API renmae k->n_neighbors

examples/plot_neighbors_regression.py

@@ -27,7 +27,7 @@ y[::5] += 1*(0.5 - np.random.rand(8))
 
 from scikits.learn import neighbors
 
-knn_barycenter = neighbors.NeighborsBarycenter(k=5)
+knn_barycenter = neighbors.NeighborsBarycenter(n_neighbors=5)
 y_ = knn_barycenter.fit(X, y).predict(T)
 
 ###############################################################################

scikits/learn/neighbors.py

@@ -23,7 +23,7 @@ class Neighbors(BaseEstimator, ClassifierMixin):
     labels : array
         An array representing labels for the data (only arrays of
         integers are supported).
-    k : int
+    n_neighbors : int
         default number of neighbors.
     window_size : int
         Window size passed to BallTree
@@ -33,9 +33,9 @@ class Neighbors(BaseEstimator, ClassifierMixin):
     >>> samples = [[0.,0.,1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]]
     >>> labels = [0,0,1,1]
     >>> from scikits.learn.neighbors import Neighbors
-    >>> neigh = Neighbors(k=3)
+    >>> neigh = Neighbors(n_neighbors=3)
     >>> neigh.fit(samples, labels)
-    Neighbors(k=3, window_size=1)
+    Neighbors(n_neighbors=3, window_size=1)
     >>> print neigh.predict([[0,0,0]])
     [ 0.]
 
@@ -44,11 +44,11 @@ class Neighbors(BaseEstimator, ClassifierMixin):
     http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
     """
 
-    def __init__(self, k=5, window_size=1):
+    def __init__(self, n_neighbors=5, window_size=1):
         """Internally uses the ball tree datastructure and algorithm for fast
         neighbors lookups on high dimensional datasets.
         """
-        self.k = k
+        self.n_neighbors = n_neighbors
         self.window_size = window_size
 
     def fit(self, X, Y=()):
@@ -57,14 +57,14 @@ class Neighbors(BaseEstimator, ClassifierMixin):
         self.ball_tree = BallTree(X, self.window_size)
         return self
 
-    def kneighbors(self, data, k=None):
+    def kneighbors(self, data, n_neighbors=None):
         """Finds the K-neighbors of a point.
 
         Parameters
         ----------
         point : array-like
             The new point.
-        k : int
+        n_neighbors : int
             Number of neighbors to get (default is the value
             passed to the constructor).
 
@@ -85,9 +85,9 @@ class Neighbors(BaseEstimator, ClassifierMixin):
         >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
         >>> labels = [0, 0, 1]
         >>> from scikits.learn.neighbors import Neighbors
-        >>> neigh = Neighbors(k=1)
+        >>> neigh = Neighbors(n_neighbors=1)
         >>> neigh.fit(samples, labels)
-        Neighbors(k=1, window_size=1)
+        Neighbors(n_neighbors=1, window_size=1)
         >>> print neigh.kneighbors([1., 1., 1.])
         (array(0.5), array(2))
 
@@ -99,18 +99,18 @@ class Neighbors(BaseEstimator, ClassifierMixin):
         (array([ 0.5       ,  1.11803399]), array([1, 2]))
 
         """
-        if k is None:
-            k = self.k
-        return self.ball_tree.query(data, k=k)
+        if n_neighbors is None:
+            n_neighbors = self.n_neighbors
+        return self.ball_tree.query(data, k=n_neighbors)
 
-    def predict(self, T, k=None):
+    def predict(self, T, n_neighbors=None):
         """Predict the class labels for the provided data.
 
         Parameters
         ----------
         test: array
             A 2-D array representing the test point.
-        k : int
+        n_neighbors : int
             Number of neighbors to get (default is the value
             passed to the constructor).
 
@@ -124,28 +124,28 @@ class Neighbors(BaseEstimator, ClassifierMixin):
         >>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
         >>> labels = [0, 0, 1]
         >>> from scikits.learn.neighbors import Neighbors
-        >>> neigh = Neighbors(k=1)
+        >>> neigh = Neighbors(n_neighbors=1)
         >>> neigh.fit(samples, labels)
-        Neighbors(k=1, window_size=1)
+        Neighbors(n_neighbors=1, window_size=1)
         >>> print neigh.predict([.2, .1, .2])
         0
         >>> print neigh.predict([[0., -1., 0.], [3., 2., 0.]])
         [0 1]
         """
         T = np.asanyarray(T)
-        if k is None:
-            k = self.k
-        return _predict_from_BallTree(self.ball_tree, self.Y, T, k=k)
+        if n_neighbors is None:
+            n_neighbors = self.n_neighbors
+        return _predict_from_BallTree(self.ball_tree, self.Y, T, n_neighbors)
 
 
-def _predict_from_BallTree(ball_tree, Y, test, k):
+def _predict_from_BallTree(ball_tree, Y, test, n_neighbors):
     """Predict target from BallTree object containing the data points.
 
     This is a helper method, not meant to be used directly. It will
     not check that input is of the correct type.
     """
-    Y_ = Y[ball_tree.query(test, k=k, return_distance=False)]
-    if k == 1:
+    Y_ = Y[ball_tree.query(test, k=n_neighbors, return_distance=False)]
+    if n_neighbors == 1:
         return Y_
     return (stats.mode(Y_, axis=1)[0]).ravel()
 
@@ -167,7 +167,7 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
     y : array
         An array representing labels for the data (only arrays of
         integers are supported).
-    k : int
+    n_neighbors : int
         default number of neighbors.
     window_size : int
         Window size passed to BallTree
@@ -177,9 +177,9 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
     >>> X = [[0], [1], [2], [3]]
     >>> y = [0, 0, 1, 1]
     >>> from scikits.learn.neighbors import NeighborsBarycenter
-    >>> neigh = NeighborsBarycenter(k=2)
+    >>> neigh = NeighborsBarycenter(n_neighbors=2)
     >>> neigh.fit(X, y)
-    NeighborsBarycenter(k=2, window_size=1)
+    NeighborsBarycenter(n_neighbors=2, window_size=1)
     >>> print neigh.predict([[1.5]])
     [ 0.5]
 
@@ -188,11 +188,11 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
     http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
     """
 
-    def __init__(self, k=5, window_size=1):
+    def __init__(self, n_neighbors=5, window_size=1):
         """Internally uses the ball tree datastructure and algorithm for fast
         neighbors lookups on high dimensional datasets.
         """
-        self.k = k
+        self.n_neighbors = n_neighbors
         self.window_size = window_size
 
     def fit(self, X, y, copy=True):
@@ -200,14 +200,14 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
         self.ball_tree = BallTree(X, self.window_size)
         return self
 
-    def predict(self, T, k=None):
+    def predict(self, T, n_neighbors=None):
         """Predict the target for the provided data.
 
         Parameters
         ----------
         T : array
             A 2-D array representing the test data.
-        k : int
+        n_neighbors : int
             Number of neighbors to get (default is the value
             passed to the constructor).
 
@@ -221,18 +221,18 @@ class NeighborsBarycenter(BaseEstimator, RegressorMixin):
         >>> X = [[0], [1], [2]]
         >>> y = [0, 0, 1]
         >>> from scikits.learn.neighbors import NeighborsBarycenter
-        >>> neigh = NeighborsBarycenter(k=2)
+        >>> neigh = NeighborsBarycenter(n_neighbors=2)
         >>> neigh.fit(X, y)
-        NeighborsBarycenter(k=2, window_size=1)
+        NeighborsBarycenter(n_neighbors=2, window_size=1)
         >>> print neigh.predict([[.5], [1.5]])
         [ 0.   0.5]
         """
         T = np.asanyarray(T)
         if T.ndim == 1:
             T = T[:,None]
-        if k is None:
-            k = self.k
-        A = kneighbors_graph(T, k=k, weight="barycenter",
+        if n_neighbors is None:
+            n_neighbors = self.n_neighbors
+        A = kneighbors_graph(T, n_neighbors=n_neighbors, weight="barycenter",
                                   ball_tree=self.ball_tree).tocsr()
         return A * self._y
 
@@ -286,7 +286,8 @@ def barycenter_weights(x, X_neighbors, tol=1e-3):
     return w
 
 
-def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
+def kneighbors_graph(X, n_neighbors, weight=None, ball_tree=None,
+                     window_size=1):
     """Computes the (weighted) graph of k-Neighbors
 
     Parameters
@@ -294,7 +295,7 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
     X : array-like, shape = [n_samples, n_features]
         Coordinates of samples. One sample per row.
 
-    k : int
+    n_neighbors : int
         Number of neighbors for each sample.
 
     weight : None (default)
@@ -319,7 +320,7 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
     Examples
     --------
     >>> X = [[0], [2], [1]]
-    >>> A = kneighbors_graph(X, k=2, weight=None)
+    >>> A = kneighbors_graph(X, n_neighbors=2, weight=None)
     >>> print A.todense()
     [[ 1.  0.  1.]
      [ 0.  1.  1.]
@@ -331,16 +332,16 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
     if ball_tree is None:
         ball_tree = BallTree(X, window_size)
     A = sparse.lil_matrix((n_samples, ball_tree.size))
-    dist, ind = ball_tree.query(X, k=k)
+    dist, ind = ball_tree.query(X, k=n_neighbors)
     if weight is None:
         for i, li in enumerate(ind):
-            if k > 1:
-                A[i, list(li)] = np.ones(k)
+            if n_neighbors > 1:
+                A[i, list(li)] = np.ones(n_neighbors)
             else:
                 A[i, li] = 1.0
     elif weight is "distance":
         for i, li in enumerate(ind):
-            if k > 1:
+            if n_neighbors > 1:
                 A[i, list(li)] = dist[i, :]
             else:
                 A[i, li] = dist[i, 0]
@@ -348,7 +349,7 @@ def kneighbors_graph(X, k, weight=None, ball_tree=None, window_size=1):
         # XXX : the next loop could be done in parallel
         # by parallelizing groups of indices
         for i, li in enumerate(ind):
-            if k > 1:
+            if n_neighbors > 1:
                 X_i = ball_tree.data[li]
                 A[i, list(li)] = barycenter_weights(X[i], X_i)
             else:

scikits/learn/tests/test_neighbors.py

@@ -17,16 +17,16 @@ def test_neighbors_1D():
     X = [[x] for x in range(0, n)]
     Y = [0]*n_2 + [1]*n_2
 
-    # k = 1
-    knn = neighbors.Neighbors(k=1)
+    # n_neighbors = 1
+    knn = neighbors.Neighbors(n_neighbors=1)
     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, 0, 0, 1, 1, 1])
     # same as before, but using predict() instead of Neighbors object
 
-    # k = 3
-    knn = neighbors.Neighbors(k=3)
+    # n_neighbors = 3
+    knn = neighbors.Neighbors(n_neighbors=3)
     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)])
@@ -59,7 +59,7 @@ def test_neighbors_barycenter():
     """
     X = [[0], [1], [2], [3]]
     y = [0, 0, 1, 1]
-    neigh = neighbors.NeighborsBarycenter(k=2)
+    neigh = neighbors.NeighborsBarycenter(n_neighbors=2)
     neigh.fit(X, y)
     assert_equal(neigh.predict([[1.5]]), 0.5)