def binarizer_deserializer_test(self):
binarizer = Binarizer(threshold=0.0)
binarizer.mlinit(input_features=['a'], output_features=['a_binary'])
Xres = binarizer.fit_transform(self.df[['a']])
# Test that the binarizer functions as expected
self.assertEqual(
float(len(self.df[self.df.a >= 0])) / 10.0, Xres.mean())
binarizer.serialize_to_bundle(self.tmp_dir, binarizer.name)
# Deserialize the Binarizer
node_name = "{}.node".format(binarizer.name)
binarizer_tf_ds = Binarizer()
binarizer_tf_ds.deserialize_from_bundle(self.tmp_dir, node_name)
# Transform some sample data
res_a = binarizer.transform(self.df[['a']])
res_b = binarizer_tf_ds.transform(self.df[['a']])
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])
self.assertEqual(res_a[3][0], res_b[3][0])
def binarizer_deserializer_test(self):
extract_features = ['a']
feature_extractor = FeatureExtractor(
input_scalars=['a'],
output_vector='extracted_a_output',
output_vector_items=["{}_out".format(x) for x in extract_features])
binarizer = Binarizer(threshold=0.0)
binarizer.mlinit(prior_tf=feature_extractor,
output_features='a_binary')
Xres = binarizer.fit_transform(self.df[['a']])
# Test that the binarizer functions as expected
self.assertEqual(
float(len(self.df[self.df.a >= 0])) / 10.0, Xres.mean())
binarizer.serialize_to_bundle(self.tmp_dir, binarizer.name)
# Deserialize the Binarizer
node_name = "{}.node".format(binarizer.name)
binarizer_tf_ds = Binarizer()
binarizer_tf_ds.deserialize_from_bundle(self.tmp_dir, node_name)
# Transform some sample data
res_a = binarizer.transform(self.df[['a']])
res_b = binarizer_tf_ds.transform(self.df[['a']])
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])
self.assertEqual(res_a[3][0], res_b[3][0])
def binarizer_test(self):
binarizer = Binarizer(threshold=0.0)
binarizer.mlinit(input_features='a',
output_features='a_binary')
Xres = binarizer.fit_transform(self.df[['a']])
# Test that the binarizer functions as expected
self.assertEqual(float(len(self.df[self.df.a >= 0]))/10.0, Xres.mean())
binarizer.serialize_to_bundle(self.tmp_dir, binarizer.name)
expected_model = {
"op": "binarizer",
"attributes": {
"threshold": {
"type": "double",
"value": 0.0
}
}
}
# Test model.json
with open("{}/{}.node/model.json".format(self.tmp_dir, binarizer.name)) as json_data:
model = json.load(json_data)
self.assertEqual(expected_model['attributes']['threshold']['value'],
model['attributes']['threshold']['value'])
def binarizer_test(self):
extract_features = ['a']
feature_extractor = FeatureExtractor(
input_scalars=['a'],
output_vector='extracted_a_output',
output_vector_items=["{}_out".format(x) for x in extract_features])
binarizer = Binarizer(threshold=0)
binarizer.mlinit(prior_tf=feature_extractor,
output_features='a_binary')
Xres = binarizer.fit_transform(self.df[['a']])
# Test that the binarizer functions as expected
self.assertEqual(
float(len(self.df[self.df.a >= 0])) / 10.0, Xres.mean())
binarizer.serialize_to_bundle(self.tmp_dir, binarizer.name)
expected_model = {
"op": "sklearn_binarizer",
"attributes": {
"threshold": {
"double": 0.0
}
}
}
# Test model.json
with open("{}/{}.node/model.json".format(self.tmp_dir,
binarizer.name)) as json_data:
model = json.load(json_data)
self.assertEqual(expected_model['attributes']['threshold']['double'],
model['attributes']['threshold']['double'])
self.assertEqual(expected_model['op'], model['op'])
# Test node.json
with open("{}/{}.node/node.json".format(self.tmp_dir,
binarizer.name)) as json_data:
node = json.load(json_data)
self.assertEqual(binarizer.name, node['name'])
self.assertEqual(binarizer.input_features,
node['shape']['inputs'][0]['name'])
self.assertEqual(binarizer.output_features,
node['shape']['outputs'][0]['name'])