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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(prior_tf = le,
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)
# 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)
le = LabelEncoder(input_features=['label_feature'],
output_features='label_feature_le_encoded')
le.fit(labels)
self.assertEqual(labels, le.classes_.tolist())
le.serialize_to_bundle(self.tmp_dir, le.name)
# Test model.json
with open("{}/{}.node/model.json".format(self.tmp_dir, le.name)) as json_data:
model = json.load(json_data)
# Deserialize the LabelEncoder
node_name = "{}.node".format(le.name)
label_encoder_tf = LabelEncoder()
label_encoder_tf.deserialize_from_bundle(self.tmp_dir, node_name)
# Transform some sample data
res_a = le.transform(labels)
res_b = label_encoder_tf.transform(labels)
print("le.output_features: {}".format(le.output_features))
print("label_encoder_tf.output_features: {}".format(label_encoder_tf.output_features))
self.assertEqual(res_a[0], res_b[0])
self.assertEqual(res_a[1], res_b[1])
self.assertEqual(res_a[2], res_b[2])
self.assertEqual(le.input_features, label_encoder_tf.input_features)
self.assertEqual(le.output_features, label_encoder_tf.output_features[0])
def label_encoder_deserializer_test(self):
labels = ['a', 'b', 'c']
le = LabelEncoder(input_features=['label_feature'],
output_features='label_feature_le_encoded')
le.fit(labels)
self.assertEqual(labels, le.classes_.tolist())
le.serialize_to_bundle(self.tmp_dir, le.name)
# Test model.json
with open("{}/{}.node/model.json".format(self.tmp_dir, le.name)) as json_data:
model = json.load(json_data)
# Deserialize the LabelEncoder
node_name = "{}.node".format(le.name)
label_encoder_tf = LabelEncoder()
label_encoder_tf.deserialize_from_bundle(self.tmp_dir, node_name)
def label_encoder_test(self):
labels = ['a', 'b', 'c']
le = LabelEncoder(input_features=['label_feature'],
output_features='label_feature_le_encoded')
le.fit(labels)
self.assertEqual(labels, le.classes_.tolist())
le.serialize_to_bundle(self.tmp_dir, le.name)
# Test model.json
with open("{}/{}.node/model.json".format(self.tmp_dir, le.name)) as json_data:
model = json.load(json_data)
self.assertEqual(le.op, model['op'])
self.assertEqual('nullable_input', model['attributes'].keys()[0])
self.assertEqual('labels', model['attributes'].keys()[1])
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(prior_tf=le,
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'])