def get_model(name):
#INPUT name: name of the wanted model
#OUPUT model: wanted model
if name == "tree":
model = ExtraTreesRegressor(n_estimators=50)
elif name == "line":
model = LinearRegression()
elif name == "NN":
model = Sequential()
model.add(Dense(200, input_dim=3))
model.add(Dense(1))
model.compile(optimizer="SGD", loss='mean_squared_error')
return model
test_size=0.3,
random_state=0)
#print(X_train.shape, X_test.shape)
#print(y_train.shape,y_test.shape)
##ANN
##create ANN model
import keras
from keras.models import Sequential
from keras.layers import Lambda, Dense, ReLU, LeakyReLU, Dropout
model = Sequential()
#input layer:
model.add(
Dense(128,
kernel_initializer='normal',
input_dim=X_train.shape[1],
activation='relu'))
##Hidden Layer:
model.add(Dense(256, kernel_initializer='normal', activation='relu'))
model.add(Dense(256, kernel_initializer='normal', activation='relu'))
model.add(Dense(256, kernel_initializer='normal', activation='relu'))
#Output layer
model.add(Dense(1, kernel_initializer='normal', activation='linear'))
#Compile the model
model.compile(loss='mean_absolute_error',
optimizer='adam',
metrics=['mean_absolute_error'])
msg = "%s: %f (%f)" % (name, score, mae)
print(msg)
model1 = ExtraTreesRegressor()
model1.fit(X_Train, Y_Train)
model_GT_T_D1 = 'finalized_model_GT_T_D1.sav'
pickle.dump(model1, open(model_GT_T_D1, 'wb'))
# Split Data to Train and Test
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.3)
# create model
model = Sequential()
model.add(
Dense(6,
input_dim=15,
kernel_initializer='random_uniform',
activation='relu'))
model.add(Dropout(0.2))
model.add(
Dense(4,
kernel_initializer='random_uniform',
activation='relu',
kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(2, kernel_initializer='random_uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='random_uniform', activation='relu'))
# Compile model
model.compile(loss='mean_absolute_error', optimizer='adam')
