def runMLPRegressor(self):
lm = MLPRegressor(hidden_layer_sizes=(250, ),
activation='tanh',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='constant',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
verbose=False,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999)
print("MLPRegressor\n")
reg = lm.fit(self.m_X_train, self.m_y_train)
predictY = lm.predict(self.m_X_test)
score = lm.score(self.m_X_test, self.m_y_test)
predictTraingY = lm.predict(self.m_X_train)
lm.coef_ = lm.coefs_
lm.fit_intercept = lm.intercepts_
self.displayPredictPlot(predictY)
self.displayResidualPlot(predictY, predictTraingY)
self.dispalyModelResult(lm, predictY, score)
