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我已经通过 sklearn OneHotEncoding 方法转换了训练和测试数据集。但是，转换后的结果具有不同的类型形状。所以不可能应用于逻辑回归等其他算法。

如何根据训练数据集的形状重塑测试数据？

最好的关于，克里斯

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder, LabelEncoder 

def data_transformation(data, dummy):  
    le = LabelEncoder()

    # Encoding the columns with multiple categorical levels
    for col1 in dummy:
        le.fit(data[col1])
        data[col1] = le.transform(data[col1])

    dummy_data = np.array(data[dummy])
    enc = OneHotEncoder()
    enc.fit(dummy_data)

    dummy_data = enc.transform(dummy_data).toarray() 

if __name__ == '__main__': 
    data = pd.read_csv('train.data', delimiter=',') 
    data_test = pd.read_csv('test.data', delimiter=',')

    dummy_columns = ['Column1', 'Column2'] 
    data = data_transformation(data, dummy_columns)
    data_test = data_transformation(data_test, dummy_columns)

# result 
# data shape : (200000, 71 ) 
# data_test shape : ( 15000, 32)
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1 回答 1

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非常感谢你，维维克！由于您的帮助，我已经解决了这个问题。 

def data_transformation2(data, data_test, dummy):
    le = LabelEncoder()

    # Encoding the columns with multiple categorical levels
    for col in dummy:
        le.fit(data[col])
        data[col] = le.transform(data[col])

    for col in dummy:
        le.fit(data_test[col])
        data_test[col] = le.transform(data_test[col])

    enc = OneHotEncoder()
    dummy_data = np.array(data[dummy])
    dummy_data_test = np.array(data_test[dummy])

    enc.fit(dummy_data)

    dummy_data = enc.transform(dummy_data).toarray()
    dummy_data_test = enc.transform(dummy_data_test).toarray()

    print(dummy_data.shape)
    print(dummy_data_test.shape)
于 2017-04-03T06:02:01.010 回答