8000 [MRG+1] Add check for sparse prediction in cross_val_predict (fixes #5132) by dubstack · Pull Request #5161 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Add check for sparse prediction in cross_val_predict (fixes #5132) #5161

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Merged
merged 6 commits into from
Aug 27, 2015
Merged

[MRG+1] Add check for sparse prediction in cross_val_predict (fixes #5132) #5161

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aa2d6bf
Add check for sparse prediction in cross_val_predict
dubstack Aug 26, 2015
48adf8b
Use a single vstack for concatenating all blocks in prediction matrix
dubstack Aug 26, 2015
e35c90c
Use Inverted locations to reorder the predictions
dubstack Aug 26, 2015
26d3323
Remove redundant p variable
dubstack Aug 26, 2015
0c6b173
Add test to check sparse predictions in cross_val_predict
dubstack Aug 26, 2015
b68f922
Fix minor indentation issues
dubstack Aug 26, 2015
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16 changes: 11 additions & 5 deletions sklearn/cross_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1042,14 +1042,20 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
train, test, verbose,
fit_params)
for train, test in cv)
p = np.concatenate([p for p, _ in preds_blocks])

preds = [p for p, _ in preds_blocks]
locs = np.concatenate([loc for _, loc in preds_blocks])
if not _check_is_partition(locs, _num_samples(X)):
raise ValueError('cross_val_predict only works for partitions')
preds = p.copy()
preds[locs] = p
return preds

inv_locs = np.empty(len(locs), dtype=int)
inv_locs[locs] = np.arange(len(locs))

# Check for sparse predictions
if sp.issparse(preds[0]):
preds = sp.vstack(preds, format=preds[0].format)
else :
preds = np.concatenate(preds)
return preds[inv_locs]

def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params):
"""Fit estimator and predict values for a given dataset split.
Expand Down
19 changes: 18 additions & 1 deletion sklearn/tests/test_cross_validation.py
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Expand Up @@ -4,6 +4,7 @@

import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats

from sklearn.utils.testing import assert_true
Expand All @@ -25,14 +26,15 @@
from sklearn.datasets import load_boston
from sklearn.datasets import load_digits
from sklearn.datasets import load_iris
from sklearn.datasets import make_multilabel_classification
from sklearn.metrics import explained_variance_score
from sklearn.metrics import make_scorer
from sklearn.metrics import precision_score

from sklearn.externals import six
from sklearn.externals.six.moves import zip

from sklearn.linear_model import Ridge
from sklearn.multiclass import OneVsRestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.cluster import KMeans
Expand Down Expand Up @@ -1094,3 +1096,18 @@ def test_check_is_partition():

p[0] = 23
assert_false(cval._check_is_partition(p, 100))

def test_cross_val_predict_sparse_prediction():
# check that cross_val_predict gives same result for sparse and dense input
X, y = make_multilabel_classification(n_classes=2, n_labels=1,
allow_unlabeled=False,
return_indicator=True,
random_state=1)
X_sparse = csr_matrix(X)
y_sparse = csr_matrix(y)
classif = OneVsRestClassifier(SVC(kernel='linear'))
preds = cval.cross_val_predict(classif, X, y, cv=10)
preds_sparse = cval.cross_val_predict(classif, X_sparse,y_sparse, cv=10)
preds_sparse = preds_sparse.toarray()
assert_array_almost_equal(preds_sparse, preds)

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