OverallConnect4Project(SourceCode).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ec1da8e",
   "metadata": {},
   "source": [
    "# CS 6045 Advanced Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99335db",
   "metadata": {},
   "source": [
    "# Optimization of connect4 game using Mini-Max Algorithm and Alpha-Beta Pruning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c422b3",
   "metadata": {},
   "source": [
    "## Imported Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eec8655d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pygame 2.1.2 (SDL 2.0.18, Python 3.8.8)\n",
      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# from IPython.display import set_matplotlib_formats\n",
    "# set_matplotlib_formats('retina', quality=100)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import pygame\n",
    "import sys\n",
    "import time\n",
    "import math\n",
    "from math import inf\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434c1fe2",
   "metadata": {},
   "source": [
    "## Code Implementation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "723bba2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select the AI ('mini-max' or 'alpha-beta')?alpha-beta\n",
      "Enter depth value between 1 and 8?3\n",
      "[[0. 0. 0. 0. 0. 0. 0.]\n",
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      " [1. 2. 1. 2. 1. 0. 1.]]\n",
      "[[0. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 2. 0. 0. 0.]\n",
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      " [1. 0. 2. 2. 2. 0. 1.]\n",
      " [1. 2. 1. 2. 1. 0. 1.]]\n",
      "[[2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 2. 0. 0. 0.]\n",
      " [1. 0. 1. 1. 2. 0. 0.]\n",
      " [1. 0. 2. 2. 2. 0. 1.]\n",
      " [1. 2. 1. 2. 1. 0. 1.]]\n",
      "[[2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 2. 0. 0. 0.]\n",
      " [1. 0. 1. 1. 2. 0. 1.]\n",
      " [1. 0. 2. 2. 2. 0. 1.]\n",
      " [1. 2. 1. 2. 1. 0. 1.]]\n",
      "[[2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 0. 0. 0. 0.]\n",
      " [2. 0. 0. 2. 0. 0. 0.]\n",
      " [1. 0. 1. 1. 2. 0. 1.]\n",
      " [1. 2. 2. 2. 2. 0. 1.]\n",
      " [1. 2. 1. 2. 1. 0. 1.]]\n",
      "\n",
      "Gathering 'Alpha-Beta' Data to analyse 'Compilation Time':\n",
      "\n",
      "Alpha-Beta time for depth value 3 is 6.822 milliseconds\n",
      "\n",
      "Gathering 'Alpha-Beta' Data to calculate 'Accuracy':\n",
      "\n",
      "                  Alphabeta Weight\n",
      "Alphabeta Column                  \n",
      "0                                0\n",
      "1                               43\n",
      "2                              144\n",
      "3                              174\n",
      "4                              140\n",
      "5                               40\n",
      "\n",
      "Alpha-Beta Algorithm resultant data as putting weights for each column at depth 3 : [0, 43, 144, 174, 140, 40]\n",
      "\n",
      "Wall time: 27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Input\n",
    "\n",
    "Select_AI = input(\"Select the AI ('mini-max' or 'alpha-beta')?\")\n",
    "\n",
    "if Select_AI == \"mini-max\":\n",
    "    DEPTH_VALUE = int(input(\"Enter depth value between 1 and 5?\"))\n",
    "if Select_AI == \"alpha-beta\":\n",
    "    DEPTH_VALUE = int(input(\"Enter depth value between 1 and 8?\"))\n",
    "    \n",
    "# Colors\n",
    "\n",
    "GREY, LBLUE, DBLUE, WHITE, TEXT = (132,132,132), (61,89,171), (0,0,128), (255,255,240), (3,3,3)\n",
    "\n",
    "# Grid Dimensions\n",
    "\n",
    "HORI, VERT = 6, 7\n",
    "\n",
    "# Utilizing Values\n",
    "\n",
    "MAN, ALGO, MAN_CHANCE, ALGO_CHANCE  = 0, 1, 1, 2\n",
    "\n",
    "# Defining weights\n",
    "\n",
    "WEIGHT_MEASURE = 4\n",
    "DUMMY = 0\n",
    "\n",
    "# Function to Prepare an empty board\n",
    "def game_grid():\n",
    "    grid = np.zeros((HORI,VERT))\n",
    "    return grid\n",
    "\n",
    "# Sectioning the board\n",
    "def drop_balls(grid, horizontal, vertical, ball):\n",
    "    grid[horizontal][vertical] = ball\n",
    "\n",
    "def true_loc(grid, vertical):\n",
    "    return grid[HORI-1][vertical] == DUMMY\n",
    "\n",
    "# Searching for vacant horizontal series\n",
    "def check_next_vacant_row(grid, vertical):\n",
    "    for ele in range(HORI):\n",
    "        if grid[ele][vertical] == DUMMY:\n",
    "            return ele\n",
    "\n",
    "# Reverse the indexing of the board \n",
    "def print_grid(grid):\n",
    "    print(np.flip(grid, DUMMY))\n",
    "\n",
    "# -- Function defines connect4 game concept \n",
    "# -- checking the strikes for winning\n",
    "def check_win_mov(grid, ball):\n",
    "    # Checking win strikes in a row\n",
    "    for v in range(VERT-3):\n",
    "        for h in range(HORI):\n",
    "            if grid[h][v] == ball and grid[h][v+1] == ball and grid[h][v+2] == ball and grid[h][v+3] == ball:\n",
    "                return True\n",
    "    # Checking win strikes in a column\n",
    "    for v in range(VERT):\n",
    "        for h in range(HORI-3):\n",
    "            if grid[h][v] == ball and grid[h+1][v] == ball and grid[h+2][v] == ball and grid[h+3][v] == ball:\n",
    "                return True\n",
    "    # Checking win strikes in +ve sloped diagonals\n",
    "    for v in range(VERT-3):\n",
    "        for h in range(HORI-3):\n",
    "            if grid[h][v] == ball and grid[h+1][v+1] == ball and grid[h+2][v+2] == ball and grid[h+3][v+3] == ball:\n",
    "                return True\n",
    "\n",
    "    # Checking win strikes in -ve sloped diagonals\n",
    "    for v in range(VERT-3):\n",
    "        for h in range(3, HORI):\n",
    "            if grid[h][v] == ball and grid[h-1][v+1] == ball and grid[h-2][v+2] == ball and grid[h-3][v+3] == ball:\n",
    "                return True\n",
    "            \n",
    "# Assigning the weights to make an action\n",
    "def declare_weights(weight, ball):\n",
    "    priority = DUMMY\n",
    "    opponent_chance = MAN_CHANCE\n",
    "    if ball == MAN_CHANCE:\n",
    "        opponent_chance = ALGO_CHANCE\n",
    "    if weight.count(ball) == 4:\n",
    "        priority += 100\n",
    "    elif weight.count(ball) == 3 and weight.count(DUMMY) == 1:\n",
    "        priority += 5\n",
    "    elif weight.count(ball) == 2 and weight.count(DUMMY) == 2:\n",
    "        priority += 2\n",
    "    if weight.count(opponent_chance) == 3 and weight.count(DUMMY) == 1:\n",
    "        priority -= 4\n",
    "    return priority\n",
    "\n",
    "# Where to focus first(Traing AI to focus)?\n",
    "def Preference_of_AI(grid, ball):\n",
    "    priority = DUMMY\n",
    "    # priority makes to choose center of the grid\n",
    "    grid_middle_series = [int(k) for k in list(grid[:, VERT//2])]\n",
    "    grid_middle_count = grid_middle_series.count(ball)\n",
    "    priority += grid_middle_count * 3\n",
    "    # prioritizing Horizontal\n",
    "    for h in range(HORI):\n",
    "        horizontal_series = [int(k) for k in list(grid[h,:])]\n",
    "        for v in range(VERT-3):\n",
    "            weight = horizontal_series[v:v+WEIGHT_MEASURE]\n",
    "            priority += declare_weights(weight, ball)\n",
    "    # prioritixing Vertical\n",
    "    for v in range(VERT):\n",
    "        vertical_series = [int(k) for k in list(grid[:,v])]\n",
    "        for h in range(HORI-3):\n",
    "            weight = vertical_series[h:h+WEIGHT_MEASURE]\n",
    "            priority += declare_weights(weight, ball)\n",
    "    # prioritizing +ve Diagonal\n",
    "    for h in range(HORI-3):\n",
    "        for v in range(VERT-3):\n",
    "            weight = [grid[h+k][v+k] for k in range(WEIGHT_MEASURE)]\n",
    "            priority += declare_weights(weight, ball)\n",
    "    # prioritizing -ve Diagonal\n",
    "    for h in range(HORI-3):\n",
    "        for v in range(VERT-3):\n",
    "            weight = [grid[h+3-k][v+k] for k in range(WEIGHT_MEASURE)]\n",
    "            priority += declare_weights(weight, ball)\n",
    "    return priority\n",
    "\n",
    "def node(grid):\n",
    "    return check_win_mov(grid, MAN_CHANCE) or check_win_mov(grid, ALGO_CHANCE) or len(get_certain_loc(grid)) == DUMMY\n",
    "\n",
    "# Implementation of Mini-Max Algorithm(Our Bench-Mark Algorithm)\n",
    "lis_mm, mm_column, mm_value = [], [], []\n",
    "#@profile\n",
    "def Mini_Max(grid, depth, maximizinggame):\n",
    "    start_time = time.time()*1000.0\n",
    "    certain_loc, term_head = get_certain_loc(grid), node(grid)\n",
    "    if depth == 0 or term_head:\n",
    "        if term_head:\n",
    "            if check_win_mov(grid, ALGO_CHANCE):\n",
    "                return (None, math.inf)\n",
    "            elif check_win_mov(grid, MAN_CHANCE):\n",
    "                return (None, -math.inf)\n",
    "            else: #Game finished, No more valid moves\n",
    "                return (None, DUMMY)\n",
    "        else: #Depth is zero\n",
    "            return (None, Preference_of_AI(grid, ALGO_CHANCE))\n",
    "    if maximizinggame:\n",
    "        value, column = -math.inf, random.choice(certain_loc)\n",
    "        for vertical in certain_loc:\n",
    "            horizontal, duplicate_grid = check_next_vacant_row(grid, vertical), grid.copy()\n",
    "            drop_balls(duplicate_grid, horizontal, vertical, ALGO_CHANCE)\n",
    "            new_priority = Mini_Max(duplicate_grid, depth-1, False)[1]\n",
    "            if new_priority > value:\n",
    "                value, column = new_priority, vertical\n",
    "        mm_column.append(column)\n",
    "        mm_value.append(value)\n",
    "        return column, value\n",
    "    else: #minimizinggame\n",
    "        value = math.inf\n",
    "        column = random.choice(certain_loc)\n",
    "        for vertical in certain_loc:\n",
    "            horizontal = check_next_vacant_row(grid, vertical)\n",
    "            duplicate_grid = grid.copy()\n",
    "            drop_balls(duplicate_grid, horizontal, vertical, MAN_CHANCE)\n",
    "            new_priority = Mini_Max(duplicate_grid, depth-1, True)[1]\n",
    "            if new_priority < value:\n",
    "                value = new_priority\n",
    "                column = vertical\n",
    "        end_time = time.time()*1000.0\n",
    "        lis_mm.append(end_time - start_time)\n",
    "        mm_column.append(column)\n",
    "        mm_value.append(value)\n",
    "        return column, value\n",
    "\n",
    "# Implementation of Alpha_Beta Algorithm(Our Optimal Algorithm)\n",
    "lis_ab, ab_column, ab_value = [], [], []\n",
    "#@profile\n",
    "def alpha_beta(grid, depth, alpha, beta, maximizingPlayer):\n",
    "    start_time = time.time()*1000.0\n",
    "    valid_locations = get_certain_loc(grid)\n",
    "    is_terminal = node(grid)\n",
    "    if depth == 0 or is_terminal:\n",
    "        if is_terminal:\n",
    "            if check_win_mov(grid, ALGO_CHANCE):\n",
    "                return (None, math.inf)\n",
    "            elif check_win_mov(grid, MAN_CHANCE):\n",
    "                return (None, -math.inf)\n",
    "            else: #Game finished, No more valid moves\n",
    "                return (None, DUMMY)\n",
    "        else: # Depth is zero\n",
    "            return (None, Preference_of_AI(grid, ALGO_CHANCE))\n",
    "    if maximizingPlayer:\n",
    "        value = -math.inf\n",
    "        vert_col = random.choice(valid_locations)\n",
    "        for vertical in valid_locations:\n",
    "            horizontal = check_next_vacant_row(grid, vertical)\n",
    "            b_copy = grid.copy()\n",
    "            drop_balls(b_copy, horizontal, vertical, ALGO_CHANCE)\n",
    "            new_priority = alpha_beta(b_copy, depth-1, alpha, beta, False)[1]\n",
    "            if new_priority > value:\n",
    "                value = new_priority\n",
    "                vert_col = vertical\n",
    "            alpha = max(alpha, value)\n",
    "            if alpha >= beta:\n",
    "                break\n",
    "        ab_column.append(vert_col)\n",
    "        ab_value.append(vert_col)\n",
    "        return vert_col, value\n",
    "    else: # Minimizing component\n",
    "        value = math.inf\n",
    "        vert_col = random.choice(valid_locations)\n",
    "        for vertical in valid_locations:\n",
    "            horizontal = check_next_vacant_row(grid, vertical)\n",
    "            b_copy = grid.copy()\n",
    "            drop_balls(b_copy, horizontal, vertical, MAN_CHANCE)\n",
    "            new_priority = alpha_beta(b_copy, depth-1, alpha, beta, True)[1]\n",
    "            if new_priority < value:\n",
    "                value = new_priority\n",
    "                vert_col = vertical\n",
    "            beta = min(beta, value)\n",
    "            if alpha >= beta:\n",
    "                break\n",
    "        end_time = time.time()*1000.0\n",
    "        lis_ab.append(end_time - start_time)\n",
    "        return vert_col, value\n",
    "\n",
    "def get_certain_loc(grid):\n",
    "    certain_loc = [(vertical) for vertical in range(VERT) if true_loc(grid, vertical)]\n",
    "    return certain_loc\n",
    "\n",
    "# Function to predict moves(priority selection)\n",
    "def optimal_prediction_mov(grid, ball):\n",
    "    efficient_priority = -12022\n",
    "    efficient_series = random.choice(get_certain_loc(grid))\n",
    "    for vertical in get_certain_loc(grid):\n",
    "        horizontal = check_next_vacant_row(grid, vertical)\n",
    "        temp_grid = grid.copy()\n",
    "        drop_balls(temp_grid, horizontal, vertical, ball)\n",
    "        priority = Preference_of_AI(temp_grid, ball)\n",
    "        if priority > efficient_priority:\n",
    "            efficient_priority = priority\n",
    "            efficient_series = vertical\n",
    "    return efficient_series\n",
    "\n",
    "# Function to creat the grid according to my screen size.\n",
    "def draw_grid(grid):\n",
    "    for v in range(VERT):\n",
    "        for h in range(HORI):\n",
    "            pygame.draw.rect(screen, GREY, (v*weigh, h*weigh+weigh, weigh, weigh))\n",
    "            pygame.draw.circle(screen, LBLUE, (int(v*weigh+weigh/2), int(h*weigh+weigh+weigh/2)), RADIUS)\n",
    "    for v in range(VERT):\n",
    "        for h in range(HORI):      \n",
    "            if grid[h][v] == MAN_CHANCE:\n",
    "                pygame.draw.circle(screen, DBLUE, (int(v*weigh+weigh/2), Length-int(h*weigh+weigh/2)), RADIUS)\n",
    "            elif grid[h][v] == ALGO_CHANCE: \n",
    "                pygame.draw.circle(screen, WHITE, (int(v*weigh+weigh/2), Length-int(h*weigh+weigh/2)), RADIUS)\n",
    "    pygame.display.update()\n",
    "\n",
    "# Variables Declaration\n",
    "grid = game_grid()\n",
    "print_grid(grid)\n",
    "end_game = False\n",
    "\n",
    "pygame.init()\n",
    "\n",
    "weigh = 100\n",
    "\n",
    "breadth = VERT * weigh\n",
    "Length = (HORI+1) * weigh\n",
    "\n",
    "size = (breadth, Length)\n",
    "\n",
    "RADIUS = int(weigh/2 - 5)\n",
    "\n",
    "screen = pygame.display.set_mode(size)\n",
    "draw_grid(grid)\n",
    "pygame.display.update()\n",
    "\n",
    "myfont = pygame.font.SysFont(\"monospace\", 75)\n",
    "\n",
    "chance = random.randint(MAN, ALGO)\n",
    "\n",
    "# Logic Intialization\n",
    "while not end_game:\n",
    "    \n",
    "    for event in pygame.event.get():\n",
    "        if event.type == pygame.QUIT:\n",
    "            sys.exit()\n",
    "            \n",
    "        if event.type == pygame.MOUSEMOTION:\n",
    "            pygame.draw.rect(screen, LBLUE, (0,0, breadth, weigh))\n",
    "            position = event.pos[0]\n",
    "            \n",
    "            if chance == MAN:\n",
    "                pygame.draw.circle(screen, DBLUE, (position, int(weigh/2)), RADIUS)\n",
    "                \n",
    "        pygame.display.update()\n",
    "        \n",
    "        if event.type == pygame.MOUSEBUTTONDOWN:\n",
    "            pygame.draw.rect(screen, LBLUE, (0,0, breadth, weigh))\n",
    "            if chance == MAN:\n",
    "                position = event.pos[0]\n",
    "                vertical = int(math.floor(position/weigh))\n",
    "                if true_loc(grid, vertical):\n",
    "                    horizontal = check_next_vacant_row(grid, vertical)\n",
    "                    drop_balls(grid, horizontal, vertical, MAN_CHANCE)\n",
    "\n",
    "                    if check_win_mov(grid, MAN_CHANCE):\n",
    "                        label = myfont.render(\"Blue wins!!\", 1, DBLUE)\n",
    "                        screen.blit(label, (20,10))\n",
    "                        end_game = True\n",
    "                    chance += 1\n",
    "                    chance = chance % 2\n",
    "                    print_grid(grid)\n",
    "                    draw_grid(grid)\n",
    "                    \n",
    "        if Select_AI == \"mini-max\":\n",
    "            if chance == ALGO and not end_game:\n",
    "                vertical, Mini_Max_priority = Mini_Max(grid, DEPTH_VALUE, True)\n",
    "                if true_loc(grid, vertical):\n",
    "                    pygame.time.wait(143)\n",
    "                    horizontal = check_next_vacant_row(grid, vertical)\n",
    "                    drop_balls(grid, horizontal, vertical, ALGO_CHANCE)\n",
    "                    if check_win_mov(grid, ALGO_CHANCE):\n",
    "                        label = myfont.render(\"White Wins!!\", 1, WHITE)\n",
    "                        screen.blit(label, (20, 10))\n",
    "                        end_game = True\n",
    "                    print_grid(grid)\n",
    "                    draw_grid(grid)\n",
    "                    chance += MAN_CHANCE\n",
    "                    chance = chance % ALGO_CHANCE\n",
    "            if end_game:\n",
    "                pygame.time.wait(2022)\n",
    "\n",
    "        if Select_AI == \"alpha-beta\":\n",
    "            if chance == ALGO and not end_game:                \n",
    "                vertical, alpha_beta_priority = alpha_beta(grid, DEPTH_VALUE, -math.inf, math.inf, True)\n",
    "                if true_loc(grid, vertical):\n",
    "                    #pygame.time.wait(500)\n",
    "                    horizontal = check_next_vacant_row(grid, vertical)\n",
    "                    drop_balls(grid, horizontal, vertical, ALGO_CHANCE)\n",
    "                    if check_win_mov(grid, ALGO_CHANCE):\n",
    "                        label = myfont.render(\"White wins!!\", 1, WHITE)\n",
    "                        screen.blit(label, (40,10))\n",
    "                        end_game = True\n",
    "                    print_grid(grid)\n",
    "                    draw_grid(grid)\n",
    "                    chance += 1\n",
    "                    chance = chance % 2\n",
    "            if end_game:\n",
    "                pygame.time.wait(2022)\n",
    "\n",
    "if Select_AI == \"mini-max\":\n",
    "    try:\n",
    "        t1 = sum(lis_mm)/len(lis_mm)\n",
    "    except ZeroDivisionError:\n",
    "        t1 = 0\n",
    "    print()\n",
    "    print(\"Gathering 'Mini-Max' Data to analyse 'Compilation Time':\")\n",
    "    print()\n",
    "    print(\"Mini-Max time for depth value\", DEPTH_VALUE, \"is\", round(t1, 3), \"milliseconds\")\n",
    "    print()\n",
    "    print(\"Gathering 'Mini-Max' Data to calculate 'Accuracy':\")\n",
    "    print()\n",
    "    minimax_return = pd.DataFrame({\"Minimax Column\":mm_column, \"Minimax Weight\":mm_value})\n",
    "    minimax_return[minimax_return == -inf], minimax_return[minimax_return == inf] = 0, 100\n",
    "    acc_data_mm = minimax_return.groupby(\"Minimax Column\").sum()\n",
    "    print(acc_data_mm)\n",
    "    print()\n",
    "    print(\"Mini-Max Algorithm resultant data as putting weights for each column at depth\", DEPTH_VALUE, \":\", \n",
    "          acc_data_mm['Minimax Weight'].tolist())\n",
    "    print()\n",
    "    \n",
    "if Select_AI == \"alpha-beta\":\n",
    "    try:\n",
    "        t2 = sum(lis_ab)/len(lis_ab)\n",
    "    except ZeroDivisionError:\n",
    "        t2 = 0\n",
    "    print()\n",
    "    print(\"Gathering 'Alpha-Beta' Data to analyse 'Compilation Time':\")\n",
    "    print()\n",
    "    print(\"Alpha-Beta time for depth value\", DEPTH_VALUE, \"is\", round(t2, 3), \"milliseconds\")\n",
    "    print()\n",
    "    print(\"Gathering 'Alpha-Beta' Data to calculate 'Accuracy':\")\n",
    "    print()\n",
    "    alphabeta_return = pd.DataFrame({\"Alphabeta Column\":ab_column, \"Alphabeta Weight\":ab_value})\n",
    "    alphabeta_return[alphabeta_return == -inf], alphabeta_return[alphabeta_return == inf] = 0, 100\n",
    "    acc_data_ab = alphabeta_return.groupby(\"Alphabeta Column\").sum()\n",
    "    print(acc_data_ab)\n",
    "    print()\n",
    "    print(\"Alpha-Beta Algorithm resultant data as putting weights for each column at depth\", DEPTH_VALUE, \":\", \n",
    "          acc_data_ab['Alphabeta Weight'].tolist())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8282f828",
   "metadata": {},
   "source": [
    "# Time Graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d6ed12",
   "metadata": {},
   "source": [
    "## Mini-Max & Alpha-Beta Time Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17ddb919",
   "metadata": {},
   "outputs": [],
   "source": [
    "minimax_time_dataset = pd.read_csv(\"Mini-Max Time Data.csv\")\n",
    "alphabeta_time_dataset = pd.read_csv(\"Alpha-Beta Time Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a61023da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Mini-Max Input</th>\n",
       "      <th>Mini-Max Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2.717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>2.893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>2.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>2.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>2.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>2.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>3.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>2.651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>2.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>3.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>15.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>18.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>16.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>17.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>16.723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>18.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>19.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>18.132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>17.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>17.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>5.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>5.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>4.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>4.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>5.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>4.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>5.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>5.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>5.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5</td>\n",
       "      <td>30.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5</td>\n",
       "      <td>32.403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5</td>\n",
       "      <td>35.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>31.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>34.134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>30.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>31.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>36.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>31.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>30.299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mini-Max Input   Mini-Max Time\n",
       "0                1           0.000\n",
       "1                1           0.000\n",
       "2                1           0.000\n",
       "3                1           0.000\n",
       "4                1           0.000\n",
       "5                1           0.000\n",
       "6                1           0.000\n",
       "7                1           0.000\n",
       "8                1           0.000\n",
       "9                1           0.000\n",
       "10               2           2.717\n",
       "11               2           2.893\n",
       "12               2           2.864\n",
       "13               2           2.930\n",
       "14               2           2.779\n",
       "15               2           2.791\n",
       "16               2           3.742\n",
       "17               2           2.651\n",
       "18               2           2.467\n",
       "19               2           3.098\n",
       "20               3          15.440\n",
       "21               3          18.582\n",
       "22               3          16.476\n",
       "23               3          17.920\n",
       "24               3          16.723\n",
       "25               3          18.017\n",
       "26               3          19.323\n",
       "27               3          18.132\n",
       "28               3          17.401\n",
       "29               3          17.342\n",
       "30               4           5.178\n",
       "31               4           5.321\n",
       "32               4           5.000\n",
       "33               4           4.986\n",
       "34               4           4.944\n",
       "35               4           5.051\n",
       "36               4           4.658\n",
       "37               4           5.107\n",
       "38               4           5.160\n",
       "39               4           5.429\n",
       "40               5          30.143\n",
       "41               5          32.403\n",
       "42               5          35.632\n",
       "43               5          31.758\n",
       "44               5          34.134\n",
       "45               5          30.017\n",
       "46               5          31.258\n",
       "47               5          36.203\n",
       "48               5          31.256\n",
       "49               5          30.299"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimax_time_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b58ffe52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alpha-Beta Input</th>\n",
       "      <th>Alpha-Beta Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>7</td>\n",
       "      <td>18.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>7</td>\n",
       "      <td>16.884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>7</td>\n",
       "      <td>15.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>7</td>\n",
       "      <td>14.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>8</td>\n",
       "      <td>7.618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alpha-Beta Input   Alpha-Beta Time\n",
       "0                  1             0.000\n",
       "1                  1             0.000\n",
       "2                  1             0.000\n",
       "3                  1             0.000\n",
       "4                  1             0.000\n",
       "..               ...               ...\n",
       "65                 7            18.339\n",
       "66                 7            16.884\n",
       "67                 7            15.482\n",
       "68                 7            14.950\n",
       "69                 8             7.618\n",
       "\n",
       "[70 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphabeta_time_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f86eab",
   "metadata": {},
   "source": [
    "## Mini-Max Time Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b0549450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "res_minimax = pd.DataFrame(minimax_time_dataset.groupby(\"Mini-Max Input\").mean())\n",
    "res_minimax.reset_index(inplace = True)\n",
    "res_minimax.columns = ('Inp','Time')\n",
    "plt.rcdefaults()\n",
    "plt.plot(res_minimax['Inp'], res_minimax['Time'], color = 'Olive')\n",
    "plt.xlabel(\"Input Depth Value\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Time(milliseconds)\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.title('Mini-Max Time Graph', color = 'Black', pad = 20, weight = 'bold')\n",
    "ax.yaxis.grid(True)\n",
    "ax.xaxis.grid(True)\n",
    "plt.scatter(res_minimax['Inp'], res_minimax['Time'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc44a0a3",
   "metadata": {},
   "source": [
    "## Alpha-Beta Time Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "745bc539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "res_alphabeta = pd.DataFrame(alphabeta_time_dataset.groupby(\"Alpha-Beta Input\").mean())\n",
    "res_alphabeta.reset_index(inplace = True)\n",
    "res_alphabeta.columns = ('Inp','Time')\n",
    "plt.rcdefaults()\n",
    "plt.plot(res_alphabeta['Inp'], res_alphabeta['Time'], color = '#8c000F')\n",
    "plt.xlabel(\"Input Depth Value\", color = 'Blue', labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Time(milliseconds)\", color = 'Blue', labelpad = 16, weight = 'bold')\n",
    "plt.title('Alpha-Beta Time Graph', color = 'Black', pad = 20, weight = 'bold')\n",
    "ax.yaxis.grid(True)\n",
    "ax.xaxis.grid(True)\n",
    "plt.scatter(res_alphabeta['Inp'], res_alphabeta['Time'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3734beb3",
   "metadata": {},
   "source": [
    "## Mini-Max & Alpha-Beta Time Graph Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d075a4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "plt.plot(res_minimax['Inp'], res_minimax['Time'], label = \"Mini-Max\", color = 'Olive')\n",
    "# plt.scatter(res_minimax['Inp'], res_minimax['Time'])\n",
    "plt.plot(res_alphabeta['Inp'], res_alphabeta['Time'], label = \"Alpha-Beta\", color = '#8c000F')\n",
    "# plt.scatter(res_alphabeta['Inp'], res_alphabeta['Time'])\n",
    "plt.xlabel(\"Input Depth Value\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Time(milliseconds)\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.title('Mini-Max & Alpha-Beta Time Graph Comparison', color = 'Black', pad = 20, weight = 'bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf24e3a3",
   "metadata": {},
   "source": [
    "# Space Allocation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307f63c8",
   "metadata": {},
   "source": [
    "# Mini-Max & Alpha-Beta Space Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd7f097a",
   "metadata": {},
   "outputs": [],
   "source": [
    "minimax_space_dataset = pd.read_csv(\"Mini-Max Space Data.csv\")\n",
    "alphabeta_space_dataset = pd.read_csv(\"Alpha-Beta Space Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9fbc1aa",
   "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>Mini-Max Input Depth</th>\n",
       "      <th>Space Required</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>99.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>99.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>99.371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>99.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>99.263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>99.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>99.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>99.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>99.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>99.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>99.574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>99.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>99.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>99.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>99.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>99.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>99.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>99.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>99.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>99.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>99.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>99.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>99.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>99.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>99.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>99.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>99.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>99.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>99.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>99.441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>99.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>99.371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>99.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>99.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>99.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>99.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>99.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>99.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>99.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>99.458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5</td>\n",
       "      <td>99.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5</td>\n",
       "      <td>99.863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5</td>\n",
       "      <td>99.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>99.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>99.868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>99.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>99.870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>99.927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>99.807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mini-Max Input Depth   Space Required\n",
       "0                      1           99.387\n",
       "1                      1           99.332\n",
       "2                      1           99.371\n",
       "3                      1           99.387\n",
       "4                      1           99.263\n",
       "5                      1           99.355\n",
       "6                      1           99.327\n",
       "7                      1           99.258\n",
       "8                      1           99.236\n",
       "9                      1           99.312\n",
       "10                     2           99.574\n",
       "11                     2           99.625\n",
       "12                     2           99.453\n",
       "13                     2           99.357\n",
       "14                     2           99.256\n",
       "15                     2           99.298\n",
       "16                     2           99.420\n",
       "17                     2           99.168\n",
       "18                     2           99.250\n",
       "19                     2           99.762\n",
       "20                     3           99.383\n",
       "21                     3           99.418\n",
       "22                     3           99.326\n",
       "23                     3           99.256\n",
       "24                     3           99.362\n",
       "25                     3           99.418\n",
       "26                     3           99.326\n",
       "27                     3           99.297\n",
       "28                     3           99.363\n",
       "29                     3           99.441\n",
       "30                     4           99.184\n",
       "31                     4           99.371\n",
       "32                     4           99.344\n",
       "33                     4           99.446\n",
       "34                     4           99.568\n",
       "35                     4           99.578\n",
       "36                     4           99.625\n",
       "37                     4           99.758\n",
       "38                     4           99.624\n",
       "39                     4           99.458\n",
       "40                     5           99.832\n",
       "41                     5           99.863\n",
       "42                     5           99.726\n",
       "43                     5           99.945\n",
       "44                     5           99.868\n",
       "45                     5           99.898\n",
       "46                     5           99.870\n",
       "47                     5           99.927\n",
       "48                     5           99.807"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimax_space_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e43d4d3b",
   "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>Alpha-Beta Input Depth</th>\n",
       "      <th>Space Allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>99.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>99.523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>99.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>99.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>99.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>8</td>\n",
       "      <td>99.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>8</td>\n",
       "      <td>101.241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>8</td>\n",
       "      <td>101.471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8</td>\n",
       "      <td>101.671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>8</td>\n",
       "      <td>101.333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alpha-Beta Input Depth   Space Allocation\n",
       "0                        1             99.578\n",
       "1                        1             99.523\n",
       "2                        1             99.545\n",
       "3                        1             99.550\n",
       "4                        1             99.533\n",
       "..                     ...                ...\n",
       "75                       8             99.999\n",
       "76                       8            101.241\n",
       "77                       8            101.471\n",
       "78                       8            101.671\n",
       "79                       8            101.333\n",
       "\n",
       "[80 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphabeta_space_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204faa72",
   "metadata": {},
   "source": [
    "# Mini-Max Space Allocation Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "280a7fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "res_minimax_space = pd.DataFrame(minimax_space_dataset.groupby(\"Mini-Max Input Depth\").mean())\n",
    "res_minimax_space.reset_index(inplace = True)\n",
    "res_minimax_space.columns = ('Inp','Space')\n",
    "plt.rcdefaults()\n",
    "plt.plot(res_minimax_space['Inp'], res_minimax_space['Space'], color = 'Olive')\n",
    "plt.xlabel(\"Input Depth Value\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Space(Mib)\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.title('Mini-Max Space Allocation Graph', color = 'Black', pad = 20, weight = 'bold')\n",
    "ax.yaxis.grid(True)\n",
    "ax.xaxis.grid(True)\n",
    "plt.scatter(res_minimax_space['Inp'], res_minimax_space['Space'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e84d33",
   "metadata": {},
   "source": [
    "# Alpha-Beta Space Allocation Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90d1b65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "res_alphabeta_space = pd.DataFrame(alphabeta_space_dataset.groupby(\"Alpha-Beta Input Depth\").mean())\n",
    "res_alphabeta_space.reset_index(inplace = True)\n",
    "res_alphabeta_space.columns = ('Inp','Space')\n",
    "plt.rcdefaults()\n",
    "plt.plot(res_alphabeta_space['Inp'], res_alphabeta_space['Space'], color = '#8c000F')\n",
    "plt.xlabel(\"Input Depth Value\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Space(Mib)\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.title('Alpha-Beta Space Allocation Graph', color = 'Black', pad = 20, weight = 'bold')\n",
    "ax.yaxis.grid(True)\n",
    "ax.xaxis.grid(True)\n",
    "plt.scatter(res_alphabeta_space['Inp'], res_alphabeta_space['Space'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ce96a6",
   "metadata": {},
   "source": [
    "# Mini-Max & Alpha-Beta Space Allocation Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e48c185d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "plt.plot(res_minimax_space['Inp'], res_minimax_space['Space'], label = \"Mini-Max\", color = 'Olive')\n",
    "# plt.scatter(res_minimax['Inp'], res_minimax['Time'])\n",
    "plt.plot(res_alphabeta_space['Inp'], res_alphabeta_space['Space'], label = \"Alpha-Beta\", color = '#8c000F')\n",
    "# plt.scatter(res_alphabeta['Inp'], res_alphabeta['Time'])\n",
    "plt.xlabel(\"Input Depth Value\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Space(Mib)\", color = '#030764', labelpad = 16, weight = 'bold')\n",
    "plt.title('Mini-Max & Alpha-Beta Space Allocation Graph Comparison', color = 'Black', pad = 20, weight = 'bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81a6110",
   "metadata": {},
   "source": [
    "# Acuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39b6982",
   "metadata": {},
   "source": [
    "## Mini-Max & Alpha-Beta Accuracy Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "357f2481",
   "metadata": {},
   "outputs": [],
   "source": [
    "minimax_acc_dataset = pd.read_csv(\"Mini-Max Accuracy Data.csv\")\n",
    "alphabeta_acc_dataset = pd.read_csv(\"Alpha-Beta Accuracy Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20559613",
   "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>Mini-Max Input</th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>430.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>130.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>250.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>184.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>204.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>250.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>213.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1187.0</td>\n",
       "      <td>3291.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>4418.0</td>\n",
       "      <td>3406.0</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>2245.0</td>\n",
       "      <td>852.0</td>\n",
       "      <td>4859.0</td>\n",
       "      <td>5262.0</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>4002.0</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>3339.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>4832.0</td>\n",
       "      <td>3987.0</td>\n",
       "      <td>1158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>4137.0</td>\n",
       "      <td>4269.0</td>\n",
       "      <td>1137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>3339.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>4832.0</td>\n",
       "      <td>5987.0</td>\n",
       "      <td>1158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1303.0</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>4137.0</td>\n",
       "      <td>5269.0</td>\n",
       "      <td>1137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>4620.0</td>\n",
       "      <td>5002.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1187.0</td>\n",
       "      <td>4291.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>4418.0</td>\n",
       "      <td>5340.0</td>\n",
       "      <td>1700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>4245.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>4859.0</td>\n",
       "      <td>5262.0</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>845.0</td>\n",
       "      <td>15493.0</td>\n",
       "      <td>15137.0</td>\n",
       "      <td>3741.0</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>8810.0</td>\n",
       "      <td>425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4</td>\n",
       "      <td>927.0</td>\n",
       "      <td>79560.0</td>\n",
       "      <td>3980.0</td>\n",
       "      <td>15705.0</td>\n",
       "      <td>5198.0</td>\n",
       "      <td>5497.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4</td>\n",
       "      <td>918.0</td>\n",
       "      <td>75080.0</td>\n",
       "      <td>3443.0</td>\n",
       "      <td>18686.0</td>\n",
       "      <td>3818.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4</td>\n",
       "      <td>839.0</td>\n",
       "      <td>10650.0</td>\n",
       "      <td>4328.0</td>\n",
       "      <td>12425.0</td>\n",
       "      <td>3202.0</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>1526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>688.0</td>\n",
       "      <td>13780.0</td>\n",
       "      <td>4856.0</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>2348.0</td>\n",
       "      <td>7894.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4</td>\n",
       "      <td>642.0</td>\n",
       "      <td>15517.0</td>\n",
       "      <td>3041.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>5574.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>4</td>\n",
       "      <td>845.0</td>\n",
       "      <td>15493.0</td>\n",
       "      <td>5137.0</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>4250.0</td>\n",
       "      <td>8810.0</td>\n",
       "      <td>4258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>839.0</td>\n",
       "      <td>10655.0</td>\n",
       "      <td>5328.0</td>\n",
       "      <td>12425.0</td>\n",
       "      <td>3202.0</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>1526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4</td>\n",
       "      <td>927.0</td>\n",
       "      <td>17956.0</td>\n",
       "      <td>4980.0</td>\n",
       "      <td>15705.0</td>\n",
       "      <td>4198.0</td>\n",
       "      <td>5497.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>839.0</td>\n",
       "      <td>11065.0</td>\n",
       "      <td>5328.0</td>\n",
       "      <td>12425.0</td>\n",
       "      <td>3202.0</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>1526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5</td>\n",
       "      <td>798.0</td>\n",
       "      <td>7246.0</td>\n",
       "      <td>5010.0</td>\n",
       "      <td>5532.0</td>\n",
       "      <td>5968.0</td>\n",
       "      <td>12859.0</td>\n",
       "      <td>356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>5</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>2818.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>4325.0</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>17021.0</td>\n",
       "      <td>507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>7113.0</td>\n",
       "      <td>3321.0</td>\n",
       "      <td>5431.0</td>\n",
       "      <td>7162.0</td>\n",
       "      <td>11765.0</td>\n",
       "      <td>778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>1718.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>5362.0</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>14996.0</td>\n",
       "      <td>449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>7003.0</td>\n",
       "      <td>3461.0</td>\n",
       "      <td>5444.0</td>\n",
       "      <td>7764.0</td>\n",
       "      <td>17578.0</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
       "      <td>654.0</td>\n",
       "      <td>7299.0</td>\n",
       "      <td>3624.0</td>\n",
       "      <td>5703.0</td>\n",
       "      <td>8677.0</td>\n",
       "      <td>12221.0</td>\n",
       "      <td>421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5</td>\n",
       "      <td>2640.0</td>\n",
       "      <td>7626.0</td>\n",
       "      <td>4796.0</td>\n",
       "      <td>5745.0</td>\n",
       "      <td>12775.0</td>\n",
       "      <td>13352.0</td>\n",
       "      <td>778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>21246.0</td>\n",
       "      <td>6010.0</td>\n",
       "      <td>6532.0</td>\n",
       "      <td>1696.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5</td>\n",
       "      <td>395.0</td>\n",
       "      <td>7003.0</td>\n",
       "      <td>3461.0</td>\n",
       "      <td>5444.0</td>\n",
       "      <td>7764.0</td>\n",
       "      <td>7578.0</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5</td>\n",
       "      <td>718.0</td>\n",
       "      <td>7004.0</td>\n",
       "      <td>10475.0</td>\n",
       "      <td>7362.0</td>\n",
       "      <td>2551.0</td>\n",
       "      <td>14996.0</td>\n",
       "      <td>449.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mini-Max Input    Col1     Col2     Col3     Col4     Col5     Col6  \\\n",
       "0                1    14.0    525.0     33.0    179.0    195.0    270.0   \n",
       "1                1    13.0     51.0    220.0     76.0    141.0    392.0   \n",
       "2                1    12.0    100.0     32.0      9.0    120.0    100.0   \n",
       "3                1    16.0    100.0     30.0     17.0    131.0    210.0   \n",
       "4                1    18.0     67.0     52.0     44.0     41.0    200.0   \n",
       "5                1    19.0      0.0    152.0     47.0    173.0    167.0   \n",
       "6                1    10.0      0.0     67.0     47.0    139.0    140.0   \n",
       "7                1    14.0    525.0     33.0    179.0    195.0     27.0   \n",
       "8                1    10.0      0.0     67.0     47.0    139.0    140.0   \n",
       "9                1    20.0    168.0    172.0    170.0    136.0     57.0   \n",
       "10               2   430.0    525.0    303.0     79.0     95.0     27.0   \n",
       "11               2   130.0    251.0    220.0     76.0     41.0    392.0   \n",
       "12               2   230.0    218.0    175.0     55.0     34.0     10.0   \n",
       "13               2   250.0    516.0    188.0     62.0    113.0    155.0   \n",
       "14               2   184.0    483.0    142.0     28.0    138.0     55.0   \n",
       "15               2   204.0    353.0    159.0     83.0    149.0     37.0   \n",
       "16               2   140.0    253.0    159.0     83.0    149.0     37.0   \n",
       "17               2   250.0    516.0    280.0     62.0    113.0    155.0   \n",
       "18               2   213.0    351.0    220.0     76.0     41.0    392.0   \n",
       "19               2   230.0    108.0    175.0     55.0     34.0     10.0   \n",
       "20               3   117.0   1187.0   3291.0   1734.0   4418.0   3406.0   \n",
       "21               3   162.0   1310.0   2245.0    852.0   4859.0   5262.0   \n",
       "22               3   195.0   1516.0   3800.0   1430.0   4462.0   4002.0   \n",
       "23               3   120.0   1049.0   3339.0   1262.0   4832.0   3987.0   \n",
       "24               3   176.0   1330.0   3107.0   1136.0   4137.0   4269.0   \n",
       "25               3   120.0   1049.0   3339.0   1262.0   4832.0   5987.0   \n",
       "26               3   176.0   1303.0   3107.0   1316.0   4137.0   5269.0   \n",
       "27               3   195.0   1006.0   3800.0   1430.0   4620.0   5002.0   \n",
       "28               3   117.0   1187.0   4291.0   1734.0   4418.0   5340.0   \n",
       "29               3   162.0   1010.0   4245.0   1852.0   4859.0   5262.0   \n",
       "30               4   845.0  15493.0  15137.0   3741.0   4250.0   8810.0   \n",
       "31               4   927.0  79560.0   3980.0  15705.0   5198.0   5497.0   \n",
       "32               4   918.0  75080.0   3443.0  18686.0   3818.0   5650.0   \n",
       "33               4   839.0  10650.0   4328.0  12425.0   3202.0   2776.0   \n",
       "34               4   688.0  13780.0   4856.0   1656.0   2348.0   7894.0   \n",
       "35               4   642.0  15517.0   3041.0   1185.0   5574.0   1986.0   \n",
       "36               4   845.0  15493.0   5137.0   1741.0   4250.0   8810.0   \n",
       "37               4   839.0  10655.0   5328.0  12425.0   3202.0   2776.0   \n",
       "38               4   927.0  17956.0   4980.0  15705.0   4198.0   5497.0   \n",
       "39               4   839.0  11065.0   5328.0  12425.0   3202.0   2776.0   \n",
       "40               5   798.0   7246.0   5010.0   5532.0   5968.0  12859.0   \n",
       "41               5  1372.0   2818.0   5232.0   4325.0   4100.0  17021.0   \n",
       "42               5  1356.0   7113.0   3321.0   5431.0   7162.0  11765.0   \n",
       "43               5  1718.0   2004.0   2475.0   5362.0   2551.0  14996.0   \n",
       "44               5  1395.0   7003.0   3461.0   5444.0   7764.0  17578.0   \n",
       "45               5   654.0   7299.0   3624.0   5703.0   8677.0  12221.0   \n",
       "46               5  2640.0   7626.0   4796.0   5745.0  12775.0  13352.0   \n",
       "47               5  5798.0  21246.0   6010.0   6532.0   1696.0   1285.0   \n",
       "48               5   395.0   7003.0   3461.0   5444.0   7764.0   7578.0   \n",
       "49               5   718.0   7004.0  10475.0   7362.0   2551.0  14996.0   \n",
       "\n",
       "      Col7  \n",
       "0      0.0  \n",
       "1    135.0  \n",
       "2      0.0  \n",
       "3      0.0  \n",
       "4      0.0  \n",
       "5      0.0  \n",
       "6      0.0  \n",
       "7      0.0  \n",
       "8      0.0  \n",
       "9     59.0  \n",
       "10     0.0  \n",
       "11   135.0  \n",
       "12     0.0  \n",
       "13   232.0  \n",
       "14    19.0  \n",
       "15     0.0  \n",
       "16     0.0  \n",
       "17   232.0  \n",
       "18   135.0  \n",
       "19     0.0  \n",
       "20   700.0  \n",
       "21  1061.0  \n",
       "22  1200.0  \n",
       "23  1158.0  \n",
       "24  1137.0  \n",
       "25  1158.0  \n",
       "26  1137.0  \n",
       "27  2010.0  \n",
       "28  1700.0  \n",
       "29  1061.0  \n",
       "30   425.0  \n",
       "31   101.0  \n",
       "32   381.0  \n",
       "33  1526.0  \n",
       "34   250.0  \n",
       "35  1788.0  \n",
       "36  4258.0  \n",
       "37  1526.0  \n",
       "38   101.0  \n",
       "39  1526.0  \n",
       "40   356.0  \n",
       "41   507.0  \n",
       "42   778.0  \n",
       "43   449.0  \n",
       "44   407.0  \n",
       "45   421.0  \n",
       "46   778.0  \n",
       "47   356.0  \n",
       "48   407.0  \n",
       "49   449.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimax_acc_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba18c068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Alpha-Beta Input</th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>8</td>\n",
       "      <td>11182.0</td>\n",
       "      <td>21682.0</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>81186.0</td>\n",
       "      <td>52152.0</td>\n",
       "      <td>9350.0</td>\n",
       "      <td>11314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>8</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>10782.0</td>\n",
       "      <td>25480.0</td>\n",
       "      <td>79308.0</td>\n",
       "      <td>42152.0</td>\n",
       "      <td>10370.0</td>\n",
       "      <td>1474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>8</td>\n",
       "      <td>10182.0</td>\n",
       "      <td>20882.0</td>\n",
       "      <td>19630.0</td>\n",
       "      <td>80499.0</td>\n",
       "      <td>40552.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>11434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8</td>\n",
       "      <td>24182.0</td>\n",
       "      <td>10982.0</td>\n",
       "      <td>29720.0</td>\n",
       "      <td>50650.0</td>\n",
       "      <td>40652.0</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>1414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>8</td>\n",
       "      <td>16182.0</td>\n",
       "      <td>20482.0</td>\n",
       "      <td>35001.0</td>\n",
       "      <td>60240.0</td>\n",
       "      <td>40352.0</td>\n",
       "      <td>9330.0</td>\n",
       "      <td>17434.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alpha-Beta Input     Col1     Col2     Col3     Col4     Col5     Col6  \\\n",
       "0                  1     14.0    525.0     33.0    179.0    195.0    270.0   \n",
       "1                  1     13.0     51.0    220.0     76.0    141.0    392.0   \n",
       "2                  1     12.0    100.0     32.0      9.0    120.0    100.0   \n",
       "3                  1     16.0    100.0     30.0     17.0    131.0    210.0   \n",
       "4                  1     18.0     67.0     52.0     44.0     41.0    200.0   \n",
       "..               ...      ...      ...      ...      ...      ...      ...   \n",
       "75                 8  11182.0  21682.0  11400.0  81186.0  52152.0   9350.0   \n",
       "76                 8  21182.0  10782.0  25480.0  79308.0  42152.0  10370.0   \n",
       "77                 8  10182.0  20882.0  19630.0  80499.0  40552.0  12300.0   \n",
       "78                 8  24182.0  10982.0  29720.0  50650.0  40652.0   9300.0   \n",
       "79                 8  16182.0  20482.0  35001.0  60240.0  40352.0   9330.0   \n",
       "\n",
       "       Col7  \n",
       "0     100.0  \n",
       "1     135.0  \n",
       "2     168.0  \n",
       "3       0.0  \n",
       "4       0.0  \n",
       "..      ...  \n",
       "75  11314.0  \n",
       "76   1474.0  \n",
       "77  11434.0  \n",
       "78   1414.0  \n",
       "79  17434.0  \n",
       "\n",
       "[80 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphabeta_acc_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3378e28c",
   "metadata": {},
   "source": [
    "## Mini-Max Accuracy Caluclation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c5a8a15b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Mini-Max Implementation: 84.61538461538461 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "mm_y = minimax_acc_dataset.iloc[:,0:1].values\n",
    "mm_X = minimax_acc_dataset.iloc[:, 1:].values\n",
    "mm_X_train, mm_X_test, mm_y_train, mm_y_test = train_test_split(mm_X, mm_y, test_size = 0.25, random_state = 0)\n",
    "sc = StandardScaler()\n",
    "mm_X_train = sc.fit_transform(mm_X_train)\n",
    "mm_X_test = sc.transform(mm_X_test)\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(mm_X_train, mm_y_train)\n",
    "mm_y_pred = classifier.predict(mm_X_test)\n",
    "cm = confusion_matrix(mm_y_test, mm_y_pred)\n",
    "#print(cm)\n",
    "print(\"Accuracy of Mini-Max Implementation:\", accuracy_score(mm_y_test, mm_y_pred)*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9e4b6a",
   "metadata": {},
   "source": [
    "## Alpha-Beta Accuracy Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6badc53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Mini-Max Implementation: 85.0 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "ab_y = alphabeta_acc_dataset.iloc[:,0:1].values\n",
    "ab_X = alphabeta_acc_dataset.iloc[:, 1:].values\n",
    "ab_X_train, ab_X_test, ab_y_train, ab_y_test = train_test_split(ab_X, ab_y, test_size = 0.25, random_state = 0)\n",
    "sc = StandardScaler()\n",
    "ab_X_train = sc.fit_transform(ab_X_train)\n",
    "ab_X_test = sc.transform(ab_X_test)\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(ab_X_train, ab_y_train)\n",
    "ab_y_pred = classifier.predict(ab_X_test)\n",
    "cm = confusion_matrix(ab_y_test, ab_y_pred)\n",
    "#print(cm)\n",
    "print(\"Accuracy of Mini-Max Implementation:\", accuracy_score(ab_y_test, ab_y_pred)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d6fa1ec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Mini-Max Implementation: 84.615 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Mini-Max Implementation:\", round(accuracy_score(mm_y_test, mm_y_pred)*100,3), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "252aff5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Alpha-Beta Implementation: 85.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Alpha-Beta Implementation:\", round(accuracy_score(ab_y_test, ab_y_pred)*100,3), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3448ce5d",
   "metadata": {},
   "source": [
    "# Accuracy Comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95392139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Method = [\"Mini-Max\", \"Alpha-Beta\"]\n",
    "Accuracy = [round(accuracy_score(mm_y_test, mm_y_pred)*100,3), round(accuracy_score(ab_y_test, ab_y_pred)*100,3)]\n",
    "plt.rcdefaults()\n",
    "plt.bar(Method, Accuracy, width = 0.35, align = \"center\", color = ['Olive', '#8c000F'])\n",
    "plt.title(\"Mini-Max & Alpha-Beta Accuracy Comparison\", pad = 20, weight = 'bold')\n",
    "plt.xlabel(\"Algorithm\", color = \"#030764\", labelpad = 16, weight = 'bold')\n",
    "plt.ylabel(\"Accuracy(%)\", color =\"#030764\", labelpad = 16, weight = 'bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d38c544",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "## Both algorithms should give the same answer. However, their main difference is that alpha-beta does not explore all paths, like minimax does, but prunes those that are guaranteed not to be an optimal state for the current player, that is max or min. So, alpha-beta is a better implementation of minimax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c0577c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}