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我最近使用 scikit-learn 进行情绪分析，所以在我训练了我的标记数据然后尝试在未标记的数据集上测试它们之后，出现了这个错误“ValueError：无法处理连续多输出和二进制的混合”

我认为我做错的是我给（y_pred）错误的假设。

错误来自：accuracy = classifier.score(test_matrix,ALL_test)

但是当我将 ALL_test 更改为 ALL_train（经过训练和标记的数据）时，它会带来 0.971251409245 的准确度；这是绝对错误的

我应该怎么办？

# -*- coding:utf-8 -*-
import sklearn.cross_validation
import sklearn.feature_extraction.text
import sklearn.metrics
import sklearn.naive_bayes
from sklearn import svm
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, precision_score, recall_score


name = ['Tweet','Label']
name2 =['Tweet','Label']
data_train = pd.read_table('unstemmedtrain.csv',sep = ';',names = name)
data_test = pd.read_table('unstemmedtest.csv',names=name2)
train_data =pd.DataFrame(data_test,columns=name2)
test_data=pd.DataFrame(data_train,columns=name)

vectorizer =  sklearn.feature_extraction.text.CountVectorizer()

train_matrix = vectorizer.fit_transform(train_data['Tweet'])
test_matrix = vectorizer.transform(test_data['Tweet'])
#print train_matrix

positive_train = (train_data['Label']=='positive')
negative_train= (train_data['Label']=='negative')
neutral_train=(train_data['Label']=='neutral')
#print negative_cases_train
ALL_train = positive_train +negative_train +neutral_train
#print positive_cases_train
ALL_test = (test_data['Tweet'])
positive_test =(test_data['Label']=='positive')
negative_test =(test_data['Label']=='negative')
neutral_test = (test_data['Label']=='neutral')
ALL_Test = positive_test + negative_test + neutral_test

#print positive_cases_test


classifier=sklearn.naive_bayes.MultinomialNB()
classifier2 = classifier.fit(train_matrix,ALL_train)

p_sentiment = classifier.predict(test_matrix)
p_prob = classifier.predict_proba(test_matrix)
#print predicted_prob
accuracy = classifier.score(test_matrix,ALL_test)
print accuracy
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2 回答 2

1

我在这里看到了一些问题。

  1. 您是在尝试预测哪条推文是正面的、哪个是负面的、哪个是中性的，或者您是在尝试预测一条推文是正面/负面/中性还是不？你在做后者。让我们假设train_data['Label'] = ['positive', 'positive', 'negative', 'neutral']。所以你的代码：

    positive_train = (train_data['Label']=='positive') # = [True, True, False, False]
negative_train= (train_data['Label']=='negative') # = [False, False, True, False]
neutral_train=(train_data['Label']=='neutral') # = [False, False, False, True]
ALL_train = positive_train +negative_train +neutral_train # = [True, True, True, True]

  2. 你给出的分数函数ALL_test = (test_data['Tweet'])是文本，而不是ALL_Test = positive_test + negative_test + neutral_test你真正的 y。这就是异常的来源。我不知道你为什么需要All_test，但如果你需要，请以不同的方式命名 - 这会让你感到困惑。

于 2014-06-10T15:41:04.467 回答
0

You have to pass All_train to classifier.score

As:

accuracy = classifier.score(test_matrix,ALL_train)
print accuracy

If you want to evaluate your model for test data then Recall,precision,f1 score and auc_score may help

于 2015-03-15T11:11:43.597 回答