From 80673ed07d9aba3bf9cea8253cd3bb1c20b14b66 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Thu, 24 Feb 2011 19:58:14 +0100
Subject: [PATCH] Rename strategy --> algorithm in Neighbors*.
---
doc/modules/neighbors.rst | 4 ++--
scikits/learn/neighbors.py | 31 ++++++++++++++-------------
scikits/learn/tests/test_neighbors.py | 12 +++++------
3 files changed, 24 insertions(+), 23 deletions(-)
diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
index d4eb4e2bc0..dc9df8c36a 100644
--- a/doc/modules/neighbors.rst
+++ b/doc/modules/neighbors.rst
@@ -20,8 +20,8 @@ The :class:`NeighborsClassifier` implements the nearest-neighbors
classification method using a vote heuristic: the class most present
in the k nearest neighbors of a point is assigned to this point.
-It is possible to use different nearest neighbor search strategies by
-using the keyword ``strategy``. Possible values are ``'auto'``,
+It is possible to use different nearest neighbor search algorithms by
+using the keyword ``algorithm``. Possible values are ``'auto'``,
``'ball_tree'``, ``'brute'`` and ``'brute_inplace'``. ``'ball_tree'``
will create an instance of :class:`BallTree` to conduct the search,
which is usually very efficient in low-dimensional spaces. In higher
diff --git a/scikits/learn/neighbors.py b/scikits/learn/neighbors.py
index 2fab1ffd32..07fca967db 100644
--- a/scikits/learn/neighbors.py
+++ b/scikits/learn/neighbors.py
@@ -22,8 +22,8 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
window_size : int, optional
Window size passed to BallTree
- strategy : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
- Strategy used to compute the nearest neighbors. 'ball_tree'
+ algorithm : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
+ Algorithm used to compute the nearest neighbors. 'ball_tree'
will construct a BallTree, 'brute' and 'brute_inplace' will
perform brute-force search.'auto' will guess the most
appropriate based on current dataset.
@@ -35,7 +35,7 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
>>> from scikits.learn.neighbors import NeighborsClassifier
>>> neigh = NeighborsClassifier(n_neighbors=1)
>>> neigh.fit(samples, labels)
- NeighborsClassifier(n_neighbors=1, window_size=1, strategy='auto')
+ NeighborsClassifier(n_neighbors=1, window_size=1, algorithm='auto')
>>> print neigh.predict([[0,0,0]])
[1]
@@ -48,10 +48,10 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
"""
- def __init__(self, n_neighbors=5, strategy='auto', window_size=1):
+ def __init__(self, n_neighbors=5, algorithm='auto', window_size=1):
self.n_neighbors = n_neighbors
self.window_size = window_size
- self.strategy = strategy
+ self.algorithm = algorithm
def fit(self, X, Y, **params):
@@ -73,8 +73,8 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
self._y = np.asanyarray(Y)
self._set_params(**params)
- if self.strategy == 'ball_tree' or \
- (self.strategy == 'auto' and X.shape[1] < 20):
+ if self.algorithm == 'ball_tree' or \
+ (self.algorithm == 'auto' and X.shape[1] < 20):
self.ball_tree = BallTree(X, self.window_size)
else:
self.ball_tree = None
@@ -119,7 +119,7 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
>>> from scikits.learn.neighbors import NeighborsClassifier
>>> neigh = NeighborsClassifier(n_neighbors=1)
>>> neigh.fit(samples, labels)
- NeighborsClassifier(n_neighbors=1, window_size=1, strategy='auto')
+ NeighborsClassifier(n_neighbors=1, window_size=1, algorithm='auto')
>>> print neigh.kneighbors([1., 1., 1.])
(array([ 0.5]), array([2]))
@@ -160,7 +160,7 @@ class NeighborsClassifier(BaseEstimator, ClassifierMixin):
# .. get neighbors ..
if self.ball_tree is None:
- if self.strategy == 'brute_inplace':
+ if self.algorithm == 'brute_inplace':
neigh_ind = knn_brute(self._fit_X, X, self.n_neighbors)
else:
from .metrics import euclidean_distances
@@ -203,8 +203,8 @@ class NeighborsRegressor(NeighborsClassifier, RegressorMixin):
mode : {'mean', 'barycenter'}, optional
Weights to apply to labels.
- strategy : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
- Strategy used to compute the nearest neighbors. 'ball_tree'
+ algorithm : {'auto', 'ball_tree', 'brute', 'brute_inplace'}, optional
+ Algorithm used to compute the nearest neighbors. 'ball_tree'
will construct a BallTree, 'brute' and 'brute_inplace' will
perform brute-force search.'auto' will guess the most
appropriate based on current dataset.
@@ -216,7 +216,8 @@ class NeighborsRegressor(NeighborsClassifier, RegressorMixin):
>>> from scikits.learn.neighbors import NeighborsRegressor
>>> neigh = NeighborsRegressor(n_neighbors=2)
>>> neigh.fit(X, y)
- NeighborsRegressor(n_neighbors=2, window_size=1, mode='mean', strategy='auto')
+ NeighborsRegressor(n_neighbors=2, window_size=1, mode='mean',
+ algorithm='auto')
>>> print neigh.predict([[1.5]])
[ 0.5]
@@ -226,12 +227,12 @@ class NeighborsRegressor(NeighborsClassifier, RegressorMixin):
"""
- def __init__(self, n_neighbors=5, mode='mean', strategy='auto',
+ def __init__(self, n_neighbors=5, mode='mean', algorithm='auto',
window_size=1):
self.n_neighbors = n_neighbors
self.window_size = window_size
self.mode = mode
- self.strategy = strategy
+ self.algorithm = algorithm
def predict(self, X, **params):
@@ -256,7 +257,7 @@ class NeighborsRegressor(NeighborsClassifier, RegressorMixin):
# .. get neighbors ..
if self.ball_tree is None:
- if self.strategy == 'brute_inplace':
+ if self.algorithm == 'brute_inplace':
neigh_ind = knn_brute(self._fit_X, X, self.n_neighbors)
else:
from .metrics.pairwise import euclidean_distances
diff --git a/scikits/learn/tests/test_neighbors.py b/scikits/learn/tests/test_neighbors.py
index 0f5f5a3206..3921fadf6b 100644
--- a/scikits/learn/tests/test_neighbors.py
+++ b/scikits/learn/tests/test_neighbors.py
@@ -24,19 +24,19 @@ def test_neighbors_1D():
for s in ('auto', 'ball_tree', 'brute', 'inplace'):
# n_neighbors = 1
- knn = neighbors.NeighborsClassifier(n_neighbors=1, strategy=s)
+ knn = neighbors.NeighborsClassifier(n_neighbors=1, algorithm=s)
knn.fit(X, Y)
test = [[i + 0.01] for i in range(0, n/2)] + \
[[i - 0.01] for i in range(n/2, n)]
assert_array_equal(knn.predict(test), [0]*3 + [1]*3)
# n_neighbors = 2
- knn = neighbors.NeighborsClassifier(n_neighbors=2, strategy=s)
+ knn = neighbors.NeighborsClassifier(n_neighbors=2, algorithm=s)
knn.fit(X, Y)
assert_array_equal(knn.predict(test), [0]*4 + [1]*2)
# n_neighbors = 3
- knn = neighbors.NeighborsClassifier(n_neighbors=3, strategy=s)
+ knn = neighbors.NeighborsClassifier(n_neighbors=3, algorithm=s)
knn.fit(X, Y)
assert_array_equal(knn.predict([[i +0.01] for i in range(0, n/2)]),
[0 for i in range(n/2)])
@@ -54,15 +54,15 @@ def test_neighbors_iris():
for s in ('auto', 'ball_tree', 'brute', 'inplace'):
clf = neighbors.NeighborsClassifier()
- clf.fit(iris.data, iris.target, n_neighbors=1, strategy=s)
+ clf.fit(iris.data, iris.target, n_neighbors=1, algorithm=s)
assert_array_equal(clf.predict(iris.data), iris.target)
- clf.fit(iris.data, iris.target, n_neighbors=9, strategy=s)
+ clf.fit(iris.data, iris.target, n_neighbors=9, algorithm=s)
assert_(np.mean(clf.predict(iris.data)== iris.target) > 0.95)
for m in ('barycenter', 'mean'):
rgs = neighbors.NeighborsRegressor()
- rgs.fit(iris.data, iris.target, mode=m, strategy=s)
+ rgs.fit(iris.data, iris.target, mode=m, algorithm=s)
assert_(np.mean(
rgs.predict(iris.data).round() == iris.target) > 0.95)
--
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