Closed
Description
Description
ValueError thrown when calling cross_val_predict on multi-output models when the method is one of 'decision_function', 'predict_proba' or 'predict_log_proba'
Steps/Code to Reproduce
import numpy
import sklearn.ensemble
import sklearn.model_selection
X = numpy.arange(400).reshape(100, 4)
y = numpy.arange(200).reshape(100, 2)
est = sklearn.ensemble.RandomForestClassifier()
sklearn.model_selection.cross_val_predict(est, X, y, method='predict_proba')Expected Results
No error is thrown.
Actual Results
Traceback (most recent call last):
File "p.py", line 9, in <module>
sklearn.model_selection.cross_val_predict(est, X, y, method='predict_proba')
File "venv/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 674, in cross_val_predict
y = le.fit_transform(y)
File "venv/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 111, in fit_transform
y = column_or_1d(y, warn=True)
File "venv/lib/python3.6/site-packages/sklearn/utils/validation.py", line 614, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (100, 2)
Versions
Python 3.6.2 (default, Jul 29 2017, 00:00:00)
[GCC 4.8.4]
NumPy 1.13.3
SciPy 1.0.0
Scikit-Learn 0.19.1
It looks like this is a regression from 0.18 introduced here: #7889
It looks like this pull request contains more multi-output friendly code-paths: #8773
Metadata
Metadata
Assignees
Labels
No labels