[FIX] KNN: Fix crash when Mahanalobis metric is used #1475
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Current coverage is 88.19% (diff: 100%)@@ master #1475 diff @@
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+ Hits 6714 6718 +4
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| @@ -11,5 +11,7 @@ class KNNLearner(SklLearner): | |||
| def __init__(self, n_neighbors=5, metric="euclidean", weights="uniform", | |||
| algorithm='auto', | |||
| preprocessors=None): | |||
| if metric == "mahalanobis": | |||
| algorithm = "brute" | |||
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Please add a comment explaining why this is necessary. For the record, this works for me:
>>> from sklearn.neighbors import KNeighborsClassifier
>>> nn = KNeighborsClassifier(metric='mahalanobis', algorithm='auto')
>>> X = np.random.random((10, 2))
>>> y = (np.random.random(10) > .5).astype(int)
>>> nn.fit(X, y)
>>> nn.predict(X)
array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0])There was a problem hiding this comment.
For me as well. But i.e. X = np.random.random((12, 2)) doesn't.
VesnaT
changed the title
VesnaT
changed the title
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| class TestKNNLearner(unittest.TestCase): | ||
| @classmethod | ||
| def setUpClass(cls): | ||
| cls.iris = Table('iris') | ||
| cls.learn = KNNLearner() | ||
| cls.learn = KNNLearner |
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I would skip assigning classes in to cls.learn and cls.learn_reg and just use their names in tests.
self.learn() is not much shorter than KNNLearner() (in fact it has the same number of characters)
KNN learners (cls and reg) need additional parameter (metric_params) when Mahalanobis distance metric is used. Since both learners have the same parameters, new base class was created.
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