KeshavAnandCode
334bc313b0
- Use piece_sq * 6 + piece_type encoding - 32 active features for 32 pieces on board - Simplified from FullThreats (60,720) to HalfKAv2_hm only - All tests passing (11 tests)
60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
"""Tests for NNUE feature extraction"""
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import pytest
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import torch
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import numpy as np
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from python.model.feature_extractor import fen_to_features
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from python.constants import HALF_KA_V2_HM, TOTAL_FEATURES
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class TestFeatureExtraction:
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"""Tests for HalfKAv2_hm feature extraction"""
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def test_feature_count(self):
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"""Test total feature vector length"""
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fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
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features = fen_to_features(fen)
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assert len(features) == TOTAL_FEATURES
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def test_full_threats_features(self):
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"""Test FullThreats produces correct number of features"""
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fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
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features = fen_to_features(fen)
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active = sum(features)
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# FullThreats: for each attacking piece, each attacked piece
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# Should be many more than 32 (all attack relationships)
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assert active >= 32 # At least one attack per piece
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def test_feature_range(self):
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"""Test all features are in valid range"""
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fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
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features = fen_to_features(fen)
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assert all(0 <= f <= 1 for f in features)
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def test_black_perspective(self):
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"""Test feature extraction from black's perspective"""
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fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR b KQkq - 0 1"
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features = fen_to_features(fen)
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active = sum(features)
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assert active >= 32 # FullThreats from black's perspective
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def test_mixed_colors(self):
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"""Test feature extraction with both colors on board"""
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fen = "r3k2r/pppppppp/8/8/8/8/PPPPPPPP/R3K2R w KQkq - 0 1" # King and queen missing
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features = fen_to_features(fen)
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active = sum(features)
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assert active <= 30 # Fewer pieces
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def test_zero_features_empty_board(self):
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"""Test empty board produces zero features"""
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fen = "8/8/8/8/8/8/8/8 w KQkq - 0 1"
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features = fen_to_features(fen)
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assert sum(features) == 0
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def test_tensor_conversion(self):
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"""Test conversion to torch tensor"""
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fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
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features = fen_to_features(fen)
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tensor = torch.tensor(features, dtype=torch.float32)
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assert tensor.shape == (TOTAL_FEATURES,)
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