def test_multilabel(tmp_dir, output_dir, dask_client):
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris',
make_multilabel=True)
automl = AutoSklearnClassifier(time_left_for_this_task=30,
per_run_time_limit=5,
tmp_folder=tmp_dir,
dask_client=dask_client,
output_folder=output_dir)
automl.fit(X_train, Y_train)
predictions = automl.predict(X_test)
assert predictions.shape == (50, 3), print_debug_information(automl)
assert count_succeses(
automl.cv_results_) > 0, print_debug_information(automl)
assert includes_train_scores(automl.performance_over_time_.columns) is True
assert performance_over_time_is_plausible(
automl.performance_over_time_) is True
score = f1_macro(Y_test, predictions)
assert score >= 0.9, print_debug_information(automl)
probs = automl.predict_proba(X_train)
assert np.mean(probs) == pytest.approx(0.33, rel=1e-1)
def test_multilabel(self):
output = os.path.join(self.test_dir, '..', '.tmp_multilabel_fit')
self._setUp(output)
X_train, Y_train, X_test, Y_test = putil.get_dataset(
'iris', make_multilabel=True)
automl = AutoSklearnClassifier(time_left_for_this_task=20,
per_run_time_limit=5,
tmp_folder=output,
output_folder=output)
automl.fit(X_train, Y_train)
predictions = automl.predict(X_test)
self.assertEqual(predictions.shape, (50, 3))
score = f1_macro(Y_test, predictions)
self.assertGreaterEqual(score, 0.9)
def test_multilabel(self):
tmp = os.path.join(self.test_dir, '..', '.tmp_multilabel_fit')
output = os.path.join(self.test_dir, '..', '.out_multilabel_fit')
self._setUp(tmp)
self._setUp(output)
X_train, Y_train, X_test, Y_test = putil.get_dataset(
'iris', make_multilabel=True)
automl = AutoSklearnClassifier(time_left_for_this_task=20,
per_run_time_limit=5,
tmp_folder=tmp,
output_folder=output)
automl.fit(X_train, Y_train)
predictions = automl.predict(X_test)
self.assertEqual(predictions.shape, (50, 3))
score = f1_macro(Y_test, predictions)
self.assertGreaterEqual(score, 0.9)
probs = automl.predict_proba(X_train)
self.assertAlmostEqual(np.mean(probs), 0.33, places=1)
def test_multilabel(self):
tmp = os.path.join(self.test_dir, '..', '.tmp_multilabel_fit')
output = os.path.join(self.test_dir, '..', '.out_multilabel_fit')
self._setUp(tmp)
self._setUp(output)
X_train, Y_train, X_test, Y_test = putil.get_dataset(
'iris', make_multilabel=True)
automl = AutoSklearnClassifier(time_left_for_this_task=30,
per_run_time_limit=5,
tmp_folder=tmp,
output_folder=output)
automl.fit(X_train, Y_train)
predictions = automl.predict(X_test)
self.assertEqual(predictions.shape, (50, 3))
self.assertGreater(self._count_succeses(automl.cv_results_), 0)
score = f1_macro(Y_test, predictions)
self.assertGreaterEqual(score, 0.9)
probs = automl.predict_proba(X_train)
self.assertAlmostEqual(np.mean(probs), 0.33, places=1)
def test_multilabel(tmp_dir, output_dir, dask_client):
X_train, Y_train, X_test, Y_test = putil.get_dataset('iris',
make_multilabel=True)
automl = AutoSklearnClassifier(time_left_for_this_task=30,
per_run_time_limit=5,
tmp_folder=tmp_dir,
dask_client=dask_client,
output_folder=output_dir)
automl.fit(X_train, Y_train)
# Log file path
log_file_path = glob.glob(os.path.join(tmp_dir, 'AutoML*.log'))[0]
predictions = automl.predict(X_test)
assert predictions.shape == (50, 3), extract_msg_from_log(log_file_path)
assert count_succeses(
automl.cv_results_) > 0, extract_msg_from_log(log_file_path)
score = f1_macro(Y_test, predictions)
assert score >= 0.9, extract_msg_from_log(log_file_path)
probs = automl.predict_proba(X_train)
assert np.mean(probs) == pytest.approx(0.33, rel=1e-1)
