Addressed review comments #5

Edited comments
Merge branch 'naneuclid' into knnimpute
scikit-learn · jnothman · Sep 3, 2019 · jnothman · Sep 3, 2019 · thomasjpfan
diff --git a/sklearn/impute/tests/test_knn.py b/sklearn/impute/tests/test_knn.py
diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py
+        return X[:, valid_idx]
+
+    def _more_tags(self):
+        return {'allow_nan': True}
-        return {'allow_nan': True}
+        return {'allow_nan': is_scalar_nan(self.missing_values)}
diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py
+
+        Parameters
+        ----------
+        dist_pot_donors : array-like, shape=(n_receivers, n_train_samples)
diff --git a/doc/modules/impute.rst b/doc/modules/impute.rst
+least one neighbor with a defined distance, the weighted or unweighted average
+of the remaining neighbors will be used during imputation. If a feature is
+always missing in training, it is removed during `transform`. For more
+information on the methodology, see ref. [OL2001]_.
diff --git a/sklearn/impute/tests/test_knn.py b/sklearn/impute/tests/test_knn.py
+    ])
+    knn = KNNImputer(missing_values=na, n_neighbors=2).fit(X)
+
+    X_transform = knn.transform(X)
diff --git a/sklearn/impute/tests/test_knn.py b/sklearn/impute/tests/test_knn.py
+    dist = nan_euclidean_distances(X)
+    r1c1_nbor_dists = dist[1, [0, 2, 3, 4, 5]]
+    r1c3_nbor_dists = dist[1, [0, 3, 4, 5, 6]]
+    r1c1_nbor_wt = (1 / r1c1_nbor_dists)
-    r1c1_nbor_wt = (1 / r1c1_nbor_dists)
+    r1c1_nbor_wt = 1 / r1c1_nbor_dists
diff --git a/sklearn/impute/tests/test_knn.py b/sklearn/impute/tests/test_knn.py
+    r1c1_nbor_dists = dist[1, [0, 2, 3, 4, 5]]
+    r1c3_nbor_dists = dist[1, [0, 3, 4, 5, 6]]
+    r1c1_nbor_wt = (1 / r1c1_nbor_dists)
+    r1c3_nbor_wt = (1 / r1c3_nbor_dists)
-    r1c3_nbor_wt = (1 / r1c3_nbor_dists)
+    r1c3_nbor_wt = 1 / r1c3_nbor_dists
diff --git a/sklearn/impute/tests/test_knn.py b/sklearn/impute/tests/test_knn.py
+
+
+@pytest.mark.parametrize("na", [-1, np.nan])
+def test_knn_imputer_with_weighted_features(na):
	return {'allow_nan': True}
	return {'allow_nan': is_scalar_nan(self.missing_values)}
	r1c1_nbor_wt = (1 / r1c1_nbor_dists)
	r1c1_nbor_wt = 1 / r1c1_nbor_dists
	r1c3_nbor_wt = (1 / r1c3_nbor_dists)
	r1c3_nbor_wt = 1 / r1c3_nbor_dists


		@pytest.mark.parametrize("na", [-1, np.nan])
		def test_knn_imputer_with_weighted_features(na):