Open Blair-Young opened 7 years ago
I'm training my classifier using clf = RVC(kernel = 'rbf') clf.fit(embeddings, labelsNum) were the number of labels = 10
When I inspect the clf I get this: with open('RVC.pkl', 'r') as rvc: le_rvc, clf_rvc = pickle.load(rvc)
array(['Ariel_Sharon', 'Colin_Powell', 'Donald_Rumsfeld', 'George_W_Bush', 'Gerhard_Schroeder', 'Hugo_Chavez', 'Jean_Chretien', 'John_Ashcroft', 'Junichiro_Koizumi', 'Tony_Blair'], dtype='|S17')
Which is correct, 10 classes.
However, when I try to predict my test set by running this
predictions = clf.predict_proba(rep).ravel() maxI = np.argmax(predictions) person = le.inverse_transform(maxI) confidence = predictions[maxI]
the length of predictions is 20
Meaning that when le.inverse_transform(maxI) is called it fails if maxl is >10
I must be doing something wrong on my side, but is there a reason why the clf is predicting more values than needed?
Hey, just wondering how this bug fix is going?
I'm training my classifier using clf = RVC(kernel = 'rbf') clf.fit(embeddings, labelsNum) were the number of labels = 10
When I inspect the clf I get this: with open('RVC.pkl', 'r') as rvc: le_rvc, clf_rvc = pickle.load(rvc)
Which is correct, 10 classes.
However, when I try to predict my test set by running this
the length of predictions is 20
Meaning that when le.inverse_transform(maxI) is called it fails if maxl is >10
I must be doing something wrong on my side, but is there a reason why the clf is predicting more values than needed?