Update README.md (#2)

Update README.md

Author: asobczyk

.github/workflows/python-package.yml

@@ -80,19 +80,20 @@ jobs:
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
         python -m pip install .
+        rm -rf ./build/
         if [ -f test_requirements.txt ]; then pip install -r test_requirements.txt; fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      run: |
-        export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:"$(pip show pylspack | grep 'Location:' | awk '{print $2}')/pylspack/" && python3 -m pytest -svvv test
     - name: Typing checks with mypy
       run: |
         mypy .
     - name: Style check with yapf
       run: |
         yapf --quiet --style "{based_on_style: pep8, blank_line_before_nested_class_or_def: true, indent_dictionary_value: true, dedent_closing_brackets: true, column_limit: 99}" --recursive .
+    - name: Test with pytest
+      run: |
+        export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:"$(pip show pylspack | grep 'Location:' | awk '{print $2}')/pylspack/" && python3 -m pytest -svvv test

README.md

@@ -16,6 +16,23 @@ The basic matrix algorithms of this package are developed in C++, using OpenMP f
 As already noted, the implementation is designed for existing data structures of SciPy and Numpy and therefore the entire codebase is **only tested** and **should only be used** via the python wrappers.
 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:
+```
+@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}
+}
+```
+
 ## Usage
 
 A simple usage example to compute the leverage scores of a sparse `csr_matrix` with the `ls_via_inv_gram` method.

pylspack/leverage_scores.py

@@ -32,7 +32,7 @@ def get_rank_from_vector(S: np.ndarray, rcond: float) -> int:
     if not np.any(S):
         return 0  # if all elements are zero, the rank is zero
     if not np.all(S[:-1] >= S[1:]):
-        raise ValueError(f'Given vector to determine the rank must be sorted.')
+        raise ValueError('Given vector to determine the rank must be sorted.')
     if rcond < 0 or rcond >= 1:
         raise ValueError(f'Given value for rcond={rcond} is invalid. Must be 0 <= rcond < 1.')
 

setup.py

@@ -47,7 +47,7 @@ def read_readme(fname):
 
 setup(
     name='pylspack',
-    version='1.0.0',
+    version='1.0.1',
     description='Python package for leverage scores computations.',
     author='Sobczyk Aleksandros',
     author_email='[email protected]',

test/test_csrrk.py

@@ -8,7 +8,12 @@
 )
 
 A_shapes = [(1, 1), (3, 1), (3, 3), (17, 5), (237, 174), (237, 237)]
-A_shapes.extend([np.random.randint(low=min_size, high=max_size, size=2) for k in range(10)])
+A_shapes.extend(
+    [
+        tuple(np.random.randint(low=min_size, high=max_size, size=2))  # type: ignore
+        for k in range(10)
+    ]
+)
 
 
 def execute_and_check(alpha: float, A: csr_matrix, beta: float, C: np.ndarray):

test/utils.py

@@ -10,7 +10,10 @@
 A_shapes_generic.extend([(8, 8), (64, 64), (128, 128)])
 B_shapes_generic.extend([(8, 8), (64, 64), (128, 128)])
 A_shapes_generic.extend(
-    [np.random.randint(low=min_size, high=max_size, size=2) for k in range(10)]
+    [
+        tuple(np.random.randint(low=min_size, high=max_size, size=2))  # type: ignore
+        for k in range(10)
+    ]
 )
 B_shapes_generic.extend(
     [