Merge pull request #16 from getspams/update_ssp

Remove unnecessary requirements (linked to Python 2 to 3 switch) and scipy random sparse number generation reimplemantation

.gitignore

+# Files generated by the tests
+dict.png
+dict1.png
+dict2.png
+
 *.pyc
 *.pyo
 *.sublime*

myscipy_rand/__init__.py

-__all__ = ['myscipy_rand']
-
-from pkg_resources import get_distribution
-__version__ = get_distribution('spams').version
-
-from .myscipy_rand import *

myscipy_rand/myscipy_rand.py

-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import scipy.sparse as ssp
-
-
-def rand(m, n, density=0.01, format="coo", dtype=None):
-    """Generate a sparse matrix of the given shape and density with uniformely
-    distributed values.
-
-    Parameters
-    ----------
-    m, n: int
-        shape of the matrix
-    density: real
-        density of the generated matrix: density equal to one means a full
-        matrix, density of 0 means a matrix with no non-zero items.
-    format: str
-        sparse matrix format.
-    dtype: dtype
-        type of the returned matrix values.
-
-    Notes
-    -----
-    Only float types are supported for now.
-    """
-    if density < 0 or density > 1:
-        raise ValueError("density expected to be 0 <= density <= 1")
-    if dtype and not dtype in [np.float32, np.float64, np.longdouble]:
-        raise NotImplementedError("type %s not supported" % dtype)
-
-    mn = m * n
-
-    # XXX: sparse uses intc instead of intp...
-    tp = np.intp
-    if mn > np.iinfo(tp).max:
-        msg = """\
-Trying to generate a random sparse matrix such as the product of dimensions is
-greater than %d - this is not supported on this machine
-"""
-        raise ValueError(msg % np.iinfo(tp).max)
-
-    # Number of non zero values
-    k = int(density * m * n)
-
-    # Generate a few more values than k so that we can get unique values
-    # afterwards.
-    # XXX: one could be smarter here
-    mlow = 5
-    fac = 1.02
-    gk = min(k + mlow, fac * k)
-
-    def _gen_unique_rand(_gk):
-        id = np.random.rand(_gk)
-        return np.unique(np.floor(id * mn))[:k]
-
-    id = _gen_unique_rand(gk)
-    while id.size < k:
-        gk *= 1.05
-        id = _gen_unique_rand(gk)
-
-    j = np.floor(id * 1. / m).astype(tp)
-    i = (id - j * m).astype(tp)
-    vals = np.random.rand(k).astype(dtype)
-    return ssp.coo_matrix((vals, (i, j)), shape=(m, n)).asformat(format)

requirements.txt

-Cython>=0.29
 numpy>=1.12
 Pillow>=6.0
 scipy>=1.0
-setuptools>=46.1
-six>=1.12
\ No newline at end of file
+setuptools>=46.1
\ No newline at end of file

setup.cfg

 # Inside of setup.cfg
 [metadata]
-description-file = README.md
+description_file = README.md
 
 [global]
 verbose = 1

setup.py

@@ -195,9 +195,8 @@ opts = dict(
     },
     license='GPLv3',
     python_requires='>=3',
-    install_requires=['Cython>=0.29', 'numpy>=1.12',
-                      'Pillow>=6.0', 'scipy>=1.0', 'six>=1.12'],
-    packages=['myscipy_rand', 'spams_wrap', 'spams', 'spams.tests'],
+    install_requires=['numpy>=1.12', 'Pillow>=6.0', 'scipy>=1.0'],
+    packages=['spams_wrap', 'spams', 'spams.tests'],
     cmdclass={'build_ext': CustomBuildExtCommand},
     ext_modules=get_extension(),
     package_data={

tests/test_decomp.py

-from __future__ import absolute_import, division, print_function
-import six.moves
-
-import sys
 import numpy as np
-import scipy
 import scipy.sparse as ssp
 
 import spams
@@ -119,7 +114,7 @@ def test_l1L2BCD():
     D = np.asfortranarray(
         D / np.tile(np.sqrt((D*D).sum(axis=0)), (D.shape[0], 1)), dtype=myfloat)
     # indices of the first signals in each group
-    ind_groups = np.array(six.moves.xrange(0, X.shape[1], 10), dtype=np.int32)
+    ind_groups = np.array(range(0, X.shape[1], 10), dtype=np.int32)
     # parameters of the optimization procedure are chosen
     itermax = 100
     tol = 1e-3
@@ -293,7 +288,7 @@ def test_somp():
     D = np.asfortranarray(np.random.normal(size=(64, 200)))
     D = np.asfortranarray(
         D / np.tile(np.sqrt((D*D).sum(axis=0)), (D.shape[0], 1)), dtype=myfloat)
-    ind_groups = np.array(six.moves.xrange(0, 10000, 10), dtype=np.int32)
+    ind_groups = np.array(range(0, 10000, 10), dtype=np.int32)
     tic = time.time()
     alpha = spams.somp(X, D, ind_groups, L=10, eps=0.1, numThreads=-1)
     tac = time.time()

tests/test_dictLearn.py

-from __future__ import absolute_import, division, print_function
-
-import sys
 import os
 import numpy as np
-import scipy
 import scipy.sparse as ssp
 from PIL import Image
 
@@ -11,23 +7,16 @@ import spams
 import time
 from test_utils import *
 
-if not ('rand' in ssp.__dict__):
-    import myscipy_rand
-    ssprand = myscipy_rand.rand
-else:
-    ssprand = ssp.rand
-    
-    
 def get_img_file_path(img):
     """Return path to an image file
-    
+
     Arguments:
-        img (string): image filename without path among 'boat.png' or 
+        img (string): image filename without path among 'boat.png' or
         'lena.png'.
-        
+
     Output:
         img_file (string): normalized path to image input filename.
-    
+
     """
     # check input
     if not img in ["boat.png", "lena.png"]:

tests/test_linalg.py

-from __future__ import absolute_import, division, print_function
-import six.moves
-
-import sys
 import numpy as np
-import scipy
 import scipy.sparse as ssp
 import spams
 import time
 from test_utils import *
 
-if not ('rand' in ssp.__dict__):
-    import myscipy_rand
-    ssprand = myscipy_rand.rand
-else:
-    ssprand = ssp.rand
-
+ssprand = ssp.rand
 
 def test_sort():
     n = 2000000
@@ -81,7 +71,7 @@ def test_conjGrad():
     itermax = int(0.5 * len(b))
 
     tic = time.time()
-    for i in six.moves.xrange(0, 20):
+    for i in range(0, 20):
         y1 = np.linalg.solve(A, b)
     tac = time.time()
     print("  Time (numpy): ", tac - tic)
@@ -89,7 +79,7 @@ def test_conjGrad():
     print("Mean error on b : %f" % (x1.sum() / b.shape[0]))
 
     tic = time.time()
-    for i in six.moves.xrange(0, 20):
+    for i in range(0, 20):
         y2 = spams.conjGrad(A, b, x0, tol, itermax)
 # *        y2 = spams.conjGrad(A,b)
     tac = time.time()

tests/test_spams.py

-from __future__ import absolute_import, division, print_function
-import six.moves
-
 import sys
 import time
 import test_utils
@@ -68,7 +65,7 @@ def main(argv):
             print("**** %s ****" % testname)
             # exec('lstm = test_%s.tests' %testname)
             lstm = locals()['test_%s' % testname].tests
-            for i in six.moves.xrange(0, len(lstm), 2):
+            for i in range(0, len(lstm), 2):
                 run_test(lstm[i], lstm[i+1])
             continue
         else:
@@ -76,7 +73,7 @@ def main(argv):
             for m in modules:
                 # exec('lstm = test_%s.tests' %m)
                 lstm = locals()['test_%s' % m].tests
-                for i in six.moves.xrange(0, len(lstm), 2):
+                for i in range(0, len(lstm), 2):
                     if (lstm[i] == testname):
                         found = True
                         run_test(lstm[i], lstm[i+1])