feature_set.py

from collections import OrderedDict
from feature_block import *
import torch
import chess

def _calculate_features_hash(features):
    if len(features) == 1:
        return features[0].hash

    tail_hash = calculate_features_hash(features[1:])
    return features[0].hash ^ (tail_hash << 1) ^ (tail_hash >> 1) & 0xffffffff

class FeatureSet:
    '''
    A feature set is nothing more than a list of named FeatureBlocks.
    It itself functions similarily to a feature block, but we don't want to be
    explicit about it as we don't want it to be used as a building block for other
    feature sets. You can think of this class as a composite, but not the full extent.
    It is basically a concatenation of feature blocks.
    '''

    def __init__(self, features):
        for feature in features:
            if not isinstance(feature, FeatureBlock):
                raise Exception('All features must subclass FeatureBlock')

        self.features = features
        self.hash = _calculate_features_hash(features)
        self.name = '+'.join(feature.name for feature in features)
        self.num_real_features = sum(feature.num_real_features for feature in features)
        self.num_virtual_features = sum(feature.num_virtual_features for feature in features)
        self.num_features = sum(feature.num_features for feature in features)

    '''
    This method returns the feature ranges for the virtual factors of the
    underlying feature blocks. This is useful to know during initialization,
    when we want to zero initialize the virtual feature weights, but give some other
    values to the real feature weights.
    '''
    def get_virtual_feature_ranges(self):
        ranges = []
        offset = 0
        for feature in self.features:
            if feature.num_virtual_features:
                ranges.append((offset + feature.num_real_features, offset + feature.num_features))
            offset += feature.num_features

        return ranges

    def get_real_feature_ranges(self):
        ranges = []
        offset = 0
        for feature in self.features:
            ranges.append((offset, offset + feature.num_real_features))
            offset += feature.num_features

        return ranges

    '''
    This method goes over all of the feature blocks and gathers the active features.
    Each block has its own index space assigned so the features from two different
    blocks will never have the same index here. Basically the thing you would expect
    to happen after concatenating many feature blocks.
    '''
    def get_active_features(self, board):
        w = torch.zeros(0)
        b = torch.zeros(0)

        offset = 0
        for feature in self.features:
            w_local, b_local = feature.get_active_features(board)
            w_local += offset
            b_local += offset
            w = torch.cat([w, w_local])
            b = torch.cat([b, b_local])
            offset += feature.num_features

        return w, b

    '''
    This method takes a feature idx and looks for the block that owns it.
    If it found the block it asks it to factorize the index, otherwise
    it throws and Exception. The idx must refer to a real feature.
    '''
    def get_feature_factors(self, idx):
        offset = 0
        for feature in self.features:
            if idx < offset + feature.num_real_features:
                return [offset + i for i in feature.get_feature_factors(idx - offset)]
            offset += feature.num_features

        raise Exception('No feature block to factorize {}'.format(idx))

    '''
    This method does what get_feature_factors does but for all
    valid features at the same time. It returns a list of length
    self.num_real_features with ith element being a list of factors
    of the ith feature.
    This method is technically redundant but it allows to perform the operation
    slightly faster when there's many feature blocks. It might be worth
    to add a similar method to the FeatureBlock itself - to make it faster
    for feature blocks with many factors.
    '''
    def get_virtual_to_real_features_gather_indices(self):
        indices = []
        real_offset = 0
        offset = 0
        for feature in self.features:
            for i_real in range(feature.num_real_features):
                i_fact = feature.get_feature_factors(i_real)
                indices.append([offset + i for i in i_fact])
            real_offset += feature.num_real_features
            offset += feature.num_features
        return indices

    def get_initial_psqt_features(self):
        init = []
        for feature in self.features:
            init += feature.get_initial_psqt_features()
        return init