14

I have applied svm on my dataset. my dataset is multi-label means each observation has more than one label.

while KFold cross-validation it raises an error not in index.

It shows the index from 601 to 6007 not in index (I have 1...6008 data samples).

This is my code:

 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)))])

Actually, I do not know how to apply KFold cross-validation in which I can get the F1 score and accuracy of each label separately. having looked at this and this did not help me how can I successfully to apply on my case.

for being reproducible, this is a small sample of the data frame the last seven features are my labels including 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

Update

when I did whatever Vivek Kumar said It raises the error 

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

in classifier part . do you have any idea how to resolve it?

there are a couple of links for this error in stackoverflow which says I need to reshape training data. I also did that but no success link Thanks :)

Improve this question
5
  • Can you elaborate? you wrote that when using KFold you get an error. is this in the code you attached? on what line Commented Aug 15, 2018 at 5:50
  • @ShaharA Thanks for the comment. it raises error when it want to do KFold. so early line in the code, the reason I put the whole code here is that to show what purpose later I want to use them. Actually the code working perfectly when I apply train_test_split, but with KFOLD it does not Commented Aug 15, 2018 at 5:53
  • Did you try kf.split(X) without the y inside? Commented Aug 15, 2018 at 6:08
  • @ShaharA Yes Actually, it seems it does not relate to that argument Commented Aug 15, 2018 at 6:09
  • I also updated with a small sample of my data frame so it is now reproducible. Commented Aug 15, 2018 at 6:18

1 Answer 1

Reset to default
42

train_index, test_index are integer indices based on the number of rows. But pandas indexing dont work like that. Newer versions of pandas are more strict in how you slice or select data from them.

You need to use .iloc to access the data. More information is available here

This is what you need:

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'])
Improve this answer
Sign up to request clarification or add additional context in comments.

13 Comments

Thank you so much for the answer, then how can I figure out when passing training to the classifier? with this I got this error: ValueError: Found input variables with inconsistent numbers of samples: [1, 5408] at this line SVC_pipeline.fit(X_train, y_train[category]). thanks for taking the time
Again thanks, Actually I have also tried this way but it raises error:
File "pandas_libs\index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc File "pandas_libs\index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc File "pandas_libs\hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas_libs\hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'ADR'
even X_train and y_train is kind of yellow shows there is something wrong with them but im not able to figure it out . I also saw this post of you here stackoverflow.com/questions/44429600/… but it did not help :|
@sariaGoudarzi On your sample data and code you provided in the question above, I am not getting this error. Please update the question with the current complete code (that you used after taking hints from this answer) and also the complete stack trace of error.
|

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.