Merge pull request #120 from matchms/matchms_0_18

Update to matchms 0.18.0

.github/workflows/CI_build.yml

@@ -106,6 +106,7 @@ jobs:
         run: pytest
       - name: Install Tensorflow version 2.8
         run: |
+          pip install --upgrade "numpy<1.24.0"
           pip install --upgrade "tensorflow>=2.8,<2.9"
       - name: Show pip list
         run: |

CHANGELOG.md

@@ -7,6 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.3.1] - 2023-01-06
+
+### Changed
+
+- Minor changes to make tests work with new matchms (>=0.18.0). Older versions should work as well though. [#120](https://github.com/matchms/ms2deepscore/pull/120)
 
 ## [0.3.0] - 2022-11-29
 
@@ -92,7 +97,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - This is the initial version of MS2DeepScore
 
-[Unreleased]: https://github.com/matchms/ms2deepscore/compare/0.3.0...HEAD
+[Unreleased]: https://github.com/matchms/ms2deepscore/compare/0.3.1...HEAD
+[0.3.1]: https://github.com/matchms/ms2deepscore/compare/0.3.1...0.3.1
 [0.3.0]: https://github.com/matchms/ms2deepscore/compare/0.2.3...0.3.0
 [0.2.3]: https://github.com/matchms/ms2deepscore/compare/0.2.2...0.2.3
 [0.2.2]: https://github.com/matchms/ms2deepscore/compare/0.2.1...0.2.2

ms2deepscore/MS2DeepScore.py

@@ -102,6 +102,7 @@ def pair(self, reference: Spectrum, query: Spectrum) -> float:
         return cosine_similarity(reference_vector[0, :], query_vector[0, :])
 
     def matrix(self, references: List[Spectrum], queries: List[Spectrum],
+               array_type: str = "numpy",
                is_symmetric: bool = False) -> np.ndarray:
         """Calculate the MS2DeepScore similarities between all references and queries.
 
@@ -111,6 +112,10 @@ def matrix(self, references: List[Spectrum], queries: List[Spectrum],
             Reference spectrum.
         queries:
             Query spectrum.
+        array_type
+            Specify the output array type. Can be "numpy" or "sparse".
+            Currently, only "numpy" is supported and will return a numpy array.
+            Future versions will include "sparse" as option to return a COO-sparse array.
         is_symmetric:
             Set to True if references == queries to speed up calculation about 2x.
             Uses the fact that in this case score[i, j] = score[j, i]. Default is False.

ms2deepscore/MS2DeepScoreMonteCarlo.py

@@ -145,6 +145,7 @@ def pair(self, reference: Spectrum, query: Spectrum) -> Tuple[float, float]:
                           dtype=self.score_datatype)
 
     def matrix(self, references: List[Spectrum], queries: List[Spectrum],
+               array_type: str = "numpy",
                is_symmetric: bool = False) -> np.ndarray:
         """Calculate the MS2DeepScoreMonteCarlo similarities between all references and queries.
 
@@ -154,6 +155,10 @@ def matrix(self, references: List[Spectrum], queries: List[Spectrum],
             Reference spectrum.
         queries:
             Query spectrum.
+        array_type
+            Specify the output array type. Can be "numpy" or "sparse".
+            Currently, only "numpy" is supported and will return a numpy array.
+            Future versions will include "sparse" as option to return a COO-sparse array.
         is_symmetric:
             Set to True if references == queries to speed up calculation about 2x.
             Uses the fact that in this case score[i, j] = score[j, i]. Default is False.

ms2deepscore/__version__.py

@@ -1 +1 @@
-__version__ = '0.3.0'
+__version__ = '0.3.1'

setup.py

@@ -29,7 +29,7 @@
     test_suite="tests",
     python_requires='>=3.7',
     install_requires=[
-        "matchms",
+        "matchms>=0.14.0",
         "numba",
         "numpy>= 1.20.3",
         "pandas",