Fix test and import for macos (#3)

* Fix imports for macos

* Bump version 1.0.2

* fix actions

.github/workflows/python-package.yml

@@ -96,4 +96,4 @@ jobs:
         yapf --quiet --style "{based_on_style: pep8, blank_line_before_nested_class_or_def: true, indent_dictionary_value: true, dedent_closing_brackets: true, column_limit: 99}" --recursive .
     - name: Test with pytest
       run: |
-        export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:"$(pip show pylspack | grep 'Location:' | awk '{print $2}')/pylspack/" && python3 -m pytest -svvv test
+        cd test && python3 -m pytest -svvv .

README.md

@@ -17,19 +17,29 @@ As already noted, the implementation is designed for existing data structures of
 The C++ API **can** be used as a standalone package, but it has not been tested.
 
 ### Citation
-The corresponding publication https://doi.org/10.1137/20m1314471 can be cited as follows:
+If you use this software in academic work, please consider citing the corresponding publications:
+- https://doi.org/10.1137/20m1314471
 ```
-@article{Sobczyk2021,
-  doi = {10.1137/20m1314471},
-  url = {https://doi.org/10.1137/20m1314471},
-  year = {2021},
-  publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
-  volume = {42},
-  number = {3},
-  pages = {1199--1228},
-  author = {Aleksandros Sobczyk and Efstratios Gallopoulos},
-  title = {Estimating Leverage Scores via Rank Revealing Methods and Randomization},
-  journal = {{SIAM} Journal on Matrix Analysis and Applications}
+@article{sobczyk2021estimating,
+  title={Estimating leverage scores via rank revealing methods and randomization},
+  author={Sobczyk, Aleksandros and Gallopoulos, Efstratios},
+  journal={SIAM Journal on Matrix Analysis and Applications},
+  volume={42},
+  number={3},
+  pages={1199--1228},
+  year={2021},
+  doi={10.1137/20m1314471},
+  url={https://doi.org/10.1137/20m1314471},
+  publisher={SIAM}
+}
+```
+- https://doi.org/10.48550/arxiv.2203.02798
+```
+@article{sobczyk2022pylspack,
+  title={pylspack: Parallel algorithms and data structures for sketching, column subset selection, regression and leverage scores},
+  author={Sobczyk, Aleksandros and Gallopoulos, Efstratios},
+  journal={arXiv preprint arXiv:2203.02798},
+  year={2022}
 }
 ```
 
@@ -92,11 +102,12 @@ pip install git+https://github.com/IBM/pylspack
 
 To run the tests:
 ```bash
+python3 -m pip install -r test_requirements.txt
+cd test
+python3 -m pytest -svvv .
 # If you get an error about liblinalg_kernels.so, do the following:
 # PYLSPACK_LOCATION="$(pip show pylspack | grep Location: | awk '{print $2}')/pylspack/"
 # export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${PYLSPACK_LOCATION}
-pip install -r test_requirements.txt
-pytest -svvv test
 ```
 
 ## Contributing

pylspack/linalg_kernels.py

@@ -7,13 +7,17 @@
 from scipy.sparse import csr_matrix
 
 libdir = os.path.dirname(os.path.realpath(__file__))
-try:
-    libfile = glob.glob('{}/liblinalg_kernels*.so'.format(libdir))[0]
-    ext_lib = CDLL(os.path.join(libdir, libfile))
-except Exception as e:
-    print('Warning: could not find {}/liblinalg_kernels*.so'.format(libdir))
-    print('Caught exception: {}. Trying to load from LD_LIBRARY_PATH...'.format(e))
-    ext_lib = CDLL('liblinalg_kernels.so')
+libfile = glob.glob(f'{libdir}/liblinalg_kernels*')
+if libfile:
+    ext_lib = CDLL(os.path.join(libdir, libfile[0]))
+else:
+    print(f'Warning: could not find {libdir}/liblinalg_kernels*')
+    try:
+        print('Trying to fild liblinalg_kernels.so from LD_LIBRARY_PATH...')
+        ext_lib = CDLL('liblinalg_kernels.so')
+    except Exception:
+        print('Trying to fild liblinalg_kernels.dylib from LD_LIBRARY_PATH...')
+        ext_lib = CDLL('liblinalg_kernels.dylib')
 
 # arg types
 ext_lib.csrcgs.argtypes = [
@@ -48,12 +52,12 @@ def assert_shape(a: int, b: int) -> None:
 
 
 def assert_dtype(A: np.ndarray, dtype: str) -> None:
-    if A.dtype != dtype:
+    if A.dtype != dtype:  # type: ignore
         raise TypeError('unsupported dtype: {}.'.format(A.dtype))
 
 
 def assert_contiguous_type(A: np.ndarray, contiguous_type: str) -> None:
-    if A.flags[contiguous_type] is False:
+    if A.flags[contiguous_type] is False:  # type: ignore
         raise TypeError('array is not {} as expected.'.format(contiguous_type))
 
 

setup.py

@@ -47,7 +47,7 @@ def read_readme(fname):
 
 setup(
     name='pylspack',
-    version='1.0.1',
+    version='1.0.2',
     description='Python package for leverage scores computations.',
     author='Sobczyk Aleksandros',
     author_email='obc@zurich.ibm.com',

src/csr_kernels.h

@@ -186,6 +186,7 @@ extern "C" {
     double *_C;
     int i, j, k, up, lo;
 
+    double *G = new double[m];
     #pragma omp parallel private(_C, i, j, k, up, lo)
     {
       double A_ki;
@@ -197,11 +198,11 @@ extern "C" {
       row_limits.second = block_size * ( thread_id + 1 );
       row_limits.second = std::min( row_limits.second, m );
       const int n_rows = row_limits.second - row_limits.first;
-      double *_G = new double[n_rows];
       std::random_device rd{};
       std::mt19937_64 gen{rd()};
       std::normal_distribution<double> dist;
 
+      double *_G = &( G[ row_limits.first ] );
       for ( k = 0; k < n; ++k ) {
         lo = A_indptr[k];
         up = A_indptr[k + 1];
@@ -224,9 +225,9 @@ extern "C" {
           }
         }
       }
-
-      delete[] _G;
     }
+
+    delete[] G;
     double scale_factor = static_cast<double>( 1 ) / sqrt( static_cast<double>( m ) );
     scale( m, d, C, scale_factor );
   }

test/test_csrrk.py

@@ -2,7 +2,7 @@
 from scipy.sparse import csr_matrix
 import pytest
 from pylspack.linalg_kernels import csrrk
-from .utils import (
+from utils import (
     eps_machine, min_size, max_size, alpha_beta_pairs_generic, get_random_matrices,
     set_arrays_elements_to_value
 )

test/test_csrsqn.py

@@ -2,11 +2,11 @@
 from scipy.sparse import csr_matrix
 import pytest
 from pylspack.linalg_kernels import csrsqn
-from .utils import (
+from utils import (
     eps_machine, alpha_beta_pairs_generic, get_random_matrices, set_arrays_elements_to_value
 )
-from .utils import A_shapes_generic as A_shapes
-from .utils import B_shapes_generic as B_shapes
+from utils import A_shapes_generic as A_shapes
+from utils import B_shapes_generic as B_shapes
 
 
 def execute_and_check(alpha: float, A: csr_matrix, B: np.ndarray, beta: float, C: np.ndarray):

test/test_gemm.py

@@ -1,11 +1,11 @@
 import numpy as np
 import pytest
 from pylspack.linalg_kernels import gemm
-from .utils import (
+from utils import (
     eps_machine, alpha_beta_pairs_generic, get_random_matrices, set_arrays_elements_to_value
 )
-from .utils import A_shapes_generic as A_shapes
-from .utils import B_shapes_generic as B_shapes
+from utils import A_shapes_generic as A_shapes
+from utils import B_shapes_generic as B_shapes
 
 
 def execute_and_check(alpha: float, A: np.ndarray, B: np.ndarray, beta: float, C: np.ndarray):

test/test_leverage_scores.py

@@ -6,7 +6,7 @@
     sample_columns, ls_via_inv_gram, ls_via_sketched_svd, ls_hrn_exact, ls_hrn_approx,
     get_rank_from_vector
 )
-from .utils import eps_machine
+from utils import eps_machine
 
 density = [0.1, 0.3, 1]
 matrices = [
@@ -19,7 +19,7 @@
 def test_get_rank_from_vector():
     s = np.zeros((10, ))
     assert get_rank_from_vector(s, rcond=0.5) == 0
-    s = np.arange(10, 0, -1)
+    s = np.arange(10, 0, -1)  # type: ignore
     assert get_rank_from_vector(s, rcond=0.65) == 4
     assert get_rank_from_vector(s, rcond=0.05) == 10
     assert get_rank_from_vector(s, rcond=0) == 10

test/test_rmdsc.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 from pylspack.linalg_kernels import rmdsc
-from .utils import eps_machine, min_size, max_size, set_arrays_elements_to_value
+from utils import eps_machine, min_size, max_size, set_arrays_elements_to_value
 
 B_shapes = [(1, 1), (3, 1), (1, 3), (3, 3), (17, 5), (17, 17), (237, 631), (631, 237), (237, 237)]
 D_shapes = [(1, 1), (1, 1), (3, 3), (3, 3), (5, 5), (17, 17), (631, 631), (237, 237), (237, 237)]

test/test_rmsqn.py

@@ -1,11 +1,11 @@
 import numpy as np
 import pytest
 from pylspack.linalg_kernels import rmsqn
-from .utils import (
+from utils import (
     eps_machine, alpha_beta_pairs_generic, get_random_matrices, set_arrays_elements_to_value
 )
-from .utils import A_shapes_generic as A_shapes
-from .utils import B_shapes_generic as B_shapes
+from utils import A_shapes_generic as A_shapes
+from utils import B_shapes_generic as B_shapes
 
 
 def execute_and_check(alpha: float, A: np.ndarray, B: np.ndarray, beta: float, C: np.ndarray):

test/test_scale.py

@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 from pylspack.linalg_kernels import scale
-from .utils import eps_machine
-from .utils import A_shapes_generic as A_shapes
+from utils import eps_machine
+from utils import A_shapes_generic as A_shapes
 
 
 def execute_and_check(A: np.ndarray, alpha: float):

test/test_set_randn.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 from pylspack.linalg_kernels import set_randn
-from .utils import A_shapes_generic as A_shapes
+from utils import A_shapes_generic as A_shapes
 
 
 def execute_and_check(A: np.ndarray):

test/test_set_value.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 from pylspack.linalg_kernels import set_value
-from .utils import A_shapes_generic as A_shapes
+from utils import A_shapes_generic as A_shapes
 
 
 def execute_and_check(A: np.ndarray, alpha: float):