| import pytest |
| import numpy as np |
| import pandas as pd |
| from backend.ml_engine.hybrid_model import CricsheetNormalizer |
|
|
| @pytest.fixture |
| def sample_match_data(): |
| """Mock match data for normalizer tests - using exact date format""" |
| return pd.DataFrame([ |
| { |
| 'match_id': '2024-05-01', |
| 'inning': 1, |
| 'over': 0.1, |
| 'batsman': 'Virat Kohli', |
| 'bowler': 'Jasprit Bumrah', |
| 'runs': 4, |
| 'wicket': False, |
| 'batting_team': 'RCB', |
| 'bowling_team': 'MI' |
| }, |
| { |
| 'match_id': '2024-05-01', |
| 'inning': 1, |
| 'over': 0.2, |
| 'batsman': 'Virat Kohli', |
| 'bowler': 'Jasprit Bumrah', |
| 'runs': 0, |
| 'wicket': True, |
| 'batting_team': 'RCB', |
| 'bowling_team': 'MI' |
| } |
| ]) |
|
|
| def test_normalizer_feature_count(normalizer, sample_match_data): |
| """ |
| Ensure the standardized feature set always contains exactly 22 columns |
| regardless of input data richness. |
| """ |
| df = normalizer.create_match_features(sample_match_data) |
| |
| |
| numeric_cols = df[normalizer.STATIC_FEATURE_COLS] |
| assert numeric_cols.shape[1] == 22 |
| assert 'bat_elo' in numeric_cols.columns |
| assert 'temp' in numeric_cols.columns |
|
|
| def test_inference_shape_matching(ensemble): |
| """ |
| Verify high-fidelity shape matching between static XGBoost vectors |
| and LSTM temporal sequences. |
| """ |
| |
| X_static = np.zeros((1, 22)) |
| |
| |
| X_seq = np.zeros((1, 18, 3)) |
| |
| |
| prediction = ensemble.predict(X_static, X_seq) |
| |
| assert 'win_probability' in prediction |
| assert isinstance(prediction['win_probability'], (float, np.float32)) |
|
|
| def test_elo_differential_calculation(normalizer, sample_match_data): |
| """ |
| Verify that ELO differentials are correctly calculated from mock lookup. |
| """ |
| df = normalizer.create_match_features(sample_match_data) |
| latest = df.iloc[-1] |
| |
| |
| assert float(latest['bat_elo']) == 1550.0 |
| assert float(latest['bowl_elo']) == 1600.0 |
| assert float(latest['elo_diff']) == -50.0 |
|
|
