Engine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import chess\n",
    "import chess.svg\n",
    "import chess.pgn\n",
    "import numpy as np\n",
    "from IPython.display import SVG\n",
    "\n",
    "from models.autoencoder import AE\n",
    "from models.siamese import Siamese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cpu\")\n",
    "\n",
    "featurizer = AE().to(device)\n",
    "comparator = Siamese().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_state_dict = torch.load('checkpoints/best_autoencoder.pth.tar', map_location=lambda storage, loc: storage)\n",
    "siamese_state_dict = torch.load('checkpoints/best_siamese.pth.tar', map_location=lambda storage, loc: storage)\n",
    "\n",
    "featurizer.load_state_dict(ae_state_dict['state_dict'])\n",
    "comparator.load_state_dict(siamese_state_dict['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Siamese(\n",
       "  (fc1): Linear(in_features=200, out_features=400, bias=True)\n",
       "  (bn1): BatchNorm1d(400, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc2): Linear(in_features=400, out_features=200, bias=True)\n",
       "  (bn2): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc3): Linear(in_features=200, out_features=100, bias=True)\n",
       "  (bn3): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (fc4): Linear(in_features=100, out_features=2, bias=True)\n",
       "  (bn4): BatchNorm1d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       ")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurizer.eval()\n",
    "comparator.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bitboard(board):\n",
    "    '''\n",
    "    params\n",
    "    ------\n",
    "\n",
    "    board : chess.pgn board object\n",
    "        board to get state from\n",
    "\n",
    "    returns\n",
    "    -------\n",
    "\n",
    "    bitboard representation of the state of the game\n",
    "    64 * 6 + 5 dim binary numpy vector\n",
    "    64 squares, 6 pieces, '1' indicates the piece is at a square\n",
    "    5 extra dimensions for castling rights queenside/kingside and whose turn\n",
    "\n",
    "    '''\n",
    "\n",
    "    bitboard = np.zeros(64*6*2+5)\n",
    "\n",
    "    piece_idx = {'p': 0, 'n': 1, 'b': 2, 'r': 3, 'q': 4, 'k': 5}\n",
    "\n",
    "    for i in range(64):\n",
    "        if board.piece_at(i):\n",
    "            color = int(board.piece_at(i).color) + 1\n",
    "            bitboard[(piece_idx[board.piece_at(i).symbol().lower()] + i * 6) * color] = 1\n",
    "\n",
    "    bitboard[-1] = int(board.turn)\n",
    "    bitboard[-2] = int(board.has_kingside_castling_rights(True))\n",
    "    bitboard[-3] = int(board.has_kingside_castling_rights(False))\n",
    "    bitboard[-4] = int(board.has_queenside_castling_rights(True))\n",
    "    bitboard[-5] = int(board.has_queenside_castling_rights(False))\n",
    "\n",
    "    return bitboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "Board('r3kb1r/1pp2pp1/2p1b2p/p2nP3/3N1BP1/2N4P/PPP2P2/R2R2K1 w - - 3 15')"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board = chess.Board()\n",
    "with open('opening.pgn') as f:\n",
    "    board = chess.pgn.read_game(f)\n",
    "board = board.end().board()\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Move.from_uci('f4h6'), Move.from_uci('f4g5'), Move.from_uci('f4g3'), Move.from_uci('f4e3'), Move.from_uci('f4h2'), Move.from_uci('f4d2'), Move.from_uci('f4c1'), Move.from_uci('d4e6'), Move.from_uci('d4c6'), Move.from_uci('d4f5'), Move.from_uci('d4b5'), Move.from_uci('d4f3'), Move.from_uci('d4b3'), Move.from_uci('d4e2'), Move.from_uci('c3d5'), Move.from_uci('c3b5'), Move.from_uci('c3e4'), Move.from_uci('c3a4'), Move.from_uci('c3e2'), Move.from_uci('c3b1'), Move.from_uci('g1h2'), Move.from_uci('g1g2'), Move.from_uci('g1h1'), Move.from_uci('g1f1'), Move.from_uci('d1d3'), Move.from_uci('d1d2'), Move.from_uci('d1f1'), Move.from_uci('d1e1'), Move.from_uci('d1c1'), Move.from_uci('d1b1'), Move.from_uci('a1c1'), Move.from_uci('a1b1'), Move.from_uci('g4g5'), Move.from_uci('h3h4'), Move.from_uci('f2f3'), Move.from_uci('b2b3'), Move.from_uci('a2a3'), Move.from_uci('b2b4'), Move.from_uci('a2a4')]\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "Board('r3kb1r/1pp2pp1/2p1b2p/pN1nP3/3N1BP1/7P/PPP2P2/R2R2K1 b - - 4 15')"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moves = board.generate_legal_moves()\n",
    "moves = list(moves)\n",
    "print(moves)\n",
    "board.push(moves[np.random.randint(len(moves))])\n",
    "#SVG(chess.svg.board(board=board))\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featurize(boards):\n",
    "    boards = torch.from_numpy(boards).type(torch.FloatTensor)\n",
    "    return featurizer(boards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_compare_array(features):\n",
    "    catcats = []\n",
    "    for feature in features:\n",
    "        cats = []\n",
    "        for compared in features:\n",
    "            cats.append(np.hstack((feature, compared)))\n",
    "        cats = np.array(cats)\n",
    "        catcats.append(cats)\n",
    "    return np.vstack(catcats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare(features):\n",
    "    features = torch.from_numpy(features).type(torch.FloatTensor)\n",
    "    return comparator(features).detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Game' object has no attribute 'generate_legal_moves'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-173-b6c88a7c1535>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmoves\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_legal_moves\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmoves\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmoves\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mbitboards\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmove\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmoves\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Game' object has no attribute 'generate_legal_moves'"
     ]
    }
   ],
   "source": [
    "moves = board.generate_legal_moves()\n",
    "moves = list(moves)\n",
    "\n",
    "bitboards = []\n",
    "for move in moves:\n",
    "    b = board.copy()\n",
    "    b.push(move)\n",
    "    bitboards.append(get_bitboard(b))\n",
    "\n",
    "bitboards = np.array(bitboards)\n",
    "\n",
    "recon, features = featurize(bitboards)\n",
    "features = features.detach().numpy()\n",
    "\n",
    "to_compare = gen_compare_array(features)\n",
    "\n",
    "to_compare_2 = np.zeros(to_compare.shape)\n",
    "to_compare_2[:, :100] = to_compare[:, 100:]\n",
    "to_compare_2[:, 100:] = to_compare[:, :100]\n",
    "\n",
    "pred = compare(to_compare)\n",
    "pred = (pred > .5).astype(int)\n",
    "splits = np.split(pred, len(moves))\n",
    "\n",
    "pred2 = compare(to_compare_2)\n",
    "pred2 = 1 - (pred2 > .5).astype(int)\n",
    "splits2 = np.split(pred2, len(moves))\n",
    "\n",
    "scores = np.array([np.sum(split[:, 0]) for split in splits])\n",
    "scores2 = np.array([np.sum(split[:, 0]) for split in splits2])\n",
    "print(scores)\n",
    "print(scores2)\n",
    "print(scores+scores2)\n",
    "best_idx = np.argmax(scores + scores2)\n",
    "board.push(moves[best_idx])\n",
    "\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.99999976 0.9999999  0.9999999  1.         1.\n",
      " 1.         0.99999976 0.99999964 1.         1.         0.9999999\n",
      " 0.9999665  1.         1.         0.99999964 1.         0.9999938\n",
      " 0.99999976 0.99904317 1.         0.9999943  0.99999976 0.9999975\n",
      " 0.9998741  0.9999999  0.999803  ]\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "Board('5r2/pp2b1p1/1kn2pn1/4p3/1P1P1P2/P5K1/2P3PP/RbB1N2R b - - 3 30')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moves = board.generate_legal_moves()\n",
    "moves = list(moves)\n",
    "\n",
    "bitboards = []\n",
    "for move in moves:\n",
    "    b = board.copy()\n",
    "    b.push(move)\n",
    "    bitboards.append(get_bitboard(b))\n",
    "\n",
    "bitboards = np.array(bitboards)\n",
    "curr_bitboard = get_bitboard(board)\n",
    "\n",
    "_, features = featurize(bitboards)\n",
    "features = features.detach().numpy()\n",
    "\n",
    "_, curr_features = featurize(curr_bitboard)\n",
    "curr_features = curr_features.detach().numpy()\n",
    "\n",
    "to_compare = np.hstack((np.repeat(curr_features, len(moves), axis=0), features))\n",
    "\n",
    "scores = compare(to_compare)\n",
    "scores = scores[:, 0]\n",
    "\n",
    "print(scores)\n",
    "best_idx = np.argmax(scores)\n",
    "\n",
    "board.push(moves[best_idx])\n",
    "\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "Board('5r2/pp2b1p1/1kn2pn1/8/1P1PpP2/P5K1/2P3PP/RbB1N2R w - - 0 31')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board.push_san('e4')\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.5127432e-10, Move.from_uci('d4c6'))"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare_boards(a,b):\n",
    "    a, b = get_bitboard(a), get_bitboard(b)\n",
    "    a, b = featurize(a)[1].detach(), featurize(b)[1].detach()\n",
    "    to_compare = np.hstack((a,b))\n",
    "    return compare(to_compare)[0][0]\n",
    "\n",
    "def minimax(board, depth, white, orig_board):\n",
    "    if depth == 0:\n",
    "        return compare_boards(board, orig_board)\n",
    "    if white:\n",
    "        best_value = -100 # very (relatively) small number\n",
    "        moves = board.generate_legal_moves()\n",
    "        moves = list(moves)\n",
    "        for move in moves:\n",
    "            new_board = board.copy()\n",
    "            new_board.push(move)\n",
    "            v = minimax(new_board, depth - 1, False, orig_board)\n",
    "            best_value = max(v, best_value)\n",
    "        return best_value\n",
    "    else:\n",
    "        best_value = 100 # very (relatively) large number\n",
    "        moves = board.generate_legal_moves()\n",
    "        moves = list(moves)\n",
    "        for move in moves:\n",
    "            new_board = board.copy()\n",
    "            new_board.push(move)\n",
    "            v = minimax(new_board, depth - 1, True, orig_board)\n",
    "            best_value = min(v, best_value)\n",
    "        return best_value\n",
    "    \n",
    "def alphabeta(board, depth, alpha, beta, white, orig_board):\n",
    "    if depth == 0:\n",
    "        return compare_boards(board, orig_board), None\n",
    "    if white:\n",
    "        v = -100 # very (relatively) small number\n",
    "        moves = board.generate_legal_moves()\n",
    "        moves = list(moves)\n",
    "        best_move = None\n",
    "        for move in moves:\n",
    "            new_board = board.copy()\n",
    "            new_board.push(move)\n",
    "            candidate_v, _ = alphabeta(new_board, depth - 1, alpha, beta, False, orig_board)\n",
    "            if candidate_v >= v:\n",
    "                v = candidate_v\n",
    "                best_move = move\n",
    "            else:\n",
    "                pass\n",
    "            alpha = max(alpha, v)\n",
    "            if beta <= alpha:\n",
    "                break\n",
    "        return v, best_move\n",
    "    else:\n",
    "        v = 100 # very (relatively) large number\n",
    "        moves = board.generate_legal_moves()\n",
    "        moves = list(moves)\n",
    "        best_move = None\n",
    "        for move in moves:\n",
    "            new_board = board.copy()\n",
    "            new_board.push(move)\n",
    "            candidate_v, _ = alphabeta(new_board, depth - 1, alpha, beta, False, orig_board)\n",
    "            if candidate_v <= v:\n",
    "                v = candidate_v\n",
    "                best_move = move\n",
    "            else:\n",
    "                pass\n",
    "            beta = min(beta, v)\n",
    "            if beta <= alpha:\n",
    "                break\n",
    "        return v, best_move\n",
    "\n",
    "#minimax(board, 3, True, board)\n",
    "alphabeta(board, 3, -100, 100, True, board)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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