OChess.ipynb

{
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     "text": [
      "Wed Oct  2 01:44:33 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.161.08             Driver Version: 535.161.08   CUDA Version: 12.2     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  Tesla V100-SXM2-16GB           On  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   36C    P0              24W / 300W |      2MiB / 16384MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|  No running processes found                                                           |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56c60668-667e-405f-8699-a7d936b9c3ac",
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    "id": "56c60668-667e-405f-8699-a7d936b9c3ac",
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   "source": [
    "# %pip install --upgrade chess -q\n",
    "# %pip install --upgrade -q torch torcheval torchtnt torchmetrics\n",
    "# # %pip install --upgrade -q torch #--index-url https://download.pytorch.org/whl/cpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "57721d08-8f3c-4bba-8aba-d72b2e0e25c9",
   "metadata": {
    "id": "57721d08-8f3c-4bba-8aba-d72b2e0e25c9",
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   "source": [
    "import gc\n",
    "import random\n",
    "import re\n",
    "from collections import defaultdict\n",
    "from glob import glob\n",
    "from itertools import chain\n",
    "from pprint import pprint\n",
    "from typing import List\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tabulate\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchmetrics\n",
    "from IPython.display import Markdown, display\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchtnt.utils.data.data_prefetcher import CudaDataPrefetcher\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "# torch.backends.cudnn.benchmark = True\n",
    "# torch.backends.cudnn.benchmark_limit = 0\n",
    "\n",
    "tqdm.pandas()\n",
    "import chess\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "\n",
    "def collect():\n",
    "    gc.collect()\n",
    "    if device.type == \"cuda\":\n",
    "        torch.cuda.empty_cache()\n",
    "        torch.cuda.reset_max_memory_allocated()\n",
    "        torch.cuda.synchronize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12d77e1a-438d-45dd-9c3a-0aa68587ef2f",
   "metadata": {
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   "source": [
    "def vis(entries):\n",
    "    display(entries[\"outcome\"].value_counts())\n",
    "    print(\"---\" * 10)\n",
    "    display(entries[\"is_capture\"].value_counts())\n",
    "    print(\"---\" * 10)\n",
    "    display(entries.groupby(\"game_id\").size().describe())\n",
    "\n",
    "\n",
    "def load_files(files):\n",
    "    entries = pd.concat(\n",
    "        [pd.read_parquet(f) for f in tqdm(files)], ignore_index=True\n",
    "    ).drop_duplicates([\"game_id\", \"board\"])\n",
    "    entries[\"outcome\"] = entries[\"outcome\"].fillna(0)\n",
    "    return entries"
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
   "id": "Rlcd_eI2VJQD",
   "metadata": {
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2428af66f61d453481d94f8912730f7c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/29 [00:00<?, ?it/s]"
      ]
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    {
     "data": {
      "text/plain": [
       "outcome\n",
       "/                        1232822\n",
       "CHECKMATE                   8298\n",
       "FIVEFOLD_REPETITION           17\n",
       "INSUFFICIENT_MATERIAL         14\n",
       "STALEMATE                     12\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "is_capture\n",
       "False    989944\n",
       "True     251219\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    12520.000000\n",
       "mean        99.134425\n",
       "std         22.854375\n",
       "min         12.000000\n",
       "25%         82.000000\n",
       "50%        105.000000\n",
       "75%        121.000000\n",
       "max        121.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "files = sorted(list(\n",
    "    set(glob('chess-dataset/chess-games-dsv5-MAX-None-*'))\n",
    "    - set(glob('chess-dataset/chess-games-dsv5-MAX-None-1-*'))\n",
    "    - set(glob('chess-dataset/chess-games-dsv5-MAX-None-2-*'))\n",
    "   \n",
    ") + list(set(glob('chess-dataset/chess-games-dsv5-top2*'))))\n",
    "\n",
    "\n",
    "entries = pd.concat( [pd.read_parquet(f)  for f in tqdm(files)    ],    ignore_index=True,).drop_duplicates([\"game_id\", \"board\"])\n",
    "entries.pop(\"legal_moves\")\n",
    "entries.pop(\"legal_captures\")\n",
    "entries[\"outcome\"] = entries[\"outcome\"].fillna(\"/\")\n",
    "vis(entries)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b42931-756b-4824-8990-0ebc7d791917",
   "metadata": {},
   "source": [
    "# Dataset is too imbalanced, trying to filter-out unuseful examples ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "259527ad-b1fd-4a2f-8339-2ba56b5b2cf7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outcome\n",
       "/            722863\n",
       "CHECKMATE      8298\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "is_capture\n",
       "False    576049\n",
       "True     155112\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    8298.000000\n",
       "mean       88.112919\n",
       "std        20.600826\n",
       "min        12.000000\n",
       "25%        74.000000\n",
       "50%        89.000000\n",
       "75%       105.000000\n",
       "max       121.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtering only the games for which we don't have any meaningful outcome\n",
    "\n",
    "entries = entries.groupby(\"game_id\").filter(\n",
    "    lambda x: x[\"outcome\"].iloc[-1] == \"CHECKMATE\"\n",
    "    # len(x) <= 200\n",
    "    # and len(x) > 51\n",
    "    # and x[\"outcome\"].iloc[-1] != \"SEVENTYFIVE_MOVES\"\n",
    "    # and x[\"outcome\"].iloc[-1] != \"STALEMATE\"\n",
    "    # and x[\"outcome\"].iloc[-1] != \"FIVEFOLD_REPETITION\"\n",
    "    # and x[\"outcome\"].iloc[-1] == \"CHECKMATE\"\n",
    ")\n",
    "vis(entries)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8311025fe7e66e8c",
   "metadata": {},
   "source": [
    "## Show a random game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27680d52-1edb-4a34-9f74-60effab7f2c5",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = entries[\"game_id\"] == random.choice(entries[\"game_id\"].unique())\n",
    "board = chess.Board()\n",
    "random_game = entries[mask]\n",
    "for fen in random_game[\"board\"].values:\n",
    "    board.set_board_fen(fen.split()[0])\n",
    "    display(board)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc8df203-2682-4806-80b4-6d9a2b340623",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def clean_score(score):\n",
    "    # FIXME: Understand a real way to encode the score\n",
    "    if score[1:].isdigit():\n",
    "        score = int(score[1:])\n",
    "    elif len(score) > 2 and score[2:].isdigit():\n",
    "        score = (100 * 7) / int(score[2:])\n",
    "    else:\n",
    "        score = int(score)\n",
    "    # For now maximize score only for 1 side: Black\n",
    "    return abs(score)\n",
    "\n",
    "\n",
    "# FIXME: Use sklearn scaler for easy serialization\n",
    "entries[\"score\"] = entries[\"score\"].progress_apply(lambda x: clean_score(x))\n",
    "_mean = entries[\"score\"].mean()\n",
    "_std = entries[\"score\"].std()\n",
    "entries[\"score\"] = (entries[\"score\"] - _mean) / _std\n",
    "# entries[\"score\"] = entries[\"score\"] / entries[\"score\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33ea05d7-6182-4f5e-92b9-89eaad1c6617",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "outcome_/  outcome_CHECKMATE\n",
       "1          0                    722863\n",
       "0          1                      8298\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "capture_False  capture_True\n",
       "1              0               576049\n",
       "0              1               155112\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outcome = pd.get_dummies(entries[\"outcome\"], dtype=int, prefix=\"outcome\")\n",
    "capture = pd.get_dummies(entries[\"is_capture\"], dtype=int, prefix=\"capture\")\n",
    "display(outcome.value_counts())\n",
    "display(capture.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a02e0405-ebb4-4792-a330-f670e30975c9",
   "metadata": {
    "id": "a02e0405-ebb4-4792-a330-f670e30975c9",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(658044, 73117)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# entries.sort_values(['game_id','#_turn'],inplace=True)\n",
    "dummies = pd.get_dummies(entries, columns=[\"is_capture\", \"outcome\"], dtype=int)\n",
    "df_train = dummies.head(int(len(dummies) * 0.9))\n",
    "df_test = dummies.tail(len(dummies) - len(df_train))\n",
    "del dummies\n",
    "gc.collect()\n",
    "len(df_train), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "SaqmUdpQT4py",
   "metadata": {
    "id": "SaqmUdpQT4py",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# FIXME: Understand a better way for get unique game batches.\n",
    "def get_unique_games_from_batches(X, y):\n",
    "    unique_games = {}\n",
    "    unique_idx = []\n",
    "    for i in tqdm(range(X.shape[0]), mininterval=1):\n",
    "        x = \"|\".join([v[:-13] for v in X[i]]) + \"|\".join(y[i])\n",
    "        if x not in unique_games:\n",
    "            unique_games[x] = None\n",
    "            unique_idx.append(i)\n",
    "    return unique_idx\n",
    "\n",
    "\n",
    "def create_timeseries_v3(y, window=6, **kwargs):\n",
    "    X = defaultdict(list)\n",
    "    _y = []\n",
    "    for k in kwargs:\n",
    "        for i in range(len(kwargs[k]) - (window - 1)):\n",
    "            X[k].append(kwargs[k][i : i + window])\n",
    "\n",
    "    for k in X:\n",
    "        X[k] = np.asarray(X[k])\n",
    "    for i in range(len(y) - (window - 1)):\n",
    "        _y.append(y[i : i + window])\n",
    "    return X, np.asarray(_y)\n",
    "\n",
    "\n",
    "def generate_dataset(df, window=6):\n",
    "    X_train = defaultdict(list)\n",
    "    y_train = []\n",
    "    capture_cols = [c for c in df.columns if \"capture\" in c]\n",
    "    outcome_cols = [c for c in df.columns if \"outcome\" in c]\n",
    "    for l, game in tqdm(df.groupby(\"game_id\"), mininterval=1):\n",
    "        a, b = create_timeseries_v3(\n",
    "            game[\"move\"].values,\n",
    "            window,\n",
    "            **{\n",
    "                # 'idxs':game['#_turn'].values,\n",
    "                \"boards\": game[\"board\"].values,\n",
    "                \"scores\": game[\"score\"].values,\n",
    "                \"is_capture\": game[capture_cols].values,\n",
    "                \"outcomes\": game[outcome_cols].values,\n",
    "            },\n",
    "        )\n",
    "        for k in a:\n",
    "            X_train[k].extend(a[k])\n",
    "        y_train.extend(b)\n",
    "    for k in X_train:\n",
    "        X_train[k] = np.stack([v for v in X_train[k]])\n",
    "\n",
    "    y_train = np.asarray(y_train)\n",
    "    n = len(y_train)\n",
    "    unique_idx = get_unique_games_from_batches(X_train[\"boards\"], y_train)\n",
    "    print(f\"Removed {n - len(unique_idx)} duplicated entries\")\n",
    "    return {k: X_train[k][unique_idx] for k in X_train}, y_train[unique_idx]\n",
    "\n",
    "\n",
    "X_train, y_train = generate_dataset(df_train.head(5000))"
   ]
  },
  {
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   "execution_count": 25,
   "id": "0cc16ae0-d900-4e57-90bd-8d0338f80cc2",
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    "scrolled": true,
    "tags": []
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['r', 'n', 'b', 'q', 'k', 'b', 'n', 'r'],\n",
      " ['p', 'p', 'p', 'p', 'p', 'p', 'p', 'p'],\n",
      " [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '],\n",
      " [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '],\n",
      " [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '],\n",
      " [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '],\n",
      " ['P', 'P', 'P', 'P', 'P', 'P', 'P', 'P'],\n",
      " ['R', 'N', 'B', 'Q', 'K', 'B', 'N', 'R']]\n",
      "tensor([[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
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      "\n",
      "        [[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
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      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
      "\n",
      "        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
      "\n",
      "        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
      "\n",
      "        [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
      "\n",
      "        [[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
      "         [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]]], dtype=torch.int32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 8, 13])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class FenParser:\n",
    "    \"\"\"\n",
    "    This is completely copied from somewhere\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.rank_re = re.compile(r\"(\\d|[kqbnrpKQBNRP])\")\n",
    "        self.piece_re = re.compile(r\"([kqbnrpKQBNRP])\")\n",
    "        self.piece_to_index = {\n",
    "            \" \": 0,\n",
    "            \"P\": 1,\n",
    "            \"N\": 2,\n",
    "            \"B\": 3,\n",
    "            \"R\": 4,\n",
    "            \"Q\": 5,\n",
    "            \"K\": 6,\n",
    "            \"p\": 7,  # White\n",
    "            \"n\": 8,\n",
    "            \"b\": 9,\n",
    "            \"r\": 10,\n",
    "            \"q\": 11,\n",
    "            \"k\": 12,  # Black\n",
    "        }\n",
    "        self.piece_map = {\n",
    "            \" \": \" \",\n",
    "            \"P\": \"PED\",\n",
    "            \"N\": \"CAV\",\n",
    "            \"B\": \"ALF\",\n",
    "            \"R\": \"TOR\",\n",
    "            \"Q\": \"REG\",\n",
    "            \"K\": \"RE\",\n",
    "            \"p\": \"ped\",\n",
    "            \"n\": \"cav\",\n",
    "            \"b\": \"alf\",\n",
    "            \"r\": \"tor\",\n",
    "            \"q\": \"reg\",\n",
    "            \"k\": \"re\",\n",
    "        }\n",
    "        self.index_to_piece = {v: k for k, v in self.piece_to_index.items()}\n",
    "\n",
    "    def parse(self, fen_str):\n",
    "        ranks = fen_str.split(\" \")[0].split(\"/\")\n",
    "        pieces_on_all_ranks = [self.parse_rank(rank) for rank in ranks]\n",
    "        return pieces_on_all_ranks\n",
    "\n",
    "    def parse_rank(self, rank):\n",
    "        piece_tokens = self.rank_re.findall(rank)\n",
    "        pieces = self.flatten(map(self.expand_or_noop, piece_tokens))\n",
    "        return pieces\n",
    "\n",
    "    def flatten(self, lst):\n",
    "        return list(chain(*lst))\n",
    "\n",
    "    def expand_or_noop(self, piece_str):\n",
    "        if self.piece_re.match(piece_str):\n",
    "            return piece_str\n",
    "        return self.expand(piece_str)\n",
    "\n",
    "    def to_tensor(self, fen):\n",
    "        tensor = torch.zeros((8, 8, 13), dtype=torch.int)\n",
    "\n",
    "        # Split the FEN string by spaces to get the board part\n",
    "        board_fen = fen.split()[0]\n",
    "\n",
    "        # Process each rank (row) in the FEN board part\n",
    "        rank_fen = board_fen.split(\"/\")\n",
    "\n",
    "        for rank_idx, rank in enumerate(rank_fen):\n",
    "            file_idx = 0\n",
    "            for char in rank:\n",
    "                if char.isdigit():\n",
    "                    # Empty squares (advance file index by the number)\n",
    "                    file_idx += int(char)\n",
    "                else:\n",
    "                    # Fill the appropriate channel for the piece\n",
    "                    piece_idx = self.piece_to_index[char]\n",
    "                    tensor[rank_idx, file_idx, piece_idx] = 1\n",
    "                    file_idx += 1\n",
    "\n",
    "        return torch.IntTensor(tensor)\n",
    "\n",
    "    def tensor_to_board_v2(self, t):\n",
    "        t = torch.Tensor.tolist(t)\n",
    "        for i in range(len(t)):\n",
    "            for j in range(len(t[0])):\n",
    "                t[i][j] = fp.index_to_piece.get(t[i][j])\n",
    "                t[i][j] = fp.piece_map[t[i][j]]\n",
    "        print(\n",
    "            tabulate.tabulate(\n",
    "                t,\n",
    "                tablefmt=\"heavy_grid\",\n",
    "                headers=[chr(ord(\"A\") + i) for i in range(8)],\n",
    "                showindex=list(range(8, 0, -1)),\n",
    "            )\n",
    "        )\n",
    "\n",
    "    def to_fen(self, tensor):\n",
    "        fen = \"\"\n",
    "        # Index-to-piece mapping (reverse of piece_to_index)\n",
    "        index_to_piece = {value: key for key, value in self.piece_to_index.items()}\n",
    "\n",
    "        # Iterate over each rank (row)\n",
    "        for rank_idx in range(8):\n",
    "            empty_squares = 0\n",
    "            for file_idx in range(8):\n",
    "                # Find the active channel (piece) for this square\n",
    "                piece_idx = np.argmax(tensor[rank_idx, file_idx])\n",
    "                if piece_idx == 0:  # Empty square\n",
    "                    empty_squares += 1\n",
    "                else:\n",
    "                    if empty_squares > 0:\n",
    "                        fen += str(empty_squares)\n",
    "                        empty_squares = 0\n",
    "                    fen += index_to_piece[piece_idx]\n",
    "            if empty_squares > 0:\n",
    "                fen += str(empty_squares)\n",
    "            fen += \"/\"\n",
    "\n",
    "        # Remove the trailing slashs\n",
    "        return fen[:-1]\n",
    "\n",
    "    def expand(self, num_str):\n",
    "        return int(num_str) * \" \"\n",
    "\n",
    "    # FIXME: Use a 2d -> 2d index instead of a flatten 4096 array\n",
    "    def uci_to_index(self, move):\n",
    "        start_square = chess.parse_square(move[:2])\n",
    "        end_square = chess.parse_square(move[2:4])\n",
    "        return start_square * 64 + end_square\n",
    "\n",
    "    def index_to_uci(self, index):\n",
    "        start_square = index // 64\n",
    "        end_square = index % 64\n",
    "        uci_move = chess.square_name(start_square) + chess.square_name(end_square)\n",
    "        return uci_move\n",
    "\n",
    "\n",
    "fp = FenParser()\n",
    "pprint(fp.parse(entries.iloc[0][\"board\"]))\n",
    "pprint(fp.to_tensor(entries.iloc[0][\"board\"]))\n",
    "t = fp.to_tensor(entries.iloc[0][\"board\"])\n",
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7246cbc2-1d27-4d68-b4f1-29cf8927ddbe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7246cbc2-1d27-4d68-b4f1-29cf8927ddbe",
    "outputId": "16d88c02-2810-496b-a2d8-2ea907d3c516",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/cuda/memory.py:343: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a482a1cb5e664a9e9c0a00156b652bb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/7494 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90d3a0167b674b22a729a66761ff07f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/628068 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 11774 duplicated entries\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c780eb1c617e448bab3afc8b1af0fd0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/805 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54b8a210d18a4e1cbba853a968297e81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/69897 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 749 duplicated entries\n",
      "board->(5,) | object\n",
      "move->(5,) | object\n",
      "score->(5,) | float64\n",
      "is_capture->(5, 2) | int64\n",
      "outcome->(5, 2) | int64\n",
      "fen->(4, 5) | <U69\n",
      "board_tensor->torch.Size([4, 5, 8, 8]) | torch.int64\n",
      "move_index->torch.Size([4, 5, 4095]) | torch.float32\n",
      "scores->torch.Size([4, 5]) | torch.float32\n",
      "active_color->torch.Size([4, 5]) | torch.int32\n",
      "is_capture->torch.Size([4, 5, 2]) | torch.float32\n",
      "outcome->torch.Size([4, 5, 2]) | torch.float32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(dict_keys(['board', 'move', 'score', 'is_capture', 'outcome']),\n",
       " dict_keys(['fen', 'board_tensor', 'move_index', 'scores', 'active_color', 'is_capture', 'outcome']))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class ChessDataset(Dataset):\n",
    "    def __init__(\n",
    "        self,\n",
    "        # idxs: List[int],\n",
    "        boards: List[List[str]],\n",
    "        moves: List[str],\n",
    "        scores: List[float],\n",
    "        is_capture: List[int],\n",
    "        outcomes: List[int],\n",
    "    ):\n",
    "        # self.idxs = idxs\n",
    "        self.boards = boards\n",
    "        self.moves = moves\n",
    "        self.scores = scores\n",
    "        self.is_capture = is_capture\n",
    "        self.outcomes = outcomes\n",
    "        self.fen_parser = FenParser()\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.boards)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        d = {\n",
    "            # \"idx\": self.idxs[idx],\n",
    "            \"board\": self.boards[idx],\n",
    "            \"move\": self.moves[idx],\n",
    "            \"score\": self.scores[idx],\n",
    "            \"is_capture\": self.is_capture[idx],\n",
    "            \"outcome\": self.outcomes[idx],\n",
    "        }\n",
    "        return d\n",
    "\n",
    "    # FIXME: This is slow + CPU Bottleneck\n",
    "    def collate_fn(self, d):\n",
    "        boards = []\n",
    "        moves = []\n",
    "        colors = []\n",
    "        scores = []\n",
    "        is_capture = []\n",
    "        outcome = []\n",
    "        # idxs = []\n",
    "        fens = []\n",
    "        for entry in d:\n",
    "            fens.append([fen for fen in entry[\"board\"]])\n",
    "            _boards = torch.stack(\n",
    "                [\n",
    "                    torch.argmax(self.fen_parser.to_tensor(fen), axis=-1).type(\n",
    "                        torch.long\n",
    "                    )\n",
    "                    for fen in entry[\"board\"]\n",
    "                ]\n",
    "            )\n",
    "            boards.append(_boards)\n",
    "\n",
    "            _moves = [\n",
    "                self.fen_parser.uci_to_index(best_move) for best_move in entry[\"move\"]\n",
    "            ]\n",
    "            _moves = torch.stack(\n",
    "                [\n",
    "                    torch.nn.functional.one_hot(\n",
    "                        torch.tensor(index).clone().detach(), num_classes=4095\n",
    "                    ).type(dtype=torch.float32)\n",
    "                    for index in _moves\n",
    "                ]\n",
    "            )\n",
    "            moves.append(_moves)\n",
    "            # idxs.append([idxs for idxs in entry[\"idx\"]])\n",
    "            scores.append([score for score in entry[\"score\"]])\n",
    "            is_capture.append([is_capture for is_capture in entry[\"is_capture\"]])\n",
    "            outcome.append([outcome for outcome in entry[\"outcome\"]])\n",
    "            colors.append([0 if _fen.split()[1] == \"w\" else 1 for _fen in fens[-1]])\n",
    "\n",
    "        out = {\n",
    "            # \"idx\": np.asarray(idxs),\n",
    "            \"fen\": np.asarray(fens),\n",
    "            \"board_tensor\": torch.stack(boards).type(torch.long),\n",
    "            \"move_index\": torch.stack(moves),\n",
    "            \"scores\": torch.FloatTensor(scores),\n",
    "            \"active_color\": torch.IntTensor(colors),\n",
    "            \"is_capture\": torch.FloatTensor(np.asarray(is_capture)),\n",
    "            \"outcome\": torch.FloatTensor(np.asarray(outcome)),\n",
    "        }\n",
    "        return out\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda\")\n",
    "\n",
    "collect()\n",
    "window = 5\n",
    "X_train, y_train = generate_dataset(df_train, window=window)\n",
    "ds_train = ChessDataset(moves=y_train, **X_train)\n",
    "del X_train, y_train, df_train\n",
    "X_test, y_test = generate_dataset(df_test, window=window)\n",
    "ds_test = ChessDataset(moves=y_test, **X_test)\n",
    "del X_test, y_test\n",
    "gc.collect()\n",
    "batch_size = 4\n",
    "_batch_size = 128\n",
    "# Avoid to load to GPU. Prefetched during training only\n",
    "dl_train = DataLoader(\n",
    "    ds_train,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    collate_fn=ds_train.collate_fn,\n",
    ")\n",
    "dl_test = DataLoader(\n",
    "    ds_test,\n",
    "    batch_size=_batch_size,\n",
    "    shuffle=True,\n",
    "    collate_fn=ds_test.collate_fn,\n",
    ")\n",
    "\n",
    "for x1 in ds_train:\n",
    "    for k in x1.keys():\n",
    "        print(f\"{k}->{x1[k].shape} | {x1[k].dtype}\")\n",
    "    break\n",
    "for x2 in dl_train:\n",
    "    for k in x2.keys():\n",
    "        print(f\"{k}->{x2[k].shape} | {x2[k].dtype}\")\n",
    "    break\n",
    "\n",
    "x1.keys(), x2.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bEhM71Vj8uWx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bEhM71Vj8uWx",
    "outputId": "7761c68b-2c8a-4ace-e9ce-1c7bb4d022af",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┏━━━━┳━━━━━┳━━━━━┳━━━━━┳━━━━━┳━━━━━┳━━━━━┳━━━━━┳━━━━━┓\n",
      "┃    ┃ A   ┃ B   ┃ C   ┃ D   ┃ E   ┃ F   ┃ G   ┃ H   ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  8 ┃     ┃ TOR ┃     ┃     ┃     ┃     ┃     ┃     ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  7 ┃     ┃     ┃ re  ┃     ┃     ┃     ┃     ┃     ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  6 ┃ ped ┃     ┃     ┃ cav ┃     ┃     ┃     ┃ reg ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  5 ┃     ┃     ┃ ped ┃ REG ┃ ped ┃     ┃ ped ┃     ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  4 ┃     ┃     ┃     ┃ ped ┃ PED ┃ alf ┃     ┃     ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  3 ┃     ┃     ┃     ┃ PED ┃     ┃     ┃     ┃     ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  2 ┃     ┃     ┃     ┃     ┃     ┃ TOR ┃     ┃ PED ┃\n",
      "┣━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━╋━━━━━┫\n",
      "┃  1 ┃     ┃     ┃     ┃     ┃     ┃     ┃     ┃ RE  ┃\n",
      "┗━━━━┻━━━━━┻━━━━━┻━━━━━┻━━━━━┻━━━━━┻━━━━━┻━━━━━┻━━━━━┛\n"
     ]
    }
   ],
   "source": [
    "fp.tensor_to_board_v2(x2[\"board_tensor\"][0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1882a86e-e1e3-4c0a-98e6-201bf7a0a6e2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/cuda/memory.py:343: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "dropout = 0.5\n",
    "\n",
    "\n",
    "class ChessNet(nn.Module):\n",
    "\n",
    "    @staticmethod\n",
    "    def build_emb(in_dim=13, out_dim=64) -> nn.Sequential:\n",
    "        return nn.Sequential(\n",
    "            nn.Embedding(in_dim, out_dim),\n",
    "            nn.Linear(out_dim, out_dim),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "\n",
    "    @staticmethod\n",
    "    def buil_conv(\n",
    "        in_dim=128,\n",
    "        out_dim=256,\n",
    "        k=(window, 2, 2),\n",
    "        padding=\"same\",\n",
    "        pool: nn.Module = nn.MaxPool3d((1, 2, 2)),\n",
    "        activation: nn.Module = nn.Mish,\n",
    "    ) -> nn.Sequential:\n",
    "        x = nn.Sequential(\n",
    "            nn.BatchNorm3d(in_dim),\n",
    "            nn.Conv3d(\n",
    "                in_channels=in_dim, out_channels=out_dim, kernel_size=k, padding=padding\n",
    "            ),\n",
    "            activation(),\n",
    "        )\n",
    "        if pool is not None:\n",
    "            x.add_module(\"pool\", pool)\n",
    "        return x\n",
    "\n",
    "    def __init__(self):\n",
    "        super(ChessNet, self).__init__()\n",
    "        self.emb_piece = ChessNet.build_emb()\n",
    "        self.emb_color = ChessNet.build_emb()\n",
    "        self.conv1 = ChessNet.buil_conv()\n",
    "        self.conv2 = ChessNet.buil_conv(k=(window, 3, 3))\n",
    "        self.conv3 = ChessNet.buil_conv(k=(window, 5, 5))\n",
    "        self.conv4 = nn.Sequential(\n",
    "            ChessNet.buil_conv(768, 1024, k=(window, 1, 1), pool=None),\n",
    "            ChessNet.buil_conv(1024, 2048, k=(window, 1, 1), pool=None),\n",
    "            nn.BatchNorm3d(2048),\n",
    "        )\n",
    "\n",
    "        self.fc_features = nn.Sequential(\n",
    "            nn.Linear(8192, 2048), nn.ReLU(), nn.Dropout(dropout)\n",
    "        )\n",
    "        self.fc_position = nn.Sequential(\n",
    "            nn.Linear(8192, 4095),\n",
    "        )\n",
    "        self.fc_score = nn.Sequential(nn.Linear(2048, 1))\n",
    "        self.fc_capture = nn.Sequential(nn.Linear(2048, x2[\"is_capture\"].shape[-1]))\n",
    "        self.fc_outcome = nn.Sequential(nn.Linear(2048, x2[\"outcome\"].shape[-1]))\n",
    "\n",
    "    def forward(\n",
    "        self, board_tensor, scores, active_color, is_capture, outcome, **kwargs\n",
    "    ):\n",
    "        emb_board = self.emb_piece(board_tensor)\n",
    "        # print(\"BOARD EMB\", emb_board.shape)\n",
    "        emb_color = self.emb_color(active_color)\n",
    "        # print(\"COLOR EMB\", emb_color.shape)\n",
    "        emb_color = emb_color.unsqueeze(dim=2).unsqueeze(dim=3).expand(-1, -1, 8, 8, -1)\n",
    "        # print(\"COLOR EXPAND\", emb_color.shape)\n",
    "\n",
    "        emb_board = torch.concat((emb_board, emb_color), axis=-1)\n",
    "        # print(\"BOARD CONCAT\", emb_board.shape)\n",
    "        # [N, D, H, W, C]) --> N, C, D, H, W\n",
    "        emb_board = emb_board.permute(0, 4, 1, 2, 3)\n",
    "        # print(\"BOARD AFTER PERMUTE\", emb_board.shape)\n",
    "\n",
    "        cnn1 = self.conv1(emb_board)\n",
    "        # print(\"conv1\", cnn1.shape)\n",
    "        cnn2 = self.conv2(emb_board)\n",
    "        # print(\"conv2\", cnn2.shape)\n",
    "        cnn3 = self.conv3(emb_board)\n",
    "        # print(\"conv3\", cnn3.shape)\n",
    "\n",
    "        cnn = torch.concat((cnn1, cnn2, cnn3), axis=1)\n",
    "        # print(\"conv1\", cnn.shape)\n",
    "        cnn = nn.MaxPool3d((1, 3, 3), padding=(0, 1, 1))(cnn)\n",
    "        cnn = self.conv4(cnn)\n",
    "\n",
    "        cnn = cnn.permute(0, 2, 1, 3, 4)\n",
    "        # print(cnn.shape)\n",
    "        cnn = cnn.reshape(cnn.size(0), cnn.size(1), -1)\n",
    "        features = self.fc_features(cnn)\n",
    "        # print(features.shape)\n",
    "        x1 = self.fc_position(cnn)\n",
    "        x2 = np.squeeze(self.fc_score(features))\n",
    "        x3 = self.fc_capture(features)\n",
    "        x4 = self.fc_outcome(features)\n",
    "\n",
    "        return {\n",
    "            \"position\": x1,\n",
    "            \"score\": x2,\n",
    "            \"capture\": x3,\n",
    "            \"outcome\": x4,\n",
    "        }\n",
    "\n",
    "\n",
    "collect()\n",
    "model = ChessNet()  # .to(device)\n",
    "# model = torch.load(\"models/chess-model-bigdata-v1-b256-epoch3-l_0.0001.pt\", weights_only=False)\n",
    "# model = torch.compile(model, mode=\"reduce-overhead\")\n",
    "# display(model)\n",
    "output = model(**x2)\n",
    "for k in output.keys():\n",
    "    print(f\"{k}->{output[k].shape} | {output[k].dtype}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d8109c4-4a8e-42bb-b864-47fa731782e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = model(**batch)\n",
    "# for k in output.keys():\n",
    "#     print(f\"{k}->{output[k].shape} | {output[k].dtype}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32d8419a-f471-4c0d-92f6-af822b9b02e6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# outcome = output['outcome']\n",
    "# print(outcome[:, 0, :].shape,batch[\"outcome\"][:, 0, :].shape)\n",
    "# print(outcome.shape,batch[\"outcome\"].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dcbea240-84b0-437f-9969-1638c8afa2e3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# del dl_train,dl_test, optimizer, loss_ce, loss, loss_mse, model\n",
    "# collect()"
   ]
  },
  {
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    "outputId": "67dc59e3-2c45-41f6-e08b-f5cf74a1f680",
    "tags": []
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   "outputs": [
    {
     "data": {
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
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     },
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       "version_major": 2,
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      "text/plain": [
       "  0%|          | 0/2407 [00:00<?, ?it/s]"
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    {
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py:603: UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at ../aten/src/ATen/native/Convolution.cpp:1031.)\n",
      "  return F.conv3d(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVG 9.451682902531928\n"
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    },
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       "version_major": 2,
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    {
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     "output_type": "stream",
     "text": [
      "AVG 6.747758463830227\n"
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    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVG 5.165015577179932\n"
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    {
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     "output_type": "stream",
     "text": [
      "AVG 3.8535757764418324\n"
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    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVG 2.9995555120116952\n"
     ]
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
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     "metadata": {},
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   ],
   "source": [
    "loss_ce = nn.CrossEntropyLoss()\n",
    "loss_mse = nn.MSELoss()\n",
    "optimizer = optim.AdamW(model.parameters(), lr=0.001)\n",
    "\n",
    "# FIXME: Understand a correct loss calculation mechanism.\n",
    "pos_weight = 3.0  # Maximize position accuracy, especially for the first frame\n",
    "capture_weight = 2.0  # Capture is important but secondary to position\n",
    "outcome_weight = 1.0  # Outcome is important, especially for the final move\n",
    "score_weight = 0.5  # Score is less important than others\n",
    "\n",
    "\n",
    "def train_step(model, inputs):\n",
    "    res = model(**inputs)\n",
    "    position = res[\"position\"].float()\n",
    "    score = torch.squeeze(res[\"score\"]).float()\n",
    "    capture = res[\"capture\"].float()\n",
    "    outcome = res[\"outcome\"].float()\n",
    "\n",
    "    loss = torch.tensor(0, device=device, dtype=torch.float)\n",
    "    loss += loss_mse(score, inputs[\"scores\"]) * score_weight\n",
    "    for i in range(position.shape[1]):\n",
    "        # Weight the loss ascending by the frame. We want to 'enforce' the network to at least understand whats happening\n",
    "        loss += (\n",
    "            (position.shape[1] - i)\n",
    "            * loss_ce(\n",
    "                torch.squeeze(position[:, i, :]),\n",
    "                torch.squeeze(inputs[\"move_index\"][:, i, :]),\n",
    "            )\n",
    "            * pos_weight\n",
    "        )\n",
    "        loss += (\n",
    "            (position.shape[1] - i)\n",
    "            * loss_ce(capture[:, i, :], inputs[\"is_capture\"][:, i, :])\n",
    "            * capture_weight\n",
    "        )\n",
    "        loss += (\n",
    "            (position.shape[1] - i)\n",
    "            * loss_ce(outcome[:, i, :], inputs[\"outcome\"][:, i, :])\n",
    "            * outcome_weight\n",
    "        )\n",
    "        # loss += (position.shape[1] - i)  * loss_mse(score[:, i], inputs[\"scores\"][:, i]) * score_weight\n",
    "\n",
    "    return loss\n",
    "\n",
    "\n",
    "collect()\n",
    "batch_size = 256\n",
    "dl_train = DataLoader(\n",
    "    ds_train,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    num_workers=3,\n",
    "    # prefetch_factor=5,\n",
    "    persistent_workers=True,\n",
    "    pin_memory=device.type == \"cuda\",\n",
    "    drop_last=True,\n",
    "    collate_fn=ds_train.collate_fn,\n",
    ")\n",
    "dl_test = DataLoader(\n",
    "    ds_test,\n",
    "    batch_size=_batch_size,\n",
    "    shuffle=False,\n",
    "    drop_last=True,\n",
    "    collate_fn=ds_test.collate_fn,\n",
    ")\n",
    "if device.type == \"cuda\":\n",
    "    dl_train = CudaDataPrefetcher(dl_train, device, 16)\n",
    "    dl_test = CudaDataPrefetcher(dl_test, device, 1)\n",
    "\n",
    "model.to(device)\n",
    "model.train()\n",
    "with torch.enable_grad():\n",
    "    for epoch in tqdm(range(15)):\n",
    "        with tqdm(\n",
    "            enumerate(dl_train),\n",
    "            total=len(ds_train.boards) // batch_size,\n",
    "            leave=False,\n",
    "            position=1,\n",
    "        ) as pbar:\n",
    "\n",
    "            avg_loss = 0\n",
    "            loss = 0.0\n",
    "            for idx, batch in pbar:\n",
    "                optimizer.zero_grad()\n",
    "                _loss = train_step(model, batch)\n",
    "                _loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                loss += _loss.item()\n",
    "                if idx % 50 == 0:\n",
    "                    # d = {k: f\"{v.detach().cpu().item():.2f}\" for k, v in loss.items()}\n",
    "                    pbar.set_description(f\"AVG: {loss/(idx+1)} |  {_loss.item()}\")\n",
    "                pbar.update(1)\n",
    "            print(f\"AVG {loss/idx}\")\n",
    "            model_name = f\"models/bigone/chess-model-alldata-v1-b{batch_size}-epoch{epoch}-l_0.001\"\n",
    "            torch.save(model, f\"{model_name}.pt\")\n",
    "            torch.save(model.state_dict(), f\"{model_name}_weight.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "24d59027-383e-4dd8-88bd-3addeeb6255f",
   "metadata": {
    "id": "24d59027-383e-4dd8-88bd-3addeeb6255f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model_name = f\"models/chess-model-alldata-v1---b{batch_size}-epoch3-l_0.001-finetuned\"\n",
    "# torch.save(model, f\"{model_name}.pt\")\n",
    "# torch.save(model.state_dict(), f\"{model_name}_weight.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a6309255-b581-4269-ae46-14ad9a33ab96",
   "metadata": {
    "id": "a6309255-b581-4269-ae46-14ad9a33ab96",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75dae9d993644a11be7a3df7a7ea9e50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/571 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.8357, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "accuracy = torchmetrics.Accuracy(task=\"multiclass\", num_classes=4095).to(device)\n",
    "\n",
    "accuracy.reset()\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for x3 in tqdm(\n",
    "        dl_test,\n",
    "        total=len(df_test) // _batch_size,\n",
    "        leave=False,\n",
    "        position=1,\n",
    "    ):\n",
    "        res = model(**x3)\n",
    "        position = res[\"position\"]\n",
    "        # Accuracy is calculated without take care about the \"time series prediction\"\n",
    "        # Instead, we are only interested in \"next move\"\n",
    "        for i in range(position.shape[1]):\n",
    "            accuracy.update(\n",
    "                torch.argmax(position[:, i], axis=-1),\n",
    "                torch.argmax(x3[\"move_index\"][:, i], axis=-1),\n",
    "            )\n",
    "\n",
    "print(accuracy.compute())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fce23c-d6a6-4d06-9811-4e355c95d23d",
   "metadata": {},
   "source": [
    "## Filter only the games for which we have a different prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "HNYG2T2PpMZh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "f84814322b614c10bb540a00de2360ba",
      "3b8762c4015a4690b46451d17d348442",
      "b40613cf58f940b1af4653c2d037c637",
      "4ae2c487c58a4504872a009892c3f093",
      "6c265c12ff3a442aa9fa7fe97a7add2f",
      "48cf54aa39334ebfbe544c9c1923d6c3",
      "2ab38b028b724757ac0b5ca007f4db7e",
      "af4167f382b64e138fa74396375a6de5",
      "19065ffb6c904aac9ee9ead53df1c088",
      "92a888103d504af3b360d4611ce6cbeb",
      "b5e58904d91f479f83f6fea324c6c9f0"
     ]
    },
    "id": "HNYG2T2PpMZh",
    "outputId": "47971b37-8748-4012-b5aa-65e13fa9ed45",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "max_example = 10\n",
    "model.eval()\n",
    "n = 0\n",
    "with torch.no_grad():\n",
    "    for x3 in tqdm(\n",
    "        dl_test,\n",
    "        total=len(df_test) // _batch_size,\n",
    "        leave=False,\n",
    "        position=1,\n",
    "    ):\n",
    "        best_moves = torch.argmax(x3[\"move_index\"], axis=-1)\n",
    "        res = model(**x3)\n",
    "        preds = torch.argmax(res[\"position\"], axis=-1)\n",
    "\n",
    "        # Print only n-window moves for which at least 1 is different (-> avoid to print correct prediction)\n",
    "        mask = torch.any(best_moves != preds, axis=1)\n",
    "        preds = preds[mask][:max_example]\n",
    "        best_moves = best_moves[mask][:max_example]\n",
    "        n += mask.sum()\n",
    "        for idx in range(preds.shape[0]):  # Batch size\n",
    "            display(Markdown(f\"#### Game START\"))\n",
    "            for move in range(preds.shape[1]):  # Frames\n",
    "                board = x3[\"board_tensor\"][idx, move]\n",
    "                best_move = torch.argmax(x3[\"move_index\"][idx, move]).item()\n",
    "\n",
    "                print(\n",
    "                    f\"Best Move: {fp.index_to_uci(best_move)} | Predicted: {fp.index_to_uci(preds[idx, move].item())}\"\n",
    "                )\n",
    "                fp.tensor_to_board_v2(board)\n",
    "            display(Markdown(f\"---------\"))\n",
    "        if n >= max_example:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a4ed74-10eb-42c1-bd76-b4120661cc75",
   "metadata": {},
   "source": [
    "# Fight with stockfish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6c310377-2f04-4fbe-86d0-f73af3e20163",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import chess.engine\n",
    "import chess\n",
    "\n",
    "STOCKFISH_PATH = \"./stockfish/stockfish-ubuntu-x86-64-avx2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "70d20857-d64f-4007-a050-e253bf881e6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"390\" height=\"390\"><desc><pre>r n b q k b n r\n",
       "p p . . p p p p\n",
       ". . . p . . . .\n",
       ". . p . . . . .\n",
       ". P . . . . . .\n",
       ". . . . . . . N\n",
       "P B P P P P P P\n",
       "R N . Q K B . 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      ],
      "text/plain": [
       "Board('rnbqkbnr/pp2pppp/3p4/2p5/1P6/7N/PBPPPPPP/RN1QKB1R b KQkq - 1 3')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def push_move(board, move):\n",
    "    board.push(move)\n",
    "    hist.append(board.fen())\n",
    "    color.append(len(board.move_stack) % 2)\n",
    "    # if len(hist) > 3:\n",
    "    #     hist.pop(0)\n",
    "    #     color.pop(0)\n",
    "    return\n",
    "\n",
    "\n",
    "WHITE = 0\n",
    "BLACK = 1\n",
    "_ = {WHITE: \"WHITE\", BLACK: \"BLACK\"}\n",
    "_map = {WHITE: [0, 1], BLACK: [1, 0]}\n",
    "\n",
    "\n",
    "def generate_moves(hist, color, model=model):\n",
    "    # The network is encoded in {0: White, 1: Black}\n",
    "    _c = [int(c) for c in color[-window:]]\n",
    "    _colors = [_map[c] for c in _c]\n",
    "    v1 = torch.stack([fp.to_tensor(v) for v in hist[-window:]])\n",
    "    v1 = torch.argmax(torch.unsqueeze(v1, 0), axis=-1)\n",
    "\n",
    "    _in = {\n",
    "        \"board_tensor\": v1.int().to(device),\n",
    "        \"active_color\": torch.ones(1, window).int().to(device),\n",
    "        \"is_capture\": torch.zeros(1, window, 2),\n",
    "        \"outcome\": torch.zeros(1, window, 2),\n",
    "        \"scores\": torch.zeros(1, window),\n",
    "    }\n",
    "\n",
    "    res = model(**_in)\n",
    "    return (\n",
    "        torch.squeeze(torch.argmax(res[\"position\"], axis=-1)).detach().cpu()[-1].item()\n",
    "    )\n",
    "\n",
    "\n",
    "engine = chess.engine.SimpleEngine.popen_uci(STOCKFISH_PATH)\n",
    "hist = []\n",
    "color = []\n",
    "\n",
    "\n",
    "def init_game():\n",
    "    model.eval()\n",
    "    board = chess.Board()\n",
    "    move = random.choice(list(board.legal_moves))\n",
    "    push_move(board, move)\n",
    "    for i in range(window - 1):\n",
    "        move = random.choice(list(board.legal_moves))\n",
    "        push_move(board, move)\n",
    "    mov_pred = generate_moves(hist, color)\n",
    "    display(board)\n",
    "    return board, hist, color, mov_pred\n",
    "\n",
    "\n",
    "board, hist, color, mov_pred = init_game()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cce57ce-fc12-488b-b80e-1f5b9d212971",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "idx = 0\n",
    "limit = chess.engine.Limit(depth=1)\n",
    "opponent = chess.engine.Opponent(is_engine=True, name=\"bot-v1\", title=\"\", rating=300)\n",
    "while not board.is_game_over():\n",
    "    # For some unkown reason the network is only able to play black\n",
    "    # I think that this reside in :\n",
    "    #  - how the score is encodednot a clear way to let the network\n",
    "    #  the real objective of the game instead of mimic best moves\n",
    "    if len(board.move_stack) % 2 == BLACK:\n",
    "        move = generate_moves(hist, color)\n",
    "        move = fp.index_to_uci(move)\n",
    "        move = chess.Move.from_uci(move)\n",
    "        print(f\"Model: {_[len(board.move_stack) % 2]} move {move}\", end=\"\")\n",
    "        if not board.is_legal(move):\n",
    "            move = random.choice(list(board.legal_moves))\n",
    "            print(\" | Non legal move! Doing random move:\", move)\n",
    "\n",
    "    else:\n",
    "        result = engine.play(\n",
    "            board, limit, info=chess.engine.INFO_SCORE, opponent=opponent\n",
    "        )\n",
    "        move = result.move\n",
    "\n",
    "    push_move(board, move)\n",
    "\n",
    "    display(board)\n",
    "    idx += 1\n",
    "    # if idx > 100:\n",
    "    #     print(\"to much!\")\n",
    "    #     break\n",
    "if board.is_game_over():\n",
    "    print(board.result())\n",
    "    print(\"Game over!\")\n",
    "    len(board.move_stack)\n",
    "    display(board)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89b8f29e-9e07-417b-99c0-3bbae3d878ef",
   "metadata": {},
   "source": [
    "# Random Game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "925cafa8-0494-4b9c-9d2a-56a2ef32aafe",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "idx = 0\n",
    "board, hist, color, mov_pred = init_game()\n",
    "while not board.is_game_over():\n",
    "    if len(board.move_stack) % 2 == BLACK:\n",
    "        move = generate_moves(hist, color)\n",
    "        move = fp.index_to_uci(move)\n",
    "        move = chess.Move.from_uci(move)\n",
    "        print(f\"Model: {_[len(board.move_stack) % 2]} move {move}\", end=\"\")\n",
    "        if not board.is_legal(move):\n",
    "            move = random.choice(list(board.legal_moves))\n",
    "            print(\" | Non legal move! Doing random move:\", move)\n",
    "    else:\n",
    "        move = random.choice(list(board.legal_moves))\n",
    "\n",
    "    push_move(board, move)\n",
    "\n",
    "    display(board)\n",
    "    idx += 1\n",
    "if board.is_game_over():\n",
    "    print(board.result())\n",
    "    print(\"Game over!\")\n",
    "    display(board)"
   ]
  }
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