{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2i8bR6Vv_vgH"
      },
      "source": [
        "# imports and installations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_t8UesX_xKF",
        "outputId": "8bbcff0d-f559-441e-8b4e-b44721450062"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "#!ls -lh /content/drive/MyDrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZC7rtPYJ_yjm",
        "outputId": "00bccb3f-21ff-42e1-8fc1-c9d8035cc71c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: zstandard in /usr/local/lib/python3.11/dist-packages (0.23.0)\n",
            "Collecting python-chess\n",
            "  Downloading python_chess-1.999-py3-none-any.whl.metadata (776 bytes)\n",
            "Collecting chess\n",
            "  Downloading chess-1.11.2.tar.gz (6.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting stockfish\n",
            "  Downloading stockfish-3.28.0-py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (4.0.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets) (3.18.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.0.2)\n",
            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.11/dist-packages (from datasets) (4.67.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n",
            "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2025.3.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.34.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from datasets) (25.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets) (6.0.2)\n",
            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (3.12.15)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.24.0->datasets) (4.14.1)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.24.0->datasets) (1.1.7)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.3)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2025.8.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.2)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2.6.1)\n",
            "Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (25.3.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.7.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.6.4)\n",
            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.3.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.20.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
            "Downloading python_chess-1.999-py3-none-any.whl (1.4 kB)\n",
            "Downloading stockfish-3.28.0-py3-none-any.whl (13 kB)\n",
            "Building wheels for collected packages: chess\n",
            "  Building wheel for chess (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for chess: filename=chess-1.11.2-py3-none-any.whl size=147775 sha256=268584f070c47d6a368557fedaf91a1c613d2e955c58678772a47bdcf9d25a37\n",
            "  Stored in directory: /root/.cache/pip/wheels/fb/5d/5c/59a62d8a695285e59ec9c1f66add6f8a9ac4152499a2be0113\n",
            "Successfully built chess\n",
            "Installing collected packages: stockfish, chess, python-chess\n",
            "Successfully installed chess-1.11.2 python-chess-1.999 stockfish-3.28.0\n"
          ]
        }
      ],
      "source": [
        "! pip3 install zstandard python-chess chess stockfish datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qkCMPfw_0DR",
        "outputId": "b260fb82-953b-4ae4-83c3-72c4b0661767"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m\r0% [Working]\u001b[0m\r            \rGet:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease [1,581 B]\n",
            "\u001b[33m\r0% [Connecting to archive.ubuntu.com] [Connecting to security.ubuntu.com (91.18\u001b[0m\u001b[33m\r0% [Connecting to archive.ubuntu.com] [Connecting to security.ubuntu.com (91.18\u001b[0m\u001b[33m\r0% [Connecting to archive.ubuntu.com] [Connecting to security.ubuntu.com (91.18\u001b[0m\r                                                                               \rGet:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  Packages [1,933 kB]\n",
            "Get:3 https://cli.github.com/packages stable InRelease [3,917 B]\n",
            "Get:4 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,632 B]\n",
            "Get:5 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n",
            "Hit:6 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n",
            "Hit:8 https://r2u.stat.illinois.edu/ubuntu jammy InRelease\n",
            "Get:9 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease [18.1 kB]\n",
            "Get:10 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [3,209 kB]\n",
            "Hit:11 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:12 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Get:13 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,575 kB]\n",
            "Get:15 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,271 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [3,521 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [35.2 kB]\n",
            "Fetched 12.0 MB in 4s (3,215 kB/s)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "35 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "\u001b[1;33mW: \u001b[0mSkipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\u001b[0m\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "Suggested packages:\n",
            "  polyglot xboard | scid\n",
            "The following NEW packages will be installed:\n",
            "  stockfish\n",
            "0 upgraded, 1 newly installed, 0 to remove and 35 not upgraded.\n",
            "Need to get 24.8 MB of archives.\n",
            "After this operation, 47.4 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 stockfish amd64 14.1-1 [24.8 MB]\n",
            "Fetched 24.8 MB in 4s (6,169 kB/s)\n",
            "Selecting previously unselected package stockfish.\n",
            "(Reading database ... 126380 files and directories currently installed.)\n",
            "Preparing to unpack .../stockfish_14.1-1_amd64.deb ...\n",
            "Unpacking stockfish (14.1-1) ...\n",
            "Setting up stockfish (14.1-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n"
          ]
        }
      ],
      "source": [
        "!apt update\n",
        "!apt install -y stockfish"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cL-g_aar_39m"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "from tokenizers import Tokenizer, models, trainers, pre_tokenizers\n",
        "from transformers import AutoTokenizer, GPT2Config, GPT2LMHeadModel, Trainer, TrainingArguments, DataCollatorForLanguageModeling\n",
        "import zstandard as zstd\n",
        "import chess.pgn\n",
        "import io\n",
        "import re\n",
        "import math\n",
        "from datasets import Dataset, DatasetDict\n",
        "from sklearn.model_selection import train_test_split\n",
        "import stockfish\n",
        "import chess\n",
        "import chess.engine\n",
        "import os\n",
        "import wandb\n",
        "import gc\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader, Sampler\n",
        "from transformers import get_cosine_schedule_with_warmup\n",
        "from torchtune.modules import RotaryPositionalEmbeddings\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYZ_FVDzNx5A"
      },
      "source": [
        "# download data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01clGLWOACgG",
        "outputId": "98f158d7-0124-4188-ca9a-72dbf9597469"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'game_text': '1. e4 1. .. d6 2. d4 2. .. N f6 3. B d3 3. .. B g4 4. f3 4. .. B c8 5. N e2 5. .. N DISAMBIG_FILE_b d7 6. O-O 6. .. g6 7. c4 7. .. c6 8. N DISAMBIG_FILE_b c3 8. .. a6 9. B e3 9. .. R b8 10. Q d2 10. .. b5 11. c x b5 11. .. a x b5 12. b4 12. .. N b6 13. a3 13. .. B g7 14. d5 14. .. c x d5 15. B x b5 + 15. .. N DISAMBIG_FILE_f d7 16. R DISAMBIG_FILE_a c1 16. .. d x e4 17. N x e4 17. .. O-O 18. N d4 18. .. N f6 19. N c6 19. .. N x e4 20. f x e4 20. .. Q c7 21. Q f2 21. .. R b7 22. N a5 22. .. Q b8 23. N x b7 23. .. Q x b7 24. B x b6 24. .. B e6 25. B c6 25. .. Q a6 26. Q e3 26. .. Q c8 27. B d4 27. .. f6 28. B d5 28. .. Q d7 29. Q b3 29. .. B x d5 30. Q x d5 + 30. .. e6 31. Q c6 31. .. R d8 32. Q x d7 32. .. R x d7 33. R c8 + 33. .. B f8 34. R DISAMBIG_FILE_f c1 34. .. K g7 35. R DISAMBIG_RANK_1 c7 35. .. B e7 36. R x d7 36. .. K h6 37. B e3 + 37. .. g5 38. R x e7 38. .. K g6 39. R x e6 39. .. h6 40. R f8 40. .. K g7 41. R DISAMBIG_FILE_e x f6 41. .. d5 42. e5 <EOS>', 'metadata': {'event': 'Bullet swiss https://lichess.org/swiss/m5SNuPal', 'site': 'https://lichess.org/USr1zaFa', 'date': '2025.02.01', 'utc_date': '2025.02.01', 'utc_time': '00:00:00', 'white': 'Solrac77', 'black': 'JoaoZugzwang_81', 'white_elo': '1946', 'black_elo': '1790', 'white_rating_diff': '+5', 'black_rating_diff': '-12', 'result': '1-0', 'termination': 'Time forfeit', 'eco': 'B00', 'opening': 'Pirc Defense', 'time_control': '120+1', 'move_count': 83}}\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 25000\n",
        "games_as_strings = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print(games_as_strings[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjmzXOsRCabA",
        "outputId": "adaba4cc-6082-4ef6-94f2-95371b773faa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2340\n"
          ]
        }
      ],
      "source": [
        "print(len(games_as_strings))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jFitNyTQoqD",
        "outputId": "896b9b2d-6942-4639-c947-900d1171da1d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "11680"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "2388 + 1404 + 1646 + 403 + 3394 + 105 + 2340"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmbDyRord4Dw",
        "outputId": "064fbb10-a7d7-4d1b-b798-fc9640059c8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[105, {'game_text': '1. e4 1. .. d6 2. B c4 2. .. N f6 3. B x f7 + 3. .. K x f7 4. N f3 4. .. g6 5. O-O 5. .. B g7 6. e5 6. .. d x e5 7. N x e5 + 7. .. K e8 8. f4 8. .. R f8 9. d4 9. .. N DISAMBIG_FILE_b d7 10. c3 10. .. N x e5 11. f x e5 11. .. N d7 12. R x f8 + 12. .. K x f8 13. Q f3 + 13. .. K g8 14. Q d5 + 14. .. K h8 15. B g5 15. .. N b6 16. Q x d8 + 16. .. B f8 17. B x e7 17. .. B f5 18. Q x f8 + 18. .. R x f8 19. B x f8 19. .. K g8 20. B h6 20. .. N d5 21. N d2 21. .. N e3 22. R f1 22. .. N x f1 23. N x f1 23. .. c5 24. N e3 24. .. c x d4 25. N x f5 25. .. g x f5 26. c x d4 26. .. K f7 27. K f2 27. .. K e6 28. K f3 28. .. K d5 29. K f4 29. .. K x d4 30. K x f5 30. .. K d5 31. e6 31. .. K d6 32. K f6 32. .. K c7 33. K f7 33. .. K d8 34. e7 + 34. .. K d7 35. e8 =Q + 35. .. K c7 36. B f4 + 36. .. K b6 37. Q e5 37. .. K a6 38. K e7 38. .. b6 39. K d7 39. .. h5 40. K c7 40. .. h4 41. b4 41. .. h3 42. a4 42. .. h x g2 43. b5 + 43. .. K a5 44. B d2 + 44. .. K x a4 45. Q d4 + 45. .. K x b5 46. Q b4 + 46. .. K a6 47. Q a4 # <EOS>', 'metadata': {'event': 'Rated Bullet game', 'site': 'https://lichess.org/OfIGdZax', 'date': '2025.03.01', 'utc_date': '2025.03.01', 'utc_time': '00:00:00', 'white': 'Kudaponi12', 'black': 'M1N1M3', 'white_elo': '1725', 'black_elo': '1716', 'white_rating_diff': '+6', 'black_rating_diff': '-5', 'result': '1-0', 'termination': 'Normal', 'eco': 'B00', 'opening': 'Pirc Defense', 'time_control': '60+0', 'move_count': 93}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_march.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 25000\n",
        "games_as_strings_march = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_march),games_as_strings_march[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUgul6W-eAAI",
        "outputId": "f62f565e-8037-46e2-8a2c-3667c2f3af56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[3394, {'game_text': '1. a4 1. .. e5 2. h4 2. .. N c6 3. c3 3. .. N f6 4. a5 4. .. h6 5. h5 5. .. a6 6. f3 6. .. B c5 7. Q b3 7. .. d6 8. e3 8. .. R b8 9. g4 9. .. B e6 10. Q c2 10. .. Q d7 11. g5 11. .. h x g5 12. h6 12. .. g x h6 13. R x h6 13. .. R x h6 14. N h3 14. .. B x h3 15. B x h3 15. .. R x h3 16. d4 16. .. B a7 17. Q b3 17. .. Q e6 18. e4 18. .. e x d4 19. Q x e6 + 19. .. f x e6 20. B x g5 20. .. N h7 21. B f4 21. .. R x f3 22. B h6 22. .. e5 23. B g7 23. .. K f7 24. B h6 24. .. R g8 25. c x d4 25. .. N x d4 26. N c3 26. .. N c2 + 27. K e2 27. .. N x a1 28. K x f3 28. .. R g6 29. B e3 29. .. N b3 30. B x a7 30. .. N c5 31. N d5 31. .. K e6 32. N x c7 + 32. .. K d7 33. N d5 33. .. K c6 34. N b6 34. .. N f6 35. N c8 35. .. N h5 36. N e7 + 36. .. K d7 37. N x g6 37. .. K e6 38. B x c5 38. .. d x c5 39. K g4 39. .. c4 40. K x h5 40. .. K f6 41. N h8 41. .. c3 42. b x c3 42. .. b6 43. c4 43. .. b x a5 44. c5 44. .. a4 45. c6 45. .. a3 46. c7 46. .. a2 47. c8 =Q 47. .. a1 =Q 48. Q c6 + <EOS>', 'metadata': {'event': 'Rated Bullet game', 'site': 'https://lichess.org/Xlu7vFVg', 'date': '2025.04.01', 'utc_date': '2025.04.01', 'utc_time': '00:00:06', 'white': 'hgomezp', 'black': 'snaraya9', 'white_elo': '1725', 'black_elo': '1719', 'white_rating_diff': '+6', 'black_rating_diff': '-6', 'result': '1-0', 'termination': 'Time forfeit', 'eco': 'A00', 'opening': 'Ware Opening: Crab Variation', 'time_control': '60+0', 'move_count': 95}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_april.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 25000\n",
        "games_as_strings_april = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_april),games_as_strings_april[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iA6sSQpSftLF",
        "outputId": "48f62be2-a373-4ced-e1d5-89fdd9da8249"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[403, {'game_text': '1. d4 1. .. N f6 2. N f3 2. .. g6 3. B f4 3. .. B g7 4. e3 4. .. d5 5. c4 5. .. O-O 6. N c3 6. .. c5 7. B d3 7. .. N c6 8. O-O 8. .. c x d4 9. e x d4 9. .. d x c4 10. B x c4 10. .. N a5 11. B e2 11. .. N d5 12. B g5 12. .. N x c3 13. b x c3 13. .. h6 14. B h4 14. .. g5 15. B g3 15. .. B f5 16. R e1 16. .. R c8 17. R c1 17. .. N c4 18. Q b3 18. .. B e6 19. B x c4 19. .. B x c4 20. Q x b7 20. .. R c7 21. Q x c7 <EOS>', 'metadata': {'event': 'Rated Blitz game', 'site': 'https://lichess.org/8qnGOgRl', 'date': '2025.05.01', 'utc_date': '2025.05.01', 'utc_time': '00:00:06', 'white': 'GASPADAREK', 'black': 'KakTebeTakoe', 'white_elo': '1949', 'black_elo': '1836', 'white_rating_diff': '+4', 'black_rating_diff': '-4', 'result': '1-0', 'termination': 'Normal', 'eco': 'D93', 'opening': 'Grünfeld Defense: Three Knights Variation, Hungarian Variation', 'time_control': '300+3', 'move_count': 41}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_may.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 25000\n",
        "games_as_strings_may = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_may),games_as_strings_may[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlqCC3h_FI71",
        "outputId": "cb35bd57-a07e-4de8-b2e9-71bc6f012e51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1646, {'game_text': '1. d4 1. .. N f6 2. N f3 2. .. g6 3. B f4 3. .. B g7 4. e3 4. .. d5 5. c4 5. .. O-O 6. N c3 6. .. c5 7. B d3 7. .. N c6 8. O-O 8. .. c x d4 9. e x d4 9. .. d x c4 10. B x c4 10. .. N a5 11. B e2 11. .. N d5 12. B g5 12. .. N x c3 13. b x c3 13. .. h6 14. B h4 14. .. g5 15. B g3 15. .. B f5 16. R e1 16. .. R c8 17. R c1 17. .. N c4 18. Q b3 18. .. B e6 19. B x c4 19. .. B x c4 20. Q x b7 20. .. R c7 21. Q x c7 <EOS>', 'metadata': {'event': 'Rated Blitz game', 'site': 'https://lichess.org/8qnGOgRl', 'date': '2025.05.01', 'utc_date': '2025.05.01', 'utc_time': '00:00:06', 'white': 'GASPADAREK', 'black': 'KakTebeTakoe', 'white_elo': '1949', 'black_elo': '1836', 'white_rating_diff': '+4', 'black_rating_diff': '-4', 'result': '1-0', 'termination': 'Normal', 'eco': 'D93', 'opening': 'Grünfeld Defense: Three Knights Variation, Hungarian Variation', 'time_control': '300+3', 'move_count': 41}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_january.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 25000\n",
        "games_as_strings_jan = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_jan),games_as_strings_may[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bMXjNOiGGK2",
        "outputId": "56cbe6e3-6c7c-40c6-bf42-7d2126a7718d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1646, {'game_text': '1. d4 1. .. N f6 2. N f3 2. .. g6 3. B f4 3. .. B g7 4. e3 4. .. d5 5. c4 5. .. O-O 6. N c3 6. .. c5 7. B d3 7. .. N c6 8. O-O 8. .. c x d4 9. e x d4 9. .. d x c4 10. B x c4 10. .. N a5 11. B e2 11. .. N d5 12. B g5 12. .. N x c3 13. b x c3 13. .. h6 14. B h4 14. .. g5 15. B g3 15. .. B f5 16. R e1 16. .. R c8 17. R c1 17. .. N c4 18. Q b3 18. .. B e6 19. B x c4 19. .. B x c4 20. Q x b7 20. .. R c7 21. Q x c7 <EOS>', 'metadata': {'event': 'Rated Blitz game', 'site': 'https://lichess.org/8qnGOgRl', 'date': '2025.05.01', 'utc_date': '2025.05.01', 'utc_time': '00:00:06', 'white': 'GASPADAREK', 'black': 'KakTebeTakoe', 'white_elo': '1949', 'black_elo': '1836', 'white_rating_diff': '+4', 'black_rating_diff': '-4', 'result': '1-0', 'termination': 'Normal', 'eco': 'D93', 'opening': 'Grünfeld Defense: Three Knights Variation, Hungarian Variation', 'time_control': '300+3', 'move_count': 41}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_january.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 50000\n",
        "games_as_strings_jan = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_jan),games_as_strings_may[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONM4dcYqgn9l",
        "outputId": "bdf033fd-0642-4b77-f284-a66112f2ce49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-07-20 14:25:19--  https://database.lichess.org/standard/lichess_db_standard_rated_2024-10.pgn.zst\n",
            "Resolving database.lichess.org (database.lichess.org)... 141.95.66.62, 2001:41d0:700:5e3e::\n",
            "Connecting to database.lichess.org (database.lichess.org)|141.95.66.62|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 30743665262 (29G) [application/octet-stream]\n",
            "Saving to: ‘lichess_games_october_2024.pgn.zst’\n",
            "\n",
            "lichess_games_octob 100%[===================>]  28.63G  12.8MB/s    in 43m 41s \n",
            "\n",
            "2025-07-20 15:09:02 (11.2 MB/s) - ‘lichess_games_october_2024.pgn.zst’ saved [30743665262/30743665262]\n",
            "\n",
            "config\t\t\t\t     lichess_games_march.pgn.zst\n",
            "lichess_games_april.pgn.zst\t     lichess_games_may.pgn.zst\n",
            "lichess_games_december_2024.pgn.zst  lichess_games_november_2024.pgn.zst\n",
            "lichess_games_january.pgn.zst\t     lichess_games_october_2024.pgn.zst\n",
            "lichess_games_june.pgn.zst\t     lichess_games.pgn.zst\n"
          ]
        }
      ],
      "source": [
        "# READ & DOWNLOAD CHESS DATASET, save to drive!\n",
        "!wget https://database.lichess.org/standard/lichess_db_standard_rated_2024-10.pgn.zst -O lichess_games_october_2024.pgn.zst\n",
        "!mkdir -p /content/drive/MyDrive/chess_datasets\n",
        "!cp lichess_games_october_2024.pgn.zst /content/drive/MyDrive/chess_datasets/\n",
        "!ls /content/drive/MyDrive/chess_datasets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bd6uyd34ksUQ",
        "outputId": "88b318b5-0164-427d-fafc-ae18766a5b97"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-07-20 15:14:51--  https://database.lichess.org/standard/lichess_db_standard_rated_2024-09.pgn.zst\n",
            "Resolving database.lichess.org (database.lichess.org)... 141.95.66.62, 2001:41d0:700:5e3e::\n",
            "Connecting to database.lichess.org (database.lichess.org)|141.95.66.62|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 28565025781 (27G) [application/octet-stream]\n",
            "Saving to: ‘lichess_games_september_2024.pgn.zst’\n",
            "\n",
            "    lichess_games_s  65%[============>       ]  17.37G  12.7MB/s    eta 15m 56s"
          ]
        }
      ],
      "source": [
        "# READ & DOWNLOAD CHESS DATASET, save to drive!\n",
        "!wget https://database.lichess.org/standard/lichess_db_standard_rated_2024-09.pgn.zst -O lichess_games_september_2024.pgn.zst\n",
        "!mkdir -p /content/drive/MyDrive/chess_datasets\n",
        "!cp lichess_games_september_2024.pgn.zst /content/drive/MyDrive/chess_datasets/\n",
        "!ls /content/drive/MyDrive/chess_datasets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "4fX78a_uk9ox",
        "outputId": "05082aa9-8e11-488b-f488-dd4e85379eaa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-07-20 16:10:29--  https://database.lichess.org/standard/lichess_db_standard_rated_2024-08.pgn.zst\n",
            "Resolving database.lichess.org (database.lichess.org)... 141.95.66.62, 2001:41d0:700:5e3e::\n",
            "Connecting to database.lichess.org (database.lichess.org)|141.95.66.62|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 30038633172 (28G) [application/octet-stream]\n",
            "Saving to: ‘lichess_games_august_2024.pgn.zst’\n",
            "\n",
            "lichess_games_augus 100%[===================>]  27.98G  11.5MB/s    in 46m 8s  \n",
            "\n",
            "2025-07-20 16:56:39 (10.3 MB/s) - ‘lichess_games_august_2024.pgn.zst’ saved [30038633172/30038633172]\n",
            "\n",
            "config\t\t\t\t     lichess_games_march.pgn.zst\n",
            "lichess_games_april.pgn.zst\t     lichess_games_may.pgn.zst\n",
            "lichess_games_august_2024.pgn.zst    lichess_games_november_2024.pgn.zst\n",
            "lichess_games_december_2024.pgn.zst  lichess_games_october_2024.pgn.zst\n",
            "lichess_games_january.pgn.zst\t     lichess_games.pgn.zst\n",
            "lichess_games_june.pgn.zst\t     lichess_games_september_2024.pgn.zst\n"
          ]
        }
      ],
      "source": [
        "# READ & DOWNLOAD CHESS DATASET, save to drive!\n",
        "!wget https://database.lichess.org/standard/lichess_db_standard_rated_2024-08.pgn.zst -O lichess_games_august_2024.pgn.zst\n",
        "!mkdir -p /content/drive/MyDrive/chess_datasets\n",
        "!cp lichess_games_august_2024.pgn.zst /content/drive/MyDrive/chess_datasets/\n",
        "!ls /content/drive/MyDrive/chess_datasets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "arjEJKLtlCxV",
        "outputId": "7672bf06-d53d-489b-a0cf-d3ee79543fec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-07-20 13:39:53--  https://database.lichess.org/standard/lichess_db_standard_rated_2024-07.pgn.zst\n",
            "Resolving database.lichess.org (database.lichess.org)... 141.95.66.62, 2001:41d0:700:5e3e::\n",
            "Connecting to database.lichess.org (database.lichess.org)|141.95.66.62|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 29295798733 (27G) [application/octet-stream]\n",
            "Saving to: ‘lichess_games_july_2024.pgn.zst’\n",
            "\n",
            "lichess_games_july_  98%[==================> ]  26.92G  12.0MB/s    in 38m 8s  \n",
            "\n",
            "\n",
            "Cannot write to ‘lichess_games_july_2024.pgn.zst’ (Success).\n",
            "cp: error writing '/content/drive/MyDrive/chess_datasets/lichess_games_july_2024.pgn.zst': No space left on device\n",
            "lichess_games_august_2024.pgn.zst  lichess_games_october_2024.pgn.zst\n",
            "lichess_games_july_2024.pgn.zst    lichess_games_september_2024.pgn.zst\n"
          ]
        }
      ],
      "source": [
        "# READ & DOWNLOAD CHESS DATASET, save to drive!\n",
        "!wget https://database.lichess.org/standard/lichess_db_standard_rated_2024-07.pgn.zst -O lichess_games_july_2024.pgn.zst\n",
        "!mkdir -p /content/drive/MyDrive/chess_datasets\n",
        "!cp lichess_games_july_2024.pgn.zst /content/drive/MyDrive/chess_datasets/\n",
        "!ls /content/drive/MyDrive/chess_datasets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "BAhPjY-8lJSP",
        "outputId": "3417b35a-505b-4395-f5fc-3f0326002089"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-07-20 14:18:03--  https://database.lichess.org/standard/lichess_db_standard_rated_2024-06.pgn.zst\n",
            "Resolving database.lichess.org (database.lichess.org)... 141.95.66.62, 2001:41d0:700:5e3e::\n",
            "Connecting to database.lichess.org (database.lichess.org)|141.95.66.62|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 29078356575 (27G) [application/octet-stream]\n",
            "Saving to: ‘lichess_games_june_2024.pgn.zst’\n",
            "\n",
            "lichess_games_june_   0%[                    ]       0  --.-KB/s    in 0s      \n",
            "\n",
            "\n",
            "Cannot write to ‘lichess_games_june_2024.pgn.zst’ (Success).\n",
            "lichess_games_august_2024.pgn.zst  lichess_games_october_2024.pgn.zst\n",
            "lichess_games_july_2024.pgn.zst    lichess_games_september_2024.pgn.zst\n",
            "lichess_games_june_2024.pgn.zst\n"
          ]
        }
      ],
      "source": [
        "# READ & DOWNLOAD CHESS DATASET, save to drive!\n",
        "!wget https://database.lichess.org/standard/lichess_db_standard_rated_2024-06.pgn.zst -O lichess_games_june_2024.pgn.zst\n",
        "!mkdir -p /content/drive/MyDrive/chess_datasets\n",
        "!cp lichess_games_june_2024.pgn.zst /content/drive/MyDrive/chess_datasets/\n",
        "!ls /content/drive/MyDrive/chess_datasets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGG4xGMLLhcl",
        "outputId": "fe2c05a0-a2cc-40af-e6e5-f31c655ebc68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1404, {'game_text': '1. d4 1. .. N f6 2. N f3 2. .. g6 3. B f4 3. .. B g7 4. e3 4. .. d5 5. c4 5. .. O-O 6. N c3 6. .. c5 7. B d3 7. .. N c6 8. O-O 8. .. c x d4 9. e x d4 9. .. d x c4 10. B x c4 10. .. N a5 11. B e2 11. .. N d5 12. B g5 12. .. N x c3 13. b x c3 13. .. h6 14. B h4 14. .. g5 15. B g3 15. .. B f5 16. R e1 16. .. R c8 17. R c1 17. .. N c4 18. Q b3 18. .. B e6 19. B x c4 19. .. B x c4 20. Q x b7 20. .. R c7 21. Q x c7 <EOS>', 'metadata': {'event': 'Rated Blitz game', 'site': 'https://lichess.org/8qnGOgRl', 'date': '2025.05.01', 'utc_date': '2025.05.01', 'utc_time': '00:00:06', 'white': 'GASPADAREK', 'black': 'KakTebeTakoe', 'white_elo': '1949', 'black_elo': '1836', 'white_rating_diff': '+4', 'black_rating_diff': '-4', 'result': '1-0', 'termination': 'Normal', 'eco': 'D93', 'opening': 'Grünfeld Defense: Three Knights Variation, Hungarian Variation', 'time_control': '300+3', 'move_count': 41}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_june.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 50000\n",
        "games_as_strings_june = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_june),games_as_strings_may[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypudT8lZPQl8",
        "outputId": "d0f06979-f18d-4ede-ac0a-0596124aef02"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2388, {'game_text': '1. d4 1. .. b6 2. e3 2. .. B b7 3. N f3 3. .. N f6 4. N c3 4. .. e6 5. B d3 5. .. B e7 6. B d2 6. .. O-O 7. Q e2 7. .. N e8 8. O-O-O 8. .. f5 9. K b1 9. .. c5 10. R DISAMBIG_FILE_d g1 10. .. c x d4 11. N x d4 11. .. N c6 12. f3 12. .. N x d4 13. e x d4 13. .. N f6 14. h3 14. .. N d5 15. N x d5 15. .. B x d5 16. c3 16. .. Q c7 17. g3 17. .. b5 18. R c1 18. .. Q a5 19. c4 19. .. b x c4 20. B x a5 20. .. c x d3 21. Q x d3 21. .. R DISAMBIG_FILE_a b8 22. B c3 22. .. R b6 23. g4 23. .. R DISAMBIG_FILE_f b8 24. g x f5 24. .. a5 25. f x e6 25. .. B x e6 26. d5 26. .. B f7 27. R DISAMBIG_FILE_h e1 27. .. B g6 28. Q x g6 28. .. h x g6 29. R c2 29. .. B f6 30. B x f6 30. .. g x f6 31. a3 31. .. K f7 32. R d1 32. .. R b3 33. K a2 33. .. R x f3 34. d6 34. .. R x h3 35. R d5 35. .. a4 36. R a5 36. .. R DISAMBIG_FILE_h b3 37. R x a4 37. .. f5 38. R a7 38. .. K e6 39. R d2 39. .. f4 40. R a4 40. .. g5 41. R e4 + 41. .. K f5 42. R DISAMBIG_FILE_e d4 42. .. f3 43. R d5 + 43. .. K f4 44. R DISAMBIG_RANK_2 d4 + 44. .. K g3 45. R b4 45. .. R DISAMBIG_RANK_3 x b4 46. a x b4 46. .. R x b4 47. R f5 47. .. R a4 + 48. K b3 48. .. R b4 + 49. K x b4 49. .. f2 50. R x g5 + <EOS>', 'metadata': {'event': 'Rated Bullet game', 'site': 'https://lichess.org/8PwzdhWe', 'date': '2024.12.01', 'utc_date': '2024.12.01', 'utc_time': '00:00:10', 'white': 'alphazero2045', 'black': 'Lamazukamba2555-17', 'white_elo': '2051', 'black_elo': '1966', 'white_rating_diff': '+5', 'black_rating_diff': '-4', 'result': '1-0', 'termination': 'Time forfeit', 'eco': 'A40', 'opening': 'English Defense', 'time_control': '60+0', 'move_count': 99}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_december_2024.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 50000\n",
        "games_as_strings_december_2024 = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_december_2024),games_as_strings_december_2024[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o31IBBXzQ2uS",
        "outputId": "c0b5193e-f699-40c4-a851-de398010ccad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[595, {'game_text': '1. d4 1. .. d5 2. N f3 2. .. N c6 3. e3 3. .. B f5 4. B d3 4. .. B x d3 5. c x d3 5. .. e6 6. O-O 6. .. N f6 7. N DISAMBIG_FILE_b d2 7. .. B d6 8. e4 8. .. d x e4 9. d x e4 9. .. B e7 10. e5 10. .. N d5 11. a3 11. .. O-O 12. b4 12. .. a6 13. N b3 13. .. f6 14. R e1 14. .. f x e5 15. N x e5 15. .. Q d6 16. f4 16. .. N x f4 17. N x c6 17. .. Q x c6 18. Q g4 18. .. N g6 19. Q x e6 + 19. .. Q x e6 20. R x e6 20. .. B d6 21. B e3 21. .. N f4 22. R e4 22. .. N d5 23. B f2 23. .. N c3 24. R DISAMBIG_FILE_e e1 24. .. B x h2 + 25. K x h2 25. .. R x f2 26. N c5 26. .. b6 27. N e6 27. .. N d5 28. R e5 28. .. N f4 29. R g1 29. .. R e8 30. N x f4 30. .. R x e5 31. d x e5 31. .. R x f4 32. R e1 32. .. K f7 33. e6 + 33. .. K e7 34. K g3 34. .. R f6 35. K g4 35. .. R x e6 36. R c1 36. .. R g6 + 37. K f3 37. .. h5 38. R x c7 + 38. .. K d6 39. R a7 39. .. a5 40. b x a5 40. .. b x a5 41. R x a5 41. .. K c6 42. R x h5 42. .. R f6 + 43. K e4 <EOS>', 'metadata': {'event': 'Rated Bullet game', 'site': 'https://lichess.org/nBYhJNjK', 'date': '2024.11.01', 'utc_date': '2024.11.01', 'utc_time': '00:00:00', 'white': 'Argala_JMB', 'black': 'Shantanu_Chetia', 'white_elo': '1668', 'black_elo': '1631', 'white_rating_diff': '+6', 'black_rating_diff': '-5', 'result': '1-0', 'termination': 'Time forfeit', 'eco': 'D02', 'opening': \"Queen's Pawn Game: Chigorin Variation\", 'time_control': '120+1', 'move_count': 85}}]\n"
          ]
        }
      ],
      "source": [
        "file_path = \"/content/drive/MyDrive/chess_datasets/lichess_games_november_2024.pgn.zst\" # find dataset here: https://database.lichess.org/\n",
        "# Replace with number of games\n",
        "number_of_games = 50000\n",
        "games_as_strings_november_2024 = read_first_n_games_as_strings(file_path, n=number_of_games)\n",
        "\n",
        "# Print the first game's PGN as a string\n",
        "print([len(games_as_strings_november_2024),games_as_strings_november_2024[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LOAdk83kt5s",
        "outputId": "5cbd259f-bf8d-4956-9a5b-dd32e901214a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cleaned_games_25k.json\t\t     lichess_games_june.pgn.zst\n",
            "config\t\t\t\t     lichess_games_march.pgn.zst\n",
            "lichess_games_april.pgn.zst\t     lichess_games_may.pgn.zst\n",
            "lichess_games_august_2024.pgn.zst    lichess_games_november_2024.pgn.zst\n",
            "lichess_games_december_2024.pgn.zst  lichess_games_october_2024.pgn.zst\n",
            "lichess_games_january.pgn.zst\t     lichess_games.pgn.zst\n",
            "lichess_games_july_2024.pgn.zst      lichess_games_september_2024.pgn.zst\n"
          ]
        }
      ],
      "source": [
        "!ls /content/drive/MyDrive/chess_datasets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tGcEaBgXYEAy"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yzK8aaY_67o"
      },
      "source": [
        "# Preprocess Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JJEVdZr_92f",
        "outputId": "ec42052b-4c62-4296-b66f-54dcb87bf203"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['DISAMBIG_RANK_1', 'e7'] ['DISAMBIG_FILE_a', 'e7'] ['DISAMBIG_FILE_a', 'DISAMBIG_RANK_3', 'e7']\n"
          ]
        }
      ],
      "source": [
        "def parse_target_corrected(target):\n",
        "    file_letters = 'abcdefgh'\n",
        "    rank_numbers = '12345678'\n",
        "\n",
        "    if len(target) == 2:\n",
        "        return [target]\n",
        "\n",
        "    elif len(target) == 3:\n",
        "        first, second, third = target\n",
        "\n",
        "        # Only the first character is a disambiguator\n",
        "        disambig_token = None\n",
        "        if first in file_letters:\n",
        "            disambig_type = 'DISAMBIG_FILE'\n",
        "            disambig_token = f'DISAMBIG_FILE_{first.lower()}'\n",
        "        elif first in rank_numbers:\n",
        "            disambig_type = 'DISAMBIG_RANK'\n",
        "            disambig_token = f'DISAMBIG_RANK_{first}'\n",
        "        else:\n",
        "            disambig_type = 'UNKNOWN'\n",
        "\n",
        "        target_square = second + third if second in file_letters and third in rank_numbers else target\n",
        "\n",
        "        return [disambig_token, target_square]\n",
        "\n",
        "    elif len(target) == 4:\n",
        "        # Full disambiguation: <from_square><to_square>\n",
        "        from_file, from_rank, to_file, to_rank = target\n",
        "        if (from_file in file_letters and from_rank in rank_numbers and\n",
        "            to_file in file_letters and to_rank in rank_numbers):\n",
        "            return [\n",
        "                f'DISAMBIG_FILE_{from_file.lower()}',\n",
        "                    f'DISAMBIG_RANK_{from_rank}',\n",
        "                    to_file + to_rank\n",
        "                ]\n",
        "\n",
        "    return [target]\n",
        "\n",
        "print(parse_target_corrected('1e7'), parse_target_corrected('ae7'), parse_target_corrected('a3e7'))\n",
        "\n",
        "def looks_like_target_square(token):\n",
        "    if not (2 <= len(token) <= 4):\n",
        "        return False\n",
        "\n",
        "    files = set(\"abcdefgh\")\n",
        "    ranks = set(\"12345678\")\n",
        "\n",
        "    # Strip possible disambiguators (first 1–2 chars), check if remaining is a valid square\n",
        "    if len(token) >= 2:\n",
        "        square = token[-2:]\n",
        "        file, rank = square[0], square[1]\n",
        "        if file in files and rank in ranks:\n",
        "            return True\n",
        "\n",
        "    return False\n",
        "\n",
        "\n",
        "def process_tokens(tokens):\n",
        "    output = []\n",
        "    for token in tokens:\n",
        "        if looks_like_target_square(token):  # Write this helper\n",
        "            disambig_parts = parse_target_corrected(token)\n",
        "            output.extend(disambig_parts)\n",
        "        else:\n",
        "            output.append(token)\n",
        "    return ' '.join(output)\n",
        "\n",
        "test_string = '1. e4 1. .. e5 2. N f3 2. .. N c6 3. B c4 3. .. N f6 4. O-O 4. .. B e7 5. d3 5. .. d6 6. c3 6. .. B g4 7. h3 7. .. B h5 8. B e3 8. .. Q d7 9. N bd2 9. .. h6 10. b4 10. .. g5 11. Q b3 11. .. g4 12. h x g4 12. .. N x g4 13. N h4 13. .. O-O -O 14. N f5 14. .. B g5 15. f4 15. .. e x f4 16. B x f4 16. .. B x f4 17. R x f4 17. .. R hg8 18. B d5 18. .. N e3 19. R f2 19. .. N x d5 20. e x d5 20. .. N e5 21. c4 21. .. N f3 + 22. N x f3 22. .. Q x f5 23. N d4 23. .. Q e5 24. N f5 24. .. R g5 25. R af1 25. .. R dg8 26. N h4 26. .. Q g3 27. d4 27. .. Q x h4 28. c5 28. .. Q x d4 29. K h1 29. .. Q h4 + 30. Q h3 + 30. .. Q x h3 + 31. g x h3 31. .. R x d5 32. c x d6 32. .. R x d6 33. a3 33. .. R d3 34. K h2 34. .. R x a3 35. R f5 35. .. B g6 36. R 5f2 36. .. R d8 37. R e2 37. .. R dd3 38. R fe1 38. .. R x h3 + 39. K g2 39. .. R ag3 + 40. K f2 40. .. b6 41. b5 41. .. B h5 42. R e8 + 42. .. K b7 43. R 1e7 43. .. R f3 + 44. K g2 44. .. R hg3 + 45. K h2 45. .. R g5 46. R d8 46. .. f5 47. R dd7 47. .. R c3 48. R h7 48. .. B g4 49. R x h6 49. .. R h5 + 50. R x h5 50. .. B x h5 51. R h7 51. .. B g4 52. R g7 52. .. R c5 53. K g3 53. .. R x b5 54. R f7 54. .. a5 55. K f4 55. .. a4 56. R d7 56. .. a3 57. R d2 57. .. R b2 58. R d3 58. .. a2 59. R a3 59. .. B d1 60. K e3 60. .. B b3 61. K d3 61. .. R b1 62. K c3 62. .. a1 =Q + 63. K b4 63. .. Q x a3 + 64. K x a3 64. .. f4 65. K b4 65. .. f3 66. K c3 66. .. f2 67. K d4 67. .. f1 =Q 68. K e5 68. .. R c1 69. K d4 69. .. Q d1 + 70. K e3 70. .. R c2 71. K f4 71. .. R e2 72. K f5 72. .. Q f1 + 73. K g4 73. .. R g2 + 74. K h5 74. .. Q h1 #'\n",
        "process_tokens(test_string.split(' '))\n",
        "\n",
        "def format_pgn(text, check_loose_letters=False):\n",
        "\n",
        "    text = re.sub(r'\\[.*?\\]', '', text)                          # ✅ remove metadata\n",
        "    text = re.sub(r'\\s*(1-0|0-1|1/2-1/2)\\s*', '', text)          # ✅ remove game result\n",
        "    text = ' '.join(text.split())                                # ✅ normalize whitespace\n",
        "    text = re.sub(r'\\{.*?\\}', '', text)                          # ✅ remove comments\n",
        "    text = ' '.join(text.split())                                # ✅ re-normalize whitespace\n",
        "    text = re.sub(r'\\$(2|4|6)\\b', '', text)                      # ✅ remove PGN annotations\n",
        "    text = re.sub(r'(\\d+)\\.\\.\\.', r'\\1. ..', text)               # ✅ adds space between '.' and '..' to indicate black move\n",
        "\n",
        "    # disambiguates special piece moves e.g. R1xe4 → R DISAMBIG_RANK_1 x e4\n",
        "    text = re.sub(r'([KQRBN])([1-8])x([a-h][1-8])', r'\\1 DISAMBIG_RANK_\\2 x \\3', text)\n",
        "    text = re.sub(r'([KQRBN])([a-h])x([a-h][1-8])', r'\\1 DISAMBIG_FILE_\\2 x \\3', text)\n",
        "    text = re.sub(r'([KQRBN])([1-8])([a-h][1-8])', r'\\1 DISAMBIG_RANK_\\2 \\3', text)\n",
        "    text = re.sub(r'([KQRBN])([a-h])([a-h][1-8])', r'\\1 DISAMBIG_FILE_\\2 \\3', text)\n",
        "\n",
        "    text = re.sub(r'(O-O-O|O-O|x|\\+|#|=Q|=R|=B|=N)', r' \\1 ', text) # ✅ adds space between special moves\n",
        "\n",
        "    piece_pattern = piece_pattern = re.compile(r'\\b([KQRBN])\\b') #re.compile(r'([KQRBN])')\n",
        "    square_pattern = re.compile(r'([a-h][1-8])')\n",
        "\n",
        "    text = piece_pattern.sub(r'\\1 ', text)  # Piece spacing\n",
        "    text = square_pattern.sub(r' \\1 ', text)\n",
        "\n",
        "    def check_loose_file_letters(tokens):\n",
        "        for i in range(len(tokens)):\n",
        "            if tokens[i] in 'abcdefgh':\n",
        "                before = tokens[i - 1] if i > 0 else None\n",
        "                after = tokens[i + 1] if i + 1 < len(tokens) else None\n",
        "                print(f\"Token: {tokens[i]} | Before: {before} | After: {after}\")\n",
        "\n",
        "    if check_loose_letters:\n",
        "      check_loose_file_letters(text.split()) #--> meant to be a sanity check for loose file or rank names\n",
        "\n",
        "    text += ' <EOS>'\n",
        "\n",
        "    return ' '.join(text.split())\n",
        "\n",
        "def extract_metadata_from_game(game):\n",
        "    \"\"\"Extract relevant metadata from a python-chess PGN game object.\"\"\"\n",
        "    headers = game.headers\n",
        "\n",
        "    return {\n",
        "        \"event\": headers.get(\"Event\", \"\"),\n",
        "        \"site\": headers.get(\"Site\", \"\"),\n",
        "        \"date\": headers.get(\"Date\", \"\"),\n",
        "        \"utc_date\": headers.get(\"UTCDate\", \"\"),\n",
        "        \"utc_time\": headers.get(\"UTCTime\", \"\"),\n",
        "        \"white\": headers.get(\"White\", \"\"),\n",
        "        \"black\": headers.get(\"Black\", \"\"),\n",
        "        \"white_elo\": headers.get(\"WhiteElo\", \"\"),\n",
        "        \"black_elo\": headers.get(\"BlackElo\", \"\"),\n",
        "        \"white_rating_diff\": headers.get(\"WhiteRatingDiff\", \"\"),\n",
        "        \"black_rating_diff\": headers.get(\"BlackRatingDiff\", \"\"),\n",
        "        \"result\": headers.get(\"Result\", \"\"),\n",
        "        \"termination\": headers.get(\"Termination\", \"\"),\n",
        "        \"eco\": headers.get(\"ECO\", \"\"),\n",
        "        \"opening\": headers.get(\"Opening\", \"\"),\n",
        "        \"time_control\": headers.get(\"TimeControl\", \"\")\n",
        "    }\n",
        "\n",
        "def is_game_legal(game):\n",
        "    board = game.board()\n",
        "    try:\n",
        "        for move in game.mainline_moves():\n",
        "            if move not in board.legal_moves:\n",
        "                return False\n",
        "            board.push(move)\n",
        "        return True\n",
        "    except Exception:\n",
        "        return False\n",
        "\n",
        "def count_full_moves(text):\n",
        "    return len(re.findall(r'\\b\\d+\\.', text))\n",
        "\n",
        "def read_first_n_games_as_strings(file_path, n=100):\n",
        "    with open(file_path, 'rb') as f:\n",
        "        dctx = zstd.ZstdDecompressor()\n",
        "        decompressed = dctx.stream_reader(f)\n",
        "        pgn_text = io.TextIOWrapper(decompressed, encoding='utf-8')\n",
        "\n",
        "        games = []\n",
        "        while len(games) < n:\n",
        "            game = chess.pgn.read_game(pgn_text)\n",
        "            if game is None or game.headers.get(\"Result\") not in {\"1-0\", \"0-1\", \"1/2-1/2\"}:\n",
        "                break  # Stop if no more games are available\n",
        "\n",
        "            if not is_game_legal(game):\n",
        "                continue\n",
        "\n",
        "            # Convert game to string\n",
        "            game_str = io.StringIO()\n",
        "            game.accept(chess.pgn.StringExporter())  # Corrected line\n",
        "            metadata = extract_metadata_from_game(game)\n",
        "            text = str(game)\n",
        "            # TODO: REMOVE LAST CLAUSE IF ONLY WANT WINNING GAMES @SUHAS\n",
        "            # TODO: POTENTIALLY DO MORE FILTERING TO ENSURE GAME HAS BEEN COMPLETED PROPERLY?\n",
        "            #print(metadata['result'],  metadata['white_elo'],  metadata['black_elo'])\n",
        "            if metadata['result']: #metadata['result'] == '1-0' and int(metadata['white_elo']) >= 1600 and int(metadata['black_elo']) >= 1600:#or '0-1' in text or '1/2-1/2' in text:\n",
        "                cleaned_game = format_pgn(text)\n",
        "                move_count = count_full_moves(cleaned_game)\n",
        "                metadata['move_count'] = move_count\n",
        "                if move_count and len(cleaned_game):\n",
        "                    record = {\n",
        "                        \"game_text\": cleaned_game,\n",
        "                        \"metadata\": metadata\n",
        "                    }\n",
        "                    games.append(record)\n",
        "\n",
        "\n",
        "    return games"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oaHkake1YyuB"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RcHB4QMOZ-6D"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5yIDCsvgOBKg"
      },
      "source": [
        "# read all games"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gurxCwFUZ-8f"
      },
      "outputs": [],
      "source": [
        "import zstandard as zstd\n",
        "import chess.pgn\n",
        "import io\n",
        "import json\n",
        "\n",
        "def process_and_write_games_from_zst(\n",
        "    file_path,\n",
        "    output_path,\n",
        "    max_games=None,  # set to None to process all games\n",
        "    min_elo=0,\n",
        "    print_every=1000,\n",
        "):\n",
        "    count = 0\n",
        "\n",
        "    with open(file_path, 'rb') as f, open(output_path, 'w') as out_f:\n",
        "        dctx = zstd.ZstdDecompressor()\n",
        "        stream = dctx.stream_reader(f)\n",
        "        pgn_text = io.TextIOWrapper(stream, encoding='utf-8')\n",
        "\n",
        "        while True:\n",
        "            game = chess.pgn.read_game(pgn_text)\n",
        "            if game is None:\n",
        "                break\n",
        "\n",
        "            # Basic filters\n",
        "            result = game.headers.get(\"Result\")\n",
        "            if result not in {\"1-0\", \"0-1\", \"1/2-1/2\"}:\n",
        "                continue\n",
        "            if not is_game_legal(game):\n",
        "                continue\n",
        "\n",
        "            # Metadata + text\n",
        "            metadata = extract_metadata_from_game(game)\n",
        "            text = str(game)\n",
        "            cleaned_game = format_pgn(text)\n",
        "            move_count = count_full_moves(cleaned_game)\n",
        "            metadata['move_count'] = move_count\n",
        "\n",
        "            # Elo filter\n",
        "            try:\n",
        "                if int(metadata[\"white_elo\"]) < min_elo or int(metadata[\"black_elo\"]) < min_elo and move_count < 40:\n",
        "                    continue\n",
        "            except (ValueError, TypeError):\n",
        "                continue\n",
        "\n",
        "            # Final check\n",
        "            if move_count and cleaned_game:\n",
        "                record = {\n",
        "                    \"game_text\": cleaned_game,\n",
        "                    \"metadata\": metadata,\n",
        "                }\n",
        "                json.dump(record, out_f)\n",
        "                out_f.write(\"\\n\")\n",
        "                count += 1\n",
        "\n",
        "                if print_every and count % print_every == 0:\n",
        "                    print(f\"{count} games processed and written.\")\n",
        "\n",
        "                if max_games and count >= max_games:\n",
        "                    break\n",
        "\n",
        "    print(f\"Done. {count} games written to {output_path}.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HCji4Y4dahFG"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "def process_all_zst_files(\n",
        "    input_dir,\n",
        "    output_path,\n",
        "    prefix=\"lichess_games_\",\n",
        "    max_games_total=None,\n",
        "    **kwargs  # passed to `process_and_write_games_from_zst`\n",
        "):\n",
        "    input_dir = Path(input_dir)\n",
        "    files = sorted(input_dir.glob(f\"{prefix}*.pgn.zst\"))\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    with open(output_path, 'w') as out_f:\n",
        "        for file in files:\n",
        "            print(f\"Processing {file.name}...\")\n",
        "            file_count = 0\n",
        "\n",
        "            with open(file, 'rb') as f:\n",
        "                dctx = zstd.ZstdDecompressor()\n",
        "                stream = dctx.stream_reader(f)\n",
        "                pgn_text = io.TextIOWrapper(stream, encoding='utf-8')\n",
        "\n",
        "                while True:\n",
        "                    game = chess.pgn.read_game(pgn_text)\n",
        "                    if game is None:\n",
        "                        break\n",
        "\n",
        "                    # Skip invalid games\n",
        "                    if game.headers.get(\"Result\") not in {\"1-0\", \"0-1\", \"1/2-1/2\"}:\n",
        "                        continue\n",
        "                    if not is_game_legal(game):\n",
        "                        continue\n",
        "\n",
        "                    metadata = extract_metadata_from_game(game)\n",
        "                    text = str(game)\n",
        "                    cleaned_game = format_pgn(text)\n",
        "                    move_count = count_full_moves(cleaned_game)\n",
        "                    metadata['move_count'] = move_count\n",
        "\n",
        "                    \"\"\"try:\n",
        "                        if int(metadata[\"white_elo\"]) < kwargs.get(\"min_elo\", 1600) or int(metadata[\"black_elo\"]) < kwargs.get(\"min_elo\", 1600):\n",
        "                            continue\n",
        "                    except (ValueError, TypeError):\n",
        "                        continue\"\"\"\n",
        "\n",
        "                    if move_count >= 40 and int(metadata['white_elo']) >= 1600 and int(metadata['black_elo']) >= 1600 and cleaned_game:\n",
        "                        json.dump({\n",
        "                            \"game_text\": cleaned_game,\n",
        "                            \"metadata\": metadata,\n",
        "                            \"result\": metadata['result'],\n",
        "                            \"white_elo\": metadata['white_elo'],\n",
        "                            \"black_elo\": metadata['black_elo'],\n",
        "                            \"move_count\": metadata['move_count']\n",
        "                        }, out_f)\n",
        "                        out_f.write(\"\\n\")\n",
        "\n",
        "                        count += 1\n",
        "                        file_count += 1\n",
        "\n",
        "                        if kwargs.get(\"print_every\", 1000) and file_count % kwargs[\"print_every\"] == 0:\n",
        "                            print(f\"{file_count} games processed from {file.name}...\")\n",
        "\n",
        "                        if max_games_total and count >= max_games_total:\n",
        "                            print(f\"Reached max total of {max_games_total} games.\")\n",
        "                            return\n",
        "\n",
        "            print(f\"Finished {file.name}: {file_count} games written.\")\n",
        "\n",
        "    print(f\"✅ Done. Total {count} games written to {output_path}.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BroWkRwwakql",
        "outputId": "49fad5d9-05f8-4978-8e7d-724f7da12761"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "2486500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "2487000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4908000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4908500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4909000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4909500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4910000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4915000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4918000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4919000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4920000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4920500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4921000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4921500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4922000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4922500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4923000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4923500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4924000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4928000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4930000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4930500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4931000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4931500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4932000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4932500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4933000 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4940000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4940500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4941000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4941500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4942000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4942500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4943000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4943500 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4947000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4947500 games processed from lichess_games_august_2024.pgn.zst...\n",
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            "4949000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4949500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4950000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4950500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4951000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4951500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4952000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4952500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4953000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4953500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4954000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4954500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4955000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4955500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4956000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4956500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4957000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4957500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4958000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4958500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4959000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4959500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4960000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4960500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4961000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4961500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4962000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4962500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4963000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4963500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4964000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4964500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4965000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4965500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4966000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4966500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4967000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4967500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4968000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4968500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4969000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4969500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4970000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4970500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4971000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4971500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4972000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4972500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4973000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4973500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4974000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4974500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4975000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4975500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4976000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4976500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4977000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4977500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4978000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4978500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4979000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4979500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4980000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4980500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4981000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4981500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4982000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4982500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4983000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4983500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4984000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4984500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4985000 games processed from lichess_games_august_2024.pgn.zst...\n",
            "4985500 games processed from lichess_games_august_2024.pgn.zst...\n",
            "Reached max total of 5000000 games.\n"
          ]
        }
      ],
      "source": [
        "process_all_zst_files(\n",
        "    input_dir=\"/content/drive/MyDrive/chess_datasets\",\n",
        "    output_path=\"/content/drive/MyDrive/processed_games_v4_5_mil_games.jsonl\",\n",
        "    max_games_total=5000000,  # Optional cap\n",
        "    min_elo=1600,\n",
        "    print_every=500\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEPpSXWZsgvn",
        "outputId": "f509352e-66c2-4859-d79b-4e94c643b9af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total games: 5000000\n"
          ]
        }
      ],
      "source": [
        "def count_games_in_jsonl(path):\n",
        "    with open(path, 'r') as f:\n",
        "        return sum(1 for line in f if line.strip())\n",
        "\n",
        "# Example\n",
        "num_games = count_games_in_jsonl(\"/content/drive/MyDrive/processed_games_v4_5_mil_games.jsonl\")\n",
        "print(f\"Total games: {num_games}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNseOw5WOXkF"
      },
      "source": [
        "# create vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJQfmzEuOYlQ",
        "outputId": "bf1d9272-0b37-4310-f12b-6c9f96316403"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['#',\n",
              "  '+',\n",
              "  '..',\n",
              "  '1.',\n",
              "  '10.',\n",
              "  '100.',\n",
              "  '101.',\n",
              "  '102.',\n",
              "  '103.',\n",
              "  '104.',\n",
              "  '105.',\n",
              "  '106.',\n",
              "  '107.',\n",
              "  '108.',\n",
              "  '109.',\n",
              "  '11.',\n",
              "  '110.',\n",
              "  '111.',\n",
              "  '112.',\n",
              "  '113.'],\n",
              " 402)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "import json\n",
        "from collections import Counter\n",
        "\n",
        "# Path to the dataset\n",
        "file_path = \"/content/drive/MyDrive/processed_games_v4_5_mil_games.jsonl\"\n",
        "\n",
        "# Initialize a counter for vocabulary\n",
        "vocab_counter = Counter()\n",
        "\n",
        "# Read and parse each game's 'game_text' field\n",
        "with open(file_path, 'r') as f:\n",
        "    for line in f:\n",
        "        data = json.loads(line)\n",
        "        game_text = data.get('game_text', '')\n",
        "        tokens = game_text.split()\n",
        "        vocab_counter.update(tokens)\n",
        "\n",
        "# Get the vocabulary as a sorted list\n",
        "vocab = sorted(vocab_counter.keys())\n",
        "\n",
        "# Display first few vocab entries and total vocab size\n",
        "vocab[:20], len(vocab)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOiFZBOWPdKE",
        "outputId": "b0090936-5946-4966-d199-1111e22c5cd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(403, 403)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "vocab = sorted(vocab)\n",
        "token_to_id = {token: idx + 1 for idx, token in enumerate(vocab)}\n",
        "id_to_token = {idx: token for token, idx in token_to_id.items()}\n",
        "token_to_id['<PAD>'] = 0\n",
        "id_to_token[0] = '<PAD>'\n",
        "\n",
        "len(token_to_id), len(id_to_token)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mCGA6SJpPvsh"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "# Set output directory\n",
        "output_dir = '/content/drive/MyDrive/chess_datasets/vocab'\n",
        "\n",
        "# Save vocab list\n",
        "with open(f'{output_dir}/vocab_5_mill_games.json', 'w') as f:\n",
        "    json.dump(vocab, f, indent=2)\n",
        "\n",
        "# Save token_to_id dictionary\n",
        "with open(f'{output_dir}/token_to_id5_mill_games.json', 'w') as f:\n",
        "    json.dump(token_to_id, f, indent=2)\n",
        "\n",
        "# Save id_to_token dictionary\n",
        "with open(f'{output_dir}/id_to_token5_mill_games.json', 'w') as f:\n",
        "    # Convert integer keys to strings for JSON\n",
        "    json.dump({str(k): v for k, v in id_to_token.items()}, f, indent=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Source file path\n",
        "src = \"/content/drive/MyDrive/chess_datasets/vocab/id_to_token5_mill_games.json\"\n",
        "\n",
        "# Destination file path with new name\n",
        "dst = \"/content/drive/MyDrive/chess_datasets/5_million_games/vocab/id_to_token.json\"\n",
        "\n",
        "# Move and rename\n",
        "shutil.move(src, dst)\n",
        "\n",
        "print(f\"Moved and renamed file to: {dst}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_m-URwflBHMO",
        "outputId": "2f4333e5-0202-4821-e48d-af3384887fc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moved and renamed file to: /content/drive/MyDrive/chess_datasets/5_million_games/vocab/id_to_token.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Source file path\n",
        "src = \"/content/drive/MyDrive/chess_datasets/vocab/token_to_id5_mill_games.json\"\n",
        "\n",
        "# Destination file path with new name\n",
        "dst = \"/content/drive/MyDrive/chess_datasets/5_million_games/vocab/token_to_id.json\"\n",
        "\n",
        "# Move and rename\n",
        "shutil.move(src, dst)\n",
        "\n",
        "print(f\"Moved and renamed file to: {dst}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YatCJkvFA_cs",
        "outputId": "04815ca1-b562-4285-bd17-e427ed561acc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moved and renamed file to: /content/drive/MyDrive/chess_datasets/5_million_games/vocab/token_to_id.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Source file path\n",
        "src = \"/content/drive/MyDrive/chess_datasets/vocab/vocab_5_mill_games.json\"\n",
        "\n",
        "# Destination file path with new name\n",
        "dst = \"/content/drive/MyDrive/chess_datasets/5_million_games/vocab/vocab.json\"\n",
        "\n",
        "# Move and rename\n",
        "shutil.move(src, dst)\n",
        "\n",
        "print(f\"Moved and renamed file to: {dst}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsr7m-HuAUcH",
        "outputId": "fa1170e8-a269-4d8e-b773-0847d41df7f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moved and renamed file to: /content/drive/MyDrive/chess_datasets/5_million_games/vocab/vocab.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Source file path\n",
        "src = \"/content/drive/MyDrive/processed_games_v4_5_mil_games.jsonl\"\n",
        "\n",
        "# Destination file path with new name\n",
        "dst = \"/content/drive/MyDrive/chess_datasets/5_million_games/processed_games.jsonl\"\n",
        "\n",
        "# Move and rename\n",
        "shutil.move(src, dst)\n",
        "\n",
        "print(f\"Moved and renamed file to: {dst}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XercR4YK_6Cx",
        "outputId": "313a93de-7435-487a-ee0f-08858dd12616"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moved and renamed file to: /content/drive/MyDrive/chess_datasets/5_million_games/processed_games.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBeuc8o_2S-2"
      },
      "source": [
        "# creating white wins only dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIKPg55zs6Xa",
        "outputId": "eb42a09d-f5a5-4247-eec8-a55302000fc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total games: 5000000\n",
            "White wins: 2422215 (48.44%)\n",
            "Black wins: 2331735 (46.63%)\n",
            "Draws: 246050 (4.92%)\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "\n",
        "def count_white_wins(jsonl_path):\n",
        "    total = 0\n",
        "    white_wins = 0\n",
        "    black_wins = 0\n",
        "    draws = 0\n",
        "\n",
        "    with open(jsonl_path, 'r') as f:\n",
        "        for line in f:\n",
        "            if not line.strip():\n",
        "                continue\n",
        "            try:\n",
        "                game = json.loads(line)\n",
        "                white_elo = game.get(\"metadata\", {}).get(\"white_elo\")\n",
        "                black_elo = game.get(\"metadata\", {}).get(\"black_elo\")\n",
        "                if white_elo and black_elo:\n",
        "                  if int(white_elo) >= 1600 and int(black_elo) >= 1600:\n",
        "                    total += 1\n",
        "                    if game.get(\"metadata\", {}).get(\"result\") == \"1-0\":\n",
        "                        white_wins += 1\n",
        "                    if game.get(\"metadata\", {}).get(\"result\") == \"0-1\":\n",
        "                        black_wins += 1\n",
        "                    if game.get(\"metadata\", {}).get(\"result\") == \"1/2-1/2\":\n",
        "                        draws += 1\n",
        "            except json.JSONDecodeError:\n",
        "                continue  # skip corrupted lines\n",
        "\n",
        "    print(f\"Total games: {total}\")\n",
        "    print(f\"White wins: {white_wins} ({white_wins / total:.2%})\")\n",
        "    print(f\"Black wins: {black_wins} ({black_wins / total:.2%})\")\n",
        "    print(f\"Draws: {draws} ({draws / total:.2%})\")\n",
        "# Example usage\n",
        "count_white_wins( \"/content/drive/MyDrive/chess_datasets/5_million_games/processed_games.jsonl\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6aNd310lyfaI"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "# Paths\n",
        "input_path =  \"/content/drive/MyDrive/chess_datasets/5_million_games/processed_games.jsonl\"\n",
        "output_all_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/all_games_with_ids.jsonl\"\n",
        "output_white_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/white_only_games.jsonl\"\n",
        "\n",
        "# Processing\n",
        "with open(input_path, \"r\") as infile, \\\n",
        "     open(output_all_path, \"w\") as all_outfile, \\\n",
        "     open(output_white_path, \"w\") as white_outfile:\n",
        "\n",
        "    for idx, line in enumerate(infile):\n",
        "        game = json.loads(line)\n",
        "        game['id'] = idx  # Assign numeric ID\n",
        "\n",
        "        # Write the full game with ID\n",
        "        all_outfile.write(json.dumps(game) + \"\\n\")\n",
        "\n",
        "        # Check conditions for white-win subset\n",
        "        meta = game.get('metadata', {})\n",
        "        if (\n",
        "            int(meta.get(\"white_elo\", 0)) >= 1600 and\n",
        "            int(meta.get(\"black_elo\", 0)) >= 1600 and\n",
        "            meta.get(\"move_count\",0) <= 100 and\n",
        "            meta.get(\"move_count\",0) >= 40 and\n",
        "            meta.get(\"result\") == \"1-0\"\n",
        "        ):\n",
        "            white_outfile.write(json.dumps(game) + \"\\n\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FKm0qwoa2amR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8565591-1013-4036-b6fb-e610c07ad5e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total white-win games available: 2013159\n",
            "✅ Done! Splits saved:\n",
            "→ /content/drive/MyDrive/chess_datasets/5_million_games/white_wins_only/train.jsonl: 270000 games\n",
            "→ /content/drive/MyDrive/chess_datasets/5_million_games/white_wins_only/val.jsonl: 23000 games\n",
            "→ /content/drive/MyDrive/chess_datasets/5_million_games/white_wins_only/test.jsonl: 23000 games\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import random\n",
        "\n",
        "# Parameters\n",
        "input_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/white_only_games.jsonl\"\n",
        "train_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/white_wins_only/train.jsonl\"\n",
        "val_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/white_wins_only/val.jsonl\"\n",
        "test_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/white_wins_only/test.jsonl\"\n",
        "train_size = 270000\n",
        "val_size = 23000\n",
        "test_size = 23000\n",
        "total_needed = train_size + val_size + test_size\n",
        "\n",
        "# Read and shuffle\n",
        "with open(input_path, \"r\") as f:\n",
        "    games = [json.loads(line) for line in f]\n",
        "\n",
        "print(f\"Total white-win games available: {len(games)}\")\n",
        "\n",
        "# Shuffle in-place with fixed seed for reproducibility\n",
        "random.seed(42)\n",
        "random.shuffle(games)\n",
        "\n",
        "# Select subset and split\n",
        "selected_games = games[:total_needed]\n",
        "train_games = selected_games[:train_size]\n",
        "val_games = selected_games[train_size:train_size+val_size]\n",
        "test_games = selected_games[train_size+val_size:]\n",
        "\n",
        "# Save splits\n",
        "def save_games(path, games):\n",
        "    with open(path, \"w\") as f:\n",
        "        for game in games:\n",
        "            f.write(json.dumps(game) + \"\\n\")\n",
        "\n",
        "save_games(train_path, train_games)\n",
        "save_games(val_path, val_games)\n",
        "save_games(test_path, test_games)\n",
        "\n",
        "print(\"✅ Done! Splits saved:\")\n",
        "print(f\"→ {train_path}: {len(train_games)} games\")\n",
        "print(f\"→ {val_path}: {len(val_games)} games\")\n",
        "print(f\"→ {test_path}: {len(test_games)} games\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4fmYyS43P1_"
      },
      "source": [
        "# create balanced dataset only"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYbVGPvR3Ts0",
        "outputId": "569e03c7-1e77-4825-c999-51b1946b7c3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "White wins: 2013159\n",
            "Black wins: 1939272\n",
            "Draws     : 92376\n",
            "✅ Balanced dataset saved to: /content/drive/MyDrive/chess_datasets/5_million_games/balanced_dataset_with_ids.jsonl\n",
            "→ Total games: 277128 (White/Black/Draw: 92376 each)\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import random\n",
        "\n",
        "# Parameters\n",
        "input_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/all_games_with_ids.jsonl\"\n",
        "output_path = \"/content/drive/MyDrive/chess_datasets/5_million_games/balanced_dataset_with_ids.jsonl\"\n",
        "random.seed(42)\n",
        "\n",
        "# Storage\n",
        "white_wins, black_wins, draws = [], [], []\n",
        "\n",
        "# Filter and group\n",
        "with open(input_path, \"r\") as f:\n",
        "    for line in f:\n",
        "        game = json.loads(line)\n",
        "        metadata = game.get(\"metadata\", {})\n",
        "        result = metadata.get(\"result\")\n",
        "        white_elo = metadata.get(\"white_elo\", 0)\n",
        "        black_elo = metadata.get(\"black_elo\", 0)\n",
        "        moves = game.get(\"move_count\", 0)\n",
        "\n",
        "        if int(white_elo) >= 1600 and int(black_elo) >= 1600 and moves >= 40 and moves <= 100:\n",
        "            if result == \"1-0\":\n",
        "                white_wins.append(game)\n",
        "            elif result == \"0-1\":\n",
        "                black_wins.append(game)\n",
        "            elif result == \"1/2-1/2\":\n",
        "                draws.append(game)\n",
        "\n",
        "print(f\"White wins: {len(white_wins)}\")\n",
        "print(f\"Black wins: {len(black_wins)}\")\n",
        "print(f\"Draws     : {len(draws)}\")\n",
        "\n",
        "# Balance based on smallest group\n",
        "min_size = min(len(white_wins), len(black_wins), len(draws))\n",
        "white_sample = random.sample(white_wins, min_size)\n",
        "black_sample = random.sample(black_wins, min_size)\n",
        "draw_sample = random.sample(draws, min_size)\n",
        "\n",
        "# Combine and shuffle\n",
        "balanced_games = white_sample + black_sample + draw_sample\n",
        "random.shuffle(balanced_games)\n",
        "\n",
        "# Assign unique numeric IDs\n",
        "for idx, game in enumerate(balanced_games):\n",
        "    game[\"id\"] = idx\n",
        "\n",
        "# Save\n",
        "with open(output_path, \"w\") as f:\n",
        "    for game in balanced_games:\n",
        "        f.write(json.dumps(game) + \"\\n\")\n",
        "\n",
        "print(f\"✅ Balanced dataset saved to: {output_path}\")\n",
        "print(f\"→ Total games: {len(balanced_games)} (White/Black/Draw: {min_size} each)\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eoreSRF26OLy"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import random\n",
        "from pathlib import Path\n",
        "\n",
        "# Load all games\n",
        "input_path = Path(\"/content/drive/MyDrive/chess_datasets/5_million_games/balanced_dataset_with_ids.jsonl\")\n",
        "with input_path.open() as f:\n",
        "    games = [json.loads(line) for line in f]\n",
        "\n",
        "# Assign numeric IDs\n",
        "for idx, game in enumerate(games):\n",
        "    game[\"id\"] = idx\n",
        "\n",
        "# Filter for ELO >= 1600 for both players and move count between 40-100\n",
        "eligible_games = [\n",
        "    g for g in games\n",
        "    if int(g[\"metadata\"][\"white_elo\"]) >= 1600 and\n",
        "       int(g[\"metadata\"][\"black_elo\"]) >= 1600 and\n",
        "       int(g[\"metadata\"][\"move_count\"]) >= 40 and\n",
        "       int(g[\"metadata\"][\"move_count\"]) <= 100\n",
        "]\n",
        "\n",
        "# Split by result\n",
        "white_wins = [g for g in eligible_games if g[\"metadata\"][\"result\"] == \"1-0\"]\n",
        "black_wins = [g for g in eligible_games if g[\"metadata\"][\"result\"] == \"0-1\"]\n",
        "draws      = [g for g in eligible_games if g[\"metadata\"][\"result\"] == \"1/2-1/2\"]\n",
        "\n",
        "# Shuffle for randomness\n",
        "random.seed(42)\n",
        "random.shuffle(white_wins)\n",
        "random.shuffle(black_wins)\n",
        "random.shuffle(draws)\n",
        "\n",
        "# Choose minimum to balance all classes\n",
        "n_train = 90000 * 3\n",
        "n_per_class_train = n_train // 3  # ~8333\n",
        "\n",
        "# Reserve 20% for validation and test (10% each)\n",
        "n_valtest_per_class = 2000 #int(n_per_class_train * 0.2)\n",
        "n_val_per_class = 2000 #n_valtest_per_class // 2\n",
        "n_test_per_class = n_valtest_per_class - n_val_per_class\n",
        "\n",
        "# Final counts\n",
        "train_white = white_wins[:n_per_class_train]\n",
        "train_black = black_wins[:n_per_class_train]\n",
        "train_draws = draws[:n_per_class_train]\n",
        "\n",
        "val_white = white_wins[n_per_class_train:n_per_class_train+n_val_per_class]\n",
        "val_black = black_wins[n_per_class_train:n_per_class_train+n_val_per_class]\n",
        "val_draws = draws[n_per_class_train:n_per_class_train+n_val_per_class]\n",
        "\n",
        "test_white = white_wins[n_per_class_train+n_val_per_class:n_per_class_train+n_val_per_class+n_test_per_class]\n",
        "test_black = black_wins[n_per_class_train+n_val_per_class:n_per_class_train+n_val_per_class+n_test_per_class]\n",
        "test_draws = draws[n_per_class_train+n_val_per_class:n_per_class_train+n_val_per_class+n_test_per_class]\n",
        "\n",
        "# Combine splits\n",
        "train_set = train_white + train_black + train_draws\n",
        "val_set = val_white + val_black + val_draws\n",
        "test_set = test_white + test_black + test_draws\n",
        "\n",
        "# Shuffle each final set\n",
        "random.shuffle(train_set)\n",
        "random.shuffle(val_set)\n",
        "random.shuffle(test_set)\n",
        "\n",
        "# Save to disk\n",
        "output_dir = Path(\"/content/drive/MyDrive/chess_datasets/5_million_games/balanced\")\n",
        "output_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def write_jsonl(path, data):\n",
        "    with path.open(\"w\") as f:\n",
        "        for game in data:\n",
        "            f.write(json.dumps(game) + \"\\n\")\n",
        "\n",
        "write_jsonl(output_dir / \"train.jsonl\", train_set)\n",
        "write_jsonl(output_dir / \"val.jsonl\", val_set)\n",
        "write_jsonl(output_dir / \"test.jsonl\", test_set)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CSz4w33BWRy-"
      },
      "outputs": [],
      "source": [
        "def count_games_in_jsonl(path):\n",
        "    with open(path, 'r') as f:\n",
        "        return sum(1 for line in f if line.strip())\n",
        "count_games_in_jsonl(output_dir / \"train.jsonl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkaVBezL2XsG"
      },
      "source": [
        "# read all games"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPYV-yTwYyw7"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "def save_games_to_jsonl(games, output_path):\n",
        "    with open(output_path, 'w') as f:\n",
        "        for game in games:\n",
        "            json.dump(game, f)\n",
        "            f.write('\\n')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hRcYaOhEYyzr"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "from pathlib import Path\n",
        "\n",
        "input_dir = Path(\"/content/drive/MyDrive/chess_datasets\")\n",
        "output_path = Path(\"/content/drive/MyDrive/processed_games.jsonl\")\n",
        "\n",
        "# Replace with your actual game processing logic\n",
        "def process_game(pgn_string):\n",
        "    # Return None if game is invalid or should be skipped\n",
        "    return {\n",
        "        \"game_text\": \"e4 e5 Nf3 Nc6\",  # Example\n",
        "        \"metadata\": {\n",
        "            \"result\": \"1-0\",\n",
        "            \"num_moves\": 42,\n",
        "            \"opening\": \"Sicilian Defense\"\n",
        "        }\n",
        "    }\n",
        "\n",
        "def process_all_games(input_dir, prefix=\"lichess_games_\", output_path=\"processed.jsonl\"):\n",
        "    input_files = sorted(input_dir.glob(f\"{prefix}*\"))\n",
        "\n",
        "    with open(output_path, \"w\") as outfile:\n",
        "        for file in input_files:\n",
        "            print(f\"Processing file: {file}\")\n",
        "            with open(file, \"r\") as f:\n",
        "                pgn_text = \"\"\n",
        "                for line in f:\n",
        "                    if line.strip() == \"\" and pgn_text.strip():\n",
        "                        game_obj = process_game(pgn_text)\n",
        "                        if game_obj:\n",
        "                            json.dump(game_obj, outfile)\n",
        "                            outfile.write(\"\\n\")\n",
        "                        pgn_text = \"\"\n",
        "                    else:\n",
        "                        pgn_text += line\n",
        "\n",
        "                # process final game if file doesn't end with newline\n",
        "                if pgn_text.strip():\n",
        "                    game_obj = process_game(pgn_text)\n",
        "                    if game_obj:\n",
        "                        json.dump(game_obj, outfile)\n",
        "                        outfile.write(\"\\n\")\n",
        "\n",
        "process_all_games(input_dir, output_path=output_path)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqPYJxpFYy3d"
      },
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      "source": []
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      "execution_count": null,
      "metadata": {
        "id": "JYBRbbM3Yy5_"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ZnD5DqNYzAc"
      },
      "outputs": [],
      "source": []
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