def check_sparse_model(self, model):
# Given
model.fit(self.X_sparse, self.y_sparse)
# When
serialized_model = skljson.to_dict(model)
deserialized_model = skljson.from_dict(serialized_model)
# Then
expected_predictions = model.predict(self.X_sparse)
actual_predictions = deserialized_model.predict(self.X_sparse)
testing.assert_array_equal(expected_predictions, actual_predictions)
def check_sparse_model(self, model, abs=False):
# Given
if abs:
model.fit(np.absolute(self.X_sparse), self.y_sparse)
else:
model.fit(self.X_sparse, self.y_sparse)
# When
serialized_model = skljson.to_dict(model)
deserialized_model = skljson.from_dict(serialized_model)
# Then
expected_predictions = model.predict(self.X)
actual_predictions = deserialized_model.predict(self.X)
testing.assert_array_equal(expected_predictions, actual_predictions)
def check_model(self, model):
expected_vectors = model.fit_transform(self.X)
serialized_model = skljson.to_dict(model)
deserialized_model = skljson.from_dict(serialized_model)
actual_vectors = deserialized_model.fit_transform(self.X)
if model.sparse:
testing.assert_array_equal(expected_vectors.indptr,
actual_vectors.indptr)
testing.assert_array_equal(expected_vectors.indices,
actual_vectors.indices)
testing.assert_array_equal(expected_vectors.data,
actual_vectors.data)
else:
testing.assert_array_equal(expected_vectors, actual_vectors)
def open_model(model_path):
"""Open and return a model from json file
:param model_path: path to the model
"""
if model_path.endswith('gz'):
with gzip.open(model_path, 'r') as f:
model = f.read()
model = json.loads(model.decode('utf-8'))
model = from_dict(model)
return model
else:
with open(model_path, 'r') as f:
a = f.readline()
if a.startswith('{"learner"'):
model = xgb.Booster()
model.load_model(model_path)
return model
else:
model = from_json(model_path)
return model
def get_tree(tree_json):
return skljson.from_dict(tree_json)