def build_preprocessor(self, X):
X_list = split_features(X)
# Google trend de
self.gt_de_enc = StandardScaler()
self.gt_de_enc.fit(X_list[32])
# Google trend state
self.gt_state_enc = StandardScaler()
self.gt_state_enc.fit(X_list[33])
def build_preprocessor(self, X):
X_list = split_features(X)
# Google trend de
self.gt_de_enc = StandardScaler()
self.gt_de_enc.fit(X_list[32])
# Google trend state
self.gt_state_enc = StandardScaler()
self.gt_state_enc.fit(X_list[33])
def preprocessing(self, X):
X_list = split_features(X)
# X_list[0] = self.store_index.transform(X_list[0])
X_list[1] = self.item_index.transform(X_list[1])
# X_list[9] = self.clas.transform(X_list[9])
X_list[12] = self.oil.transform(X_list[12])
# X_list[33] = self.gt_state_enc.transform(X_list[33])
# print(X_list)
return X_list
def build_preprocessor(self, X):
X_list = split_features(X)
# return True
# Google trend de
# self.store_index = StandardScaler()
# self.store_index.fit(X_list[0])
self.item_index = StandardScaler()
self.item_index.fit(X_list[1])
# self.clas = StandardScaler()
# self.clas.fit(X_list[9])
self.oil = StandardScaler()
self.oil.fit(X_list[12])
def preprocessing(self, X):
X_list = split_features(X)
X_list[32] = self.gt_de_enc.transform(X_list[32])
X_list[33] = self.gt_state_enc.transform(X_list[33])
return X_list
def preprocessing(self, X):
X_list = split_features(X)
X_list[32] = self.gt_de_enc.transform(X_list[32])
X_list[33] = self.gt_state_enc.transform(X_list[33])
return X_list
