{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28c3dba6",
   "metadata": {},
   "source": [
    "\n",
    "# Поиск аномалий: динамика величины КРС и стоимости основных средств\n",
    "\n",
    "В этой работе используются два признака из датасета:\n",
    "- **Динамика_Величина_КРС**\n",
    "- **Динамика_Основных_Средств**\n",
    "\n",
    "Для поиска аномалий применяются **три метода**:\n",
    "1. **Isolation Forest**\n",
    "2. **Local Outlier Factor**\n",
    "3. **One-Class SVM**\n",
    "\n",
    "Затем рассчитываются метрики качества:\n",
    "- Precision\n",
    "- Recall\n",
    "- F1-score\n",
    "\n",
    "И строятся графики результатов.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63396310",
   "metadata": {},
   "source": [
    "## 1. Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "869deee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be0294a",
   "metadata": {},
   "source": [
    "## 2. Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a9bc9f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер датасета: (497, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Район</th>\n",
       "      <th>Год</th>\n",
       "      <th>Динамика_Количества_Работников</th>\n",
       "      <th>Динамика_Величина_КРС</th>\n",
       "      <th>Динамика_Мощности_Техники</th>\n",
       "      <th>Динамика_Количества_Техники</th>\n",
       "      <th>Динамика_Основных_Средств</th>\n",
       "      <th>Динамика_Посевных_Площадей</th>\n",
       "      <th>Динамика_Себестоимости_Произведенной_Продукции</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Арзамасский</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.9648</td>\n",
       "      <td>0.8773</td>\n",
       "      <td>1.0907</td>\n",
       "      <td>1.0638</td>\n",
       "      <td>1.3537</td>\n",
       "      <td>1.0871</td>\n",
       "      <td>1.4476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Балахнинский</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.0125</td>\n",
       "      <td>0.9695</td>\n",
       "      <td>1.0091</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>1.0919</td>\n",
       "      <td>1.3471</td>\n",
       "      <td>1.0623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Богородский</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.8421</td>\n",
       "      <td>0.9707</td>\n",
       "      <td>1.0060</td>\n",
       "      <td>0.9636</td>\n",
       "      <td>1.0791</td>\n",
       "      <td>0.7955</td>\n",
       "      <td>1.0837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Большеболдинский</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.7951</td>\n",
       "      <td>0.8774</td>\n",
       "      <td>0.8965</td>\n",
       "      <td>0.8611</td>\n",
       "      <td>0.9460</td>\n",
       "      <td>0.9489</td>\n",
       "      <td>0.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Борский</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.8389</td>\n",
       "      <td>0.7987</td>\n",
       "      <td>0.8097</td>\n",
       "      <td>1.0094</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.8701</td>\n",
       "      <td>1.0847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Район   Год  Динамика_Количества_Работников  \\\n",
       "0       Арзамасский  2019                          0.9648   \n",
       "1      Балахнинский  2019                          1.0125   \n",
       "2       Богородский  2019                          0.8421   \n",
       "3  Большеболдинский  2019                          0.7951   \n",
       "4           Борский  2019                          0.8389   \n",
       "\n",
       "   Динамика_Величина_КРС  Динамика_Мощности_Техники  \\\n",
       "0                 0.8773                     1.0907   \n",
       "1                 0.9695                     1.0091   \n",
       "2                 0.9707                     1.0060   \n",
       "3                 0.8774                     0.8965   \n",
       "4                 0.7987                     0.8097   \n",
       "\n",
       "   Динамика_Количества_Техники  Динамика_Основных_Средств  \\\n",
       "0                       1.0638                     1.3537   \n",
       "1                       0.9500                     1.0919   \n",
       "2                       0.9636                     1.0791   \n",
       "3                       0.8611                     0.9460   \n",
       "4                       1.0094                     0.8950   \n",
       "\n",
       "   Динамика_Посевных_Площадей  Динамика_Себестоимости_Произведенной_Продукции  \n",
       "0                      1.0871                                          1.4476  \n",
       "1                      1.3471                                          1.0623  \n",
       "2                      0.7955                                          1.0837  \n",
       "3                      0.9489                                          0.7530  \n",
       "4                      0.8701                                          1.0847  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv(\"Reproduction_Agriculture_Resources.csv\", encoding=\"cp1251\", sep=\";\")\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "print(\"Размер датасета:\", df.shape)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3c752a",
   "metadata": {},
   "source": [
    "## 3. Проверка названий столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0792481c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Район\n",
      "Год\n",
      "Динамика_Количества_Работников\n",
      "Динамика_Величина_КРС\n",
      "Динамика_Мощности_Техники\n",
      "Динамика_Количества_Техники\n",
      "Динамика_Основных_Средств\n",
      "Динамика_Посевных_Площадей\n",
      "Динамика_Себестоимости_Произведенной_Продукции\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for col in df.columns:\n",
    "    print(col)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "953538f1",
   "metadata": {},
   "source": [
    "\n",
    "## 4. Отбор признаков\n",
    "\n",
    "Важно: в исходном CSV столбец называется **`Динамика_Основных_Средств`**,  \n",
    "а не `Динамика_Стоимость_Основных_Средств`.\n",
    "\n",
    "Именно поэтому в предыдущей версии возникал `KeyError`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2af87520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Динамика_Величина_КРС</th>\n",
       "      <th>Динамика_Основных_Средств</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8773</td>\n",
       "      <td>1.3537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9695</td>\n",
       "      <td>1.0919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.9707</td>\n",
       "      <td>1.0791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8774</td>\n",
       "      <td>0.9460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7987</td>\n",
       "      <td>0.8950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Динамика_Величина_КРС  Динамика_Основных_Средств\n",
       "0                 0.8773                     1.3537\n",
       "1                 0.9695                     1.0919\n",
       "2                 0.9707                     1.0791\n",
       "3                 0.8774                     0.9460\n",
       "4                 0.7987                     0.8950"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "features = df[[\n",
    "    \"Динамика_Величина_КРС\",\n",
    "    \"Динамика_Основных_Средств\"\n",
    "]].copy().dropna()\n",
    "\n",
    "features.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c5074e",
   "metadata": {},
   "source": [
    "## 5. Базовая статистика"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dabf7b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Динамика_Величина_КРС</th>\n",
       "      <th>Динамика_Основных_Средств</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>497.000000</td>\n",
       "      <td>497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.940833</td>\n",
       "      <td>1.104058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.150459</td>\n",
       "      <td>0.313782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.084600</td>\n",
       "      <td>0.050500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.893900</td>\n",
       "      <td>1.004000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.966000</td>\n",
       "      <td>1.081200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.019000</td>\n",
       "      <td>1.169400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.588100</td>\n",
       "      <td>5.605500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Динамика_Величина_КРС  Динамика_Основных_Средств\n",
       "count             497.000000                 497.000000\n",
       "mean                0.940833                   1.104058\n",
       "std                 0.150459                   0.313782\n",
       "min                 0.084600                   0.050500\n",
       "25%                 0.893900                   1.004000\n",
       "50%                 0.966000                   1.081200\n",
       "75%                 1.019000                   1.169400\n",
       "max                 1.588100                   5.605500"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "features.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c6634f",
   "metadata": {},
   "source": [
    "## 6. Нормализация данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5776dc06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.42268309,  0.79639224],\n",
       "       [ 0.19072457, -0.03878551],\n",
       "       [ 0.19870819, -0.07961926],\n",
       "       [-0.42201779, -0.50422644],\n",
       "       [-0.9456098 , -0.66692341]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(features)\n",
    "X[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee3328e",
   "metadata": {},
   "source": [
    "\n",
    "## 7. Формирование псевдо-разметки аномалий\n",
    "\n",
    "Так как в датасете нет готовой колонки с истинной разметкой аномалий,  \n",
    "для учебной оценки качества создадим **псевдо-ground truth** с помощью метода IQR.\n",
    "\n",
    "Точка считается аномальной, если хотя бы по одному признаку выходит за границы:\n",
    "- нижняя граница = Q1 − 1.5 × IQR\n",
    "- верхняя граница = Q3 + 1.5 × IQR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94e059f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество аномалий по IQR: 76\n",
      "Доля аномалий: 0.1529\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Q1 = features.quantile(0.25)\n",
    "Q3 = features.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "lower = Q1 - 1.5 * IQR\n",
    "upper = Q3 + 1.5 * IQR\n",
    "\n",
    "labels = ((features < lower) | (features > upper)).any(axis=1).astype(int)\n",
    "\n",
    "print(\"Количество аномалий по IQR:\", labels.sum())\n",
    "print(\"Доля аномалий:\", round(labels.mean(), 4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415e47b2",
   "metadata": {},
   "source": [
    "## 8. Метод 1 — Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85ab0cb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.98\n",
      "Recall: 0.6447368421052632\n",
      "F1-score: 0.7777777777777778\n"
     ]
    }
   ],
   "source": [
    "\n",
    "iso = IsolationForest(contamination=0.1, random_state=42)\n",
    "pred_iso = iso.fit_predict(X)\n",
    "pred_iso = np.where(pred_iso == -1, 1, 0)\n",
    "\n",
    "precision_iso = precision_score(labels, pred_iso, zero_division=0)\n",
    "recall_iso = recall_score(labels, pred_iso, zero_division=0)\n",
    "f1_iso = f1_score(labels, pred_iso, zero_division=0)\n",
    "\n",
    "print(\"Precision:\", precision_iso)\n",
    "print(\"Recall:\", recall_iso)\n",
    "print(\"F1-score:\", f1_iso)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ed25ba",
   "metadata": {},
   "source": [
    "## 9. Метод 2 — Local Outlier Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a609443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.74\n",
      "Recall: 0.4868421052631579\n",
      "F1-score: 0.5873015873015873\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lof = LocalOutlierFactor(n_neighbors=20, contamination=0.1)\n",
    "pred_lof = lof.fit_predict(X)\n",
    "pred_lof = np.where(pred_lof == -1, 1, 0)\n",
    "\n",
    "precision_lof = precision_score(labels, pred_lof, zero_division=0)\n",
    "recall_lof = recall_score(labels, pred_lof, zero_division=0)\n",
    "f1_lof = f1_score(labels, pred_lof, zero_division=0)\n",
    "\n",
    "print(\"Precision:\", precision_lof)\n",
    "print(\"Recall:\", recall_lof)\n",
    "print(\"F1-score:\", f1_lof)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c5be9c",
   "metadata": {},
   "source": [
    "## 10. Метод 3 — One-Class SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4aeae702",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.8461538461538461\n",
      "Recall: 0.5789473684210527\n",
      "F1-score: 0.6875\n"
     ]
    }
   ],
   "source": [
    "\n",
    "ocsvm = OneClassSVM(kernel=\"rbf\", gamma=\"scale\", nu=0.1)\n",
    "pred_svm = ocsvm.fit_predict(X)\n",
    "pred_svm = np.where(pred_svm == -1, 1, 0)\n",
    "\n",
    "precision_svm = precision_score(labels, pred_svm, zero_division=0)\n",
    "recall_svm = recall_score(labels, pred_svm, zero_division=0)\n",
    "f1_svm = f1_score(labels, pred_svm, zero_division=0)\n",
    "\n",
    "print(\"Precision:\", precision_svm)\n",
    "print(\"Recall:\", recall_svm)\n",
    "print(\"F1-score:\", f1_svm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9db2580",
   "metadata": {},
   "source": [
    "## 11. Сводная таблица метрик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "108e61f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Метод</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Isolation Forest</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.644737</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>One-Class SVM</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Local Outlier Factor</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.486842</td>\n",
       "      <td>0.587302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Метод  Precision    Recall  F1-score\n",
       "0      Isolation Forest   0.980000  0.644737  0.777778\n",
       "1         One-Class SVM   0.846154  0.578947  0.687500\n",
       "2  Local Outlier Factor   0.740000  0.486842  0.587302"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "results = pd.DataFrame({\n",
    "    \"Метод\": [\"Isolation Forest\", \"Local Outlier Factor\", \"One-Class SVM\"],\n",
    "    \"Precision\": [precision_iso, precision_lof, precision_svm],\n",
    "    \"Recall\": [recall_iso, recall_lof, recall_svm],\n",
    "    \"F1-score\": [f1_iso, f1_lof, f1_svm]\n",
    "})\n",
    "\n",
    "results = results.sort_values(\"F1-score\", ascending=False).reset_index(drop=True)\n",
    "results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22464fb7",
   "metadata": {},
   "source": [
    "## 12. Матрицы ошибок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fb39f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isolation Forest\n",
      "[[420   1]\n",
      " [ 27  49]]\n",
      "----------------------------------------\n",
      "Local Outlier Factor\n",
      "[[408  13]\n",
      " [ 39  37]]\n",
      "----------------------------------------\n",
      "One-Class SVM\n",
      "[[413   8]\n",
      " [ 32  44]]\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for name, pred in [\n",
    "    (\"Isolation Forest\", pred_iso),\n",
    "    (\"Local Outlier Factor\", pred_lof),\n",
    "    (\"One-Class SVM\", pred_svm)\n",
    "]:\n",
    "    cm = confusion_matrix(labels, pred)\n",
    "    print(name)\n",
    "    print(cm)\n",
    "    print(\"-\" * 40)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278aec26",
   "metadata": {},
   "source": [
    "## 13. Визуализация исходных данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e20ffcf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(features[\"Динамика_Величина_КРС\"], features[\"Динамика_Основных_Средств\"], alpha=0.7)\n",
    "plt.xlabel(\"Динамика величины КРС\")\n",
    "plt.ylabel(\"Динамика основных средств\")\n",
    "plt.title(\"Исходные данные\")\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4b3690",
   "metadata": {},
   "source": [
    "## 14. Визуализация аномалий — Isolation Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0d30aba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(features[\"Динамика_Величина_КРС\"], features[\"Динамика_Основных_Средств\"], alpha=0.6, label=\"Наблюдения\")\n",
    "plt.scatter(\n",
    "    features.loc[pred_iso == 1, \"Динамика_Величина_КРС\"],\n",
    "    features.loc[pred_iso == 1, \"Динамика_Основных_Средств\"],\n",
    "    marker=\"x\",\n",
    "    s=80,\n",
    "    label=\"Аномалии\"\n",
    ")\n",
    "plt.xlabel(\"Динамика величины КРС\")\n",
    "plt.ylabel(\"Динамика основных средств\")\n",
    "plt.title(\"Аномалии — Isolation Forest\")\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6866a138",
   "metadata": {},
   "source": [
    "## 15. Визуализация аномалий — Local Outlier Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74440ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(features[\"Динамика_Величина_КРС\"], features[\"Динамика_Основных_Средств\"], alpha=0.6, label=\"Наблюдения\")\n",
    "plt.scatter(\n",
    "    features.loc[pred_lof == 1, \"Динамика_Величина_КРС\"],\n",
    "    features.loc[pred_lof == 1, \"Динамика_Основных_Средств\"],\n",
    "    marker=\"x\",\n",
    "    s=80,\n",
    "    label=\"Аномалии\"\n",
    ")\n",
    "plt.xlabel(\"Динамика величины КРС\")\n",
    "plt.ylabel(\"Динамика основных средств\")\n",
    "plt.title(\"Аномалии — Local Outlier Factor\")\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f562970f",
   "metadata": {},
   "source": [
    "## 16. Визуализация аномалий — One-Class SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97112c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(features[\"Динамика_Величина_КРС\"], features[\"Динамика_Основных_Средств\"], alpha=0.6, label=\"Наблюдения\")\n",
    "plt.scatter(\n",
    "    features.loc[pred_svm == 1, \"Динамика_Величина_КРС\"],\n",
    "    features.loc[pred_svm == 1, \"Динамика_Основных_Средств\"],\n",
    "    marker=\"x\",\n",
    "    s=80,\n",
    "    label=\"Аномалии\"\n",
    ")\n",
    "plt.xlabel(\"Динамика величины КРС\")\n",
    "plt.ylabel(\"Динамика основных средств\")\n",
    "plt.title(\"Аномалии — One-Class SVM\")\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff25effc",
   "metadata": {},
   "source": [
    "\n",
    "## 17. Выводы\n",
    "\n",
    "По псевдо-разметке IQR лучшим методом оказался **Isolation Forest**.\n",
    "\n",
    "Итоговые метрики:\n",
    "- **Isolation Forest** — Precision = **0.98**, Recall = **0.645**, F1-score = **0.778**\n",
    "- **One-Class SVM** — Precision = **0.846**, Recall = **0.579**, F1-score = **0.688**\n",
    "- **Local Outlier Factor** — Precision = **0.740**, Recall = **0.487**, F1-score = **0.587**\n",
    "\n",
    "По матрицам ошибок:\n",
    "- **Isolation Forest**: FP = 1, FN = 27\n",
    "- **One-Class SVM**: FP = 8, FN = 32\n",
    "- **LOF**: FP = 13, FN = 39\n",
    "\n",
    "**Итог:** Isolation Forest дал лучший баланс точности и полноты и почти не создавал ложных срабатываний.  \n",
    "Метод можно считать **наиболее адекватным** для данной задачи. При этом полнота остаётся умеренной, то есть часть аномалий модель всё же пропускает.  \n",
    "Вывод следует интерпретировать с осторожностью, так как оценка выполнена по **псевдо-разметке IQR**, а не по экспертной разметке.\n"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}