feat: implement HalfKAv2_hm feature extraction (352 features)

- Implement piece-square feature extraction
- 32 active features for 32 pieces on board
- Tests for feature extraction (7 tests)
- Fix: piece_sq * 6 + piece_type mapping

python/tests/test_features.py

@@ -0,0 +1,57 @@
+"""Tests for NNUE feature extraction"""
+
+import pytest
+import torch
+import numpy as np
+from python.model.feature_extractor import fen_to_features
+from python.constants import HALF_KA_V2_HM, TOTAL_FEATURES
+
+
+class TestFeatureExtraction:
+    """Tests for HalfKAv2_hm feature extraction"""
+
+    def test_feature_count(self):
+        """Test total feature vector length"""
+        fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
+        features = fen_to_features(fen)
+        assert len(features) == TOTAL_FEATURES
+
+    def test_half_ka_hm_features(self):
+        """Test HalfKAv2_hm produces correct number of features (32 pieces on full board)"""
+        fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
+        features = fen_to_features(fen)
+        active = sum(features)
+        assert active == 32  # 32 pieces on full board
+
+    def test_feature_range(self):
+        """Test all features are in valid range"""
+        fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
+        features = fen_to_features(fen)
+        assert all(0 <= f <= 1 for f in features)
+
+    def test_black_perspective(self):
+        """Test feature extraction from black's perspective"""
+        fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR b KQkq - 0 1"
+        features = fen_to_features(fen)
+        active = sum(features)
+        assert active == 32  # 32 pieces
+
+    def test_mixed_colors(self):
+        """Test feature extraction with both colors on board"""
+        fen = "r3k2r/pppppppp/8/8/8/8/PPPPPPPP/R3K2R w KQkq - 0 1"  # King and queen missing
+        features = fen_to_features(fen)
+        active = sum(features)
+        assert active <= 30  # Fewer pieces
+
+    def test_zero_features_empty_board(self):
+        """Test empty board produces zero features"""
+        fen = "8/8/8/8/8/8/8/8 w KQkq - 0 1"
+        features = fen_to_features(fen)
+        assert sum(features) == 0
+
+    def test_tensor_conversion(self):
+        """Test conversion to torch tensor"""
+        fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
+        features = fen_to_features(fen)
+        tensor = torch.tensor(features, dtype=torch.float32)
+        assert tensor.shape == (TOTAL_FEATURES,)