def test_model_predict_untrained(dummy_pipeline, dummy_data_single):
"""check error value for model without `trained` flag set.
"""
df, y, yp = dummy_data_single
with pytest.raises(ValueError) as e:
build_prediction(dummy_pipeline, df)
assert "untrained" in str(e.value)
def test_model_predict_missing_data(dummy_pipeline_trained, missing_data):
"""check error value for inputs with missing data.
"""
df, y, yp = missing_data
with pytest.raises(KeyError) as e:
build_prediction(dummy_pipeline_trained, df)
assert "not in index" in str(e.value)
def test_model_predict_bad_data(dummy_pipeline_logistic, bad_data):
"""check error value for malformed data.
Requires use of real model rather than `DummyClassifier`.
"""
df, y, yp = bad_data
with pytest.raises(ValueError) as e:
build_prediction(dummy_pipeline_logistic, df)
assert "misformatted" in str(e.value)
def test_model_predict_multi(dummy_pipeline_trained, dummy_data_multi):
"""check formation for multi-sample prediction.
"""
df, y, yp = dummy_data_multi
pred, pred_prob = build_prediction(dummy_pipeline_trained, df)
assert ((y == pred) & (yp == pred_prob))