diff --git a/sklearn/neighbors/base.py b/sklearn/neighbors/base.py
index 7cda17f1d40a7a5dd52acef70ad934a13ce20a4d..c2e554793212a2d50120fe6273ee3a69d1ec64a0 100644
--- a/sklearn/neighbors/base.py
+++ b/sklearn/neighbors/base.py
@@ -78,13 +78,19 @@ class NeighborsBase(BaseEstimator):
self._fit_method = None
def _fit(self, X):
- if isinstance(X, BallTree):
+ if isinstance(X, NeighborsBase):
+ self._fit_X = X._fit_X
+ self._tree = X._tree
+ self._fit_method = X._fit_method
+ return self
+
+ elif isinstance(X, BallTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'ball_tree'
return self
- if isinstance(X, cKDTree):
+ elif isinstance(X, cKDTree):
self._fit_X = X.data
self._tree = X
self._fit_method = 'kd_tree'
@@ -92,6 +98,9 @@ class NeighborsBase(BaseEstimator):
X = safe_asanyarray(X)
+ if X.ndim != 2:
+ raise ValueError("data type not understood")
+
if issparse(X):
if self.algorithm not in ('auto', 'brute'):
warnings.warn("cannot use tree with sparse input: "
@@ -320,7 +329,7 @@ class RadiusNeighborsMixin(object):
>>> neigh.fit(samples) # doctest: +ELLIPSIS
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print neigh.radius_neighbors([1., 1., 1.]) # doctest: +ELLIPSIS
- (array([[ 1.5 0.5]]...), array([[1 2]]...)
+ (array([[ 1.5, 0.5]]...), array([[1, 2]]...)
The first array returned contains the distances to all points which
are closer than 1.6, while the second array returned contains their
@@ -328,8 +337,8 @@ class RadiusNeighborsMixin(object):
Because the number of neighbors of each point is not necessarily
equal, `radius_neighbors` returns an array of objects, where each
object is a 1D array of indices.
-
"""
+
if self._fit_method == None:
raise ValueError("must fit neighbors before querying")
diff --git a/sklearn/neighbors/regression.py b/sklearn/neighbors/regression.py
index cbfd7b1837edc363cc08237b6e2ebde657d61777..9db821a97ff660f9380bc2eca53344b68edb35b9 100644
--- a/sklearn/neighbors/regression.py
+++ b/sklearn/neighbors/regression.py
@@ -1,4 +1,4 @@
-"""Nearest Neighbor Classification"""
+"""Nearest Neighbor Regression"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
index b2159609e38a5670a61925baa288a7ae3fee3c14..d15fc3940980f73e24c459a083eb3620ae59ef95 100644
--- a/sklearn/neighbors/tests/test_neighbors.py
+++ b/sklearn/neighbors/tests/test_neighbors.py
@@ -1,7 +1,9 @@
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_raises
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix)
+from scipy.spatial import cKDTree
from sklearn import neighbors, datasets
@@ -30,6 +32,7 @@ def test_unsupervised_kneighbors(n_samples=20, n_features=5,
test = rng.rand(n_query_pts, n_features)
+ results_nodist = []
results = []
for algorithm in ALGORITHMS:
@@ -37,13 +40,33 @@ def test_unsupervised_kneighbors(n_samples=20, n_features=5,
algorithm=algorithm)
neigh.fit(X)
+ results_nodist.append(neigh.kneighbors(test, return_distance=False))
results.append(neigh.kneighbors(test, return_distance=True))
for i in range(len(results) - 1):
+ assert_array_almost_equal(results_nodist[i], results[i][1])
assert_array_almost_equal(results[i][0], results[i + 1][0])
assert_array_almost_equal(results[i][1], results[i + 1][1])
+def test_unsupervised_inputs():
+ X = np.random.random((10,3))
+
+ nbrs_fid = neighbors.NearestNeighbors(n_neighbors=1)
+ nbrs_fid.fit(X)
+
+ dist1, ind1 = nbrs_fid.kneighbors(X)
+
+ nbrs = neighbors.NearestNeighbors(n_neighbors=1)
+
+ for input in (nbrs_fid, neighbors.BallTree(X), cKDTree(X)):
+ nbrs.fit(input)
+ dist2, ind2 = nbrs.kneighbors(X)
+
+ assert_array_almost_equal(dist1, dist2)
+ assert_array_almost_equal(ind1, ind2)
+
+
def test_unsupervised_radius_neighbors(n_samples=20, n_features=5,
n_query_pts=2, radius=0.5,
random_state=0):
@@ -63,15 +86,20 @@ def test_unsupervised_radius_neighbors(n_samples=20, n_features=5,
algorithm=algorithm)
neigh.fit(X)
+ ind1 = neigh.radius_neighbors(test, return_distance=False)
+
# sort the results: this is not done automatically for
# radius searches
dist, ind = neigh.radius_neighbors(test, return_distance=True)
- for (d, i) in zip(dist, ind):
+ for (d, i, i1) in zip(dist, ind, ind1):
j = d.argsort()
d[:] = d[j]
i[:] = i[j]
results.append((dist, ind))
+ assert_array_almost_equal(np.concatenate(list(ind)),
+ np.concatenate(list(ind1)))
+
for i in range(len(results) - 1):
assert_array_almost_equal(np.concatenate(list(results[i][0])),
np.concatenate(list(results[i + 1][0]))),
@@ -189,6 +217,7 @@ def test_radius_neighbors_regressor(n_samples=40,
for algorithm in ALGORITHMS:
for weights in ['uniform', 'distance', weight_func]:
neigh = neighbors.RadiusNeighborsRegressor(radius=radius,
+ weights=weights,
algorithm=algorithm)
neigh.fit(X, y)
epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
@@ -304,6 +333,44 @@ def test_radius_neighbors_graph():
[ 1.01 , 0. , 1.40716026],
[ 0. , 1.40716026, 0. ]])
+def test_neighbors_badargs():
+ neigh_types = [neighbors.KNeighborsClassifier,
+ neighbors.RadiusNeighborsClassifier,
+ neighbors.KNeighborsRegressor,
+ neighbors.RadiusNeighborsRegressor]
+
+
+ assert_raises(ValueError,
+ neighbors.NearestNeighbors,
+ algorithm='blah')
+
+ X = np.random.random((10,2))
+
+ for cls in neigh_types:
+ assert_raises(ValueError,
+ cls,
+ weights='blah')
+
+ for cls in neigh_types:
+ nbrs = cls()
+ assert_raises(ValueError,
+ nbrs.predict,
+ X)
+
+
+
+ nbrs = neighbors.NearestNeighbors().fit(X)
+
+ assert_raises(ValueError,
+ nbrs.kneighbors_graph,
+ X, mode='blah')
+ assert_raises(ValueError,
+ nbrs.radius_neighbors_graph,
+ X, mode='blah')
+
+
+
+
if __name__ == '__main__':
import nose
nose.runmodule()
diff --git a/sklearn/neighbors/unsupervised.py b/sklearn/neighbors/unsupervised.py
index 15d6c4478e2815eb78ac2b2cb4dcbf0259fc7016..74f42a0e2e937694bd5ac34af3d15b5320415287 100644
--- a/sklearn/neighbors/unsupervised.py
+++ b/sklearn/neighbors/unsupervised.py
@@ -47,10 +47,10 @@ class NearestNeighbors(NeighborsBase, KNeighborsMixin,
NearestNeighbors(algorithm='auto', leaf_size=30, n_neighbors=2, radius=0.4)
>>> neigh.kneighbors([[0, 0, 1.3]], 2, return_distance=False)
- array([[2, 0]], dtype=int32)
+ array([[2, 0]])
>>> neigh.radius_neighbors([0, 0, 1.3], 0.4, return_distance=False)
- array([[2]], dtype=object)
+ array([[2]])
See also
--------