LabelEncoder save 离线使用

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For me the easiest way was exporting LabelEncoder as .pkl file for each column. You have to export the encoder for each column after using the fit_transform() function

For example

from sklearn.preprocessing import LabelEncoder
import pickle
import pandas as pd
df_train = pd.read_csv(‘traing_data.csv‘)
le = LabelEncoder()    
df_train[‘Departure‘] = le.fit_transform(df_train[‘Departure‘])
#exporting the departure encoder
output = open(‘Departure_encoder.pkl‘, ‘wb‘)
pickle.dump(le, output)
output.close()

Then in the testing project, you can load the LabelEncoder object and apply transform() function directly

from sklearn.preprocessing import LabelEncoder
import pandas as pd
df_test = pd.read_csv(‘testing_data.csv‘)
#load the encoder file
import pickle 
pkl_file = open(‘Departure_encoder.pkl‘, ‘rb‘)
le_departure = pickle.load(pkl_file) 
pkl_file.close()
df_test[‘Departure‘] = le_departure.transform(df_test[‘Departure‘])

LabelEncoder save 离线使用

标签：out   sklearn   nbsp   import   object   dir   cti   dep   roc   

原文地址：https://www.cnblogs.com/bonelee/p/10861506.html