Moved sparsefuncs to sparsefuncs_fast

MechCoder · MechCoder · commit bbb8f1d1bf41 · 2014-03-27T13:20:45.000+05:30
diff --git a/sklearn/cluster/_k_means.c b/sklearn/cluster/_k_means.c
diff --git a/sklearn/cluster/_k_means.pyx b/sklearn/cluster/_k_means.pyx
@@ -15,7 +15,7 @@ cimport numpy as np
 cimport cython
 
 from ..utils.extmath import norm
-from sklearn.utils.sparsefuncs cimport add_row_csr
+from sklearn.utils.sparsefuncs_fast cimport add_row_csr
 
 ctypedef np.float64_t DOUBLE
 ctypedef np.int32_t INT
diff --git a/sklearn/cluster/k_means_.py b/sklearn/cluster/k_means_.py
@@ -19,7 +19,8 @@
 from ..base import BaseEstimator, ClusterMixin, TransformerMixin
 from ..metrics.pairwise import euclidean_distances
 from ..utils.extmath import row_norms
-from ..utils.sparsefuncs import assign_rows_csr, mean_variance_axis0
+from ..utils.sparsefuncs_fast import assign_rows_csr
+from ..utils.sparsefuncs import mean_variance_axis0
 from ..utils import check_arrays
 from ..utils import check_random_state
 from ..utils import atleast2d_or_csr
diff --git a/sklearn/feature_selection/variance_threshold.py b/sklearn/feature_selection/variance_threshold.py
@@ -5,7 +5,7 @@
 from ..base import BaseEstimator
 from .base import SelectorMixin
 from ..utils import atleast2d_or_csr
-from ..utils.sparsefuncs import csr_mean_variance_axis0
+from ..utils.sparsefuncs_fast import csr_mean_variance_axis0
 
 
 class VarianceThreshold(BaseEstimator, SelectorMixin):
diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
@@ -20,9 +20,9 @@
 from ..utils import safe_asarray
 from ..utils import warn_if_not_float
 from ..utils.extmath import row_norms
-from ..utils.sparsefuncs import inplace_csr_row_normalize_l1
-from ..utils.sparsefuncs import inplace_csr_row_normalize_l2
-from ..utils.sparsefuncs import inplace_csr_column_scale
+from ..utils.sparsefuncs_fast import inplace_csr_row_normalize_l1
+from ..utils.sparsefuncs_fast import inplace_csr_row_normalize_l2
+from ..utils.sparsefuncs_fast import inplace_csr_column_scale
 from ..utils.sparsefuncs import mean_variance_axis0
 
 zip = six.moves.zip
diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
@@ -17,7 +17,7 @@
 from .fixes import np_version
 from ._logistic_sigmoid import _log_logistic_sigmoid
 from ..externals.six.moves import xrange
-from .sparsefuncs import csr_row_norms
+from .sparsefuncs_fast import csr_row_norms
 from .validation import array2d, NonBLASDotWarning
 
 
diff --git a/sklearn/utils/setup.py b/sklearn/utils/setup.py
@@ -22,7 +22,7 @@ def configuration(parent_package='', top_path=None):
         libraries.append('m')
         cblas_libs.append('m')
 
-    config.add_extension('sparsefuncs', sources=['sparsefuncs.c'],
+    config.add_extension('sparsefuncs_fast', sources=['sparsefuncs_fast.c'],
                          libraries=libraries)
 
     config.add_extension('arrayfuncs',
diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py
@@ -0,0 +1,58 @@
+# Authors: Manoj Kumar
+
+# License: BSD 3 clause
+import scipy.sparse as sp
+
+from .sparsefuncs_fast import (csr_mean_variance_axis0,
+                               csc_mean_variance_axis0,
+                               inplace_csr_column_scale,
+                               inplace_csc_column_scale)                
+
+def mean_variance_axis0(X):
+    """Compute mean and variance along axis 0 on a CSR or CSC matrix
+
+    Parameters
+    ----------
+    X: CSR or CSC sparse matrix, shape (n_samples, n_features)
+        Input data.
+
+    Returns
+    -------
+
+    means: float array with shape (n_features,)
+        Feature-wise means
+
+    variances: float array with shape (n_features,)
+        Feature-wise variances
+
+    """
+    if isinstance(X, sp.csr_matrix):
+        return csr_mean_variance_axis0(X)
+    elif isinstance(X, sp.csc_matrix):
+        return csc_mean_variance_axis0(X)
+    else:
+        raise TypeError(
+                "Unsupported type; expected a CSR or CSC sparse matrix.")
+
+
+def inplace_column_scale(X, scale):
+    """Inplace column scaling of a CSC/CSR matrix.
+
+    Scale each feature of the data matrix by multiplying with specific scale
+    provided by the caller assuming a (n_samples, n_features) shape.
+
+    Parameters
+    ----------
+    X: CSC or CSR matrix with shape (n_samples, n_features)
+        Matrix to normalize using the variance of the features.
+
+    scale: float array with shape (n_features,)
+        Array of precomputed feature-wise values to use for scaling.
+    """
+    if isinstance(X, sp.csr_matrix):
+        return inplace_csr_column_scale(X, scale)
+    elif isinstance(X, sp.csc_matrix):
+        return inplace_csc_column_scale(X, scale)
+    else:
+        raise TypeError(
+                "Unsupported type; expected a CSR or CSC sparse matrix.")
diff --git a/sklearn/utils/sparsefuncs_fast.c b/sklearn/utils/sparsefuncs_fast.c
diff --git a/sklearn/utils/sparsefuncs_fast.pxd b/sklearn/utils/sparsefuncs_fast.pxd
diff --git a/sklearn/utils/sparsefuncs_fast.pyx b/sklearn/utils/sparsefuncs_fast.pyx
@@ -291,56 +291,6 @@ def inplace_csc_column_scale(X, np.ndarray[DOUBLE, ndim=1] scale):
             X_data[j] *= scale[i]
 
 
-def inplace_column_scale(X, np.ndarray[DOUBLE, ndim=1] scale):
-    """Inplace column scaling of a CSC/CSR matrix.
-
-    Scale each feature of the data matrix by multiplying with specific scale
-    provided by the caller assuming a (n_samples, n_features) shape.
-
-    Parameters
-    ----------
-    X: CSC or CSR matrix with shape (n_samples, n_features)
-        Matrix to normalize using the variance of the features.
-
-    scale: float array with shape (n_features,)
-        Array of precomputed feature-wise values to use for scaling.
-    """
-    if isinstance(X, sp.csr_matrix):
-        return inplace_csr_column_scale(X, scale)
-    elif isinstance(X, sp.csc_matrix):
-        return inplace_csc_column_scale(X, scale)
-    else:
-        raise TypeError(
-                "Unsupported type; expected a CSR or CSC sparse matrix.")
-
-
-def mean_variance_axis0(X):
-    """Compute mean and variance along axis 0 on a CSR or CSC matrix
-
-    Parameters
-    ----------
-    X: CSR or CSC sparse matrix, shape (n_samples, n_features)
-        Input data.
-
-    Returns
-    -------
-
-    means: float array with shape (n_features,)
-        Feature-wise means
-
-    variances: float array with shape (n_features,)
-        Feature-wise variances
-
-    """
-    if isinstance(X, sp.csr_matrix):
-        return csr_mean_variance_axis0(X)
-    elif isinstance(X, sp.csc_matrix):
-        return csc_mean_variance_axis0(X)
-    else:
-        raise TypeError(
-                "Unsupported type; expected a CSR or CSC sparse matrix.")
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 cdef void add_row_csr(np.ndarray[np.float64_t, ndim=1] data,
diff --git a/sklearn/utils/tests/test_sparsefuncs.py b/sklearn/utils/tests/test_sparsefuncs.py
@@ -3,9 +3,10 @@
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 
 from sklearn.datasets import make_classification
-from sklearn.utils.sparsefuncs import (assign_rows_csr, mean_variance_axis0,
-                                       inplace_csc_column_scale,
-                                       inplace_csr_column_scale)
+from sklearn.utils.sparsefuncs import mean_variance_axis0
+from sklearn.utils.sparsefuncs_fast import (assign_rows_csr,
+                                            inplace_csc_column_scale,
+                                            inplace_csr_column_scale)
 
 def test_mean_variance_axis0():
     X, _ = make_classification(5, 4, random_state=0)
