File size: 2,387 Bytes
dccc3ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | 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)
# Check numeric feature count against defined STATIC_FEATURE_COLS
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.
"""
# 22 features, 1 row
X_static = np.zeros((1, 22))
# 18 balls, 3 features [runs, wickets, overs]
X_seq = np.zeros((1, 18, 3))
# This shouldn't crash if scalers are correctly initialized
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]
# RCB (1550) vs MI (1600) -> elo_diff = -50.0
assert float(latest['bat_elo']) == 1550.0
assert float(latest['bowl_elo']) == 1600.0
assert float(latest['elo_diff']) == -50.0
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