python code for vowpal wabbit multilabel classifier with option --multilabel_oaa

write python code for vowpal wabbit multilabel classifier with option --multilabel_oaa data find by yourself with many rows and features features are both categorical and continues >>> import numpy as np >>> X = [login to view URL]([ [10, 10], [8, 10], [-5, 5.5], [-5.4, 5.5], [-20, -20], [-15, -20] ]) >>> y = [login to view URL]([1, 1, 2, 2, 3, 3]) >>> from vowpalwabbit.sklearn_vw import VWMultiClassifier >>> model = VWMultiClassifier(oaa=3, loss_function='logistic') >>> _ = [login to view URL](X, y) >>> model.predict_proba(X) array([[0.38928846, 0.30534211, 0.30536944], [0.40664235, 0.29666999, 0.29668769], [0.52324486, 0.23841164, 0.23834346], [0.5268591 , 0.23660533, 0.23653553], [0.65397811, 0.17312808, 0.17289382], [0.61190444, 0.19416356, 0.19393198]]) [login to view URL] # Test 85: check multilabel_oaa {VW} --multilabel_oaa 10 -d train-sets/multilabel -p [login to view URL]