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[MRG + 1] Too few arguments in formatting call #9298

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Merged
merged 3 commits into from
Jul 13, 2017
Merged

[MRG + 1] Too few arguments in formatting call #9298

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68e5487
Too few arguments in formatting call
SebastinSanty Jul 7, 2017
d2e0b0f
Add test
SebastinSanty Jul 13, 2017
60467b5
Covered with tests
SebastinSanty Jul 13, 2017
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4 changes: 2 additions & 2 deletions sklearn/ensemble/bagging.py
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Original file line number Diff line number Diff line change
Expand Up @@ -773,8 +773,8 @@ def decision_function(self, X):

if self.n_features_ != X.shape[1]:
raise ValueError("Number of features of the model must "
"match the input. Model n_features is {1} and "
"input n_features is {2} "
"match the input. Model n_features is {0} and "
"input n_features is {1} "
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this means it was not covered by any test. Can you update existing test to catch this pb?

thx

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@SebastinSanty SebastinSanty Jul 10, 2017 •
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Yes, I'll check in with tests

"".format(self.n_features_, X.shape[1]))

# Parallel loop
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8 changes: 8 additions & 0 deletions sklearn/ensemble/tests/test_bagging.py
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Expand Up @@ -19,6 +19,7 @@
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import assert_raise_message

from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.model_selection import GridSearchCV, ParameterGrid
Expand Down Expand Up @@ -449,6 +450,13 @@ def test_parallel_classification():
decisions2 = ensemble.decision_function(X_test)
assert_array_almost_equal(decisions1, decisions2)

X_err = np.hstack((X_test, np.zeros((X_test.shape[0], 1))))
assert_raise_message(ValueError, "Number of features of the model "
"must match the input. Model n_features is {0} "
"and input n_features is {1} "
"".format(X_test.shape[1], X_err.shape[1]),
ensemble.decision_function, X_err)

ensemble = BaggingClassifier(SVC(decision_function_shape='ovr'),
n_jobs=1,
random_state=0).fit(X_train, y_train)
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2 changes: 1 addition & 1 deletion sklearn/multiclass.py
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Expand Up @@ -721,7 +721,7 @@ def fit(self, X, y):
"""
X, y = check_X_y(X, y)
if self.code_size <= 0:
raise ValueError("code_size should be greater than 0, got {1}"
raise ValueError("code_size should be greater than 0, got {0}"
"".format(self.code_size))

_check_estimator(self.estimator)
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3 changes: 3 additions & 0 deletions sklearn/tests/test_multiclass.py
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Expand Up @@ -704,6 +704,9 @@ def test_ecoc_float_y():

ovo = OutputCodeClassifier(LinearSVC())
assert_raise_message(ValueError, "Unknown label type", ovo.fit, X, y)
ovo = OutputCodeClassifier(LinearSVC(), code_size=-1)
assert_raise_message(ValueError, "code_size should be greater than 0,"
" got -1", ovo.fit, X, y)


def test_pairwise_indices():
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