Merge pull request #54 from vecxoz/dev5

Global maintenance 2025 turn 7

Author: vecxoz

CHANGELOG.md

@@ -1,8 +1,12 @@
 # Changelog
 
+### v0.5.2 -- November 1, 2025 -- Maintenance release
+
+Enhance doc-strings
+
 ### v0.5.1 -- September 28, 2025 -- Maintenance release
 
-Set minimum scikit-learn version to 1.6.0.
+Set minimum scikit-learn version to 1.6.0.  
 Function `check_estimator` requires usage of `validate_data` which was introduced in 1.6.0
 
 ### v0.5.0 -- September 8, 2025 -- Maintenance release

README.md

@@ -18,10 +18,10 @@ Convenient way to automate OOF computation, prediction and bagging using any num
     * And of course `FeatureUnion` is also invited to the party
 * Overall specs:
     * Use any sklearn-like estimators
-    * Perform [classification and regression](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L85) tasks
-    * Predict [class labels or probabilities](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L121) in classification task
-    * Apply any [user-defined metric](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L126)
-    * Apply any [user-defined transformations](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L89) for target and prediction
+    * Perform [classification and regression](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L87) tasks
+    * Predict [class labels or probabilities](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L123) in classification task
+    * Apply any [user-defined metric](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L128)
+    * Apply any [user-defined transformations](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L91) for target and prediction
     * Python 3.9+, [unofficial support for Python 2.7 and 3.4](https://github.com/vecxoz/vecstack/blob/master/PY2.md)
     * Win, Linux, Mac
     * [MIT license](https://github.com/vecxoz/vecstack/blob/master/LICENSE.txt)
@@ -44,7 +44,7 @@ Convenient way to automate OOF computation, prediction and bagging using any num
         * [Regression + Multilevel stacking using Pipeline](https://github.com/vecxoz/vecstack/blob/master/examples/04_sklearn_api_regression_pipeline.ipynb)
 * Documentation:
     * [Functional API](https://github.com/vecxoz/vecstack/blob/master/vecstack/core.py#L133) or type ```>>> help(stacking)```
-    * [Scikit-learn API](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L66) or type ```>>> help(StackingTransformer)```
+    * [Scikit-learn API](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L68) or type ```>>> help(StackingTransformer)```
 
 # Installation
 

setup.py

@@ -9,7 +9,7 @@
 '''
 
 setup(name='vecstack',
-      version='0.5.1',
+      version='0.5.2',
       description='Python package for stacking (machine learning technique)',
       long_description=long_desc,
       classifiers=[

vecstack/__init__.py

@@ -38,7 +38,7 @@
 
 __author__ = 'Igor Ivanov > kaggle.com/vecxoz'
 __license__ = 'MIT'
-__version__ = '0.5.1'
+__version__ = '0.5.2'
 
 __all__ = ['stacking', 'StackingTransformer']
 

vecstack/coresk.py

@@ -78,6 +78,8 @@ class StackingTransformer(TransformerMixin, BaseEstimator):
         estimators = [('lr', LinearRegression()),
                       ('ridge', Ridge(random_state=0))]
 
+        Note. Dropping individual estimators using 'drop' is not supported.
+
         Note. According to sklearn convention for binary classification
             task with probabilities estimator must return probabilities
             for each class (i.e. two columns).