def __trends(self):
from model_holder import load_model
self.result_map = {}
for model_file in self.models:
print('Running regression ', model_file, '....')
mh = load_model(model_file)
model, most_dependent_columns = mh.get()
formatter = dtFrm.LANLDataFormatter(data_df=self.data_df, data_type='test', doTransform=True, doScale=True, \
most_dependent_columns=most_dependent_columns)
train_df = formatter.transform()
train_df = train_df.drop(['segment_id'], axis=1)
y_train_pred = model.predict(train_df)
self.result_map[model_file] = y_train_pred
formatter = dtFrm.LANLDataFormatter(data_df=data_df, data_type='train', doTransform=True, doScale=True, cols_to_keep=50)
data_df = formatter.transform()
most_dependent_columns = formatter.getMostImpCols()
# data_df = data_df.drop(['acc_max','acc_min','chg_acc_max','chg_acc_min'],axis=1)
# Splitting data into test_random_forest and train
# train_set, test_set = train_test_split(data_df, test_size=0.2, random_state=np.random.randint(1, 1000))
# Separate output from inputs
y_train = data_df['time_to_failure']
x_train_seg = data_df['segment_id']
x_train = data_df.drop(['time_to_failure', 'segment_id'], axis=1)
y_train = np.around(y_train.values, decimals=2)
# mlpReg = MLPRegressor(verbose=True, tol=0.0001, max_iter=200000, n_iter_no_change=10000, hidden_layer_sizes=(200,))
mlpReg = MLPRegressor(verbose=True, max_iter=1000)
mlpReg.fit(x_train, y_train)
# Create an variable to pickle and open it in write mode
mh = ModelHolder(mlpReg, most_dependent_columns)
mh.save('mlp_regression.model')
mlpReg = None
mh_new = load_model(model_name)
mlpReg, most_dependent_columns = mh_new.get()
y_pred = mlpReg.predict(x_train)
# y_pred = pd.Series(y_pred).apply(lambda x: float(x / 10))
print('MAE for Multi Layer Perceptron', mean_absolute_error(y_train, y_pred))