8000 Exception When Calling cross_val_predict with Multi-Output Estimators and Methods Besides 'predict' · Issue #10028 · scikit-learn/scikit-learn · GitHub
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Exception When Calling cross_val_predict with Multi-Output Estimators and Methods Besides 'predict' #10028

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waltaskew opened this issue Oct 27, 2017 · 2 comments
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Exception When Calling cross_val_predict with Multi-Output Estimators and Methods Besides 'predict' #10028

waltaskew opened this issue Oct 27, 2017 · 2 comments

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@waltaskew
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waltaskew commented Oct 27, 2017 •
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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
@amueller
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So that's the same issue as reported in #8773 right?

@waltaskew
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6081

So that's the same issue as reported in #8773 right?

Ah, sorry -- you're right -- different manifestation of the same issue.

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