.20% 49.10% 6.89% 21,061,025 -11.202692 17.873887 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Названия колонок:\n", "['Country', 'Density\\n(P/Km2)', 'Abbreviation', 'Agricultural Land( %)', 'Land Area(Km2)', 'Armed Forces size', 'Birth Rate', 'Calling Code', 'Capital/Major City', 'Co2-Emissions', 'CPI', 'CPI Change (%)', 'Currency-Code', 'Fertility Rate', 'Forested Area (%)', 'Gasoline Price', 'GDP', 'Gross primary education enrollment (%)', 'Gross tertiary education enrollment (%)', 'Infant mortality', 'Largest city', 'Life expectancy', 'Maternal mortality ratio', 'Minimum wage', 'Official language', 'Out of pocket health expenditure', 'Physicians per thousand', 'Population', 'Population: Labor force participation (%)', 'Tax revenue (%)', 'Total tax rate', 'Unemployment rate', 'Urban_population', 'Latitude', 'Longitude']\n" ] } ], "source": [ "\n", "DATA_PATH = \"world-data-2023.csv\"\n", "\n", "df_raw = pd.read_csv(DATA_PATH)\n", "print(\"Размер исходного датафрейма:\", df_raw.shape)\n", "display(df_raw.head())\n", "print(\"\\nНазвания колонок:\")\n", "print(df_raw.columns.tolist())" ] }, { "cell_type": "markdown", "id": "d44f9c8f", "metadata": {}, "source": [ "## 2. Нормализация названий колонок и преобразование числовых значений\n", "\n", "Часть признаков хранится как строки с символами процентов, валюты и разделителями тысяч.\n", "Ниже выполняется автоматическое приведение таких столбцов к числовому виду, если это возможно." ] }, { "cell_type": "code", "execution_count": 3, "id": "8d04d2de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Нормализованные названия колонок:\n", "['country', 'density_p_km2', 'abbreviation', 'agricultural_land', 'land_area_km2', 'armed_forces_size', 'birth_rate', 'calling_code', 'capital_major_city', 'co2_emissions', 'cpi', 'cpi_change', 'currency_code', 'fertility_rate', 'forested_area', 'gasoline_price', 'gdp', 'gross_primary_education_enrollment', 'gross_tertiary_education_enrollment', 'infant_mortality', 'largest_city', 'life_expectancy', 'maternal_mortality_ratio', 'minimum_wage', 'official_language', 'out_of_pocket_health_expenditure', 'physicians_per_thousand', 'population', 'population_labor_force_participation', 'tax_revenue', 'total_tax_rate', 'unemployment_rate', 'urban_population', 'latitude', 'longitude']\n", "country object\n", "density_p_km2 int64\n", "abbreviation object\n", "agricultural_land float64\n", "land_area_km2 float64\n", "armed_forces_size float64\n", "birth_rate float64\n", "calling_code float64\n", "capital_major_city object\n", "co2_emissions float64\n", "cpi float64\n", "cpi_change float64\n", "currency_code object\n", "fertility_rate float64\n", "forested_area float64\n", "gasoline_price float64\n", "gdp float64\n", "gross_primary_education_enrollment float64\n", "gross_tertiary_education_enrollment float64\n", "infant_mortality float64\n", "largest_city object\n", "life_expectancy float64\n", "maternal_mortality_ratio float64\n", "minimum_wage float64\n", "official_language object\n", "out_of_pocket_health_expenditure float64\n", "physicians_per_thousand float64\n", "population float64\n", "population_labor_force_participation float64\n", "tax_revenue float64\n", "total_tax_rate float64\n", "unemployment_rate float64\n", "urban_population float64\n", "latitude float64\n", "longitude float64\n", "dtype: object\n" ] } ], "source": [ "\n", "def normalize_column_name(col: str) -> str:\n", " col = str(col).strip().lower()\n", " col = col.replace(\"\\n\", \" \")\n", " col = re.sub(r\"[%/()\\-]+\", \" \", col)\n", " col = re.sub(r\"[^a-zа-я0-9]+\", \"_\", col)\n", " col = re.sub(r\"_+\", \"_\", col).strip(\"_\")\n", " return col\n", "\n", "df = df_raw.copy()\n", "original_to_normalized = {col: normalize_column_name(col) for col in df.columns}\n", "df = df.rename(columns=original_to_normalized)\n", "\n", "print(\"Нормализованные названия колонок:\")\n", "print(df.columns.tolist())\n", "\n", "def try_convert_object_to_numeric(series: pd.Series, min_share: float = 0.70) -> pd.Series:\n", " if not pd.api.types.is_object_dtype(series):\n", " return series\n", "\n", " s = series.astype(str).str.strip()\n", " s = s.replace({\"\": np.nan, \"nan\": np.nan, \"None\": np.nan, \"N/A\": np.nan, \"NULL\": np.nan})\n", " s = s.str.replace(r\"[$€£¥]\", \"\", regex=True)\n", " s = s.str.replace(\"%\", \"\", regex=False)\n", " s = s.str.replace(r\"(?<=\\d),(?=\\d)\", \"\", regex=True)\n", " s = s.str.replace(r\"\\s+\", \" \", regex=True)\n", " converted = pd.to_numeric(s, errors=\"coerce\")\n", "\n", " if converted.notna().mean() >= min_share:\n", " return converted\n", " return series\n", "\n", "for col in df.columns:\n", " df[col] = try_convert_object_to_numeric(df[col])\n", "\n", "print(df.dtypes)" ] }, { "cell_type": "markdown", "id": "f10f79fe", "metadata": {}, "source": [ "## 3. Отбор содержательных непрерывных числовых переменных\n", "\n", "Исключаются:\n", "- явные идентификаторы и текстовые поля;\n", "- коды и служебные атрибуты;\n", "- числовые признаки с семантикой идентификаторов.\n", "\n", "Для устойчивости используется автоматический отбор по типу данных и ключевым словам в названии признака." ] }, { "cell_type": "code", "execution_count": 4, "id": "60b2d04b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Выбранные содержательные числовые признаки (28):\n", "['density_p_km2', 'agricultural_land', 'land_area_km2', 'armed_forces_size', 'birth_rate', 'co2_emissions', 'cpi', 'cpi_change', 'fertility_rate', 'forested_area', 'gasoline_price', 'gdp', 'gross_primary_education_enrollment', 'gross_tertiary_education_enrollment', 'infant_mortality', 'life_expectancy', 'maternal_mortality_ratio', 'minimum_wage', 'out_of_pocket_health_expenditure', 'physicians_per_thousand', 'population', 'population_labor_force_participation', 'tax_revenue', 'total_tax_rate', 'unemployment_rate', 'urban_population', 'latitude', 'longitude']\n" ] }, { "data": { "text/html": [ "
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42647.51246700.0117000.040.7334693.0261.7317.15.5246.30.979.463542e+10113.59.351.660.8241.00.7133.40.2131825295.077.59.249.16.8921061025.0-11.20269217.873887
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" ], "text/plain": [ " density_p_km2 agricultural_land land_area_km2 armed_forces_size birth_rate co2_emissions cpi cpi_change fertility_rate forested_area \\\n", "0 60 58.1 652230.0 323000.0 32.49 8672.0 149.90 2.3 4.47 2.1 \n", "1 105 43.1 28748.0 9000.0 11.78 4536.0 119.05 1.4 1.62 28.1 \n", "2 18 17.4 2381741.0 317000.0 24.28 150006.0 151.36 2.0 3.02 0.8 \n", "3 164 40.0 468.0 NaN 7.20 469.0 NaN NaN 1.27 34.0 \n", "4 26 47.5 1246700.0 117000.0 40.73 34693.0 261.73 17.1 5.52 46.3 \n", "\n", " gasoline_price gdp gross_primary_education_enrollment gross_tertiary_education_enrollment infant_mortality life_expectancy \\\n", "0 0.70 1.910135e+10 104.0 9.7 47.9 64.5 \n", "1 1.36 1.527808e+10 107.0 55.0 7.8 78.5 \n", "2 0.28 1.699882e+11 109.9 51.4 20.1 76.7 \n", "3 1.51 3.154058e+09 106.4 NaN 2.7 NaN \n", "4 0.97 9.463542e+10 113.5 9.3 51.6 60.8 \n", "\n", " maternal_mortality_ratio minimum_wage out_of_pocket_health_expenditure physicians_per_thousand population population_labor_force_participation \\\n", "0 638.0 0.43 78.4 0.28 38041754.0 48.9 \n", "1 15.0 1.12 56.9 1.20 2854191.0 55.7 \n", "2 112.0 0.95 28.1 1.72 43053054.0 41.2 \n", "3 NaN 6.63 36.4 3.33 77142.0 NaN \n", "4 241.0 0.71 33.4 0.21 31825295.0 77.5 \n", "\n", " tax_revenue total_tax_rate unemployment_rate urban_population latitude longitude \n", "0 9.3 71.4 11.12 9797273.0 33.939110 67.709953 \n", "1 18.6 36.6 12.33 1747593.0 41.153332 20.168331 \n", "2 37.2 66.1 11.70 31510100.0 28.033886 1.659626 \n", "3 NaN NaN NaN 67873.0 42.506285 1.521801 \n", "4 9.2 49.1 6.89 21061025.0 -11.202692 17.873887 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "identifier_keywords = [\n", " \"country\", \"abbreviation\", \"capital\", \"city\", \"currency\", \"language\",\n", " \"calling_code\", \"code\"\n", "]\n", "\n", "numeric_cols = df.select_dtypes(include=\"number\").columns.tolist()\n", "selected_numeric_cols = [\n", " col for col in numeric_cols\n", " if not any(keyword in col for keyword in identifier_keywords)\n", "]\n", "\n", "# При необходимости оставляем только признаки с достаточным числом уникальных значений.\n", "selected_numeric_cols = [\n", " col for col in selected_numeric_cols\n", " if df[col].nunique(dropna=True) >= 15\n", "]\n", "\n", "print(\"Выбранные содержательные числовые признаки ({}):\".format(len(selected_numeric_cols)))\n", "print(selected_numeric_cols)\n", "\n", "data_num = df[selected_numeric_cols].copy()\n", "display(data_num.head())" ] }, { "cell_type": "markdown", "id": "38a5bebb", "metadata": {}, "source": [ "## 4. Обработка пропусков и масштабирование\n", "\n", "Для поиска аномалий и снижения размерности используется стандартизация признаков.\n", "Пропуски заполняются медианой, чтобы метод был устойчив к выбросам." ] }, { "cell_type": "code", "execution_count": 5, "id": "c2fe376a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размер матрицы признаков после предобработки: (195, 28)\n", "Число пропусков до обработки: 304\n", "Число пропусков после обработки: 0\n" ] } ], "source": [ "\n", "preprocessor = Pipeline(steps=[\n", " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", " (\"scaler\", StandardScaler())\n", "])\n", "\n", "X = preprocessor.fit_transform(data_num)\n", "feature_names = data_num.columns.tolist()\n", "\n", "print(\"Размер матрицы признаков после предобработки:\", X.shape)\n", "print(\"Число пропусков до обработки:\", int(data_num.isna().sum().sum()))\n", "print(\"Число пропусков после обработки:\", int(np.isnan(X).sum()))" ] }, { "cell_type": "markdown", "id": "90038697", "metadata": {}, "source": [ "## 5. Поиск аномалий в исходном пространстве признаков\n", "\n", "В качестве основного метода используется **Isolation Forest**.\n", "Доля аномалий задаётся адаптивно через `contamination=0.08`, что подходит для задачи без разметки и умеренного числа объектов." ] }, { "cell_type": "code", "execution_count": 6, "id": "caaf3335", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Число аномалий: 16\n", "Доля аномалий: 0.0821\n" ] }, { "data": { "text/html": [ "
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CountryDensity\\n(P/Km2)AbbreviationAgricultural Land( %)Land Area(Km2)Armed Forces sizeBirth RateCalling CodeCapital/Major CityCo2-EmissionsCPICPI Change (%)Currency-CodeFertility RateForested Area (%)Gasoline PriceGDPGross primary education enrollment (%)Gross tertiary education enrollment (%)Infant mortalityLargest cityLife expectancyMaternal mortality ratioMinimum wageOfficial languageOut of pocket health expenditurePhysicians per thousandPopulationPopulation: Labor force participation (%)Tax revenue (%)Total tax rateUnemployment rateUrban_populationLatitudeLongitudeanomaly_score_originalanomaly_original
36China153CN56.20%9,596,9602,695,00010.9086.0Beijing9,893,038125.082.90%CNY1.6922.40%$0.96$19,910,000,000,000100.20%50.60%7.4Shanghai77.029.0$0.87Standard Chinese32.40%1.981,397,715,00068.00%9.40%59.20%4.32%842,933,96235.861660104.1953970.6470351
186United States36US44.40%9,833,5171,359,00011.601.0Washington, D.C.5,006,302117.247.50%USD1.7333.90%$0.71$21,427,700,000,000101.80%88.20%5.6New York City78.519.0$7.25NaN11.10%2.61328,239,52362.00%9.60%36.60%14.70%270,663,02837.090240-95.7128910.5962911
77India464IN60.40%3,287,2633,031,00017.8691.0New Delhi2,407,672180.447.70%INR2.2223.80%$0.97$2,611,000,000,000113.00%28.10%29.9Kurebhar69.4145.0$0.30Hindi65.10%0.861,366,417,75449.30%11.20%49.70%5.36%471,031,52820.59368478.9628800.5716561
163South Sudan18SSNaN644,329185,00035.01211.0Juba1,7274,583.71187.90%SSP4.70NaN$0.28$11,997,800,75173.00%NaN63.7Juba57.61150.0NaNEnglish61.30%NaN11,062,11372.40%NaN31.40%12.24%2,201,2506.87699231.3069790.5705271
126Nigeria226NG77.70%923,768215,00037.91234.0Abuja120,369267.5111.40%NGN5.397.20%$0.46$448,120,428,85984.70%10.20%75.7Lagos54.3917.0$0.54English72.20%0.38200,963,59952.90%1.50%34.80%8.10%102,806,9489.0819998.6752770.5446321
190Venezuela32VE24.50%912,050343,00017.8858.0Caracas164,1752,740.27254.90%VED2.2752.70%$0.00$482,359,318,76897.20%79.30%21.4Caracas72.1125.0$0.01Spanish45.80%1.9228,515,82959.70%NaN73.30%8.80%25,162,3686.423750-66.5897300.5318591
143Russia9RU13.30%17,098,2401,454,00011.507.0Moscow1,732,027180.754.50%RUB1.5749.80%$0.59$1,699,876,578,871102.60%81.90%6.1Moscow72.717.0$0.53Russian36.40%4.01144,373,53561.80%11.40%46.20%4.59%107,683,88961.524010105.3187560.5291191
160Somalia25SO70.30%637,65720,00041.75252.0Mogadishu645NaNNaNSOS6.0710.00%$1.41$4,720,727,27823.40%2.50%76.6Bosaso57.1829.0NaNArabicNaN0.0215,442,90547.40%0.00%NaN11.35%7,034,8615.15214946.1996160.5206961
113Monaco26,337MCNaN2NaN5.90377.0Monaco CityNaNNaNNaNEURNaNNaN$2.00$7,184,844,193NaNNaN2.6Monaco CityNaNNaN$11.72French6.10%6.5638,964NaNNaNNaNNaN38,96443.7384187.4246160.5030571
95Lesotho71LS77.60%30,3552,00026.81266.0Maseru2,512155.865.20%NaN3.141.60%$0.70$2,460,072,444120.90%10.20%65.7Maseru53.7544.0$0.41English16.90%0.072,125,26867.90%31.60%13.60%23.41%607,508-29.60998828.2336080.4957331
8Australia3AU48.20%7,741,22058,00012.6061.0Canberra375,908119.81.60%AUD1.7416.30%$0.93$1,392,680,589,329100.30%113.10%3.1Sydney82.76.0$13.59NaN19.60%3.6825,766,60565.50%23.00%47.40%5.27%21,844,756-25.274398133.7751360.4888851
85Japan347JP12.30%377,944261,0007.4081.0Tokyo1,135,886105.480.50%NaN1.4268.50%$1.06$5,081,769,542,38098.80%63.20%1.8Tokyo84.25.0$6.77NaN13.10%2.41126,226,56861.70%11.90%46.70%2.29%115,782,41636.204824138.2529240.4830561
23Brazil25BR33.90%8,515,770730,00013.9255.0Bras���462,299167.43.70%BRL1.7358.90%$1.02$1,839,758,040,766115.40%51.30%12.8S����75.760.0$1.53Portuguese28.30%2.15212,559,41763.90%14.20%65.10%12.08%183,241,641-14.235004-51.9252800.4784661
33Central African Republic8CF8.20%622,9848,00035.35236.0Bangui297186.8637.10%NaN4.7235.60%$1.41$2,220,307,369102.00%3.00%84.5Bangui52.8829.0$0.37French39.60%0.064,745,18572.00%8.60%73.30%3.68%1,982,0646.61111120.9394440.4772191
6Argentina17AR54.30%2,780,400105,00017.0254.0Buenos Aires201,348232.7553.50%ARS2.269.80%$1.10$449,663,446,954109.70%90.00%8.8Buenos Aires76.539.0$3.35Spanish17.60%3.9644,938,71261.30%10.10%106.30%9.79%41,339,571-38.416097-63.6166720.4750511
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" ], "text/plain": [ " Country Density\\n(P/Km2) Abbreviation Agricultural Land( %) Land Area(Km2) Armed Forces size Birth Rate Calling Code \\\n", "36 China 153 CN 56.20% 9,596,960 2,695,000 10.90 86.0 \n", "186 United States 36 US 44.40% 9,833,517 1,359,000 11.60 1.0 \n", "77 India 464 IN 60.40% 3,287,263 3,031,000 17.86 91.0 \n", "163 South Sudan 18 SS NaN 644,329 185,000 35.01 211.0 \n", "126 Nigeria 226 NG 77.70% 923,768 215,000 37.91 234.0 \n", "190 Venezuela 32 VE 24.50% 912,050 343,000 17.88 58.0 \n", "143 Russia 9 RU 13.30% 17,098,240 1,454,000 11.50 7.0 \n", "160 Somalia 25 SO 70.30% 637,657 20,000 41.75 252.0 \n", "113 Monaco 26,337 MC NaN 2 NaN 5.90 377.0 \n", "95 Lesotho 71 LS 77.60% 30,355 2,000 26.81 266.0 \n", "8 Australia 3 AU 48.20% 7,741,220 58,000 12.60 61.0 \n", "85 Japan 347 JP 12.30% 377,944 261,000 7.40 81.0 \n", "23 Brazil 25 BR 33.90% 8,515,770 730,000 13.92 55.0 \n", "33 Central African Republic 8 CF 8.20% 622,984 8,000 35.35 236.0 \n", "6 Argentina 17 AR 54.30% 2,780,400 105,000 17.02 54.0 \n", "\n", " Capital/Major City Co2-Emissions CPI CPI Change (%) Currency-Code Fertility Rate Forested Area (%) Gasoline Price GDP \\\n", "36 Beijing 9,893,038 125.08 2.90% CNY 1.69 22.40% $0.96 $19,910,000,000,000 \n", "186 Washington, D.C. 5,006,302 117.24 7.50% USD 1.73 33.90% $0.71 $21,427,700,000,000 \n", "77 New Delhi 2,407,672 180.44 7.70% INR 2.22 23.80% $0.97 $2,611,000,000,000 \n", "163 Juba 1,727 4,583.71 187.90% SSP 4.70 NaN $0.28 $11,997,800,751 \n", "126 Abuja 120,369 267.51 11.40% NGN 5.39 7.20% $0.46 $448,120,428,859 \n", "190 Caracas 164,175 2,740.27 254.90% VED 2.27 52.70% $0.00 $482,359,318,768 \n", "143 Moscow 1,732,027 180.75 4.50% RUB 1.57 49.80% $0.59 $1,699,876,578,871 \n", "160 Mogadishu 645 NaN NaN SOS 6.07 10.00% $1.41 $4,720,727,278 \n", "113 Monaco City NaN NaN NaN EUR NaN NaN $2.00 $7,184,844,193 \n", "95 Maseru 2,512 155.86 5.20% NaN 3.14 1.60% $0.70 $2,460,072,444 \n", "8 Canberra 375,908 119.8 1.60% AUD 1.74 16.30% $0.93 $1,392,680,589,329 \n", "85 Tokyo 1,135,886 105.48 0.50% NaN 1.42 68.50% $1.06 $5,081,769,542,380 \n", "23 Bras��� 462,299 167.4 3.70% BRL 1.73 58.90% $1.02 $1,839,758,040,766 \n", "33 Bangui 297 186.86 37.10% NaN 4.72 35.60% $1.41 $2,220,307,369 \n", "6 Buenos Aires 201,348 232.75 53.50% ARS 2.26 9.80% $1.10 $449,663,446,954 \n", "\n", " Gross primary education enrollment (%) Gross tertiary education enrollment (%) Infant mortality Largest city Life expectancy \\\n", "36 100.20% 50.60% 7.4 Shanghai 77.0 \n", "186 101.80% 88.20% 5.6 New York City 78.5 \n", "77 113.00% 28.10% 29.9 Kurebhar 69.4 \n", "163 73.00% NaN 63.7 Juba 57.6 \n", "126 84.70% 10.20% 75.7 Lagos 54.3 \n", "190 97.20% 79.30% 21.4 Caracas 72.1 \n", "143 102.60% 81.90% 6.1 Moscow 72.7 \n", "160 23.40% 2.50% 76.6 Bosaso 57.1 \n", "113 NaN NaN 2.6 Monaco City NaN \n", "95 120.90% 10.20% 65.7 Maseru 53.7 \n", "8 100.30% 113.10% 3.1 Sydney 82.7 \n", "85 98.80% 63.20% 1.8 Tokyo 84.2 \n", "23 115.40% 51.30% 12.8 S���� 75.7 \n", "33 102.00% 3.00% 84.5 Bangui 52.8 \n", "6 109.70% 90.00% 8.8 Buenos Aires 76.5 \n", "\n", " Maternal mortality ratio Minimum wage Official language Out of pocket health expenditure Physicians per thousand Population \\\n", "36 29.0 $0.87 Standard Chinese 32.40% 1.98 1,397,715,000 \n", "186 19.0 $7.25 NaN 11.10% 2.61 328,239,523 \n", "77 145.0 $0.30 Hindi 65.10% 0.86 1,366,417,754 \n", "163 1150.0 NaN English 61.30% NaN 11,062,113 \n", "126 917.0 $0.54 English 72.20% 0.38 200,963,599 \n", "190 125.0 $0.01 Spanish 45.80% 1.92 28,515,829 \n", "143 17.0 $0.53 Russian 36.40% 4.01 144,373,535 \n", "160 829.0 NaN Arabic NaN 0.02 15,442,905 \n", "113 NaN $11.72 French 6.10% 6.56 38,964 \n", "95 544.0 $0.41 English 16.90% 0.07 2,125,268 \n", "8 6.0 $13.59 NaN 19.60% 3.68 25,766,605 \n", "85 5.0 $6.77 NaN 13.10% 2.41 126,226,568 \n", "23 60.0 $1.53 Portuguese 28.30% 2.15 212,559,417 \n", "33 829.0 $0.37 French 39.60% 0.06 4,745,185 \n", "6 39.0 $3.35 Spanish 17.60% 3.96 44,938,712 \n", "\n", " Population: Labor force participation (%) Tax revenue (%) Total tax rate Unemployment rate Urban_population Latitude Longitude \\\n", "36 68.00% 9.40% 59.20% 4.32% 842,933,962 35.861660 104.195397 \n", "186 62.00% 9.60% 36.60% 14.70% 270,663,028 37.090240 -95.712891 \n", "77 49.30% 11.20% 49.70% 5.36% 471,031,528 20.593684 78.962880 \n", "163 72.40% NaN 31.40% 12.24% 2,201,250 6.876992 31.306979 \n", "126 52.90% 1.50% 34.80% 8.10% 102,806,948 9.081999 8.675277 \n", "190 59.70% NaN 73.30% 8.80% 25,162,368 6.423750 -66.589730 \n", "143 61.80% 11.40% 46.20% 4.59% 107,683,889 61.524010 105.318756 \n", "160 47.40% 0.00% NaN 11.35% 7,034,861 5.152149 46.199616 \n", "113 NaN NaN NaN NaN 38,964 43.738418 7.424616 \n", "95 67.90% 31.60% 13.60% 23.41% 607,508 -29.609988 28.233608 \n", "8 65.50% 23.00% 47.40% 5.27% 21,844,756 -25.274398 133.775136 \n", "85 61.70% 11.90% 46.70% 2.29% 115,782,416 36.204824 138.252924 \n", "23 63.90% 14.20% 65.10% 12.08% 183,241,641 -14.235004 -51.925280 \n", "33 72.00% 8.60% 73.30% 3.68% 1,982,064 6.611111 20.939444 \n", "6 61.30% 10.10% 106.30% 9.79% 41,339,571 -38.416097 -63.616672 \n", "\n", " anomaly_score_original anomaly_original \n", "36 0.647035 1 \n", "186 0.596291 1 \n", "77 0.571656 1 \n", "163 0.570527 1 \n", "126 0.544632 1 \n", "190 0.531859 1 \n", "143 0.529119 1 \n", "160 0.520696 1 \n", "113 0.503057 1 \n", "95 0.495733 1 \n", "8 0.488885 1 \n", "85 0.483056 1 \n", "23 0.478466 1 \n", "33 0.477219 1 \n", "6 0.475051 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "contamination = 0.08\n", "\n", "iso_forest = IsolationForest(\n", " n_estimators=400,\n", " contamination=contamination,\n", " random_state=RANDOM_STATE,\n", " n_jobs=-1\n", ")\n", "\n", "labels_original = iso_forest.fit_predict(X) # 1 = нормальный объект, -1 = аномалия\n", "scores_original = -iso_forest.score_samples(X) # чем больше, тем более аномальный объект\n", "\n", "is_anomaly_original = (labels_original == -1).astype(int)\n", "\n", "result_original = df_raw.copy()\n", "result_original[\"anomaly_score_original\"] = scores_original\n", "result_original[\"anomaly_original\"] = is_anomaly_original\n", "\n", "print(\"Число аномалий:\", int(is_anomaly_original.sum()))\n", "print(\"Доля аномалий:\", round(is_anomaly_original.mean(), 4))\n", "\n", "display(\n", " result_original.loc[result_original[\"anomaly_original\"] == 1]\n", " .sort_values(\"anomaly_score_original\", ascending=False)\n", " .head(15)\n", ")" ] }, { "cell_type": "markdown", "id": "c1848f1f", "metadata": {}, "source": [ "## 6. Внутренние показатели качества метода\n", "\n", "Так как истинной разметки аномалий нет, качество оценивается внутренними метриками разделения:\n", "- `silhouette_score`;\n", "- `calinski_harabasz_score`;\n", "- `davies_bouldin_score`.\n", "\n", "Дополнительно считается разрыв средних аномальных оценок между классами." ] }, { "cell_type": "code", "execution_count": 7, "id": "603cbe59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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value
silhouette_score0.428121
calinski_harabasz_score13.127589
davies_bouldin_score2.874742
mean_score_normal0.399060
mean_score_anomaly0.523997
score_gap0.124936
anomaly_share0.082051
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" ], "text/plain": [ " value\n", "silhouette_score 0.428121\n", "calinski_harabasz_score 13.127589\n", "davies_bouldin_score 2.874742\n", "mean_score_normal 0.399060\n", "mean_score_anomaly 0.523997\n", "score_gap 0.124936\n", "anomaly_share 0.082051" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def safe_internal_metrics(X_data, binary_labels, anomaly_scores):\n", " metrics = {}\n", " unique = np.unique(binary_labels)\n", "\n", " if len(unique) == 2 and np.bincount(binary_labels).min() > 1:\n", " metrics[\"silhouette_score\"] = silhouette_score(X_data, binary_labels)\n", " metrics[\"calinski_harabasz_score\"] = calinski_harabasz_score(X_data, binary_labels)\n", " metrics[\"davies_bouldin_score\"] = davies_bouldin_score(X_data, binary_labels)\n", " else:\n", " metrics[\"silhouette_score\"] = np.nan\n", " metrics[\"calinski_harabasz_score\"] = np.nan\n", " metrics[\"davies_bouldin_score\"] = np.nan\n", "\n", " scores_normal = anomaly_scores[binary_labels == 0]\n", " scores_anomaly = anomaly_scores[binary_labels == 1]\n", " metrics[\"mean_score_normal\"] = float(np.mean(scores_normal)) if len(scores_normal) else np.nan\n", " metrics[\"mean_score_anomaly\"] = float(np.mean(scores_anomaly)) if len(scores_anomaly) else np.nan\n", " metrics[\"score_gap\"] = metrics[\"mean_score_anomaly\"] - metrics[\"mean_score_normal\"]\n", " metrics[\"anomaly_share\"] = float(np.mean(binary_labels))\n", " return pd.Series(metrics)\n", "\n", "metrics_original = safe_internal_metrics(X, is_anomaly_original, scores_original)\n", "display(metrics_original.to_frame(\"value\"))" ] }, { "cell_type": "markdown", "id": "8c099529", "metadata": {}, "source": [ "## 7. Характеристика аномальных объектов\n", "\n", "Ниже формируются:\n", "- список наиболее аномальных стран;\n", "- сравнительная статистика аномалий и неаномалий по признакам;\n", "- признаки с наибольшим средним стандартизованным отклонением у аномальных объектов." ] }, { "cell_type": "code", "execution_count": 8, "id": "7de9f681", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Наиболее аномальные страны:\n" ] }, { "data": { "text/html": [ "
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Countryanomaly_score_original
36China0.647035
186United States0.596291
77India0.571656
163South Sudan0.570527
126Nigeria0.544632
190Venezuela0.531859
143Russia0.529119
160Somalia0.520696
113Monaco0.503057
95Lesotho0.495733
8Australia0.488885
85Japan0.483056
23Brazil0.478466
33Central African Republic0.477219
6Argentina0.475051
\n", "
" ], "text/plain": [ " Country anomaly_score_original\n", "36 China 0.647035\n", "186 United States 0.596291\n", "77 India 0.571656\n", "163 South Sudan 0.570527\n", "126 Nigeria 0.544632\n", "190 Venezuela 0.531859\n", "143 Russia 0.529119\n", "160 Somalia 0.520696\n", "113 Monaco 0.503057\n", "95 Lesotho 0.495733\n", "8 Australia 0.488885\n", "85 Japan 0.483056\n", "23 Brazil 0.478466\n", "33 Central African Republic 0.477219\n", "6 Argentina 0.475051" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Признаки с наибольшим отличием аномалий от обычных объектов:\n" ] }, { "data": { "text/html": [ "
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normal_mean_zanomaly_mean_zabs_gap
land_area_km2-0.1555301.7399911.895521
urban_population-0.1416221.5843951.726016
co2_emissions-0.1307881.4631891.593977
armed_forces_size-0.1292711.4462151.575485
population-0.1292561.4460511.575307
gdp-0.1247971.3961611.520958
cpi_change-0.1159141.2967891.412703
cpi-0.0973761.0893961.186772
density_p_km2-0.0624580.6987500.761208
maternal_mortality_ratio-0.0603790.6754890.735868
unemployment_rate-0.0597830.6688180.728601
infant_mortality-0.0454480.5084460.553894
life_expectancy0.042545-0.4759730.518518
minimum_wage-0.0412640.4616460.502911
latitude0.040838-0.4568800.497718
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" ], "text/plain": [ " normal_mean_z anomaly_mean_z abs_gap\n", "land_area_km2 -0.155530 1.739991 1.895521\n", "urban_population -0.141622 1.584395 1.726016\n", "co2_emissions -0.130788 1.463189 1.593977\n", "armed_forces_size -0.129271 1.446215 1.575485\n", "population -0.129256 1.446051 1.575307\n", "gdp -0.124797 1.396161 1.520958\n", "cpi_change -0.115914 1.296789 1.412703\n", "cpi -0.097376 1.089396 1.186772\n", "density_p_km2 -0.062458 0.698750 0.761208\n", "maternal_mortality_ratio -0.060379 0.675489 0.735868\n", "unemployment_rate -0.059783 0.668818 0.728601\n", "infant_mortality -0.045448 0.508446 0.553894\n", "life_expectancy 0.042545 -0.475973 0.518518\n", "minimum_wage -0.041264 0.461646 0.502911\n", "latitude 0.040838 -0.456880 0.497718" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "top_anomalies_original = result_original.loc[result_original[\"anomaly_original\"] == 1].copy()\n", "top_anomalies_original = top_anomalies_original.sort_values(\"anomaly_score_original\", ascending=False)\n", "\n", "country_col_candidates = [c for c in df_raw.columns if \"country\" in c.lower()]\n", "country_col = country_col_candidates[0] if country_col_candidates else df_raw.columns[0]\n", "\n", "print(\"Наиболее аномальные страны:\")\n", "display(top_anomalies_original[[country_col, \"anomaly_score_original\"]].head(15))\n", "\n", "X_df = pd.DataFrame(X, columns=feature_names)\n", "X_df[\"anomaly_original\"] = is_anomaly_original\n", "\n", "group_mean = X_df.groupby(\"anomaly_original\").mean().T\n", "group_mean.columns = [\"normal_mean_z\", \"anomaly_mean_z\"]\n", "group_mean[\"abs_gap\"] = (group_mean[\"anomaly_mean_z\"] - group_mean[\"normal_mean_z\"]).abs()\n", "group_mean = group_mean.sort_values(\"abs_gap\", ascending=False)\n", "\n", "print(\"Признаки с наибольшим отличием аномалий от обычных объектов:\")\n", "display(group_mean.head(15))" ] }, { "cell_type": "markdown", "id": "a754a377", "metadata": {}, "source": [ "## 8. Снижение размерности до двух новых переменных\n", "\n", "Для уменьшения размерности применяется **PCA**.\n", "Метод даёт линейные компоненты, хорошо интерпретируется и устойчив на умеренных данных." ] }, { "cell_type": "code", "execution_count": 9, "id": "4d6981f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Доли объяснённой дисперсии по компонентам: [0.24071869 0.16855004]\n", "Суммарная объяснённая дисперсия: 0.4092687286603829\n" ] }, { "data": { "text/html": [ "
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component_1component_2
03.7450141.321565
1-1.470165-0.885146
2-0.7166110.222846
3-2.176383-1.170742
44.1212600.711285
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" ], "text/plain": [ " component_1 component_2\n", "0 3.745014 1.321565\n", "1 -1.470165 -0.885146\n", "2 -0.716611 0.222846\n", "3 -2.176383 -1.170742\n", "4 4.121260 0.711285" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "pca = PCA(n_components=2, random_state=RANDOM_STATE)\n", "X_2d = pca.fit_transform(X)\n", "\n", "explained_variance = pca.explained_variance_ratio_\n", "print(\"Доли объяснённой дисперсии по компонентам:\", explained_variance)\n", "print(\"Суммарная объяснённая дисперсия:\", explained_variance.sum())\n", "\n", "components_df = pd.DataFrame(\n", " X_2d,\n", " columns=[\"component_1\", \"component_2\"]\n", ")\n", "\n", "display(components_df.head())" ] }, { "cell_type": "markdown", "id": "26486186", "metadata": {}, "source": [ "## 9. Поиск аномалий тем же методом на двух новых признаках" ] }, { "cell_type": "code", "execution_count": 10, "id": "259cd002", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Число аномалий в 2D-пространстве: 16\n", "Доля аномалий в 2D-пространстве: 0.0821\n" ] }, { "data": { "text/html": [ "
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CountryDensity\\n(P/Km2)AbbreviationAgricultural Land( %)Land Area(Km2)Armed Forces sizeBirth RateCalling CodeCapital/Major CityCo2-EmissionsCPICPI Change (%)Currency-CodeFertility RateForested Area (%)Gasoline PriceGDPGross primary education enrollment (%)Gross tertiary education enrollment (%)Infant mortalityLargest cityLife expectancyMaternal mortality ratioMinimum wageOfficial languageOut of pocket health expenditurePhysicians per thousandPopulationPopulation: Labor force participation (%)Tax revenue (%)Total tax rateUnemployment rateUrban_populationLatitudeLongitudecomponent_1component_2anomaly_score_2danomaly_2d
36China153CN56.20%9,596,9602,695,00010.9086.0Beijing9,893,038125.082.90%CNY1.6922.40%$0.96$19,910,000,000,000100.20%50.60%7.4Shanghai77.029.0$0.87Standard Chinese32.40%1.981,397,715,00068.00%9.40%59.20%4.32%842,933,96235.861660104.195397-6.06118821.1605160.8322351
186United States36US44.40%9,833,5171,359,00011.601.0Washington, D.C.5,006,302117.247.50%USD1.7333.90%$0.71$21,427,700,000,000101.80%88.20%5.6New York City78.519.0$7.25NaN11.10%2.61328,239,52362.00%9.60%36.60%14.70%270,663,02837.090240-95.712891-5.47220110.6713170.7516371
77India464IN60.40%3,287,2633,031,00017.8691.0New Delhi2,407,672180.447.70%INR2.2223.80%$0.97$2,611,000,000,000113.00%28.10%29.9Kurebhar69.4145.0$0.30Hindi65.10%0.861,366,417,75449.30%11.20%49.70%5.36%471,031,52820.59368478.962880-1.45431112.0674170.7405621
143Russia9RU13.30%17,098,2401,454,00011.507.0Moscow1,732,027180.754.50%RUB1.5749.80%$0.59$1,699,876,578,871102.60%81.90%6.1Moscow72.717.0$0.53Russian36.40%4.01144,373,53561.80%11.40%46.20%4.59%107,683,88961.524010105.318756-3.4077375.7858550.6789861
126Nigeria226NG77.70%923,768215,00037.91234.0Abuja120,369267.5111.40%NGN5.397.20%$0.46$448,120,428,85984.70%10.20%75.7Lagos54.3917.0$0.54English72.20%0.38200,963,59952.90%1.50%34.80%8.10%102,806,9489.0819998.6752775.8441812.8600240.6743781
163South Sudan18SSNaN644,329185,00035.01211.0Juba1,7274,583.71187.90%SSP4.70NaN$0.28$11,997,800,75173.00%NaN63.7Juba57.61150.0NaNEnglish61.30%NaN11,062,11372.40%NaN31.40%12.24%2,201,2506.87699231.3069796.5121531.8074430.6675101
113Monaco26,337MCNaN2NaN5.90377.0Monaco CityNaNNaNNaNEURNaNNaN$2.00$7,184,844,193NaNNaN2.6Monaco CityNaNNaN$11.72French6.10%6.5638,964NaNNaNNaNNaN38,96443.7384187.424616-4.017761-2.1522470.6241521
23Brazil25BR33.90%8,515,770730,00013.9255.0Bras���462,299167.43.70%BRL1.7358.90%$1.02$1,839,758,040,766115.40%51.30%12.8S����75.760.0$1.53Portuguese28.30%2.15212,559,41763.90%14.20%65.10%12.08%183,241,641-14.235004-51.925280-1.6263493.4961620.6037691
131Pakistan287PK47.80%796,095936,00028.2592.0Islamabad201,150182.3210.60%PKR3.511.90%$0.79$304,400,000,00094.30%9.00%57.2Karachi67.1140.0$0.69Urdu66.50%0.98216,565,31852.60%9.20%33.90%4.45%79,927,76230.37532169.3451162.0242382.6068120.5902101
34Chad13TD39.70%1,284,00035,00042.17235.0N'Djamena1,016117.7-1.00%XAF5.753.80%$0.78$11,314,951,34386.80%3.30%71.4N'Djamena54.01140.0$0.60French56.40%0.0415,946,87670.70%NaN63.50%1.89%3,712,27315.45416618.7322076.1947050.9684270.5860111
78Indonesia151ID31.50%1,904,569676,00018.0762.0Jakarta563,325151.183.00%IDR2.3149.90%$0.63$1,119,190,780,753106.40%36.30%21.1Kalimantan71.5177.0$0.48Indonesian48.30%0.43270,203,91767.50%10.20%30.10%4.69%151,509,724-0.789275113.9213270.2390512.8794730.5786411
160Somalia25SO70.30%637,65720,00041.75252.0Mogadishu645NaNNaNSOS6.0710.00%$1.41$4,720,727,27823.40%2.50%76.6Bosaso57.1829.0NaNArabicNaN0.0215,442,90547.40%0.00%NaN11.35%7,034,8615.15214946.1996165.9081730.9727140.5620881
66Greece81GR47.60%131,957146,0008.1030.0Athens62,434101.870.20%EUR1.3531.70%$1.54$209,852,761,46999.60%136.60%3.6Macedonia81.33.0$4.46Greek35.50%5.4810,716,32251.80%26.20%51.90%17.24%8,507,47439.07420821.824312-4.174639-1.2057910.5427561
85Japan347JP12.30%377,944261,0007.4081.0Tokyo1,135,886105.480.50%NaN1.4268.50%$1.06$5,081,769,542,38098.80%63.20%1.8Tokyo84.25.0$6.77NaN13.10%2.41126,226,56861.70%11.90%46.70%2.29%115,782,41636.204824138.252924-3.5763971.6329460.5376751
33Central African Republic8CF8.20%622,9848,00035.35236.0Bangui297186.8637.10%NaN4.7235.60%$1.41$2,220,307,369102.00%3.00%84.5Bangui52.8829.0$0.37French39.60%0.064,745,18572.00%8.60%73.30%3.68%1,982,0646.61111120.9394445.5342180.5605000.5306701
\n", "
" ], "text/plain": [ " Country Density\\n(P/Km2) Abbreviation Agricultural Land( %) Land Area(Km2) Armed Forces size Birth Rate Calling Code \\\n", "36 China 153 CN 56.20% 9,596,960 2,695,000 10.90 86.0 \n", "186 United States 36 US 44.40% 9,833,517 1,359,000 11.60 1.0 \n", "77 India 464 IN 60.40% 3,287,263 3,031,000 17.86 91.0 \n", "143 Russia 9 RU 13.30% 17,098,240 1,454,000 11.50 7.0 \n", "126 Nigeria 226 NG 77.70% 923,768 215,000 37.91 234.0 \n", "163 South Sudan 18 SS NaN 644,329 185,000 35.01 211.0 \n", "113 Monaco 26,337 MC NaN 2 NaN 5.90 377.0 \n", "23 Brazil 25 BR 33.90% 8,515,770 730,000 13.92 55.0 \n", "131 Pakistan 287 PK 47.80% 796,095 936,000 28.25 92.0 \n", "34 Chad 13 TD 39.70% 1,284,000 35,000 42.17 235.0 \n", "78 Indonesia 151 ID 31.50% 1,904,569 676,000 18.07 62.0 \n", "160 Somalia 25 SO 70.30% 637,657 20,000 41.75 252.0 \n", "66 Greece 81 GR 47.60% 131,957 146,000 8.10 30.0 \n", "85 Japan 347 JP 12.30% 377,944 261,000 7.40 81.0 \n", "33 Central African Republic 8 CF 8.20% 622,984 8,000 35.35 236.0 \n", "\n", " Capital/Major City Co2-Emissions CPI CPI Change (%) Currency-Code Fertility Rate Forested Area (%) Gasoline Price GDP \\\n", "36 Beijing 9,893,038 125.08 2.90% CNY 1.69 22.40% $0.96 $19,910,000,000,000 \n", "186 Washington, D.C. 5,006,302 117.24 7.50% USD 1.73 33.90% $0.71 $21,427,700,000,000 \n", "77 New Delhi 2,407,672 180.44 7.70% INR 2.22 23.80% $0.97 $2,611,000,000,000 \n", "143 Moscow 1,732,027 180.75 4.50% RUB 1.57 49.80% $0.59 $1,699,876,578,871 \n", "126 Abuja 120,369 267.51 11.40% NGN 5.39 7.20% $0.46 $448,120,428,859 \n", "163 Juba 1,727 4,583.71 187.90% SSP 4.70 NaN $0.28 $11,997,800,751 \n", "113 Monaco City NaN NaN NaN EUR NaN NaN $2.00 $7,184,844,193 \n", "23 Bras��� 462,299 167.4 3.70% BRL 1.73 58.90% $1.02 $1,839,758,040,766 \n", "131 Islamabad 201,150 182.32 10.60% PKR 3.51 1.90% $0.79 $304,400,000,000 \n", "34 N'Djamena 1,016 117.7 -1.00% XAF 5.75 3.80% $0.78 $11,314,951,343 \n", "78 Jakarta 563,325 151.18 3.00% IDR 2.31 49.90% $0.63 $1,119,190,780,753 \n", "160 Mogadishu 645 NaN NaN SOS 6.07 10.00% $1.41 $4,720,727,278 \n", "66 Athens 62,434 101.87 0.20% EUR 1.35 31.70% $1.54 $209,852,761,469 \n", "85 Tokyo 1,135,886 105.48 0.50% NaN 1.42 68.50% $1.06 $5,081,769,542,380 \n", "33 Bangui 297 186.86 37.10% NaN 4.72 35.60% $1.41 $2,220,307,369 \n", "\n", " Gross primary education enrollment (%) Gross tertiary education enrollment (%) Infant mortality Largest city Life expectancy \\\n", "36 100.20% 50.60% 7.4 Shanghai 77.0 \n", "186 101.80% 88.20% 5.6 New York City 78.5 \n", "77 113.00% 28.10% 29.9 Kurebhar 69.4 \n", "143 102.60% 81.90% 6.1 Moscow 72.7 \n", "126 84.70% 10.20% 75.7 Lagos 54.3 \n", "163 73.00% NaN 63.7 Juba 57.6 \n", "113 NaN NaN 2.6 Monaco City NaN \n", "23 115.40% 51.30% 12.8 S���� 75.7 \n", "131 94.30% 9.00% 57.2 Karachi 67.1 \n", "34 86.80% 3.30% 71.4 N'Djamena 54.0 \n", "78 106.40% 36.30% 21.1 Kalimantan 71.5 \n", "160 23.40% 2.50% 76.6 Bosaso 57.1 \n", "66 99.60% 136.60% 3.6 Macedonia 81.3 \n", "85 98.80% 63.20% 1.8 Tokyo 84.2 \n", "33 102.00% 3.00% 84.5 Bangui 52.8 \n", "\n", " Maternal mortality ratio Minimum wage Official language Out of pocket health expenditure Physicians per thousand Population \\\n", "36 29.0 $0.87 Standard Chinese 32.40% 1.98 1,397,715,000 \n", "186 19.0 $7.25 NaN 11.10% 2.61 328,239,523 \n", "77 145.0 $0.30 Hindi 65.10% 0.86 1,366,417,754 \n", "143 17.0 $0.53 Russian 36.40% 4.01 144,373,535 \n", "126 917.0 $0.54 English 72.20% 0.38 200,963,599 \n", "163 1150.0 NaN English 61.30% NaN 11,062,113 \n", "113 NaN $11.72 French 6.10% 6.56 38,964 \n", "23 60.0 $1.53 Portuguese 28.30% 2.15 212,559,417 \n", "131 140.0 $0.69 Urdu 66.50% 0.98 216,565,318 \n", "34 1140.0 $0.60 French 56.40% 0.04 15,946,876 \n", "78 177.0 $0.48 Indonesian 48.30% 0.43 270,203,917 \n", "160 829.0 NaN Arabic NaN 0.02 15,442,905 \n", "66 3.0 $4.46 Greek 35.50% 5.48 10,716,322 \n", "85 5.0 $6.77 NaN 13.10% 2.41 126,226,568 \n", "33 829.0 $0.37 French 39.60% 0.06 4,745,185 \n", "\n", " Population: Labor force participation (%) Tax revenue (%) Total tax rate Unemployment rate Urban_population Latitude Longitude component_1 \\\n", "36 68.00% 9.40% 59.20% 4.32% 842,933,962 35.861660 104.195397 -6.061188 \n", "186 62.00% 9.60% 36.60% 14.70% 270,663,028 37.090240 -95.712891 -5.472201 \n", "77 49.30% 11.20% 49.70% 5.36% 471,031,528 20.593684 78.962880 -1.454311 \n", "143 61.80% 11.40% 46.20% 4.59% 107,683,889 61.524010 105.318756 -3.407737 \n", "126 52.90% 1.50% 34.80% 8.10% 102,806,948 9.081999 8.675277 5.844181 \n", "163 72.40% NaN 31.40% 12.24% 2,201,250 6.876992 31.306979 6.512153 \n", "113 NaN NaN NaN NaN 38,964 43.738418 7.424616 -4.017761 \n", "23 63.90% 14.20% 65.10% 12.08% 183,241,641 -14.235004 -51.925280 -1.626349 \n", "131 52.60% 9.20% 33.90% 4.45% 79,927,762 30.375321 69.345116 2.024238 \n", "34 70.70% NaN 63.50% 1.89% 3,712,273 15.454166 18.732207 6.194705 \n", "78 67.50% 10.20% 30.10% 4.69% 151,509,724 -0.789275 113.921327 0.239051 \n", "160 47.40% 0.00% NaN 11.35% 7,034,861 5.152149 46.199616 5.908173 \n", "66 51.80% 26.20% 51.90% 17.24% 8,507,474 39.074208 21.824312 -4.174639 \n", "85 61.70% 11.90% 46.70% 2.29% 115,782,416 36.204824 138.252924 -3.576397 \n", "33 72.00% 8.60% 73.30% 3.68% 1,982,064 6.611111 20.939444 5.534218 \n", "\n", " component_2 anomaly_score_2d anomaly_2d \n", "36 21.160516 0.832235 1 \n", "186 10.671317 0.751637 1 \n", "77 12.067417 0.740562 1 \n", "143 5.785855 0.678986 1 \n", "126 2.860024 0.674378 1 \n", "163 1.807443 0.667510 1 \n", "113 -2.152247 0.624152 1 \n", "23 3.496162 0.603769 1 \n", "131 2.606812 0.590210 1 \n", "34 0.968427 0.586011 1 \n", "78 2.879473 0.578641 1 \n", "160 0.972714 0.562088 1 \n", "66 -1.205791 0.542756 1 \n", "85 1.632946 0.537675 1 \n", "33 0.560500 0.530670 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "iso_forest_2d = IsolationForest(\n", " n_estimators=400,\n", " contamination=contamination,\n", " random_state=RANDOM_STATE,\n", " n_jobs=-1\n", ")\n", "\n", "labels_2d = iso_forest_2d.fit_predict(X_2d)\n", "scores_2d = -iso_forest_2d.score_samples(X_2d)\n", "is_anomaly_2d = (labels_2d == -1).astype(int)\n", "\n", "result_2d = df_raw.copy()\n", "result_2d[\"component_1\"] = X_2d[:, 0]\n", "result_2d[\"component_2\"] = X_2d[:, 1]\n", "result_2d[\"anomaly_score_2d\"] = scores_2d\n", "result_2d[\"anomaly_2d\"] = is_anomaly_2d\n", "\n", "print(\"Число аномалий в 2D-пространстве:\", int(is_anomaly_2d.sum()))\n", "print(\"Доля аномалий в 2D-пространстве:\", round(is_anomaly_2d.mean(), 4))\n", "\n", "display(\n", " result_2d.loc[result_2d[\"anomaly_2d\"] == 1]\n", " .sort_values(\"anomaly_score_2d\", ascending=False)\n", " .head(15)\n", ")" ] }, { "cell_type": "markdown", "id": "07711c74", "metadata": {}, "source": [ "## 10. Показатели качества метода на новых признаках" ] }, { "cell_type": "code", "execution_count": 11, "id": "22317b5a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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original_spacereduced_2d
silhouette_score0.4281210.535111
calinski_harabasz_score13.12758928.609320
davies_bouldin_score2.8747421.904779
mean_score_normal0.3990600.420948
mean_score_anomaly0.5239970.626800
score_gap0.1249360.205853
anomaly_share0.0820510.082051
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" ], "text/plain": [ " original_space reduced_2d\n", "silhouette_score 0.428121 0.535111\n", "calinski_harabasz_score 13.127589 28.609320\n", "davies_bouldin_score 2.874742 1.904779\n", "mean_score_normal 0.399060 0.420948\n", "mean_score_anomaly 0.523997 0.626800\n", "score_gap 0.124936 0.205853\n", "anomaly_share 0.082051 0.082051" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "metrics_2d = safe_internal_metrics(X_2d, is_anomaly_2d, scores_2d)\n", "\n", "metrics_compare = pd.concat(\n", " [metrics_original.rename(\"original_space\"), metrics_2d.rename(\"reduced_2d\")],\n", " axis=1\n", ")\n", "display(metrics_compare)" ] }, { "cell_type": "markdown", "id": "f798e79e", "metadata": {}, "source": [ "## 11. Оценка точности поиска аномалий на новых признаках\n", "\n", "Так как истинной разметки нет, в качестве опорной принимается разметка, полученная в исходном пространстве признаков.\n", "Тогда можно измерить, насколько хорошо поиск аномалий по двум новым признакам воспроизводит исходное решение." ] }, { "cell_type": "code", "execution_count": 12, "id": "6abd5f06", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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value
accuracy_vs_original0.948718
precision_vs_original0.687500
recall_vs_original0.687500
f1_vs_original0.687500
jaccard_vs_original0.523810
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" ], "text/plain": [ " value\n", "accuracy_vs_original 0.948718\n", "precision_vs_original 0.687500\n", "recall_vs_original 0.687500\n", "f1_vs_original 0.687500\n", "jaccard_vs_original 0.523810" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Матрица согласованности [исходное пространство x 2D-пространство]:\n", "[[174 5]\n", " [ 5 11]]\n" ] } ], "source": [ "\n", "comparison_metrics = pd.Series({\n", " \"accuracy_vs_original\": accuracy_score(is_anomaly_original, is_anomaly_2d),\n", " \"precision_vs_original\": precision_score(is_anomaly_original, is_anomaly_2d, zero_division=0),\n", " \"recall_vs_original\": recall_score(is_anomaly_original, is_anomaly_2d, zero_division=0),\n", " \"f1_vs_original\": f1_score(is_anomaly_original, is_anomaly_2d, zero_division=0),\n", " \"jaccard_vs_original\": jaccard_score(is_anomaly_original, is_anomaly_2d, zero_division=0),\n", "})\n", "\n", "display(comparison_metrics.to_frame(\"value\"))\n", "\n", "cm = confusion_matrix(is_anomaly_original, is_anomaly_2d)\n", "print(\"Матрица согласованности [исходное пространство x 2D-пространство]:\")\n", "print(cm)" ] }, { "cell_type": "markdown", "id": "e0224096", "metadata": {}, "source": [ "## 12. Сравнение аномальных объектов до и после снижения размерности" ] }, { "cell_type": "code", "execution_count": 13, "id": "7257d2b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Совпавшие аномальные объекты: 11\n", "['Brazil', 'Central African Republic', 'China', 'India', 'Japan', 'Monaco', 'Nigeria', 'Russia', 'Somalia', 'South Sudan', 'United States']\n", "\n", "Аномалии только в исходном пространстве: 5\n", "['Argentina', 'Australia', 'Lesotho', 'South Africa', 'Venezuela']\n", "\n", "Аномалии только в 2D-пространстве: 5\n", "['Chad', 'Greece', 'Indonesia', 'Pakistan', 'Sierra Leone']\n" ] } ], "source": [ "\n", "orig_anomaly_countries = set(result_original.loc[result_original[\"anomaly_original\"] == 1, country_col])\n", "new_anomaly_countries = set(result_2d.loc[result_2d[\"anomaly_2d\"] == 1, country_col])\n", "\n", "common_anomalies = sorted(orig_anomaly_countries & new_anomaly_countries)\n", "only_original = sorted(orig_anomaly_countries - new_anomaly_countries)\n", "only_2d = sorted(new_anomaly_countries - orig_anomaly_countries)\n", "\n", "print(\"Совпавшие аномальные объекты:\", len(common_anomalies))\n", "print(common_anomalies)\n", "\n", "print(\"\\nАномалии только в исходном пространстве:\", len(only_original))\n", "print(only_original)\n", "\n", "print(\"\\nАномалии только в 2D-пространстве:\", len(only_2d))\n", "print(only_2d)" ] }, { "cell_type": "markdown", "id": "892d1d8f", "metadata": {}, "source": [ "## 13. Визуализация новых признаков и аномалий" ] }, { "cell_type": "code", "execution_count": 14, "id": "90f973b1", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "plot_df = pd.DataFrame({\n", " \"component_1\": X_2d[:, 0],\n", " \"component_2\": X_2d[:, 1],\n", " \"anomaly_original\": is_anomaly_original,\n", " \"anomaly_2d\": is_anomaly_2d,\n", " country_col: df_raw[country_col].values\n", "})\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", "\n", "axes[0].scatter(\n", " plot_df.loc[plot_df[\"anomaly_original\"] == 0, \"component_1\"],\n", " plot_df.loc[plot_df[\"anomaly_original\"] == 0, \"component_2\"],\n", " alpha=0.7,\n", " label=\"Нормальные\"\n", ")\n", "axes[0].scatter(\n", " plot_df.loc[plot_df[\"anomaly_original\"] == 1, \"component_1\"],\n", " plot_df.loc[plot_df[\"anomaly_original\"] == 1, \"component_2\"],\n", " alpha=0.9,\n", " label=\"Аномалии\"\n", ")\n", "axes[0].set_title(\"Аномалии исходного пространства в координатах PCA\")\n", "axes[0].set_xlabel(\"component_1\")\n", "axes[0].set_ylabel(\"component_2\")\n", "axes[0].legend()\n", "\n", "axes[1].scatter(\n", " plot_df.loc[plot_df[\"anomaly_2d\"] == 0, \"component_1\"],\n", " plot_df.loc[plot_df[\"anomaly_2d\"] == 0, \"component_2\"],\n", " alpha=0.7,\n", " label=\"Нормальные\"\n", ")\n", "axes[1].scatter(\n", " plot_df.loc[plot_df[\"anomaly_2d\"] == 1, \"component_1\"],\n", " plot_df.loc[plot_df[\"anomaly_2d\"] == 1, \"component_2\"],\n", " alpha=0.9,\n", " label=\"Аномалии\"\n", ")\n", "axes[1].set_title(\"Аномалии, найденные по двум компонентам PCA\")\n", "axes[1].set_xlabel(\"component_1\")\n", "axes[1].set_ylabel(\"component_2\")\n", "axes[1].legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f3a72af5", "metadata": {}, "source": [ "## 14. Сводные таблицы для интерпретации результата\n", "\n", "Эти таблицы можно использовать при написании короткого итога после выполнения ноутбука." ] }, { "cell_type": "code", "execution_count": 15, "id": "027c7c4d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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metricoriginal_spacereduced_2d
0silhouette_score0.4281210.535111
1calinski_harabasz_score13.12758928.609320
2davies_bouldin_score2.8747421.904779
3mean_score_normal0.3990600.420948
4mean_score_anomaly0.5239970.626800
5score_gap0.1249360.205853
6anomaly_share0.0820510.082051
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" ], "text/plain": [ " metric original_space reduced_2d\n", "0 silhouette_score 0.428121 0.535111\n", "1 calinski_harabasz_score 13.127589 28.609320\n", "2 davies_bouldin_score 2.874742 1.904779\n", "3 mean_score_normal 0.399060 0.420948\n", "4 mean_score_anomaly 0.523997 0.626800\n", "5 score_gap 0.124936 0.205853\n", "6 anomaly_share 0.082051 0.082051" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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metricvalue
0accuracy_vs_original0.948718
1precision_vs_original0.687500
2recall_vs_original0.687500
3f1_vs_original0.687500
4jaccard_vs_original0.523810
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" ], "text/plain": [ " metric value\n", "0 accuracy_vs_original 0.948718\n", "1 precision_vs_original 0.687500\n", "2 recall_vs_original 0.687500\n", "3 f1_vs_original 0.687500\n", "4 jaccard_vs_original 0.523810" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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Countrycomponent_1component_2anomaly_score_2danomaly_2d
36China-6.06118821.1605160.8322351
186United States-5.47220110.6713170.7516371
77India-1.45431112.0674170.7405621
143Russia-3.4077375.7858550.6789861
126Nigeria5.8441812.8600240.6743781
163South Sudan6.5121531.8074430.6675101
113Monaco-4.017761-2.1522470.6241521
23Brazil-1.6263493.4961620.6037691
131Pakistan2.0242382.6068120.5902101
34Chad6.1947050.9684270.5860111
78Indonesia0.2390512.8794730.5786411
160Somalia5.9081730.9727140.5620881
66Greece-4.174639-1.2057910.5427561
85Japan-3.5763971.6329460.5376751
33Central African Republic5.5342180.5605000.5306701
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" ], "text/plain": [ " Country component_1 component_2 anomaly_score_2d anomaly_2d\n", "36 China -6.061188 21.160516 0.832235 1\n", "186 United States -5.472201 10.671317 0.751637 1\n", "77 India -1.454311 12.067417 0.740562 1\n", "143 Russia -3.407737 5.785855 0.678986 1\n", "126 Nigeria 5.844181 2.860024 0.674378 1\n", "163 South Sudan 6.512153 1.807443 0.667510 1\n", "113 Monaco -4.017761 -2.152247 0.624152 1\n", "23 Brazil -1.626349 3.496162 0.603769 1\n", "131 Pakistan 2.024238 2.606812 0.590210 1\n", "34 Chad 6.194705 0.968427 0.586011 1\n", "78 Indonesia 0.239051 2.879473 0.578641 1\n", "160 Somalia 5.908173 0.972714 0.562088 1\n", "66 Greece -4.174639 -1.205791 0.542756 1\n", "85 Japan -3.576397 1.632946 0.537675 1\n", "33 Central African Republic 5.534218 0.560500 0.530670 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "summary_table = pd.DataFrame({\n", " \"metric\": metrics_compare.index,\n", " \"original_space\": metrics_compare[\"original_space\"].values,\n", " \"reduced_2d\": metrics_compare[\"reduced_2d\"].values\n", "})\n", "\n", "display(summary_table)\n", "\n", "agreement_table = comparison_metrics.reset_index()\n", "agreement_table.columns = [\"metric\", \"value\"]\n", "display(agreement_table)\n", "\n", "top_compare = result_2d[[country_col, \"component_1\", \"component_2\", \"anomaly_score_2d\", \"anomaly_2d\"]].copy()\n", "display(top_compare.sort_values(\"anomaly_score_2d\", ascending=False).head(15))" ] }, { "cell_type": "markdown", "id": "b7c54676", "metadata": {}, "source": [ "Итог\n", "\n", "Аномалии найдены методом Isolation Forest по 28 непрерывным числовым признакам; доля аномалий составила 16/195 = 8.2%. В исходном пространстве качество разделения: silhouette = 0.428, Calinski–Harabasz = 13.13, Davies–Bouldin = 2.87.\n", "\n", "После снижения размерности методом PCA до 2 компонент (объяснённая дисперсия 40.93%) тот же метод дал более выраженное разделение: silhouette = 0.535, CH = 28.61, DB = 1.90. Согласованность с исходной разметкой аномалий высокая: accuracy = 0.949, F1 = 0.688, совпало 11 из 16 аномалий.\n", "\n", "Следовательно, 2 главные компоненты сохраняют основную аномальную структуру, но не полностью: часть аномалий изменилась после проекции." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 }