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FIX exclude samples with nan distance in KNNImputer for uniform weights #29135

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Merged
merged 6 commits into from
Jun 6, 2024
Merged

FIX exclude samples with nan distance in KNNImputer for uniform weights #29135

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2925794
FIX exclude samples with nan distance in KNNImputer for uniform weights
xuefeng-xu May 22, 2024
1d114f6
update weight_matrix
xuefeng-xu May 30, 2024
7cdee80
Merge branch 'main' into nan_dist_knnimpute
xuefeng-xu Jun 6, 2024
8761001
add changelog
xuefeng-xu Jun 6, 2024
be7dab8
add another test data with more rows and columns
xuefeng-xu Jun 6, 2024
9215e67
resolve conflicts
xuefeng-xu Jun 6, 2024
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7 changes: 7 additions & 0 deletions doc/whats_new/v1.6.rst
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Expand Up @@ -96,6 +96,13 @@ Changelog
can be silenced using the `reg_param` attribute.
:pr:`19731` by :user:`Alihan Zihna <azihna>`.

:mod:`sklearn.impute`
.....................

- |Fix| :class:`impute.KNNImputer` excludes samples with nan distances when
computing the mean value for uniform weights.
:pr:`29135` by :user:`Xuefeng Xu <xuefeng-xu>`.

:mod:`sklearn.linear_model`
...........................

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3 changes: 3 additions & 0 deletions sklearn/impute/_knn.py
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Expand Up @@ -195,6 +195,9 @@ def _calc_impute(self, dist_pot_donors, n_neighbors, fit_X_col, mask_fit_X_col):
# fill nans with zeros
if weight_matrix is not None:
weight_matrix[np.isnan(weight_matrix)] = 0.0
else:
weight_matrix = np.ones_like(donors_dist)
weight_matrix[np.isnan(donors_dist)] = 0.0

# Retrieve donor values and calculate kNN average
donors = fit_X_col.take(donors_idx)
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25 changes: 24 additions & 1 deletion sklearn/impute/tests/test_knn.py
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Expand Up @@ -239,7 +239,9 @@ def test_knn_imputer_one_n_neighbors(na):
def test_knn_imputer_all_samples_are_neighbors(na):
X = np.array([[0, 0], [na, 2], [4, 3], [5, na], [7, 7], [na, 8], [14, 13]])

X_imputed = np.array([[0, 0], [6, 2], [4, 3], [5, 5.5], [7, 7], [6, 8], [14, 13]])
X_imputed = np.array(
[[0, 0], [6.25, 2], [4, 3], [5, 5.75], [7, 7], [6.25, 8], [14, 13]]
)

n_neighbors = X.shape[0] - 1
imputer = KNNImputer(n_neighbors=n_neighbors, missing_values=na)
Expand Down Expand Up @@ -505,6 +507,27 @@ def test_knn_imputer_not_enough_valid_distances(na, weights):
assert_allclose(knn.transform(X2), X2_imputed)


@pytest.mark.parametrize("na", [-1, np.nan])
@pytest.mark.parametrize("weights", ["uniform", "distance"])
def test_knn_imputer_nan_distance(na, weights):
# Samples with nan distance should be excluded from the mean computation
X1_train = np.array([[1, 1], [na, 2]])
X1_test = np.array([[0, na]])
X1_test_expected = np.array([[0, 1]])

knn1 = KNNImputer(n_neighbors=2, missing_values=na, weights=weights)
knn1.fit(X1_train)
assert_allclose(knn1.transform(X1_test), X1_test_expected)

X2_train = np.array([[na, 1, 1], [2, na, 2], [3, 3, na]])
X2_test = np.array([[na, 0, na], [0, na, na], [na, na, 0]])
X2_test_expected = np.array([[3, 0, 1], [0, 3, 2], [2, 1, 0]])

knn2 = KNNImputer(n_neighbors=2, missing_values=na, weights=weights)
knn2.fit(X2_train)
assert_allclose(knn2.transform(X2_test), X2_test_expected)

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Would it be possible to add another scenario where we have like 3 or 4 features, 3 or 4 rows in X_train and more 2 or 3 rows in X_test?

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Sure, I added another test scenario.


@pytest.mark.parametrize("na", [-1, np.nan])
def test_knn_imputer_drops_all_nan_features(na):
X1 = np.array([[na, 1], [na, 2]])
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