def one_hot_encoder_serializer_test(self):
labels = ['a', 'b', 'c']
le = LabelEncoder(input_features=['label_feature'],
output_features='label_feature_le_encoded')
oh_data = le.fit_transform(labels).reshape(3, 1)
one_hot_encoder_tf = OneHotEncoder(sparse=False)
one_hot_encoder_tf.mlinit(input_features=le.output_features,
output_features='{}_one_hot_encoded'.format(
le.output_features))
one_hot_encoder_tf.fit(oh_data)
one_hot_encoder_tf.serialize_to_bundle(self.tmp_dir,
one_hot_encoder_tf.name)
# Test model.json
with open("{}/{}.node/model.json".format(
self.tmp_dir, one_hot_encoder_tf.name)) as json_data:
model = json.load(json_data)
self.assertEqual(one_hot_encoder_tf.op, model['op'])
self.assertEqual(3, model['attributes']['size']['value'])
self.assertEqual(True, model['attributes']['drop_last']['value'])
def test_one_hot_encoder_serialization_succeeds_when_handle_unknown_is_set_to_ignore(self):
labels = ['a', 'b', 'c', 'a', 'b', 'b']
le = LabelEncoder(input_features=['label'], output_features='label_le_encoded')
oh_data = le.fit_transform(labels).reshape(-1, 1)
ohe = OneHotEncoder(handle_unknown='ignore')
ohe.mlinit(prior_tf=le, output_features='{}_one_hot_encoded'.format(le.output_features))
ohe.fit(oh_data)
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
with open("{}/{}.node/model.json".format(self.tmp_dir, ohe.name)) as json_data:
model = json.load(json_data)
self.assertEqual('one_hot_encoder', model['op'])
self.assertEqual(3, model['attributes']['size']['long'])
self.assertEqual('keep', model['attributes']['handle_invalid']['string'])
self.assertEqual(True, model['attributes']['drop_last']['boolean'])
def one_hot_encoder_deserializer_test(self):
labels = ['a', 'b', 'c']
le = LabelEncoder(input_features=['label_feature'],
output_features='label_feature_le_encoded')
oh_data = le.fit_transform(labels).reshape(3, 1)
one_hot_encoder_tf = OneHotEncoder(sparse=False)
one_hot_encoder_tf.mlinit(input_features=le.output_features,
output_features=[
'{}_one_hot_encoded'.format(
le.output_features[0])
])
one_hot_encoder_tf.fit(oh_data)
one_hot_encoder_tf.serialize_to_bundle(self.tmp_dir,
one_hot_encoder_tf.name)
# Deserialize the OneHotEncoder
node_name = "{}.node".format(one_hot_encoder_tf.name)
one_hot_encoder_tf_ds = OneHotEncoder()
one_hot_encoder_tf_ds.deserialize_from_bundle(self.tmp_dir, node_name)
# Transform some sample data
res_a = one_hot_encoder_tf.transform(oh_data)
res_b = one_hot_encoder_tf_ds.transform(oh_data)
self.assertEqual(res_a[0][0], res_b[0][0])
self.assertEqual(res_a[1][0], res_b[1][0])
self.assertEqual(res_a[2][0], res_b[2][0])
# Test node.json
with open("{}/{}.node/node.json".format(
self.tmp_dir, one_hot_encoder_tf.name)) as json_data:
node = json.load(json_data)
self.assertEqual(one_hot_encoder_tf_ds.name, node['name'])
self.assertEqual(one_hot_encoder_tf_ds.input_features[0],
node['shape']['inputs'][0]['name'])
self.assertEqual(one_hot_encoder_tf_ds.output_features,
node['shape']['outputs'][0]['name'])
class TestOneHotEncoder(unittest.TestCase):
def setUp(self):
labels = ['a', 'b', 'c', 'a', 'b', 'b']
self.le = LabelEncoder(input_features=['label'], output_features='label_le_encoded')
self.oh_data = self.le.fit_transform(labels).reshape(-1, 1)
self.tmp_dir = tempfile.mkdtemp(prefix="mleap.python.tests")
def tearDown(self):
shutil.rmtree(self.tmp_dir)
def test_one_hot_encoder_serialization_fails_on_multiple_feature_columns(self):
self.oh_data = np.hstack((self.oh_data, self.oh_data)) # make two feature columns
ohe = OneHotEncoder(handle_unknown='error')
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
with self.assertRaises(NotImplementedError):
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
def test_one_hot_encoder_serialization_fails_on_an_invalid_category_range(self):
self.oh_data[2][0] = 3 # make invalid category range
ohe = OneHotEncoder(handle_unknown='error')
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
with self.assertRaises(ValueError):
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
def test_one_hot_encoder_serialization_fails_when_using_the_drop_param(self):
ohe = OneHotEncoder(handle_unknown='error', drop='first') # try to use `drop` parameter
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
with self.assertRaises(NotImplementedError):
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
def test_one_hot_encoder_serialization_fails_when_using_the_dtype_param(self):
ohe = OneHotEncoder(handle_unknown='error', dtype=int) # try to use `dtype` parameter
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
with self.assertRaises(NotImplementedError):
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
def test_one_hot_encoder_serialization_succeeds_when_handle_unknown_is_set_to_error(self):
ohe = OneHotEncoder(handle_unknown='error')
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
with open("{}/{}.node/model.json".format(self.tmp_dir, ohe.name)) as json_data:
model = json.load(json_data)
self.assertEqual('one_hot_encoder', model['op'])
self.assertEqual(3, model['attributes']['size']['long'])
self.assertEqual('error', model['attributes']['handle_invalid']['string'])
self.assertEqual(False, model['attributes']['drop_last']['boolean'])
def test_one_hot_encoder_deserialization_succeeds_when_handle_unknown_is_set_to_error(self):
ohe = OneHotEncoder(handle_unknown='error')
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
node_name = "{}.node".format(ohe.name)
ohe_ds = OneHotEncoder()
ohe_ds.deserialize_from_bundle(self.tmp_dir, node_name)
self.oh_data[2][0] = 3 # Add an unknown category
with self.assertRaises(ValueError):
ohe_ds.transform(self.oh_data)
def test_one_hot_encoder_serialization_succeeds_when_handle_unknown_is_set_to_ignore(self):
labels = ['a', 'b', 'c', 'a', 'b', 'b']
le = LabelEncoder(input_features=['label'], output_features='label_le_encoded')
oh_data = le.fit_transform(labels).reshape(-1, 1)
ohe = OneHotEncoder(handle_unknown='ignore')
ohe.mlinit(prior_tf=le, output_features='{}_one_hot_encoded'.format(le.output_features))
ohe.fit(oh_data)
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
with open("{}/{}.node/model.json".format(self.tmp_dir, ohe.name)) as json_data:
model = json.load(json_data)
self.assertEqual('one_hot_encoder', model['op'])
self.assertEqual(3, model['attributes']['size']['long'])
self.assertEqual('keep', model['attributes']['handle_invalid']['string'])
self.assertEqual(True, model['attributes']['drop_last']['boolean'])
def test_one_hot_encoder_deserialization_succeeds_when_handle_unknown_is_set_to_ignore(self):
ohe = OneHotEncoder(handle_unknown='ignore')
ohe.mlinit(prior_tf=self.le, output_features='{}_one_hot_encoded'.format(self.le.output_features))
ohe.fit(self.oh_data)
ohe.serialize_to_bundle(self.tmp_dir, ohe.name)
node_name = "{}.node".format(ohe.name)
ohe_ds = OneHotEncoder()
ohe_ds.deserialize_from_bundle(self.tmp_dir, node_name)
self.oh_data[2][0] = 3 # Add an unknown category
expected = ohe.transform(self.oh_data).todense()
actual = ohe_ds.transform(self.oh_data)
np.testing.assert_array_equal(expected, actual)