From 51d4310c6feeb1ace394cc577dac06f22b6e1a75 Mon Sep 17 00:00:00 2001
From: Lars Buitinck <L.J.Buitinck@uva.nl>
Date: Fri, 21 Oct 2011 13:58:08 +0200
Subject: [PATCH] COSMIT rename safe_asanyarray to safe_asarray to prevent
confusion
---
doc/developers/index.rst | 11 +----------
sklearn/cluster/affinity_propagation_.py | 1 +
sklearn/feature_selection/univariate_selection.py | 6 +++---
sklearn/linear_model/base.py | 6 +++---
sklearn/linear_model/ridge.py | 6 +++---
sklearn/metrics/pairwise.py | 8 ++++----
sklearn/naive_bayes.py | 4 ++--
sklearn/neighbors/base.py | 4 ++--
sklearn/svm/base.py | 4 ++--
sklearn/utils/__init__.py | 4 ++--
sklearn/utils/tests/test___init__.py | 8 ++++----
11 files changed, 27 insertions(+), 35 deletions(-)
diff --git a/doc/developers/index.rst b/doc/developers/index.rst
index a6824be432..19bbf8ae05 100644
--- a/doc/developers/index.rst
+++ b/doc/developers/index.rst
@@ -276,7 +276,7 @@ Input validation
The module ``sklearn.utils`` contains various functions for doing input
validation/conversion. Sometimes, ``np.atleast_2d`` suffices for validation;
-in other cases, be sure to call ``safe_asanyarray``, ``atleast2d_or_csr`` or
+in other cases, be sure to call ``safe_asarray``, ``atleast2d_or_csr`` or
``as_float_array`` on any array-like argument passed to a scikit-learn API
function.
@@ -430,15 +430,6 @@ you call ``fit`` a second time without taking any previous value into
account: **fit should be idempotent**.
-Python tuples
-^^^^^^^^^^^^^
-
-In addition to numpy arrays, all methods should be able to accept
-Python tuples as arguments. In practice, this means you should call
-``numpy.asanyarray`` at the beginning at each public method that accepts
-arrays.
-
-
Optional Arguments
^^^^^^^^^^^^^^^^^^
diff --git a/sklearn/cluster/affinity_propagation_.py b/sklearn/cluster/affinity_propagation_.py
index eceb412c69..e84b138d2c 100644
--- a/sklearn/cluster/affinity_propagation_.py
+++ b/sklearn/cluster/affinity_propagation_.py
@@ -10,6 +10,7 @@ clustering.
import numpy as np
from ..base import BaseEstimator
+from ..utils import as_float_array
def affinity_propagation(S, p=None, convit=30, max_iter=200, damping=0.5,
diff --git a/sklearn/feature_selection/univariate_selection.py b/sklearn/feature_selection/univariate_selection.py
index a553d36a08..f839c5ea92 100644
--- a/sklearn/feature_selection/univariate_selection.py
+++ b/sklearn/feature_selection/univariate_selection.py
@@ -13,7 +13,7 @@ from scipy.sparse import issparse
from ..base import BaseEstimator, TransformerMixin
from ..preprocessing import LabelBinarizer
-from ..utils import safe_asanyarray
+from ..utils import safe_asarray
from ..utils.extmath import safe_sparse_dot
######################################################################
@@ -146,7 +146,7 @@ def chi2(X, y):
# XXX: we might want to do some of the following in logspace instead for
# numerical stability.
- X = safe_asanyarray(X)
+ X = safe_asarray(X)
Y = LabelBinarizer().fit_transform(y)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
@@ -248,7 +248,7 @@ class _AbstractUnivariateFilter(BaseEstimator, TransformerMixin):
"""
Transform a new matrix using the selected features
"""
- return safe_asanyarray(X)[:, self.get_support(indices=issparse(X))]
+ return safe_asarray(X)[:, self.get_support(indices=issparse(X))]
def inverse_transform(self, X):
"""
diff --git a/sklearn/linear_model/base.py b/sklearn/linear_model/base.py
index ba29aef268..2bbf2ba8f0 100644
--- a/sklearn/linear_model/base.py
+++ b/sklearn/linear_model/base.py
@@ -19,7 +19,7 @@ from ..base import RegressorMixin
from ..base import ClassifierMixin
from ..base import TransformerMixin
from ..utils.extmath import safe_sparse_dot
-from ..utils import as_float_array, safe_asanyarray
+from ..utils import as_float_array, safe_asarray
from ..utils import atleast2d_or_csr, check_arrays
from .sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber
@@ -49,7 +49,7 @@ class LinearModel(BaseEstimator, RegressorMixin):
C : array, shape = [n_samples]
Returns predicted values.
"""
- X = safe_asanyarray(X)
+ X = safe_asarray(X)
return safe_sparse_dot(X, self.coef_.T) + self.intercept_
@staticmethod
@@ -355,7 +355,7 @@ class BaseSGDClassifier(BaseSGD, ClassifierMixin):
-------
self : returns an instance of self.
"""
- X = safe_asanyarray(X)
+ X = safe_asarray(X)
y = np.asarray(y, dtype=np.float64, order='C')
n_samples, n_features = X.shape
diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py
index 388d94af1d..ee11e55de1 100644
--- a/sklearn/linear_model/ridge.py
+++ b/sklearn/linear_model/ridge.py
@@ -9,7 +9,7 @@ import numpy as np
from .base import LinearModel
from ..utils.extmath import safe_sparse_dot
-from ..utils import safe_asanyarray
+from ..utils import safe_asarray
from ..preprocessing import LabelBinarizer
from ..grid_search import GridSearchCV
@@ -201,7 +201,7 @@ class Ridge(LinearModel):
-------
self : returns an instance of self.
"""
- X = safe_asanyarray(X, dtype=np.float)
+ X = safe_asarray(X, dtype=np.float)
y = np.asarray(y, dtype=np.float)
X, y, X_mean, y_mean, X_std = \
@@ -394,7 +394,7 @@ class _RidgeGCV(LinearModel):
-------
self : Returns self.
"""
- X = safe_asanyarray(X, dtype=np.float)
+ X = safe_asarray(X, dtype=np.float)
y = np.asarray(y, dtype=np.float)
n_samples = X.shape[0]
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index ccf1476e04..00f4930f83 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -37,7 +37,7 @@ kernel:
import numpy as np
from scipy.spatial import distance
from scipy.sparse import csr_matrix, issparse
-from ..utils import safe_asanyarray, atleast2d_or_csr, deprecated
+from ..utils import safe_asarray, atleast2d_or_csr, deprecated
from ..utils.extmath import safe_sparse_dot
@@ -71,10 +71,10 @@ def check_pairwise_arrays(X, Y):
"""
if Y is X or Y is None:
- X = Y = safe_asanyarray(X)
+ X = Y = safe_asarray(X)
else:
- X = safe_asanyarray(X)
- Y = safe_asanyarray(Y)
+ X = safe_asarray(X)
+ Y = safe_asarray(Y)
X = atleast2d_or_csr(X)
Y = atleast2d_or_csr(Y)
if len(X.shape) < 2:
diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
index 0ce352eafa..30e8a6e93e 100644
--- a/sklearn/naive_bayes.py
+++ b/sklearn/naive_bayes.py
@@ -25,7 +25,7 @@ from scipy.sparse import issparse
from .base import BaseEstimator, ClassifierMixin
from .preprocessing import binarize, LabelBinarizer
-from .utils import safe_asanyarray, atleast2d_or_csr
+from .utils import safe_asarray, atleast2d_or_csr
from .utils.extmath import safe_sparse_dot, logsum
from .utils.fixes import unique
@@ -230,7 +230,7 @@ class BaseDiscreteNB(BaseNB):
Returns self.
"""
X = atleast2d_or_csr(X)
- y = safe_asanyarray(y)
+ y = safe_asarray(y)
if X.shape[0] != y.shape[0]:
msg = "X and y have incompatible shapes."
diff --git a/sklearn/neighbors/base.py b/sklearn/neighbors/base.py
index fd5bfe7dfe..87c3407c99 100644
--- a/sklearn/neighbors/base.py
+++ b/sklearn/neighbors/base.py
@@ -14,7 +14,7 @@ from scipy.spatial.ckdtree import cKDTree
from .ball_tree import BallTree
from ..base import BaseEstimator
from ..metrics import euclidean_distances
-from ..utils import safe_asanyarray, atleast2d_or_csr
+from ..utils import safe_asarray, atleast2d_or_csr
def warn_equidistant():
@@ -104,7 +104,7 @@ class NeighborsBase(BaseEstimator):
self._fit_method = 'kd_tree'
return self
- X = safe_asanyarray(X)
+ X = safe_asarray(X)
if X.ndim != 2:
raise ValueError("data type not understood")
diff --git a/sklearn/svm/base.py b/sklearn/svm/base.py
index d8ebcf11b1..a8575f9055 100644
--- a/sklearn/svm/base.py
+++ b/sklearn/svm/base.py
@@ -2,7 +2,7 @@ import numpy as np
from . import libsvm, liblinear
from ..base import BaseEstimator
-from ..utils import safe_asanyarray
+from ..utils import safe_asarray
LIBSVM_IMPL = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
@@ -383,7 +383,7 @@ class BaseLibLinear(BaseEstimator):
self.class_weight, self.class_weight_label = \
_get_class_weight(class_weight, y)
- X = safe_asanyarray(X, dtype=np.float64, order='C')
+ X = safe_asarray(X, dtype=np.float64, order='C')
if not isinstance(X, np.ndarray): # sparse X passed in by user
raise ValueError("Training vectors should be array-like, not %s"
% type(X))
diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py
index bbe776686c..3e329385d1 100644
--- a/sklearn/utils/__init__.py
+++ b/sklearn/utils/__init__.py
@@ -15,7 +15,7 @@ def assert_all_finite(X):
raise ValueError("array contains NaN or infinity")
-def safe_asanyarray(X, dtype=None, order=None):
+def safe_asarray(X, dtype=None, order=None):
"""Convert X to an array or sparse matrix.
Prevents copying X when possible; sparse matrices are passed through."""
if not sp.issparse(X):
@@ -47,7 +47,7 @@ def as_float_array(X, copy=True):
if isinstance(X, np.matrix):
X = X.A
elif not isinstance(X, np.ndarray) and not sp.issparse(X):
- return safe_asanyarray(X, dtype=np.float64)
+ return safe_asarray(X, dtype=np.float64)
if X.dtype in [np.float32, np.float64]:
return X.copy() if copy else X
if X.dtype == np.int32:
diff --git a/sklearn/utils/tests/test___init__.py b/sklearn/utils/tests/test___init__.py
index 9db225327c..69a1305f36 100644
--- a/sklearn/utils/tests/test___init__.py
+++ b/sklearn/utils/tests/test___init__.py
@@ -1,7 +1,7 @@
import numpy as np
import scipy.sparse as sp
-from .. import as_float_array, atleast2d_or_csr, safe_asanyarray
+from .. import as_float_array, atleast2d_or_csr, safe_asarray
def test_as_float_array():
@@ -39,6 +39,6 @@ def test_np_matrix():
assert not isinstance(atleast2d_or_csr(np.matrix(X)), np.matrix)
assert not isinstance(atleast2d_or_csr(sp.csc_matrix(X)), np.matrix)
- assert not isinstance(safe_asanyarray(X), np.matrix)
- assert not isinstance(safe_asanyarray(np.matrix(X)), np.matrix)
- assert not isinstance(safe_asanyarray(sp.lil_matrix(X)), np.matrix)
+ assert not isinstance(safe_asarray(X), np.matrix)
+ assert not isinstance(safe_asarray(np.matrix(X)), np.matrix)
+ assert not isinstance(safe_asarray(sp.lil_matrix(X)), np.matrix)
--
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