How do I classify documents with SciKitLearn using

问题:

The following example shows how one can train a classifier with the Sklearn 20 newsgroups data.

>>> from sklearn.feature_extraction.text import TfidfVectorizer 
>>> categories = ['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space']
>>> newsgroups_train = fetch_20newsgroups(subset='train', ... categories=categories) 
>>> vectorizer = TfidfVectorizer() >>> vectors = vectorizer.fit_transform(newsgroups_train.data) 
>>> vectors.shape (2034, 34118)

However, I have my own labeled corpus that I would like to use.

After getting a tfidfvector of my own data, would I train a classifier like this?

classif_nb = nltk.NaiveBayesClassifier.train(vectorizer)

To recap: How can I use my own corpus instead of the 20newsgroups, but in the same way used here? How can I then use my TFIDFVectorized corpus to train a classifier?

Thanks!

回答1:

To address questions from comments; The whole basic process of working with tfidf representation in some classification task you should:

1. You fit the vectorizer to your training data and save it in some variable, lets call it tfidf
2. You transform training data (without labels, just text) through data = tfidf.transform(...)
3. You fit the model (classifier) using some_classifier.fit( data, labels ), where labels are in the same order as documnents in data
4. During testing you use tfidf.transform( ... ) on new data, and check the prediction of your model

回答2:

In general, for sklearn the flow is:

1. Convert your string data to numeric values usinf some vectorizer for e.g. TfIDF,count etcs
2. fit and transform
3. Pass it to train/fit of your choice of classifier.

You did not mention your data format but if it is csv file with some rows,flow could be:

1. Read each row of text
2. Pre process, like remove the stop words etc.
3. raw_data_list = [row1,row2,rown...]
4. vectorizer = TfidfVectorizer()
5. x_transformed = vectorizer.fit_transform(raw_data_list)
6. x_transformed can be passed to fit/train function of classifiers.

And once you have trained classifier you can call predict for new data. Remeber to convert new data to same format as data on which you trained by using above used and fitted vectorizer before passing it to classif.predict.