def test_logistic_regression_deserializer(self):
logistic_regression = LogisticRegression(fit_intercept=True)
logistic_regression.mlinit(input_features='a',
prediction_column='e_binary')
extract_features = ['e']
feature_extractor = FeatureExtractor(
input_scalars=['e'],
output_vector='extracted_e_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='e_binary')
Xres = binarizer.fit_transform(self.df[['a']])
logistic_regression.fit(self.df[['a']], Xres)
logistic_regression.serialize_to_bundle(self.tmp_dir,
logistic_regression.name)
# Test model.json
with open("{}/{}.node/model.json".format(
self.tmp_dir, logistic_regression.name)) as json_data:
model = json.load(json_data)
# Now deserialize it back
node_name = "{}.node".format(logistic_regression.name)
logistic_regression_tf = LogisticRegression()
logistic_regression_tf = logistic_regression_tf.deserialize_from_bundle(
self.tmp_dir, node_name)
res_a = logistic_regression.predict(self.df[['a']])
res_b = logistic_regression_tf.predict(self.df[['a']])
self.assertEqual(res_a[0], res_b[0])
self.assertEqual(res_a[1], res_b[1])
self.assertEqual(res_a[2], res_b[2])
def test_multinomial_logistic_regression_deserializer(self):
logistic_regression = LogisticRegression(fit_intercept=True)
logistic_regression.mlinit(input_features='a',
prediction_column='prediction')
X = self.df[['a']]
y = np.array(
[to_standard_normal_quartile(elem) for elem in X.to_numpy()])
logistic_regression.fit(X, y)
logistic_regression.serialize_to_bundle(self.tmp_dir,
logistic_regression.name)
node_name = "{}.node".format(logistic_regression.name)
logistic_regression_tf = LogisticRegression()
logistic_regression_tf = logistic_regression_tf.deserialize_from_bundle(
self.tmp_dir, node_name)
expected = logistic_regression.predict(self.df[['a']])
actual = logistic_regression_tf.predict(self.df[['a']])
np.testing.assert_array_equal(expected, actual)
def test_logistic_regression_deserializer(self):
logistic_regression = LogisticRegression(fit_intercept=True)
logistic_regression.mlinit(input_features=['a', 'b', 'c'],
prediction_column=['e_binary'])
binarizer = Binarizer(threshold=0.0)
binarizer.mlinit(input_features=['e'], output_features=['e_binary'])
Xres = binarizer.fit_transform(self.df[['a']])
logistic_regression.fit(self.df[logistic_regression.input_features],
Xres)
logistic_regression.serialize_to_bundle(self.tmp_dir,
logistic_regression.name)
# Test model.json
with open("{}/{}.node/model.json".format(
self.tmp_dir, logistic_regression.name)) as json_data:
model = json.load(json_data)
# Now deserialize it back
node_name = "{}.node".format(logistic_regression.name)
logistic_regression_tf = LogisticRegression()
logistic_regression_tf = logistic_regression_tf.deserialize_from_bundle(
self.tmp_dir, node_name)
res_a = logistic_regression.predict(
self.df[logistic_regression.input_features])
res_b = logistic_regression_tf.predict(
self.df[logistic_regression_tf.input_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])
