How to get Top 3 or Top N predictions using sklearn's SGDClassifier

There is no built-in function, but what is wrong with

probs = clf.predict_proba(test)
best_n = np.argsort(probs, axis=1)[-n:]

?

As suggested by one of the comment, should change [-n:] to [:,-n:]

probs = clf.predict_proba(test)
best_n = np.argsort(probs, axis=1)[:,-n:]

I know this has been answered...but I can add a bit more...

#both preds and truths are same shape m by n (m is number of predictions and n is number of classes)
def top_n_accuracy(preds, truths, n):
    best_n = np.argsort(preds, axis=1)[:,-n:]
    ts = np.argmax(truths, axis=1)
    successes = 0
    for i in range(ts.shape[0]):
      if ts[i] in best_n[i,:]:
        successes += 1
    return float(successes)/ts.shape[0]

It's quick and dirty but I find it useful. One can add their own error checking, etc..

Hopefully, Andreas will help with this. predict_probs is not available when loss='hinge'. To get top n class when loss='hinge' do:

calibrated_clf = CalibratedClassifierCV(clfSDG, cv=3, method='sigmoid')
model = calibrated_clf.fit(train.data, train.label)

probs = model.predict_proba(test_data)
sorted( zip( calibrated_clf.classes_, probs[0] ), key=lambda x:x[1] )[-n:]

Not sure if clfSDG.predict and calibrated_clf.predict will always predict the same class.