Add release_notes.rst

breimanntools · Apr 18, 2024 · b6e46fa · b6e46fa
1 parent da400fc
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Showing 4 changed files with 97 additions and 15 deletions.
diff --git a/aaanalysis/config.py b/aaanalysis/config.py
@@ -166,5 +166,6 @@ def __str__(self) -> str:
         """Return a string representation of the settings dictionary."""
         return str(self._settings)
 
+
 # Global settings instance
 options = Settings()
diff --git a/aaanalysis/utils.py b/aaanalysis/utils.py
@@ -298,7 +298,6 @@ def _folder_path(super_folder, folder_name):
 STR_DICT_CAT = "DICT_CAT"
 
 
-
 # I Helper functions
 def _retrieve_string_starting_at_end(seq, start=None, end=None):
     """Reverse_string_start_end"""
@@ -330,9 +329,6 @@ def get_dict_part_seq(tmd=None, jmd_n=None, jmd_c=None):
     return part_seq_dict
 
 
-
-
-
 # II Main functions
 # Caching for data loading for better performance (data loaded ones)
 @lru_cache(maxsize=None)
@@ -443,7 +439,7 @@ def plot_get_cmap_(cmap="CPP", n_colors=101, facecolor_dark=None, only_pos=False
     if cmap == STR_CMAP_CPP:
         cmap = _get_diverging_cmap("RdBu_r", **args)
     elif cmap == STR_CMAP_SHAP:
-        cmap =  _get_shap_cmap(**args)
+        cmap = _get_shap_cmap(**args)
     else:
         cmap = _get_diverging_cmap(cmap=cmap, **args)
     return cmap

diff --git a/docs/release_notes.rst b/docs/release_notes.rst
@@ -0,0 +1,85 @@
+# Release notes
+
+## Version 0.1 (beta version)
+
+## v0.1.5 (Released 2024-04-18)
+
+### Added
+- **Code of Conduct**: Introduced a Code of Conduct to foster a welcoming and inclusive community environment.
+    We encourage all contributors to review the Code of Conduct to understand the expectations and
+    responsibilities when participating in the project.
+
+### Changed
+- **License update**: Transitioned the project license from MIT to BSD-3-Clause to better align with our project's
+    community engagement and protection goals. This change affects how the software can be used and redistributed.
+
+### Fixed
+- **Multiprocessing**: Replaced native multiprocessing with the `joblib` module for CPP and internal feature matrix
+    creation. This change prevents a `RuntimeError` that occurred when the main function is not explicitly used.
+
+
+## v0.1.4 (Released 2024-04-09)
+
+### Added
+- **Installation options**: Introduced separate installation profiles for the core and professional versions.
+    The core version now has reduced dependencies, enhancing installation robustness. For advanced usage, the
+    professional version includes necessary packages such as SHAP for the AAanalysis explainable AI module
+    (`TreeModel` and `ShapExplainer`). This version can be installed using `pip install aaanalysis[pro]`.
+
+### Changed
+- **API improvements**: General improvement of API for consistency and higher user-friendliness.
+
+### Fixed
+- **General issues**: Fix of different check function related API issues.
+
+### Other
+- **Python dependency**: Updated the Python version compatibility from <= 3.10 to <= 3.12.
+
+
+## v0.1.3 (Released 2024-02-09)
+
+### Added
+- **TreeModel**: Wrapper class of tree-based models for Monte Carlo estimates of predictions and feature importance.
+- **ShapExplainer**: A wrapper for SHAP (SHapley Additive exPlanations) explainers to obtain Monte Carlo estimates
+    for feature impact.
+
+### Changed
+- **API improvements**: General improvement of API for consistency and higher user-friendliness.
+
+### Fixed
+- **Interface**: Change of internal documentation decorator to hard coded documentation for better IDE responsiveness.
+- **General issues**: Fix of different check function related API issues.
+
+### Other
+
+
+## v0.1.2 (Released 2023-11-06)
+
+### Added
+- **CPPPlot**: Plotting class for CPP features.
+- **dPULearnPlot**: Plotting class for results of negative identifications by dPULearn.
+- **AAclustPlot**: Plotting class for AAclust clustering results.
+- **options**: Set system-level settings by a dictionary-like interface (similar to pandas)
+
+### Changed
+- **API improvements**: General improvement of API (Application Programming Interface).
+
+### Fixed
+- **API improvements**: General improvement of API (Application Programming Interface).
+
+### Other
+- **Python dependency**: Requires Python <= 3.10 (>3.8).
+
+
+## v0.1.1 (Released 2023-09-11)
+Test release of first beta version.
+
+
+## v0.1.0 (Released 2023-09-11)
+First release of beta version including
+`CPP <https://aaanalysis.readthedocs.io/en/latest/generated/aaanalysis.CPP.html>`_,
+`dPULearn <https://aaanalysis.readthedocs.io/en/latest/generated/aaanalysis.dPULearn.html>`_,
+and `AAclust <https://aaanalysis.readthedocs.io/en/latest/generated/aaanalysis.AAclust.html>`_ algorithms
+as well as
+`SequenceFeature <https://aaanalysis.readthedocs.io/en/latest/generated/aaanalysis.SequenceFeature.html>`_
+utility class.
diff --git a/tests/unit/dpulearn_tests/test_dpulearn_fit.py b/tests/unit/dpulearn_tests/test_dpulearn_fit.py
@@ -50,7 +50,7 @@ def test_X_parameter(self, X):
         if size >= 2:
             labels = create_labels(X.shape[0])
             valid_labels = sum([x for x in set(labels) if x in [1, 2]]) == 2
-            is_invalid =  check_invalid_conditions(X=X, labels=labels)
+            is_invalid = check_invalid_conditions(X=X, labels=labels)
             if not is_invalid and valid_labels:
                 df_pu = dpul.fit(X, labels).df_pu_
                 assert isinstance(df_pu, pd.DataFrame)
@@ -61,7 +61,7 @@ def test_labels_parameter(self, labels):
         """Test the 'labels' parameter with valid inputs."""
         X = np.random.rand(100, 5)  # Assuming 100 samples, 5 features
         dpul = aa.dPULearn()
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         valid_labels = sum([x for x in set(labels) if x in [1, 2]]) == 2
         if len(set(labels)) < 2 or 1 not in labels or 2 not in labels:
             valid_labels = False
@@ -72,9 +72,9 @@ def test_labels_parameter(self, labels):
     def test_labels(self):
         """Test the 'labels' parameter with valid inputs."""
         X = np.random.rand(100, 5)  # Assuming 100 samples, 5 features
-        labels = np.asarray([1]* 50 + [2]* 50)
+        labels = np.asarray([1] * 50 + [2] * 50)
         dpul = aa.dPULearn()
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         valid_labels = sum([x for x in set(labels) if x in [1, 2]]) == 2
         if not is_invalid and valid_labels:
             df_pu = dpul.fit(X, labels).df_pu_
@@ -88,7 +88,7 @@ def test_n_unl_to_neg_parameter(self, n_unl_to_neg):
         labels = create_labels(100)
         dpul = aa.dPULearn()
         n_unl = sum([x == 2 for x in labels])
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         valid_labels = sum([x for x in set(labels) if x in [1, 2]]) == 2
         if not is_invalid and valid_labels and n_unl > n_unl_to_neg :
             df_pu = dpul.fit(X, labels, n_unl_to_neg=n_unl_to_neg).df_pu_
@@ -101,7 +101,7 @@ def test_metric_parameter(self, metric):
         X = np.random.rand(100, 5)
         labels = create_labels(100)
         dpul = aa.dPULearn()
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         if not is_invalid:
             for n in [5, 25, 39]:
                 df_pu = dpul.fit(X, labels, metric=metric, n_unl_to_neg=n).df_pu_
@@ -115,7 +115,7 @@ def test_n_components_parameter(self, n_components):
         labels = create_labels(100)
         dpul = aa.dPULearn()
         n_samples, n_features = X.shape
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         for i in [5, 26, 39]:
             if not is_invalid:
                 if n_components < min(n_features, n_samples) and n_components not in [0.0, 1.0]:
@@ -165,7 +165,7 @@ def test_n_unl_to_neg_invalid(self, n_unl_to_neg):
         X = np.random.rand(100, 5)
         labels = create_labels(100)
         dpul = aa.dPULearn()
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         if is_invalid:
             with pytest.raises(ValueError):
                 dpul.fit(X, labels, n_unl_to_neg=n_unl_to_neg)
@@ -178,7 +178,7 @@ def test_invalid_metric(self, metric):
         X = np.random.rand(100, 5)
         labels = create_labels(100)
         dpul = aa.dPULearn()
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         if not is_invalid and not metric in valid_metrics:
             with pytest.raises(ValueError):
                dpul.fit(X, labels, metric=metric)
@@ -191,7 +191,7 @@ def test_n_components_invalid(self, n_components):
         X = np.random.rand(100, 5)
         labels = create_labels(100)
         dpul = aa.dPULearn()
-        is_invalid =  check_invalid_conditions(X=X, labels=labels)
+        is_invalid = check_invalid_conditions(X=X, labels=labels)
         valid_labels = sum([x for x in set(labels) if x in [1, 2]]) == 2
         if is_invalid and valid_labels:
             with pytest.raises(ValueError):