Spotify Playlist MCP Server

"""Tests for spotify_mcp/similarity/engine.py (pure functions only).""" import pytest from spotify_mcp.similarity.engine import average_features class TestAverageFeatures: """Tests for average_features function.""" def test_single_feature_set(self, sample_audio_features): """Test averaging a single feature set.""" result = average_features([sample_audio_features]) # Should return the same values assert result["acousticness"] == sample_audio_features["acousticness"] assert result["danceability"] == sample_audio_features["danceability"] assert result["energy"] == sample_audio_features["energy"] assert result["instrumentalness"] == sample_audio_features["instrumentalness"] assert result["liveness"] == sample_audio_features["liveness"] assert result["loudness"] == sample_audio_features["loudness"] assert result["speechiness"] == sample_audio_features["speechiness"] assert result["valence"] == sample_audio_features["valence"] assert result["tempo"] == sample_audio_features["tempo"] def test_two_feature_sets(self, sample_audio_features, sample_audio_features_2): """Test averaging two feature sets.""" result = average_features([sample_audio_features, sample_audio_features_2]) # Should return averages expected_acousticness = ( sample_audio_features["acousticness"] + sample_audio_features_2["acousticness"] ) / 2 expected_energy = ( sample_audio_features["energy"] + sample_audio_features_2["energy"] ) / 2 assert abs(result["acousticness"] - expected_acousticness) < 0.001 assert abs(result["energy"] - expected_energy) < 0.001 def test_multiple_feature_sets(self): """Test averaging multiple feature sets.""" features_list = [ {"acousticness": 0.1, "energy": 0.9, "tempo": 120.0}, {"acousticness": 0.3, "energy": 0.7, "tempo": 130.0}, {"acousticness": 0.2, "energy": 0.8, "tempo": 125.0}, ] result = average_features(features_list) # Average acousticness: (0.1 + 0.3 + 0.2) / 3 = 0.2 # Average energy: (0.9 + 0.7 + 0.8) / 3 = 0.8 # Average tempo: (120 + 130 + 125) / 3 = 125 assert abs(result["acousticness"] - 0.2) < 0.001 assert abs(result["energy"] - 0.8) < 0.001 assert abs(result["tempo"] - 125.0) < 0.001 def test_empty_list_raises_error(self): """Test that empty list raises ValueError.""" with pytest.raises(ValueError, match="Cannot average empty features list"): average_features([]) def test_missing_features(self): """Test handling of missing features (uses default 0.0).""" features_list = [ {"acousticness": 0.5}, # Missing other features {"acousticness": 0.3, "energy": 0.8}, ] result = average_features(features_list) # Acousticness: (0.5 + 0.3) / 2 = 0.4 # Energy: (0.0 + 0.8) / 2 = 0.4 (first dict has 0.0 default) assert abs(result["acousticness"] - 0.4) < 0.001 assert abs(result["energy"] - 0.4) < 0.001 def test_all_numeric_keys_present(self): """Test that all expected numeric keys are present in result.""" features_list = [ { "acousticness": 0.1, "danceability": 0.2, "energy": 0.3, "instrumentalness": 0.4, "liveness": 0.5, "loudness": -10.0, "speechiness": 0.6, "valence": 0.7, "tempo": 120.0, } ] result = average_features(features_list) expected_keys = [ "acousticness", "danceability", "energy", "instrumentalness", "liveness", "loudness", "speechiness", "valence", "tempo", ] for key in expected_keys: assert key in result def test_extreme_values(self): """Test averaging extreme values.""" features_list = [ { "acousticness": 0.0, "energy": 0.0, "loudness": -60.0, "tempo": 50.0, }, {"acousticness": 1.0, "energy": 1.0, "loudness": 0.0, "tempo": 200.0}, ] result = average_features(features_list) assert abs(result["acousticness"] - 0.5) < 0.001 assert abs(result["energy"] - 0.5) < 0.001 assert abs(result["loudness"] - (-30.0)) < 0.001 assert abs(result["tempo"] - 125.0) < 0.001