multilabel decision_function and predict_proba output shapes

check_multilabel_output_shapes.py

import warnings
import sklearn
warnings.simplefilter('ignore')
from sklearn import *

X, y = datasets.make_multilabel_classification()
for clf in [tree.DecisionTreeClassifier(),
            neighbors.KNeighborsClassifier(),
            neural_network.MLPClassifier(),
            multioutput.MultiOutputClassifier(linear_model.LogisticRegression()),
            multiclass.OneVsRestClassifier(linear_model.LogisticRegression()),
            ]:
    clf.fit(X[:-10], y[:-10])
    for method in ['decision_function', 'predict_proba']:
        if not hasattr(clf, method):
            continue
        s = getattr(clf, method)(X[-3:])
        if hasattr(s, 'shape'):
            print(type(clf).__name__, method, s.shape)
        else:
            print(type(clf).__name__, method, [x.shape for x in s])


"""Output:

DecisionTreeClassifier predict_proba [(3, 2), (3, 2), (3, 2), (3, 2), (3, 2)]
KNeighborsClassifier predict_proba [(3, 2), (3, 2), (3, 2), (3, 2), (3, 2)]
MLPClassifier predict_proba (3, 5)
MultiOutputClassifier predict_proba [(3, 2), (3, 2), (3, 2), (3, 2), (3, 2)]
OneVsRestClassifier decision_function (3, 5)
OneVsRestClassifier predict_proba (3, 5)
"""