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Accept column vectors when having binary or multiclass targets #673

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Merged
merged 10 commits into from
Feb 2, 2020
Merged

Accept column vectors when having binary or multiclass targets #673

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ff4ae73
Accept column vectors when having binary or multiclass targets
chkoar Jan 7, 2020
e860876
Skip the failed test
chkoar Jan 8, 2020
4df7c38
Revert "Skip the failed test"
Jan 31, 2020
b087cee
Update an irrelevant doctest
Jan 31, 2020
7969973
Update test_iht_fit_resample_half
Jan 31, 2020
9676582
Update test_iht_fit_resample_half
Jan 31, 2020
6b471ea
Update test_iht_fit_resample_half
Jan 31, 2020
b321366
Update test_iht_fit_resample_half
Jan 31, 2020
ebc25b4
Revert "Skip the failed test"
Jan 31, 2020
d210ac0
Merge branch 'master' of https://github.com/scikit-learn-contrib/imba…
Jan 31, 2020
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2 changes: 2 additions & 0 deletions .gitignore
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Expand Up @@ -22,6 +22,8 @@ var/
*.egg-info/
.installed.cfg
*.egg
Pipfile
Pipfile.lock

# PyInstaller
# Usually these files are written by a python script from a template
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3 changes: 3 additions & 0 deletions imblearn/utils/_validation.py
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Expand Up @@ -12,6 +12,7 @@
from sklearn.base import clone
from sklearn.neighbors._base import KNeighborsMixin
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import column_or_1d
from sklearn.utils.multiclass import type_of_target

from ..exceptions import raise_isinstance_error
Expand Down Expand Up @@ -96,6 +97,8 @@ def check_target_type(y, indicate_one_vs_all=False):
"multioutput targets are not supported."
)
y = y.argmax(axis=1)
else:
y = column_or_1d(y)

return (y, type_y == "multilabel-indicator") if indicate_one_vs_all else y

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15 changes: 15 additions & 0 deletions imblearn/utils/estimator_checks.py
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Expand Up @@ -44,6 +44,7 @@ def _yield_sampler_checks(name, Estimator):
yield check_samplers_multiclass_ova
yield check_samplers_preserve_dtype
yield check_samplers_sample_indices
yield check_samplers_2d_target


def _yield_classifier_checks(name, Estimator):
Expand Down Expand Up @@ -283,6 +284,20 @@ def check_samplers_multiclass_ova(name, Sampler):
assert_allclose(y_res, y_res_ova.argmax(axis=1))


def check_samplers_2d_target(name, Sampler):
X, y = make_classification(
n_samples=100,
n_classes=3,
n_informative=4,
weights=[0.2, 0.3, 0.5],
random_state=0,
)

y = y.reshape(-1, 1) # Make the target 2d
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Could you add another check when having pandas Series and DataFrame

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I was thinking to make a follow up PR to address #666. So, this check could be added in that same PR. What do you think?

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OK good then

sampler = Sampler()
sampler.fit_resample(X, y)


def check_samplers_preserve_dtype(name, Sampler):
X, y = make_classification(
n_samples=1000,
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