def deserialize_mlp(model_dict):
model = MLPClassifier(**model_dict['params'])
model.coefs_ = np.array(model_dict['coefs_'])
model.loss_ = model_dict['loss_']
model.intercepts_ = np.array(model_dict['intercepts_'])
model.n_iter_ = model_dict['n_iter_']
model.n_layers_ = model_dict['n_layers_']
model.n_outputs_ = model_dict['n_outputs_']
model.out_activation_ = model_dict['out_activation_']
model._label_binarizer = deserialize_label_binarizer(
model_dict['_label_binarizer'])
model.classes_ = np.array(model_dict['classes_'])
return model
def deserialize_model(path):
"""Deserialize JSON object storing the ml model.
Model (an MLPClassifier from sklearn) is re-instantiated
with proper values.
INPUT:
--path: filepath for loading the JSON object
OUTPUT:
--model: Returns an MLPClassifier (sklearn) object
"""
def deserialize_label_binarizer(label_binarizer_dict):
label_binarizer = LabelBinarizer()
label_binarizer.neg_label = label_binarizer_dict['neg_label']
label_binarizer.pos_label = label_binarizer_dict['pos_label']
label_binarizer.sparse_output = label_binarizer_dict[
'sparse_output']
label_binarizer.y_type_ = label_binarizer_dict['y_type_']
label_binarizer.sparse_input_ = label_binarizer_dict[
'sparse_input_']
label_binarizer.classes_ = np.array(
label_binarizer_dict['classes_'])
return label_binarizer
# Load (or deserialize) model from JSON
model_dict = {}
with open(path, 'r') as in_file:
model_dict = json.load(in_file)
model = MLPClassifier(**model_dict['params'])
model.coefs_ = np.array(model_dict['coefs_'], dtype=object)
model.loss_ = model_dict['loss_']
model.intercepts_ = np.array(model_dict['intercepts_'], dtype=object)
model.n_iter_ = model_dict['n_iter_']
model.n_layers_ = model_dict['n_layers_']
model.n_outputs_ = model_dict['n_outputs_']
model.out_activation_ = model_dict['out_activation_']
model._label_binarizer = deserialize_label_binarizer(
model_dict['_label_binarizer'])
model.features = list(model_dict['features'])
model.classes_ = np.array(model_dict['classes_'])
# Convert coeficients to numpy arrays to enable JSON deserialization
# This is a hack to compensate for a bug in sklearn_json
for i, x in enumerate(model.coefs_):
model.coefs_[i] = np.array(x)
return model
def test_serialize_model():
instance = HostFootprint()
model = MLPClassifier()
label_binarizer = LabelBinarizer()
label_binarizer.neg_label = 0
label_binarizer.pos_label = 1
label_binarizer.sparse_output = False
label_binarizer.y_type_ = "binary"
label_binarizer.sparse_input_ = False
label_binarizer.classes_ = np.array([0])
parameters = {'hidden_layer_sizes': [(64, 32)]}
GridSearchCV(model, parameters, cv=5, n_jobs=-1, scoring='f1_weighted')
model.coefs_ = np.array([[1], [2]])
model.loss_ = 42
model.intercepts_ = np.array([[3], [4]])
model.classes_ = np.array([[5], [6]])
model.n_iter_ = 42
model.n_layers_ = 2
model.n_outputs_ = 1
model.out_activation_ = "logistic"
model._label_binarizer = label_binarizer
model.features = ['test_1', 'test_2', 'test_3']
with tempfile.TemporaryDirectory() as tmpdir:
model_file = os.path.join(tmpdir, 'host_footprint.json')
instance.serialize_model(model, model_file)
new_model = instance.deserialize_model(model_file)
assert model.features == new_model.features
print(f"model params: {model.get_params()}")
print(f"new_model params: {new_model.get_params()}")
assert len(model.get_params()['hidden_layer_sizes']) == len(
new_model.get_params()['hidden_layer_sizes'])
assert model._label_binarizer.y_type_ == new_model._label_binarizer.y_type_
assert len(model.coefs_) == len(new_model.coefs_)
assert len(model.intercepts_) == len(new_model.intercepts_)
