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