class Classifier:
def __init__(self, model_type, **kwargs):
self.model_type = model_type
if model_type == 'LR': # Logistic Regression
# modeling
self.model = LogisticRegression(**kwargs)
elif model_type == 'DT': # Decision Tree
# modeling
self.model = DecisionTreeClassifier(**kwargs)
elif model_type == 'RF': # Random Forest
# modeling
self.model = RandomForestClassifier(**kwargs)
elif model_type == 'XGB': # XGboost
# default
kwargs['objective'] = "binary:logistic"
# modeling
self.model = XGBClassifier(**kwargs)
elif model_type == 'DNN': # Deep Neural Network
# default params
nb_features = kwargs['nb_features'] if 'nb_features' in kwargs.keys(
) else NameError("name 'nb_features' is not defined")
nb_class = kwargs['nb_class'] if 'nb_class' in kwargs.keys(
) else NameError("name 'nb_class' is not defined")
nb_layers = kwargs['nb_layers'] if 'nb_layers' in kwargs.keys(
) else NameError("name 'nb_layers' is not defiend")
loss = 'categorical_crossentropy' if nb_class > 2 else 'binary_crossentropy'
act_func = 'softmax' if nb_class > 2 else 'sigmoid'
# modeling
input_ = tf.keras.layers.Input(shape=(nb_features, ))
x = input_
for i in range(len(nb_layers)):
x = tf.keras.layers.Dense(nb_layers[i], activation='relu')(x)
output = tf.keras.layers.Dense(nb_class, activation=act_func)(x)
self.model = tf.keras.models.Model(input_, output)
# complie
self.model.compile(optimizer=kwargs['optimizer'],
loss=loss,
metrics=['acc'])
def train(self, X, y, savedir=None, **kwargs):
# set evaluation dataset when model selected as XGB
if self.model_type == 'XGB':
kwargs['eval_set'] = [(X, y)]
# model training
self.model.fit(X, y, **kwargs)
# save model
if savedir != None:
# check save directory
if not os.path.isdir('../saved_models'):
os.mkdir('../saved_models')
# model save to pickle except DNN
if self.model_type == 'DNN':
self.model.save(savedir)
else:
pickle.dump(self.model, open(savedir, "wb"))
