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import joblib
import numpy as np
import pandas as pd
from predicting_outcomes_in_heart_failure.config import MODELS_DIR, PREPROCESSED_CSV, TARGET_COL
import pytest
@pytest.fixture
def sample_features():
df = pd.read_csv(PREPROCESSED_CSV).iloc[100:].copy()
return df.iloc[[1]].drop(columns=[TARGET_COL])
@pytest.fixture
def trained_models():
"""
Load all saved models.
"""
models = {}
models_path = MODELS_DIR / "all"
for model_file in models_path.iterdir():
if model_file.suffix == ".joblib":
model_name = model_file.stem
models[model_name] = joblib.load(model_file)
return models
class TestModelInvariance:
def test_prediction_determinism(self, trained_models, sample_features):
"""
Same input should always produce same prediction.
"""
models = trained_models
for model_name, model in models.items():
pred1 = model.predict(sample_features)
pred2 = model.predict(sample_features)
pred3 = model.predict(sample_features)
assert np.array_equal(pred1, pred2), f"{model_name}: non-deterministic predictions"
assert np.array_equal(pred2, pred3), f"{model_name}: non-deterministic predictions"
def test_uniform_scaling_invariance(self, trained_models, sample_features):
"""
Uniform scaling should always produce same prediction.
"""
models = trained_models
original = sample_features.copy()
modified = original.copy() * 1.1
for model_name, model in models.items():
pred_original = model.predict(original)
pred_modified = model.predict(modified)
assert np.array_equal(pred_original, pred_modified), (
f"{model_name}: prediction changed after uniform scaling"
)
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