Experimental chess position evaluation project
The goal of this project is to evaluate chess position with machine learning
Current code in repository transforms PGN files into
-
tensors of size
6x8x8and12x8x8,8x8represents board itself while6and12represent depth of tensor. Fordepth = 6each piece is placed in one of 6 dimensions (as we have 6 types of pieces) with values1or-1indicating the piece color. In case ofdepth = 12each piece is placed in one of 12 dimensions (6 black + 6 white pieces) with one value =1. In other words every piece on8x8board is represented by vector of dimensionality6or12(depending what board representation is chosen) in a 'bag of words' fashion. -
flat vectors of size
384 (6*8*8)and768 (12*8*8)being flattened versions of tensor described above -
position metadata (result, number_of_moves, castlings potential) is inlcuded
Requirements:
numpy>=1.12.0
python-chess>=0.22.0
torch>=0.3.0.post4
torchvision>=0.2.0
pytest>=3.3.1
To install requirements run:
pip install -r requirements.txtTo translate PGN file into tensor-like data (coordinates and values of non-zero tensor entries + game metadata):
from common.readers import PgnReader as reader
from common.io import FileSystemDataSaverWithShuffling as saver
from common.transformations import DataSpecs
# memory_size indicates how many prev moves to keep
with reader("data.pgn", memory_size = 5) as r, saver('output', chunk_size = 5000, number_of_buckets=50) as s:
for position in iter(r):
# if you don't want to include draws in your dataset
if position['current'].draw():
continue
black_to_move = position['current'].black_to_move
# flipping board instead of encoding who moves next - board always seen from white perspective
current_data = position['current'].get_training_data(DataSpecs.vector12x8x8_flat, flip = black_to_move)
prev_data = [prev.get_training_data(flip = black_to_move) for prev in position['prev']]
s.insert_next({'current': current_data, 'prev_positions': prev_data})To later use tensor-like data to train chess value network:
from common.io import FlatVector6x8x8PositionReader
from common.models import FeedForwardNetwork
from training.trainers import ValueNetworkTrainer
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
# training data reader - reads previously created data files
training_data_reader = FlatVector6x8x8PositionReader("data_dir", number_of_files_in_memory = 10, batch_size = 1000)
# create dense feed forward neural network
network = FeedForwardNetwork(
layers_shapes = [(6*8*8, 5000), (5000,500), (500,50), (50, 1)],
activations = [F.leaky_relu, F.leaky_relu, F.leaky_relu, F.sigmoid]
)
optimizer = optim.Adam(network.parameters(), lr=0.0001)
trainer = ValueNetworkTrainer(
training_data_reader = training_data_reader,
model = network, # network itself
loss_function = F.mse_loss, # mean square error loss
x_tensorize_f = lambda x : Variable(x), # in case you need additional transformation
y_tensorize_f = lambda x : Variable(x), # in case you need additional transformation
optimizer = optimizer, # adam optimizer
use_cuda = False # set to true to run on CUDA
)
# train 10 epochs
for epoch in range(0,10):
trainer.train_single_epoch()
# save after each epoch
trainer.save_model_state(prefix = 'epoch_' + str(epoch) + '_')To run tests try:
py.test -v tests/