Closed
Description
Description
If two target columns are categorical and have a different number unique values, MultiOutputClassifier.predict_proba raises a value error when trying to dstack the probability matrices.
Steps/Code to Reproduce
Example:
from sklearn.linear_model import LogisticRegression
from sklearn.multioutput import MultiOutputClassifier
import numpy as np
# random features
X = np.random.normal(size=(100, 100))
# random labels
Y = np.concatenate([
np.random.choice(['a', 'b'], (100, 1)), # first column can have 2 values
np.random.choice(['d', 'e', 'f'], (100, 1)) # second column can have 3
], axis=1)
clf = MultiOutputClassifier(LogisticRegression())
clf.fit(X, Y)
clf.predict_proba(X)Expected Results
No error is thrown. It looks like the RandomForestClassifier handles data of this shape and returns a list of numpy arrays. I would expect the same behavior in this case.
Actual Results
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-40-84c34a558c92> in <module>()
18 clf.fit(X, Y)
19
---> 20 clf.predict_proba(X)
anaconda/lib/python3.5/site-packages/sklearn/multioutput.py in predict_proba(self, X)
224
225 results = np.dstack([estimator.predict_proba(X) for estimator in
--> 226 self.estimators_])
227 return results
228
anaconda/lib/python3.5/site-packages/numpy/lib/shape_base.py in dstack(tup)
366
367 """
--> 368 return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
369
370 def _replace_zero_by_x_arrays(sub_arys):
ValueError: all the input array dimensions except for the concatenation axis must match exactly
Versions
Darwin-15.6.0-x86_64-i386-64bit
Python 3.5.2 |Anaconda custom (x86_64)| (default, Jul 2 2016, 17:52:12)
[GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]
NumPy 1.11.1
SciPy 0.18.1
Scikit-Learn 0.18.1
If returning a list is the right fix, happy to submit a PR for this.