我正在尝试找到2个以上预测数据帧结果的组合精度, 在对结果进行预测之后,将结果保存到csv中并获得最终的合并准确性,所有保存的结果都将导入并合并。
但是在计算过程中,它并没有达到预期的效果,并且随着结果的出现而改变,结果保存到csv中的结果被更改了。我们还有另一种将结果保存到csv中的方法,因此,保留了结果的原始形式。
test_result is test data having
test_result['text'] and test_result['labels]
train_result is train data having
train_result['text'] and t_result['sentiment]
time classifier = grid_svm.fit(train_result['text'], train_result['sentiment'])
predicted= classifier.predict(test_result['lemma_text'])
print(classification_report(test_result['labels'], predicted))
预测的分类报告如下
# below code is used to save result to csv
test_result.to_csv('result/2_entertainment.csv')
# below code is used to import same result from csv again
test_data_2 = pd.read_csv('result/2_entertainment.csv')
#But now strangely the classification report changes drastically.
# I'm not sure where the error is happening
#it seems that while saving result the result has been swaped
print(classification_report(test_data_2['sentiment'], test_data_2['labels']))