def test_final_model(self, X, y):
"""
Test the (already trained) model pipeline on the provided test data
(X and y). Store the test judgment metric and return the rest of the
metrics as a hierarchical dictionary.
"""
# time the prediction
start_time = time.time()
total = time.time() - start_time
self.avg_predict_time = old_div(total, float(len(y)))
# TODO: this is hacky. See https://github.com/HDI-Project/ATM/issues/48
binary = self.num_classes == 2
kwargs = {}
if self.verbose_metrics:
kwargs['include_curves'] = True
if not binary:
kwargs['include_per_class'] = True
# compute the actual test scores!
test_scores = test_pipeline(self.pipeline, X, y, binary, **kwargs)
# save meta-metrics
self.test_judgment_metric = test_scores.get(self.judgment_metric)
return test_scores
def test_final_model(self, X, y):
"""
Test the (already trained) model pipeline on the provided test data (X
and y). Store the test judgment metric and return the rest of the
metrics as a hierarchical dictionary.
"""
# time the prediction
starttime = time.time()
y_preds = self.pipeline.predict(X)
binary = self.num_classes == 2
test_scores = test_pipeline(self.pipeline, X, y, binary)
total = time.time() - starttime
self.avg_prediction_time = total / float(len(y))
self.test_judgment_metric = test_scores.get(self.judgment_metric)
return test_scores
