交叉验证时不在索引中的关键错误

Question

我在我的数据集上应用了 svm。我的数据集是多标签的，这意味着每个观察都有多个标签。

虽然KFold cross-validation 会引发错误not in index。

它显示了从 601 到 6007 not in index 的索引（我有 1...6008 个数据样本）。

这是我的代码：

   df = pd.read_csv("finalupdatedothers.csv")
categories = ['ADR','WD','EF','INF','SSI','DI','others']
X= df[['sentences']]
y = df[['ADR','WD','EF','INF','SSI','DI','others']]
kf = KFold(n_splits=10)
kf.get_n_splits(X)
for train_index, test_index in kf.split(X,y):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
    y_train, y_test = y.iloc[train_index], y.iloc[test_index]

SVC_pipeline = Pipeline([
                ('tfidf', TfidfVectorizer(stop_words=stop_words)),
                ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
            ])

for category in categories:
    print('... Processing {} '.format(category))
    # train the model using X_dtm & y
    SVC_pipeline.fit(X_train['sentences'], y_train[category])

    prediction = SVC_pipeline.predict(X_test['sentences'])
    print('SVM Linear Test accuracy is {} '.format(accuracy_score(X_test[category], prediction)))
    print 'SVM Linear f1 measurement is {} '.format(f1_score(X_test[category], prediction, average='weighted'))
    print([{X_test[i]: categories[prediction[i]]} for i in range(len(list(prediction)))])

实际上，我不知道如何应用 KFold 交叉验证，其中我可以分别获得每个标签的 F1 分数和准确率。 看过this 和this 并没有帮助我如何成功申请我的案子。

为了可重现，这是数据框的一个小样本 最后七个特征是我的标签，包括 ADR、WD、...

,sentences,ADR,WD,EF,INF,SSI,DI,others
0,"extreme weight gain, short-term memory loss, hair loss.",1,0,0,0,0,0,0
1,I am detoxing from Lexapro now.,0,0,0,0,0,0,1
2,I slowly cut my dosage over several months and took vitamin supplements to help.,0,0,0,0,0,0,1
3,I am now 10 days completely off and OMG is it rough.,0,0,0,0,0,0,1
4,"I have flu-like symptoms, dizziness, major mood swings, lots of anxiety, tiredness.",0,1,0,0,0,0,0
5,I have no idea when this will end.,0,0,0,0,0,0,1

更新

当我做任何 Vivek Kumar 所说的事情时，它会引发错误

ValueError: Found input variables with inconsistent numbers of samples: [1, 5408]

在分类器部分。你知道如何解决吗？

stackoverflow 中有几个链接表示我需要重塑训练数据。我也这样做了，但没有成功link 谢谢:)

Answer 1

train_index、test_index 是基于行数的整数索引。但是熊猫索引不是那样工作的。较新版本的 pandas 在如何从它们中切片或选择数据方面更加严格。

您需要使用.iloc 访问数据。更多信息是available here

这是你需要的：

for train_index, test_index in kf.split(X,y):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
    y_train, y_test = y.iloc[train_index], y.iloc[test_index]

    ...
    ...

    # TfidfVectorizer dont work with DataFrame, 
    # because iterating a DataFrame gives the column names, not the actual data
    # So specify explicitly the column name, to get the sentences

    SVC_pipeline.fit(X_train['sentences'], y_train[category])

    prediction = SVC_pipeline.predict(X_test['sentences'])