8000 [MRG + 1] Labels of clustering should start at 0 or -1 if noise by albertcthomas · Pull Request #10015 · scikit-learn/scikit-learn · GitHub
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[MRG + 1] Labels of clustering should start at 0 or -1 if noise #10015

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Merged
agramfort merged 3 commits into from
Oct 27, 2017
Merged

[MRG + 1] Labels of clustering should start at 0 or -1 if noise #10015

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3f28fc9
test labels of clustering should start at 0 or -1 if noise
albertcthomas Oct 26, 2017
14314bb
take into account agramfort's comment
albertcthomas Oct 26, 2017
0543a96
fix test
albertcthomas Oct 26, 2017
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21 changes: 13 additions & 8 deletions sklearn/utils/estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1051,20 +1051,25 @@ def check_clustering(name, clusterer_orig):
assert_in(pred.dtype, [np.dtype('int32'), np.dtype('int64')])
assert_in(pred2.dtype, [np.dtype('int32'), np.dtype('int64')])

# Add noise to X to test the possible values of the labels
rng = np.random.RandomState(7)
X_noise = np.concatenate([X, rng.uniform(low=-3, high=3, size=(5, 2))])
labels = clusterer.fit_predict(X_noise)

# There should be at least one sample in every cluster. Equivalently
# labels_ should contain all the consecutive values between its
# min and its max.
pred_sorted = np.unique(pred)
assert_array_equal(pred_sorted, np.arange(pred_sorted[0],
pred_sorted[-1] + 1))
labels_sorted = np.unique(labels)
assert_array_equal(labels_sorted, np.arange(labels_sorted[0],
labels_sorted[-1] + 1))

# labels_ should be greater than -1
assert_greater_equal(pred_sorted[0], -1)
# labels_ should be less than n_clusters - 1
# Labels are expected to start at 0 (no noise) or -1 (if noise)
assert_true(labels_sorted[0] in [0, -1])
# Labels should be less than n_clusters - 1
if hasattr(clusterer, 'n_clusters'):
n_clusters = getattr(clusterer, 'n_clusters')
assert_greater_equal(n_clusters - 1, pred_sorted[-1])
# else labels_ should be less than max(labels_) which is necessarily true
assert_greater_equal(n_clusters - 1, labels_sorted[-1])
# else labels should be less than max(labels_) which is necessarily true


@ignore_warnings(category=DeprecationWarning)
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