AIM-PIbd-31-Masenkin-M-S/lab_7/fuzzy_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Датасет: [Tesla Insider Trading](https://www.kaggle.com/datasets/ilyaryabov/tesla-insider-trading).\n",
    "\n",
    "### Описание датасета:\n",
    "\n",
    "Датасет представляет собой выборку операций с ценными бумагами компании Tesla, совершённых инсайдерами, и является частью более крупного проекта \"Insider Trading S&P500 – Inside Info\". Данные охватывают транзакции с участием крупных акционеров и должностных лиц компании, включая такие операции, как покупка, продажа и опционы, начиная с 10 ноября 2021 года и до 27 июля 2022 года.\n",
    "\n",
    "---\n",
    "\n",
    "### Анализ сведений:\n",
    "\n",
    "**Проблемная область:**\n",
    "Проблемная область данного датасета касается анализа инсайдерских сделок в публичных компаниях, а также их влияния на ценообразование акций. Инсайдерские транзакции, совершаемые людьми с доступом к непубличной информации (такими как руководители, крупные акционеры или члены совета директоров), могут быть индикаторами будущих изменений стоимости акций. Исследование таких транзакций помогает понять, как информация внутри компании отражается в действиях ключевых участников, и может выявить паттерны поведения, которые влияют на рынки.\n",
    "\n",
    "**Актуальность:**\n",
    "Анализ инсайдерских сделок становится особенно важным в условиях высокой волатильности рынка и неопределенности. Инвесторы, аналитики и компании используют такие данные, чтобы лучше понимать сигналы от крупных акционеров и должностных лиц. Действия инсайдеров, такие как покупки и продажи акций, нередко рассматриваются как индикаторы доверия к компании, что может оказывать значительное влияние на рыночные ожидания и прогнозы.\n",
    "\n",
    "**Объекты наблюдений:**\n",
    "Объектами наблюдений в датасете являются инсайдеры компании Tesla — лица, имеющие значительное влияние на управление и информацию компании. Каждый объект характеризуется различными параметрами, включая должность, тип транзакции, количество акций и общую стоимость сделок.\n",
    "\n",
    "**Атрибуты объектов:**\n",
    "- Insider Trading: ФИО лица, совершившего транзакцию.\n",
    "- Relationship: Должность или статус данного лица в компании Tesla.\n",
    "- Date: Дата завершения транзакции.\n",
    "- Transaction: Тип транзакции.\n",
    "- Cost: Цена одной акции на момент совершения транзакции.\n",
    "- Shares: Количество акций, участвующих в транзакции.\n",
    "- Value ($): Общая стоимость транзакции в долларах США.\n",
    "- Shares Total: Общее количество акций, принадлежащих этому лицу после завершения данной транзакции.\n",
    "- SEC Form 4: Дата записи транзакции в форме SEC Form 4, обязательной для отчётности о сделках инсайдеров.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выгрузка данных из файла в DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "\n",
    "\n",
    "df: DataFrame = pd.read_csv(\"..//static//csv//TSLA.csv\")\n",
    "\n",
    "# Преобразование типов данных\n",
    "df[\"Insider Trading\"] = df[\"Insider Trading\"].astype(\"category\")                             # Преобразование в категорию\n",
    "df[\"Relationship\"] = df[\"Relationship\"].astype(\"category\")                                   # Преобразование в категорию\n",
    "df[\"Transaction\"] = df[\"Transaction\"].astype(\"category\")                                     # Преобразование в категорию\n",
    "df[\"Cost\"] = pd.to_numeric(df[\"Cost\"], errors=\"coerce\")                                      # Преобразование в float\n",
    "df[\"Shares\"] = pd.to_numeric(df[\"Shares\"].str.replace(\",\", \"\"), errors=\"coerce\")             # Преобразование в float с удалением запятых\n",
    "df[\"Value ($)\"] = pd.to_numeric(df[\"Value ($)\"].str.replace(\",\", \"\"), errors=\"coerce\")       # Преобразование в float с удалением запятых\n",
    "df[\"Shares Total\"] = pd.to_numeric(df[\"Shares Total\"].str.replace(\",\", \"\"), errors=\"coerce\") # Преобразование в float с удалением запятых"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Краткая информация о DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Non-Null Count  Dtype   \n",
      "---  ------           --------------  -----   \n",
      " 0   Insider Trading  156 non-null    category\n",
      " 1   Relationship     156 non-null    category\n",
      " 2   Date             156 non-null    object  \n",
      " 3   Transaction      156 non-null    category\n",
      " 4   Cost             156 non-null    float64 \n",
      " 5   Shares           156 non-null    int64   \n",
      " 6   Value ($)        156 non-null    int64   \n",
      " 7   Shares Total     156 non-null    int64   \n",
      " 8   SEC Form 4       156 non-null    object  \n",
      "dtypes: category(3), float64(1), int64(3), object(2)\n",
      "memory usage: 8.6+ KB\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>156.0</td>\n",
       "      <td>4.787856e+02</td>\n",
       "      <td>4.489229e+02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.5225</td>\n",
       "      <td>240.225</td>\n",
       "      <td>9.341075e+02</td>\n",
       "      <td>1.171040e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shares</th>\n",
       "      <td>156.0</td>\n",
       "      <td>5.404666e+05</td>\n",
       "      <td>1.530835e+06</td>\n",
       "      <td>121.0</td>\n",
       "      <td>3500.0000</td>\n",
       "      <td>10500.000</td>\n",
       "      <td>3.017978e+05</td>\n",
       "      <td>1.192000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value ($)</th>\n",
       "      <td>156.0</td>\n",
       "      <td>1.818582e+08</td>\n",
       "      <td>4.131734e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271008.0000</td>\n",
       "      <td>2026823.000</td>\n",
       "      <td>1.487132e+08</td>\n",
       "      <td>2.278695e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shares Total</th>\n",
       "      <td>156.0</td>\n",
       "      <td>3.347679e+07</td>\n",
       "      <td>9.553593e+07</td>\n",
       "      <td>49.0</td>\n",
       "      <td>25103.5000</td>\n",
       "      <td>73488.000</td>\n",
       "      <td>1.507274e+06</td>\n",
       "      <td>4.554674e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count          mean           std    min          25%  \\\n",
       "Cost          156.0  4.787856e+02  4.489229e+02    0.0      50.5225   \n",
       "Shares        156.0  5.404666e+05  1.530835e+06  121.0    3500.0000   \n",
       "Value ($)     156.0  1.818582e+08  4.131734e+08    0.0  271008.0000   \n",
       "Shares Total  156.0  3.347679e+07  9.553593e+07   49.0   25103.5000   \n",
       "\n",
       "                      50%           75%           max  \n",
       "Cost              240.225  9.341075e+02  1.171040e+03  \n",
       "Shares          10500.000  3.017978e+05  1.192000e+07  \n",
       "Value ($)     2026823.000  1.487132e+08  2.278695e+09  \n",
       "Shares Total    73488.000  1.507274e+06  4.554674e+08  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Краткая информация о DataFrame\n",
    "df.info()\n",
    "\n",
    "# Статистическое описание числовых столбцов\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выбор входных данных и целевого признака:\n",
    "\n",
    "**Входные данные:**\n",
    "\n",
    "- Transaction (Тип транзакции): Категориальная переменная, указывающая на тип операции (Sale, Option Exercise).\n",
    "- Shares (Количество акций): Числовая переменная, указывающая количество акций в транзакции.\n",
    "- Value ($): Числовая переменная, представляющая общую стоимость транзакции.\n",
    "\n",
    "**Целевой признак:**\n",
    "\n",
    "Cost (Цена акции): Числовая переменная, показывающая стоимость одной акции на момент совершения транзакции.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определение лингвистических переменных:\n",
    "\n",
    "**Лингвистическая переменная** – это переменная, значениями которой являются слова или фразы вместо чисел. Она используется в нечеткой логике и теории нечетких множеств для описания понятий, которые нельзя точно выразить числовыми значениями.\n",
    "\n",
    "Для каждой переменной определим количество термов, типы и параметры функций принадлежности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "\n",
    "transaction = ctrl.Antecedent(np.arange(0, 2, 1), \"Transaction\")\n",
    "shares = ctrl.Antecedent(np.arange(0, 12000000, 1), \"Shares\")\n",
    "value = ctrl.Antecedent(np.arange(0, 2.3e9, 1e6), \"Value\")\n",
    "cost = ctrl.Consequent(np.arange(0, 1200, 1), \"Cost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определение нечетких переменных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ULSTU\\Семестр 5\\AIM-PIbd-31-Masenkin-M-S\\aimenv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n",
      "d:\\ULSTU\\Семестр 5\\AIM-PIbd-31-Masenkin-M-S\\aimenv\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  func(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ULSTU\\Семестр 5\\AIM-PIbd-31-Masenkin-M-S\\aimenv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import skfuzzy as fuzz\n",
    "\n",
    "\n",
    "transaction[\"Option Exercise\"] = fuzz.trimf(transaction.universe, [0, 0, 1])\n",
    "transaction[\"Sale\"] = fuzz.trimf(transaction.universe, [0, 1, 1])\n",
    "transaction.view()\n",
    "\n",
    "# shares[\"Few\"] = fuzz.zmf(shares.universe, 0, 20000)\n",
    "# shares[\"Moderate\"] = fuzz.trimf(shares.universe, [10000, 50000, 500000])\n",
    "# shares[\"Many\"] = fuzz.smf(shares.universe, 400000, 12000000)\n",
    "shares.automf(3, names=[\"Few\", \"Moderate\", \"Many\"])\n",
    "shares.view()\n",
    "\n",
    "# value[\"Low\"] = fuzz.zmf(value.universe, 0, 5e7)\n",
    "# value[\"Medium\"] = fuzz.trimf(value.universe, [5e7, 1e8, 1e9])\n",
    "# value[\"High\"] = fuzz.smf(value.universe, 1e9, 2.3e9)\n",
    "value.automf(3, names=[\"Low\", \"Medium\", \"High\"])\n",
    "value.view()\n",
    "\n",
    "# cost[\"Low\"] = fuzz.zmf(cost.universe, 0, 300)\n",
    "# cost[\"Medium\"] = fuzz.trimf(cost.universe, [250, 500, 750])\n",
    "# cost[\"High\"] = fuzz.smf(cost.universe, 700, 1200)\n",
    "cost.automf(3, names=[\"Low\", \"Medium\", \"High\"])\n",
    "cost.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определение нечётких правил:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule1 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Few\"] & value[\"Low\"], cost[\"Low\"])\n",
    "rule2 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Few\"] & value[\"Medium\"], cost[\"Low\"])\n",
    "rule3 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Few\"] & value[\"High\"], cost[\"Medium\"])\n",
    "rule4 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Moderate\"] & value[\"Low\"], cost[\"Low\"])\n",
    "rule5 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Moderate\"] & value[\"Medium\"], cost[\"Medium\"])\n",
    "rule6 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Moderate\"] & value[\"High\"], cost[\"High\"])\n",
    "rule7 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Many\"] & value[\"Low\"], cost[\"Medium\"])\n",
    "rule8 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Many\"] & value[\"Medium\"], cost[\"High\"])\n",
    "rule9 = ctrl.Rule(transaction[\"Option Exercise\"] & shares[\"Many\"] & value[\"High\"], cost[\"High\"])\n",
    "\n",
    "rule10 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Few\"] & value[\"Low\"], cost[\"Low\"])\n",
    "rule11 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Few\"] & value[\"Medium\"], cost[\"Low\"])\n",
    "rule12 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Few\"] & value[\"High\"], cost[\"Medium\"])\n",
    "rule13 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Moderate\"] & value[\"Low\"], cost[\"Low\"])\n",
    "rule14 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Moderate\"] & value[\"Medium\"], cost[\"Medium\"])\n",
    "rule15 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Moderate\"] & value[\"High\"], cost[\"High\"])\n",
    "rule16 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Many\"] & value[\"Low\"], cost[\"Medium\"])\n",
    "rule17 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Many\"] & value[\"Medium\"], cost[\"High\"])\n",
    "rule18 = ctrl.Rule(transaction[\"Sale\"] & shares[\"Many\"] & value[\"High\"], cost[\"High\"])\n",
    "# Всё ещё не нравится"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Создание нечеткой системы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[IF (Transaction[Option Exercise] AND Shares[Few]) AND Value[Low] THEN Cost[Low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Few]) AND Value[Medium] THEN Cost[Low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Few]) AND Value[High] THEN Cost[Medium]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Moderate]) AND Value[Low] THEN Cost[Low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Moderate]) AND Value[Medium] THEN Cost[Medium]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Moderate]) AND Value[High] THEN Cost[High]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Many]) AND Value[Low] THEN Cost[Medium]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Many]) AND Value[Medium] THEN Cost[High]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Option Exercise] AND Shares[Many]) AND Value[High] THEN Cost[High]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Few]) AND Value[Low] THEN Cost[Low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Few]) AND Value[Medium] THEN Cost[Low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Few]) AND Value[High] THEN Cost[Medium]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Moderate]) AND Value[Low] THEN Cost[Low]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Moderate]) AND Value[Medium] THEN Cost[Medium]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Moderate]) AND Value[High] THEN Cost[High]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Many]) AND Value[Low] THEN Cost[Medium]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Many]) AND Value[Medium] THEN Cost[High]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax,\n",
       " IF (Transaction[Sale] AND Shares[Many]) AND Value[High] THEN Cost[High]\n",
       " \tAND aggregation function : fmin\n",
       " \tOR aggregation function  : fmax]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzzy_rules: list[ctrl.Rule] = [\n",
    "    rule1,\n",
    "    rule2,\n",
    "    rule3,\n",
    "    rule4,\n",
    "    rule5,\n",
    "    rule6,\n",
    "    rule7,\n",
    "    rule8,\n",
    "    rule9,\n",
    "    rule10,\n",
    "    rule11,\n",
    "    rule12,\n",
    "    rule13,\n",
    "    rule14,\n",
    "    rule15,\n",
    "    rule16,\n",
    "    rule17,\n",
    "    rule18,\n",
    "]\n",
    "\n",
    "# Создание системы управления\n",
    "cost_ctrl = ctrl.ControlSystem(fuzzy_rules)\n",
    "\n",
    "# Стимуляция системы управления\n",
    "cost_sim = ctrl.ControlSystemSimulation(cost_ctrl)\n",
    "\n",
    "fuzzy_rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Тестирование нечёткой системы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>Transaction</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Value ($)</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Predicted cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>2500</td>\n",
       "      <td>138300</td>\n",
       "      <td>55.32</td>\n",
       "      <td>200.001889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>2152681</td>\n",
       "      <td>13432729</td>\n",
       "      <td>6.24</td>\n",
       "      <td>236.449927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>2134440</td>\n",
       "      <td>13318906</td>\n",
       "      <td>6.24</td>\n",
       "      <td>235.991667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Sale</td>\n",
       "      <td>543452</td>\n",
       "      <td>555171415</td>\n",
       "      <td>1021.56</td>\n",
       "      <td>353.734614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Sale</td>\n",
       "      <td>390639</td>\n",
       "      <td>408039939</td>\n",
       "      <td>1044.54</td>\n",
       "      <td>304.168211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Sale</td>\n",
       "      <td>3500</td>\n",
       "      <td>2527350</td>\n",
       "      <td>722.10</td>\n",
       "      <td>200.649273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>8228</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.833987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sale</td>\n",
       "      <td>10500</td>\n",
       "      <td>1885485</td>\n",
       "      <td>179.57</td>\n",
       "      <td>202.119571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>3283</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.833671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Sale</td>\n",
       "      <td>215528</td>\n",
       "      <td>212841259</td>\n",
       "      <td>987.53</td>\n",
       "      <td>252.897330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Sale</td>\n",
       "      <td>917</td>\n",
       "      <td>899091</td>\n",
       "      <td>980.47</td>\n",
       "      <td>200.047433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Sale</td>\n",
       "      <td>3750</td>\n",
       "      <td>1127212</td>\n",
       "      <td>300.59</td>\n",
       "      <td>200.706593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>3500</td>\n",
       "      <td>219520</td>\n",
       "      <td>62.72</td>\n",
       "      <td>200.100676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>16871</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.835187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>2133441</td>\n",
       "      <td>13312672</td>\n",
       "      <td>6.24</td>\n",
       "      <td>235.966640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Transaction   Shares  Value ($)     Cost  Predicted cost\n",
       "56   Option Exercise     2500     138300    55.32      200.001889\n",
       "138  Option Exercise  2152681   13432729     6.24      236.449927\n",
       "123  Option Exercise  2134440   13318906     6.24      235.991667\n",
       "128             Sale   543452  555171415  1021.56      353.734614\n",
       "127             Sale   390639  408039939  1044.54      304.168211\n",
       "66              Sale     3500    2527350   722.10      200.649273\n",
       "42   Option Exercise     8228          0     0.00      199.833987\n",
       "26              Sale    10500    1885485   179.57      202.119571\n",
       "134  Option Exercise     3283          0     0.00      199.833671\n",
       "79              Sale   215528  212841259   987.53      252.897330\n",
       "131             Sale      917     899091   980.47      200.047433\n",
       "36              Sale     3750    1127212   300.59      200.706593\n",
       "75   Option Exercise     3500     219520    62.72      200.100676\n",
       "22   Option Exercise    16871          0     0.00      199.835187\n",
       "135  Option Exercise  2133441   13312672     6.24      235.966640"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Transaction</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Value ($)</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Predicted cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>3500</td>\n",
       "      <td>219520</td>\n",
       "      <td>62.72</td>\n",
       "      <td>200.100676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Sale</td>\n",
       "      <td>121</td>\n",
       "      <td>107447</td>\n",
       "      <td>887.99</td>\n",
       "      <td>199.861821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Sale</td>\n",
       "      <td>2258486</td>\n",
       "      <td>2007978676</td>\n",
       "      <td>889.08</td>\n",
       "      <td>619.839689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Sale</td>\n",
       "      <td>3500</td>\n",
       "      <td>3147970</td>\n",
       "      <td>899.42</td>\n",
       "      <td>200.649805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Sale</td>\n",
       "      <td>340564</td>\n",
       "      <td>333647031</td>\n",
       "      <td>979.69</td>\n",
       "      <td>286.706558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sale</td>\n",
       "      <td>9650000</td>\n",
       "      <td>2012758726</td>\n",
       "      <td>208.58</td>\n",
       "      <td>753.891218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>25000</td>\n",
       "      <td>1309500</td>\n",
       "      <td>52.38</td>\n",
       "      <td>201.426112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>1786</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.833629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Option Exercise</td>\n",
       "      <td>1786</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>199.833629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Sale</td>\n",
       "      <td>126471</td>\n",
       "      <td>148102963</td>\n",
       "      <td>1171.04</td>\n",
       "      <td>231.142091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sale</td>\n",
       "      <td>3768</td>\n",
       "      <td>670935</td>\n",
       "      <td>178.07</td>\n",
       "      <td>200.648862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Sale</td>\n",
       "      <td>1535</td>\n",
       "      <td>1297675</td>\n",
       "      <td>845.39</td>\n",
       "      <td>200.191485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sale</td>\n",
       "      <td>6870000</td>\n",
       "      <td>1088011570</td>\n",
       "      <td>158.37</td>\n",
       "      <td>604.702496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Sale</td>\n",
       "      <td>764980</td>\n",
       "      <td>738683277</td>\n",
       "      <td>965.62</td>\n",
       "      <td>363.950049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sale</td>\n",
       "      <td>3750</td>\n",
       "      <td>710625</td>\n",
       "      <td>189.50</td>\n",
       "      <td>200.697003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Transaction   Shares   Value ($)     Cost  Predicted cost\n",
       "96   Option Exercise     3500      219520    62.72      200.100676\n",
       "69              Sale      121      107447   887.99      199.861821\n",
       "82              Sale  2258486  2007978676   889.08      619.839689\n",
       "76              Sale     3500     3147970   899.42      200.649805\n",
       "114             Sale   340564   333647031   979.69      286.706558\n",
       "29              Sale  9650000  2012758726   208.58      753.891218\n",
       "94   Option Exercise    25000     1309500    52.38      201.426112\n",
       "132  Option Exercise     1786           0     0.00      199.833629\n",
       "93   Option Exercise     1786           0     0.00      199.833629\n",
       "139             Sale   126471   148102963  1171.04      231.142091\n",
       "19              Sale     3768      670935   178.07      200.648862\n",
       "90              Sale     1535     1297675   845.39      200.191485\n",
       "15              Sale  6870000  1088011570   158.37      604.702496\n",
       "125             Sale   764980   738683277   965.62      363.950049\n",
       "24              Sale     3750      710625   189.50      200.697003"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# Функция для предсказания значений\n",
    "def predict_value(row):\n",
    "    cost_sim.input[\"Transaction\"] = row[\"Transaction\"]\n",
    "    cost_sim.input[\"Shares\"] = row[\"Shares\"]\n",
    "    cost_sim.input[\"Value\"] = row[\"Value ($)\"]\n",
    "    cost_sim.compute()\n",
    "    return cost_sim.output[\"Cost\"]\n",
    "\n",
    "\n",
    "# Выбор нужных столбцов\n",
    "data: DataFrame = df[[\"Transaction\", \"Shares\", \"Value ($)\", \"Cost\"]]\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% / 20%)\n",
    "train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)\n",
    "\n",
    "# Применение модели к обучающей выборке\n",
    "train_data[\"Predicted cost\"] = train_data.apply(predict_value, axis=1)\n",
    "display(train_data.head(15))\n",
    "\n",
    "# Применение модели к тестовой выборке\n",
    "test_data[\"Predicted cost\"] = test_data.apply(predict_value, axis=1)\n",
    "display(test_data.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка результатов на метриках:\n",
    "\n",
    "~ Спустя n часов, проведенных в попытках улучшить показатели метрик и танцев с бубном вокруг настройки параметров лингвистических переменных я сдался."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RMSE_train': 510.9053801945241,\n",
       " 'RMSE_test': 454.5448906860201,\n",
       " 'RMAE_test': 19.12051761053488,\n",
       " 'R2_test': -0.16291982992374576}"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from typing import Union\n",
    "from numpy import ndarray\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "metrics_results: dict[str, Union[float, ndarray]] = {}\n",
    "\n",
    "metrics_results[\"RMSE_train\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(train_data[\"Cost\"], train_data[\"Predicted cost\"])\n",
    ")\n",
    "metrics_results[\"RMSE_test\"] = math.sqrt(\n",
    "    metrics.mean_squared_error(test_data[\"Cost\"], test_data[\"Predicted cost\"])\n",
    ")\n",
    "metrics_results[\"RMAE_test\"] = math.sqrt(\n",
    "    metrics.mean_absolute_error(test_data[\"Cost\"], test_data[\"Predicted cost\"])\n",
    ")\n",
    "metrics_results[\"R2_test\"] = metrics.r2_score(  # type: ignore\n",
    "    test_data[\"Cost\"], test_data[\"Predicted cost\"]\n",
    ")\n",
    "\n",
    "metrics_results"
   ]
  }
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