forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторна робота 04\n",
    "\n",
    "## 1. Завдання лабораторної роботи\n",
    "Задачею є виконати передбачення на основі створених даних про подальші результати гри.\n",
    "\n",
    "## 2. Опис використаних технологій\n",
    "Для виконання задачі я використав\n",
    "- Jupyter Notebook для кращої візуалізації\n",
    "- Мову Python\n",
    "\n",
    "Бібліотеки\n",
    "- `numpy` для математичних операцій\n",
    "- `pandas` для маніпуляцій з даними\n",
    "- `matplotlib` для візуалізації графіків\n",
    "- `scikit-learn` для моделі лінійної регресії, розбиття датасету на тестові і тренувальні дані, та метрики для аналізу результатуц \n",
    "\n",
    "\n",
    "## 3. Опис програмного коду\n",
    "Весь код можна побачити нижче, а репозиторій можна знайти за адресою https://github.com/gazinaft/PIIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Імпорт бібліотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Читання з файлу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Time   Score\n",
      "0    843   311.0\n",
      "1     52  -401.0\n",
      "2     73  -365.0\n",
      "3    477   194.0\n",
      "4     52  -401.0\n",
      "5    164  -253.0\n",
      "6    374    45.0\n",
      "7    194  -169.0\n",
      "8    259  -162.0\n",
      "9    240  -158.0\n",
      "10   144  -279.0\n",
      "11    62  -383.0\n",
      "12   929   344.0\n",
      "13   913   337.0\n",
      "14  1858  1098.0\n",
      "15  1473  1125.0\n",
      "16   139  -288.0\n",
      "17  1299   840.0\n",
      "18  1030   294.0\n",
      "19   459   178.0\n",
      "20   174  -235.0\n",
      "21   473   145.0\n",
      "22   162  -233.0\n",
      "23  1267   596.0\n",
      "24  1074   915.0\n",
      "25   268   -54.0\n",
      "26    52  -401.0\n",
      "27   924   455.0\n",
      "28   378    94.0\n",
      "29    78  -366.0\n",
      "    Time  Score\n",
      "30   419  166.0\n",
      "31   212 -133.0\n",
      "32   777  374.0\n",
      "33   454  139.0\n",
      "34   298   10.0\n",
      "35   177 -206.0\n",
      "36   194 -199.0\n",
      "37  1413 -393.0\n",
      "38   258 -102.0\n",
      "39   139 -288.0\n",
      "40   757  538.0\n",
      "41   354   39.0\n",
      "42  2318 -574.0\n",
      "43   704  389.0\n",
      "44    52 -401.0\n",
      "45   468   96.0\n",
      "46  1092  381.0\n",
      "47   194 -169.0\n",
      "48   179 -216.0\n",
      "49    53 -401.0\n",
      "50   189 -218.0\n",
      "51   834  403.0\n",
      "52   174 -215.0\n",
      "53   189 -168.0\n",
      "54   738  302.0\n",
      "55    67 -374.0\n",
      "56    52 -401.0\n",
      "57   233 -137.0\n",
      "58   458 -142.0\n",
      "59    88 -348.0\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./search/result.csv')\n",
    "\n",
    "minimax = df[df.Agent == 'ExpectimaxAgent'][['Time', 'Score']]\n",
    "expectimax = df[df.Agent == 'MiniMaxAgent'][['Time', 'Score']]\n",
    "print(minimax)\n",
    "print(expectimax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розподіл даних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minimax.plot(x = 'Time', y = 'Score', kind = 'scatter', title = 'Minimax')\n",
    "expectimax.plot(x = 'Time', y = 'Score', kind = 'scatter', title = 'Expectimax')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розподіл даних на тренувальні та контрольні"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_min_train, x_min_test, y_min_train, y_min_test = model_selection.train_test_split(\n",
    "    minimax.Time, minimax.Score, test_size=0.25,\n",
    "    random_state=42,\n",
    "    shuffle=True,\n",
    ")\n",
    "x_exp_train, x_exp_test, y_exp_train, y_exp_test = model_selection.train_test_split(\n",
    "    expectimax.Time, expectimax.Score, test_size=0.25,\n",
    "    random_state=42,\n",
    "    shuffle=True,\n",
    "\n",
    ")\n",
    "\n",
    "class_names = ['Time', 'Score']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Тренування моделі"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_regr = LinearRegression()\n",
    "exp_regr = LinearRegression()\n",
    "\n",
    "min_regr.fit(np.array(x_min_train).reshape(-1, 1), np.array(y_min_train).reshape(-1, 1))\n",
    "exp_regr.fit(np.array(x_exp_train).reshape(-1, 1), np.array(y_exp_train).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Результати тренування моделі"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_preds = min_regr.predict(np.array(x_min_test).reshape(-1, 1))\n",
    "exp_preds = exp_regr.predict(np.array(x_exp_test).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Аналіз результатів досліджень"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minimax.plot(x = 'Time', y = 'Score', kind = 'scatter', title = 'Minimax')\n",
    "plt.plot(np.array(minimax.Time).reshape(-1, 1), min_regr.predict(np.array(minimax.Time).reshape(-1, 1)), color='blue', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vfAPuvTfragqkfQeNlyyBgw/OujKrNmmvCD8RuAZ4hMK/9hdKOj0iflm+0kq6E1gs6QhgC7AS+B8VrsFsH7t2wQ03FHoE3/te1tWUduqp8N73woknQs6TIth+Snt46vPA30fEBgBJRwLfBY4tV2GlRMSIpHOBn1I45fYbEXFfJWuwxtLbC//0T3DzzVlXUtqxx8K73w0rV8LcuVlXY40gbWg0jwYGQEQ8KKmCt8jaIyJ+DPw4i/e2+vDXv8Ill8CnP511JaW1tBR6BO99byEUfIGZVZO0odEt6Srg28nzU4G7ylOS2f655Rb44Aezv6hsPK97XaFX8La3lWeeA7NKSBsaZwPnAB+iMKbxS+DychVlNurRR+Htb4dbb826kvEtXQqXXVYIBbN6lzY0ZgBfiIhLYfeV2T6tx/ZLBJxwAvz611lXMrF//Vf46EfhWc/KuhKz6pH2HIqbgOLLkWcBN05/OVbLbrqpcPx9skcul11gnHRS4R5JEZM/PvlJB4bZWGl7GjMjYnD0SUQMSvL/Tg1gaKhwX5+sp66cyEEHwRe/CGecATM8gbFZWaXtaTwt6RWjTyR1AjvKU5JVwlVXpesVzJyZbWDceefkPYKhIXj/+x0YZpWQ9n+zjwDfl7SVwm07XgD8Y9mqsv3y+OOF2cKq3atfDb/6lS8wM6tFE/5vK+mVkp4fEXcCLwW+B4wA/wFsqkB9BnzoQ+l6BVkHRk9PurGCW291YJjVqsn+1/0a8Eyy/CrgExTmsxgguSGg7Z8NG9IFgQRf+lJ2dZ55ZrogiIBjjsmuTjOrjMkOTzVFxOiUK/8IXBERXUCXpJ7yllZ7IgpXGq9alXUlk9u61RPXmNnUTdbTaJI0GiyvB4rvwNMww45btxZO1UxzKmmWgbFqVfpegQPDzPbHZF/83wVukfQEhbOlfgUgaRGQ9Sx+ZbN+feEW0dVi+3bfdsLMqsOEoRERF0q6CZgP/CwiRic0ygEfLHdxWalEYKxdW7g9xvrNT3LalbezfWhk97rZLTO49sy/4ZiFh5a/EDOzKZj0HJaIuC0ifhQRxdO8PhgRd5e3tOy8613797oLLijMq5Dm8NDb3154jecDN7Na4hMfS7jmGujuLiy///3wpz+lC4LPfnbqp5KOzgc+sznH7JYZzGzOeT5wM6taDTOYPVXHHlsIgunUPzi0e85vYPey5wM3s1rh0KiQdT1bWN3VS3Mux47hESQxc0YTw/k8a1YsYXnHgoqERXFwOZzMbKocGhXQPzjE6q5edg7n2cno+EUwvKsw+L2qq5dli+aW/Uu8OLiKw8rMLC2PaVRA38AOmicY7GjO5egbKO/9H4uDa/vQCDuH86zq6qV/cKis72tm9cWhUQGlzpAqVomzpUoFVyXCyszqi0OjAsaeITUjB81NqujZUj6118ymg8c0yqx/cIj7tv6FQ591EP/33Nfw9DO79jl7qhID0qPBtWrMmIYHw81sKhwaZbSuZwsf//56hncVzt2dkYNL39Gx+0rvSn9h+9ReMztQPjxVJv2DQ6z6Qe/uwAAYycM//2B9poPPba0tHLPwUAeGme0Xh0aZ9A3soCmnfdqb5MFnM6tdDo0yaZ8zi135fS8p3xUefDaz2pVJaEj6nKTfSeqV9CNJhxatO1/SRkkbJL2xqP1YSfck674oad9/xleRttYWPve2JTQ37SmzSfC5tx3jQ0NmVrOy6mncALwsIpYADwLnA0g6ClgJHA2cDFwuqSl5zVeBs4DFyePkShc9VcsWzeWM1xzBjBzMmpFjRlNV55yZ2aQyCY2I+FlEjE4gcRvQniyfAlwXEUMRsQnYCBwnaT5wcET8JpnT41vAWype+BSs69nCqy+6iX+/5WFG8rBjJM/QSPgqbDOradUwpvFe4CfJ8gJgc9G6vqRtQbI8tr0kSWdJ6pbUvW3btmkud3KFM6fWMzSy75iGr8I2s1pWtus0JN0IPL/EqgsiYl2yzQXACPCd0ZeV2D4maC8pIq4ArgDo7Oyc5hucT+47t/+xZGCAr8I2s9pWttCIiDdMtF7S6cCbgdcXTSPbByws2qwd2Jq0t5dorzr9g0N85ecbS65rEr4K28xqWlZnT50MrAaWR8Rfi1ZdD6yU1CLpCAoD3ndExKPAdknHJ2dNvQtYV/HCUxjv+gyAppxYtmhuhSsyM5s+Wd1G5MtAC3BDcubsbRHxgYi4T9Ja4H4Kh63OiYhdyWvOBq4GZlEYA/nJPr+1Cox3fQZA84zCeIZ7GmZWqzIJjYhYNMG6C4ELS7R3Ay8rZ13TYfT6jI8V3XNq1K58eDzDzGpaNZw9VXeWdyzgtvNfz9knvojmJvHslqaK3QLdzKyctGcMuj51dnZGd3d3Zu/vObnNrBZJuisiOse2+9boZdbW2uKwMLO64cNTZmaWmkPDzMxSc2iYmVlqDg0zM0vNoTFN+geHWL/5Sd/B1szqms+emgbrerawuquX5lyO4XyeNSuWsLxj3JvwmpnVLPc0DlD/4BCru3rZOZxn+9AIO4fznjPDzOqWQ+MA9Q3soDm39270nBlmVq8cGgeofc4shvP5vdo8Z4aZ1SuHxgFqa21hzYolzGzOMbtlhu8xZWZ1zQPh02B5xwKWLZrre0yZWd1zaEwT32PKzBqBD0+ZmVlqDg0zM0vNoWFmZqk5NKbAtwoxs0bngfCUfKsQMzP3NFLxrULMzAocGin4ViFmZgUOjUn0Dw7xlx3DPLPLtwoxM/OYxgSKxzF25fM0N4mZM5p2j2n4Yj4zazQOjXEUj2PspNDLaJkBXzl1KUe/4BAHhpk1JB+eGkepcYyDmpo4ZNZBDgwza1gOjXH4ludWr3y9kR2ITEND0sclhaS5RW3nS9ooaYOkNxa1HyvpnmTdFyWpnLVt2jbI6xbP5aAm+ZbnVjfW9Wxh2cU3c9qVt7Ps4pu5vmdL1iVZjclsTEPSQuAk4I9FbUcBK4GjgRcAN0o6MiJ2AV8FzgJuA34MnAz8pBy1nXblbfx6Y//u50e94FlcdfpxDgyraaXG6VZ19bJs0Vz/bVtqWfY0LgNWAVHUdgpwXUQMRcQmYCNwnKT5wMER8ZuICOBbwFvKUVT3pv69AgOgZ/NTbNo2WI63M6sYX29k0yGT0JC0HNgSEevHrFoAbC563pe0LUiWx7aP9/vPktQtqXvbtm1Tqu2XDz0xpXazWuFxOpsOZQsNSTdKurfE4xTgAuBTpV5Woi0maC8pIq6IiM6I6Jw3b96U6j5h8dwptZvVCk9NbNOhbGMaEfGGUu2SXg4cAaxPxrLbgbslHUehB7GwaPN2YGvS3l6ifdp1HtHGaxe18auiQ1SvXdRG5xFt5Xg7s4ry1MR2oCo+EB4R9wDPHX0u6RGgMyKekHQ98L8kXUphIHwxcEdE7JK0XdLxwO3Au4AvlavGb595PN2b+vnlQ09wwuK5DgyrK56a2A5EVV0RHhH3SVoL3A+MAOckZ04BnA1cDcyicNZUWc6cGtV5hHsXZmZjZR4aEXH4mOcXAheW2K4beFmFyjIzsxJ8RbiZmaXm0DAzs9QcGmZmlppDw8zMUnNomJlZag4NMzNLzaFhZmapOTTMzCw1h4aZmaXm0DAzs9QcGmZmlppDw8zMUnNomJlZag4NMzNLzaFhZmapOTTMzCw1h4aZmaXm0DAzs9QcGmZmlppDw8zMUnNomJlZag4NMzNLzaFhZlZn+geHWL/5SfoHh6b9d8+Y9t9oZmaZWdezhdVdvTTncgzn86xZsYTlHQum7fe7p2FmVif6B4dY3dXLzuE824dG2DmcZ1VX77T2OBwaZmZ1om9gB825vb/Wm3M5+gZ2TNt7ZBYakj4oaYOk+yStKWo/X9LGZN0bi9qPlXRPsu6LkpRN5WZm1al9ziyG8/m92obzedrnzJq298gkNCT9LXAKsCQijgYuSdqPAlYCRwMnA5dLakpe9lXgLGBx8ji50nWbmVWzttYW1qxYwszmHLNbZjCzOceaFUtoa22ZtvfIaiD8bOCiiBgCiIjHk/ZTgOuS9k2SNgLHSXoEODgifgMg6VvAW4CfVLxyM7MqtrxjAcsWzaVvYAftc2ZNa2BAdoenjgReK+l2SbdIemXSvgDYXLRdX9K2IFke225mZmO0tbZwzMJDpz0woIw9DUk3As8vseqC5H3nAMcDrwTWSnoRUGqcIiZoH++9z6JwKIvDDjtsaoWbmdm4yhYaEfGG8dZJOhv4YUQEcIekPDCXQg9iYdGm7cDWpL29RPt4730FcAVAZ2fnuOFiZmZTk9Xhqf8N/B2ApCOBg4AngOuBlZJaJB1BYcD7joh4FNgu6fjkrKl3AeuyKd3MrHFlNRD+DeAbku4FngFOT3od90laC9wPjADnRMSu5DVnA1cDsygMgHsQ3MyswlT4rq5fnZ2d0d3dnXUZZmY1RdJdEdG5T3u9h4akbcAfJthkLoVDY+Z9Ucz7Yg/vi701yv54YUTMG9tY96ExGUndpdK0EXlf7OF9sYf3xd4afX/43lNmZpaaQ8PMzFJzaCTXcxjgfVHM+2IP74u9NfT+aPgxDTMzS889DTMzS82hYWZmqTVsaEg6OZnoaaOk87KupxIkPZJMZNUjqTtpe46kGyQ9lPycU7R9yQmxapWkb0h6PLkTwWjblD9/PUwINs6++IykLcnfR4+kNxWtq+d9sVDSzyU9kEwK9+GkvSH/NiYVEQ33AJqA3wMvonDfq/XAUVnXVYHP/Qgwd0zbGuC8ZPk84OJk+ahkv7QARyT7qynrz3CAn/8E4BXAvQfy+YE7gFdRuPvyT4B/yPqzTdO++Azw8RLb1vu+mA+8IlmeDTyYfOaG/NuY7NGoPY3jgI0R8XBEPANcR2ECqEZ0CnBNsnwNhcmtRtuvi4ihiNgEbKSw32pWRPwS+POY5il9fknzSSYEi8K3xLeKXlMzxtkX46n3ffFoRNydLG8HHqAwX09D/m1MplFDY7zJnupdAD+TdFcy5wjA86JwF2GSn89N2htlH03189f7hGDnSupNDl+NHo5pmH0h6XBgKXA7/tsoqVFDY0qTOtWRZRHxCuAfgHMknTDBto26j0ZNy4RgNearwIuBDuBR4PNJe0PsC0mtQBfwkYh4aqJNS7TV3f4YT6OGxniTPdW1iNia/Hwc+BGFw02PJd1qkp+j87U3yj6a6uef0oRgtSQiHouIXRGRB77OnsORdb8vJDVTCIzvRMQPk2b/bZTQqKFxJ7BY0hGSDgJWUpgAqm5Jerak2aPLwN8D91L43Kcnm53OnsmtSk6IVdmqK2JKnz/qeEKw0S/IxFsp/H1Ane+LpPargAci4tKiVf7bKCXrkfisHsCbKJwl8XvggqzrqcDnfRGFMz7WA/eNfmagDbgJeCj5+Zyi11yQ7J8N1MFZIMB3KRx2Gabwr8Iz9ufzA50UvlB/D3yZ5M4KtfQYZ198G7gH6KXwxTi/QfbFaygcRuoFepLHmxr1b2Oyh28jYmZmqTXq4SkzM9sPDg0zM0vNoWFmZqk5NMzMLDWHhpmZpTYj6wLM6oWk0VM0AZ4P7AK2Jc//GhGvzqQws2nkU27NykDSZ4DBiLgk61rMppMPT5lVgKTB5OeJkm6RtFbSg5IuknSqpDuSeRhenGw3T1KXpDuTx7JsP4FZgUPDrPKOAT4MvBx4J3BkRBwHXAl8MNnmC8BlEfFKYEWyzixzHtMwq7w7I7nltqTfAz9L2u8B/jZZfgNwVNHEbwdLmh2F+R7MMuPQMKu8oaLlfNHzPHv+n8wBr4qIHZUszGwyPjxlVp1+Bpw7+kRSR4a1mO3m0DCrTh8COpNZ9O4HPpB1QWbgU27NzGwK3NMwM7PUHBpmZpaaQ8PMzFJzaJiZWWoODTMzS82hYWZmqTk0zMwstf8PJmbXPTEDpI8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expectimax.plot(x = 'Time', y = 'Score', kind = 'scatter', title = 'Expectimax')\n",
    "plt.plot(np.array(expectimax.Time).reshape(-1, 1), exp_regr.predict(np.array(expectimax.Time).reshape(-1, 1)), color='blue', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Перевірка на валідність результату\n",
    "Результат валідний, якщо абсолютна помилка менше за стандартне відхилення"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "Predicted results of 8 games for expectimax\n",
      "Predicted results of 8 games for minimax\n"
     ]
    }
   ],
   "source": [
    "dev_min = np.std(y_min_train)\n",
    "dev_exp = np.std(y_exp_train)\n",
    "\n",
    "err_min = metrics.mean_absolute_error(y_pred=min_preds, y_true=y_min_test)\n",
    "err_exp = metrics.mean_absolute_error(y_pred=exp_preds, y_true=y_exp_test)\n",
    "\n",
    "print(dev_min > err_min)\n",
    "print(dev_exp > err_exp)\n",
    "print('Predicted results of ' + str(len(y_exp_test)) + ' games for expectimax')\n",
    "print('Predicted results of ' + str(len(y_exp_test)) + ' games for minimax')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Висновки\n",
    "У результаті я отимав 2 регресії. Обидві не перевищують критичні значення і робочі. Але регресія з експектімаксом виглядає дивно, через те, що агент експектімаксу знайшов оптимальну позицію і довго стояв на одному місці, і через це втрачав очки, а потім його знайшли привиди і він помер"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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