AIM-PIbd-32-Chernyshev-G-Y/lab_7/Lab7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "games.csv - датасет игр из steam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"..//static//csv//games.csv\")\n",
    "\n",
    "# df['date_release'] = pd.to_datetime(df['date_release'])\n",
    "# # Разделение даты на год, месяц и день\n",
    "# df['year'] = df['date_release'].dt.year\n",
    "# df['month'] = df['date_release'].dt.month\n",
    "# df['day'] = df['date_release'].dt.day\n",
    "\n",
    "# # Удаляем исходный столбец 'date', если он больше не нужен\n",
    "# df.drop('date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Входные переменные: user_reviews (кол-во отзывов игроков; reviews в коде), price_original (цена на момент выхода; price в коде)\n",
    "\n",
    "Выходная перменная: positive_ratio (отношение положительных отзывов к негативным; pos в коде)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime as dt\n",
    "from datetime import timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from skfuzzy import control as ctrl\n",
    "import skfuzzy as fuzz\n",
    "\n",
    "#входная переменная кол-ва отзывов с таким универсумом (от 0 до 100k)\n",
    "reviews = ctrl.Antecedent(np.arange(0, 100000, 1), \"reviews\")\n",
    "\n",
    "#входная переменная цены с таким универсумом (от 0 до 100 долларов)\n",
    "price = ctrl.Antecedent(np.arange(0, 100, 0.01), \"price\")\n",
    "\n",
    "#удалить пару игр, у которых цена больше 100 долларов, чтобы графики норм. были\n",
    "df = df[df['price_original'] <= 100]\n",
    "\n",
    "#удалить несколько игр, у которых больше 100k отзывов, чтобы графики норм. были\n",
    "df = df[df['user_reviews'] <= 100000]\n",
    "\n",
    "#выходная переменная позитивности с таким универсумом (от 0 до 100)\n",
    "pos = ctrl.Consequent(np.arange(0, 100, 1), \"pos\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Лингвистические переменные и их визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\AI labs\\aimenv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\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": [
    "#ЛИНГВИСТИЧЕСКАЯ ПЕР. КОЛ-ВО ОТЗЫВОВ\n",
    "\n",
    "#определял примерные диапазоны для кол-ва отзывов с помощью boxplot:\n",
    "# plt.figure(figsize=(12, 8))\n",
    "# df_temp = df[df['user_reviews'] < 100000] \n",
    "# plt.boxplot(x=df_temp['user_reviews'])\n",
    "# plt.show()\n",
    "\n",
    "#автоматическое формирование нечёт. переменной отзывов, но с поправкой для many (s - ф-ия) \n",
    "reviews.automf(3, variable_type=\"quant\", names=['few', 'avg', 'many'])\n",
    "reviews[\"few\"] = fuzz.zmf(reviews.universe, 0, 10000)\n",
    "\n",
    "reviews[\"many\"] = fuzz.smf(reviews.universe, reviews.universe.max()/2, reviews.universe.max())\n",
    "reviews.view()\n",
    "\n",
    "#СТАРЫЙ КОД\n",
    "#---------------------------------------------------------------------------\n",
    "# #до 15 обзоров - мало\n",
    "# #z-функция\n",
    "# reviews[\"few\"] = fuzz.zmf(reviews.universe, 0, 15)\n",
    "\n",
    "# #10-40 обзоров - средне\n",
    "# #треуг. ф-ия\n",
    "# # 10  - левая вершина треугольника\n",
    "# # 25 - верхняя вершина\n",
    "# # 40 - правая вершина\n",
    "# reviews[\"average\"] = fuzz.trimf(reviews.universe, [10, 25, 40]) \n",
    "\n",
    "# #больше 30 обзоров - много\n",
    "# #s-функция\n",
    "# reviews[\"many\"] = fuzz.smf(reviews.universe, 30, reviews.universe.max())\n",
    "\n",
    "# reviews.view()\n",
    "\n",
    "#---------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "\n",
    "#ЛИНГВИСТИЧЕСКАЯ ПЕР. ЦЕНА\n",
    "\n",
    "#определял примерные диапазоны для цены с помощью boxplot:\n",
    "# plt.figure(figsize=(12, 8))\n",
    "# df_temp = df[df['price_original'] < 60]\n",
    "# plt.boxplot(x=df_temp['price_original'])\n",
    "# plt.show()\n",
    "\n",
    "#От 0 до 10 долларов можно считать дёшево (переводил на рубли)\n",
    "#z-функция\n",
    "# price[\"cheap\"] = fuzz.zmf(price.universe, 0, 10)\n",
    "price[\"cheap\"] = fuzz.zmf(price.universe, 0, 12)\n",
    "\n",
    "#от 5 до 22 долларов можно считать средне (переводил на рубли)\n",
    "#треуг. ф-ия\n",
    "# 5  - левая вершина треугольника\n",
    "# 15 - верхняя вершина\n",
    "# 22 - правая вершина\n",
    "# price[\"medium\"] = fuzz.trimf(price.universe, [5, 15, 22])\n",
    "price[\"medium\"] = fuzz.trimf(price.universe, [5, 16, 22])\n",
    "\n",
    "#больше 15 долларов можно считать дорого (переводил на рубли)\n",
    "#s-функция\n",
    "price[\"expensive\"] = fuzz.smf(price.universe, 15, price.universe.max())\n",
    "\n",
    "price.view()\n",
    "\n",
    "\n",
    "#ЛИНГВИСТИЧЕСКАЯ ПЕР. СООТНОШЕНИЕ ПОЛОЖ./НЕГАТИВ. ОТЗЫВОВ\n",
    "#до 60% можно считать низким отношением позитивных к негативным\n",
    "\n",
    "pos[\"low\"] = fuzz.zmf(pos.universe, 0, 60)\n",
    "\n",
    "#от 50 до 80 можно считать средним\n",
    "pos[\"avg\"] = fuzz.trimf(pos.universe, [50, 70, 80])\n",
    "\n",
    "#от 75 можно считать высоким\n",
    "pos[\"high\"] = fuzz.smf(pos.universe, 75, 100)\n",
    "\n",
    "\n",
    "\n",
    "pos.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## База нечётких правил"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       user_reviews  price_original  positive_ratio\n",
      "15251            46            0.99              95\n",
      "13770           491            6.99              83\n",
      "21390           365            0.00              76\n",
      "16365            55            2.99              96\n",
      "49100            78            0.00              74\n",
      "...             ...             ...             ...\n",
      "2652             55           14.99              74\n",
      "30351            41            9.99              82\n",
      "1810           4598           59.99              92\n",
      "27959           115            2.99              73\n",
      "2864             15            4.99              86\n",
      "\n",
      "[10142 rows x 3 columns]\n"
     ]
    },
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_reviews</th>\n",
       "      <th>price_original</th>\n",
       "      <th>positive_ratio</th>\n",
       "      <th>positive_ratio_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15251</th>\n",
       "      <td>46</td>\n",
       "      <td>0.99</td>\n",
       "      <td>95</td>\n",
       "      <td>92.044490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13770</th>\n",
       "      <td>491</td>\n",
       "      <td>6.99</td>\n",
       "      <td>83</td>\n",
       "      <td>74.785910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21390</th>\n",
       "      <td>365</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76</td>\n",
       "      <td>91.640313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16365</th>\n",
       "      <td>55</td>\n",
       "      <td>2.99</td>\n",
       "      <td>96</td>\n",
       "      <td>91.739221</td>\n",
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       "    <tr>\n",
       "      <th>49100</th>\n",
       "      <td>78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>74</td>\n",
       "      <td>92.010413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <td>55</td>\n",
       "      <td>14.99</td>\n",
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       "      <td>66.698909</td>\n",
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       "    <tr>\n",
       "      <th>30351</th>\n",
       "      <td>41</td>\n",
       "      <td>9.99</td>\n",
       "      <td>82</td>\n",
       "      <td>66.142143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <td>4598</td>\n",
       "      <td>59.99</td>\n",
       "      <td>92</td>\n",
       "      <td>24.863128</td>\n",
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       "    <tr>\n",
       "      <th>27959</th>\n",
       "      <td>115</td>\n",
       "      <td>2.99</td>\n",
       "      <td>73</td>\n",
       "      <td>91.657627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10142 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_reviews  price_original  positive_ratio  positive_ratio_pred\n",
       "15251            46            0.99              95            92.044490\n",
       "13770           491            6.99              83            74.785910\n",
       "21390           365            0.00              76            91.640313\n",
       "16365            55            2.99              96            91.739221\n",
       "49100            78            0.00              74            92.010413\n",
       "...             ...             ...             ...                  ...\n",
       "2652             55           14.99              74            66.698909\n",
       "30351            41            9.99              82            66.142143\n",
       "1810           4598           59.99              92            24.863128\n",
       "27959           115            2.99              73            91.657627\n",
       "2864             15            4.99              86            90.861111\n",
       "\n",
       "[10142 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rule1 = ctrl.Rule(reviews['many'] & price['cheap'], pos['high'])\n",
    "rule2 = ctrl.Rule(reviews['many'] & price['medium'], pos['high']) #avg\n",
    "rule3 = ctrl.Rule(reviews['many'] & price['expensive'], pos['high']) \n",
    "\n",
    "rule4 = ctrl.Rule(reviews['few'] & price['cheap'], pos['high']) #avg\n",
    "rule5 = ctrl.Rule(reviews['few'] & price['medium'], pos['avg']) #avg\n",
    "rule6 = ctrl.Rule(reviews['few'] & price['expensive'], pos['low'])\n",
    "\n",
    "rule7 = ctrl.Rule(reviews['avg'] & price['cheap'], pos['avg'])\n",
    "rule8 = ctrl.Rule(reviews['avg'] & price['medium'], pos['high'])\n",
    "rule9 = ctrl.Rule(reviews['avg'] & price['expensive'], pos['avg'])\n",
    "\n",
    "\n",
    "\n",
    "#ЗДЕСЬ ДОПОЛНИТЬ, ЕСЛИ БУДЕТ ДОП. ПРАВИЛО:\n",
    "fuzzy_rules = [\n",
    "    rule1,\n",
    "    rule2,\n",
    "    rule3,\n",
    "    rule4,\n",
    "    rule5,\n",
    "    rule6,\n",
    "    rule7,\n",
    "    rule8,\n",
    "    rule9\n",
    "]\n",
    "\n",
    "fuzzy_sys = ctrl.ControlSystemSimulation(ctrl.ControlSystem(fuzzy_rules))\n",
    "\n",
    "#ТУТ ПОМЕНЯТЬ НАЗВАНИЯ СТОЛБЦОВ И НЕЧЁТКИХ ПЕРЕМЕННЫХ НА СВОИ\n",
    "def fuzzy_pred(row):\n",
    "    # al - нечёт. пер. ; al2o3 - столбец датасета\n",
    "    fuzzy_sys.input[\"reviews\"] = row[\"user_reviews\"]\n",
    "    fuzzy_sys.input[\"price\"] = row[\"price_original\"]\n",
    "    fuzzy_sys.compute()\n",
    "    # return fuzzy_sys.output[\"positive_ratio\"]\n",
    "    return fuzzy_sys.output[\"pos\"]\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "#РАЗДЕЛИТЬ ДАТАСЕТ НА ОБУЧАЮЩУЮ, ТЕСТОВУЮ ВЫБОРКИ\n",
    "#Тестирование нечеткой системы на тестовой выборке\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X = df[['user_reviews', 'price_original']]\n",
    "y = df['positive_ratio']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "test = pd.concat([X_test, y_test], axis=1)\n",
    "\n",
    "\n",
    "print(test)\n",
    "\n",
    "result_test = test.copy()\n",
    "result_test[\"positive_ratio_pred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
    "result_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 = -0.9549582516266943\n",
      "RMSE = 25.637064888955464\n"
     ]
    }
   ],
   "source": [
    "#Оценка\n",
    "import math\n",
    "from sklearn import metrics\n",
    "print(f\"R^2 = {metrics.r2_score(result_test[\"positive_ratio\"], result_test[\"positive_ratio_pred\"])}\")\n",
    "print(f\"RMSE = {math.sqrt(\n",
    "    metrics.mean_squared_error(result_test[\"positive_ratio\"], result_test[\"positive_ratio_pred\"])\n",
    ")}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценки показали, что нечёткая система очень плохо справляется с предсказанием соотношения положительных отзывов к негативным. \n",
    "\n",
    "Средняя квадратичная ошибка составляет почти 41%, что является большой ошибкой для диапазона от 0 до 100. \n",
    "\n",
    "Коэффициент детерминации получился отрицательным. Это означает, что система работает хуже, чем простое предсказание средним значением. Это говорит о том, что гипотезы о зависимости положительности отзывов от цены игры и количества отзывов, проявляемые в виде базы нечётких правил, неправильно отражают действительность. Возможно, что распределение отношений положительных отзывов к отрицательным имеет скорее случайный характер, поэтому оно не может быть описано с хорошей точностью нечёткими правилами"
   ]
  }
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