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#!/usr/bin/env python
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2018-2019 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
"""Test of the sparse module"""
import numpy as np
import unittest
import logging
from itertools import product
from ..common import ocl
if ocl:
import pyopencl.array as parray
from silx.opencl.sparse import CSR
try:
import scipy.sparse as sp
except ImportError:
sp = None
logger = logging.getLogger(__name__)
def generate_sparse_random_data(
shape=(1000,),
data_min=0, data_max=100,
density=0.1,
use_only_integers=True,
dtype="f"):
"""
Generate random sparse data where.
Parameters
------------
shape: tuple
Output data shape.
data_min: int or float
Minimum value of data
data_max: int or float
Maximum value of data
density: float
Density of non-zero elements in the output data.
Low value of density mean low number of non-zero elements.
use_only_integers: bool
If set to True, the output data items will be primarily integers,
possibly casted to float if dtype is a floating-point type.
This can be used for ease of debugging.
dtype: str or numpy.dtype
Output data type
"""
mask = np.random.binomial(1, density, size=shape)
if use_only_integers:
d = np.random.randint(data_min, high=data_max, size=shape)
else:
d = data_min + (data_max - data_min) * np.random.rand(*shape)
return (d * mask).astype(dtype)
@unittest.skipUnless(ocl and sp, "PyOpenCl/scipy is missing")
class TestCSR(unittest.TestCase):
"""Test CSR format"""
def setUp(self):
self.array = generate_sparse_random_data(shape=(512, 511))
# Compute reference sparsification
a_s = sp.csr_matrix(self.array)
self.ref_data = a_s.data
self.ref_indices = a_s.indices
self.ref_indptr = a_s.indptr
self.ref_nnz = a_s.nnz
# Test possible configurations
input_on_device = [False, True]
output_on_device = [False, True]
self._test_configs = list(product(input_on_device, output_on_device))
def test_sparsification(self):
for input_on_device, output_on_device in self._test_configs:
self._test_sparsification(input_on_device, output_on_device)
def _test_sparsification(self, input_on_device, output_on_device):
current_config = "input on device: %s, output on device: %s" % (
str(input_on_device), str(output_on_device)
)
# Sparsify on device
csr = CSR(self.array.shape)
if input_on_device:
# The array has to be flattened
arr = parray.to_device(csr.queue, self.array.ravel())
else:
arr = self.array
if output_on_device:
d_data = parray.zeros_like(csr.data)
d_indices = parray.zeros_like(csr.indices)
d_indptr = parray.zeros_like(csr.indptr)
output = (d_data, d_indices, d_indptr)
else:
output = None
data, indices, indptr = csr.sparsify(arr, output=output)
if output_on_device:
data = data.get()
indices = indices.get()
indptr = indptr.get()
# Compare
nnz = self.ref_nnz
self.assertTrue(
np.allclose(data[:nnz], self.ref_data),
"something wrong with sparsified data (%s)"
% current_config
)
self.assertTrue(
np.allclose(indices[:nnz], self.ref_indices),
"something wrong with sparsified indices (%s)"
% current_config
)
self.assertTrue(
np.allclose(indptr, self.ref_indptr),
"something wrong with sparsified indices pointers (indptr) (%s)"
% current_config
)
def test_desparsification(self):
for input_on_device, output_on_device in self._test_configs:
self._test_desparsification(input_on_device, output_on_device)
def _test_desparsification(self, input_on_device, output_on_device):
current_config = "input on device: %s, output on device: %s" % (
str(input_on_device), str(output_on_device)
)
# De-sparsify on device
csr = CSR(self.array.shape, max_nnz=self.ref_nnz)
if input_on_device:
data = parray.to_device(csr.queue, self.ref_data)
indices = parray.to_device(csr.queue, self.ref_indices)
indptr = parray.to_device(csr.queue, self.ref_indptr)
else:
data = self.ref_data
indices = self.ref_indices
indptr = self.ref_indptr
if output_on_device:
d_arr = parray.zeros_like(csr.array)
output = d_arr
else:
output = None
arr = csr.densify(data, indices, indptr, output=output)
if output_on_device:
arr = arr.get()
# Compare
self.assertTrue(
np.allclose(arr.reshape(self.array.shape), self.array),
"something wrong with densified data (%s)"
% current_config
)
def suite():
suite = unittest.TestSuite()
suite.addTest(
unittest.defaultTestLoader.loadTestsFromTestCase(TestCSR)
)
return suite
if __name__ == '__main__':
unittest.main(defaultTest="suite")
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