Pickle을 사용하여 저장된 모델로드-로드 된 프로그램에서 fit_transform이 수행됨에 따라 오류 발생

알고리즘을 훈련시키고 저장하는 첫 번째 프로그램을 만들었습니다.

프로그램 1

import numpy as np
from sklearn.model_selection import StratifiedShuffleSplit 
from sklearn.impute import SimpleImputer  
from sklearn.tree import DecisionTreeRegressor # import for Decision Tree Algorithm
import pickle
from sklearn.preprocessing import StandardScaler

SourceData=pd.read_excel("ASML Stock Predict.xlsx") # Load the data into Pandas DataFrame
SourceData["Nasdaq Category"]=pd.cut(SourceData["Adj Close Nasdaq 100"],
                                     bins=[0., 4500, 5500, 6500, 7500,8500, 9500, 10500, np.inf],
                                     labels=[1, 2, 3, 4,5,6,7,8])

""" Split the data source into test and train subset """
split = StratifiedShuffleSplit(n_splits=1, test_size=0.01, random_state=42)
for train_index, test_index in split.split(SourceData, SourceData["Nasdaq Category"]):
    strat_train_set = SourceData.loc[train_index]  # stratfied train dataset with all columns in original source data 
    strat_test_set = SourceData.loc[test_index] #stratified test dataset with all columns in original source data

""" Drop the new Nasdaq Category Cloumn from the data source after the train and test subset is prepared"""
for set_ in (strat_train_set, strat_test_set): 
    set_.drop("Nasdaq Category", axis=1, inplace=True)

DataSource_train_independent= strat_train_set.drop(["Date", "Adj Close ASML"], axis=1) # Drop depedent variable from training dataset
DataSource_train_dependent=strat_train_set["Adj Close ASML"].copy() #  New dataframe with only independent variable value for training dataset



imputer = SimpleImputer(strategy="median") # declated imputer to fill the blank values with Median value of the variable
imputer.fit(DataSource_train_independent) # calulate the median for different independent variables

""" Scale the independent variables training set. No need to scale the dependent variable """
sc_X = StandardScaler()
X=sc_X.fit_transform(DataSource_train_independent.values) # scale the independent variables
X_test=sc_X.transform(testdata.values) # scale the independent variables for test data
##sc_y = StandardScaler()
y=DataSource_train_dependent # scaling is not required for dependent variable


"""Decision Tree Regressor """

tree_reg = DecisionTreeRegressor()
tree_reg.fit(X,y)

filename = 'DecisionTree_TrainedModel.sav'
pickle.dump(tree_reg, open(filename, 'wb'))

프로그램 2

from sklearn.tree import DecisionTreeRegressor # import for Decision Tree Algorithm
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor # import for Decision Tree Algorithm
import pandas as pd

testdata=pd.read_excel("ASML Test  Stock Predict.xlsx") # Load the test data

sc_X = StandardScaler()
X_test=sc_X.transform(testdata.values) # scale the independent variables for test data



loaded_model = pickle.load(open('DecisionTree_TrainedModel.sav', 'rb'))
decision_predictions = loaded_model.predict(X_test) # Predict the value of dependent variable
print("The prediction by Decision Treemodel is " , decision_predictions )

프로그램 1에 "fit_transform"이 있고 모델을 저장 했으므로 모델을로드 한 후 두 번째 프로그램에서는 독립 변수 만 변환했습니다.

두 번째 프로그램을 실행할 때 "sklearn.exceptions.NotFittedError :이 StandardScaler 인스턴스가 아직 적합하지 않습니다.이 추정기를 사용하기 전에 적절한 인수로 'fit'을 호출하십시오."라는 오류 메시지가 표시됩니다.

제안 해주세요. 나는 테스트 독립 변수에 적합하지 않고 변환 만하면된다는 것을 이해합니다.