Global maintenance 2025 turn 3 pull

.github/workflows/actions.yaml

@@ -0,0 +1,27 @@
+name: Build
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+      - name: Checkout the ${{ github.repository }} repository
+        uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[test]
+      - name: Test with Pytest
+        run: |
+          pytest --cov=vecstack --cov-report=term-missing tests
+      - name: Coveralls
+        uses: coverallsapp/github-action@v2

CHANGELOG.md

@@ -1,5 +1,17 @@
 # Changelog
 
+### v0.5.0 -- September 8, 2025 -- Maintenance release
+
+* Python 3.9+
+* Testing: pytest and pytest-cov
+* CI: GitHub Actions
+
+* Scikit-learn API:
+  * Fixed `_set_params` method which was not resetting individual estimators in the `estimators` collection
+
+* Functional API
+  * Fixed saving OOF arrays in file
+
 ### v0.4.0 -- August 12, 2019
 
 Since v0.4.0 vecstack provides official support for Python 3.5 and higher only,  

LICENSE.txt

@@ -1,7 +1,7 @@
 MIT License
 
 Vecstack. Python package for stacking (machine learning technique)
-Copyright (c) 2016-2019 Igor Ivanov
+Copyright (c) 2016-2025 Igor Ivanov
 Email: [email protected]
 
 Permission is hereby granted, free of charge, to any person obtaining a copy

README.md

@@ -1,6 +1,6 @@
 [![PyPI version](https://img.shields.io/pypi/v/vecstack.svg?colorB=4cc61e)](https://pypi.python.org/pypi/vecstack)
 [![PyPI license](https://img.shields.io/pypi/l/vecstack.svg)](https://github.com/vecxoz/vecstack/blob/master/LICENSE.txt)
-[![Build Status](https://travis-ci.org/vecxoz/vecstack.svg?branch=master)](https://travis-ci.org/vecxoz/vecstack)
+[![Build status](https://github.com/vecxoz/vecstack/actions/workflows/actions.yaml/badge.svg?branch=master)](https://github.com/vecxoz/vecstack/actions)
 [![Coverage Status](https://coveralls.io/repos/github/vecxoz/vecstack/badge.svg?branch=master)](https://coveralls.io/github/vecxoz/vecstack?branch=master)
 [![PyPI pyversions](https://img.shields.io/pypi/pyversions/vecstack.svg)](https://pypi.python.org/pypi/vecstack/)
 
@@ -11,18 +11,18 @@ Convenient way to automate OOF computation, prediction and bagging using any num
 * [Functional API](https://github.com/vecxoz/vecstack#usage-functional-api):
     * Minimalistic. Get your stacked features in a single line
     * RAM-friendly. The lowest possible memory consumption
-    * Kaggle-ready. Stacked features and hyperparameters from each run can be [automatically saved](https://github.com/vecxoz/vecstack/blob/master/vecstack/core.py#L209) in files. No more mess at the end of the competition.  [Log example](https://github.com/vecxoz/vecstack/blob/master/examples/03_log_example.txt)
+    * Kaggle-ready. Stacked features and hyperparameters from each run can be [automatically saved](https://github.com/vecxoz/vecstack/blob/master/vecstack/core.py#L210) in files. No more mess at the end of the competition.  [Log example](https://github.com/vecxoz/vecstack/blob/master/examples/03_log_example.txt)
 * [Scikit-learn API](https://github.com/vecxoz/vecstack#usage-scikit-learn-api):
     * Standardized. Fully scikit-learn compatible transformer class exposing `fit` and `transform` methods
     * Pipeline-certified. Implement and deploy [multilevel stacking](https://github.com/vecxoz/vecstack/blob/master/examples/04_sklearn_api_regression_pipeline.ipynb) like it's no big deal using `sklearn.pipeline.Pipeline` 
     * 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#L83) tasks
-    * Predict [class labels or probabilities](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L119) in classification task
-    * Apply any [user-defined metric](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L124)
-    * Apply any [user-defined transformations](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L87) for target and prediction
-    * Python 3.5 and higher, [unofficial support for Python 2.7 and 3.4](https://github.com/vecxoz/vecstack/blob/master/PY2.md)
+    * 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
+    * 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)
     * Depends on **numpy**, **scipy**, **scikit-learn>=0.18**
@@ -44,19 +44,19 @@ 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#L64) or type ```>>> help(StackingTransformer)```
+    * [Scikit-learn API](https://github.com/vecxoz/vecstack/blob/master/vecstack/coresk.py#L66) or type ```>>> help(StackingTransformer)```
 
 # Installation
 
-***Note:*** Python 3.5 or higher is required. If you’re still using Python 2.7 or 3.4 see [installation details here](https://github.com/vecxoz/vecstack/blob/master/PY2.md)  
+***Note:*** Python 3.9+ is officially supported and tested. If you’re still using Python 2.7 or 3.4 see [installation details here](https://github.com/vecxoz/vecstack/blob/master/PY2.md)  
 
 * ***Classic 1st time installation (recommended):*** 
     * `pip install vecstack`
 * Install for current user only (if you have some troubles with write permission):
     * `pip install --user vecstack`
 * If your PATH doesn't work: 
     * `/usr/bin/python -m pip install vecstack`
-    * `C:/Python36/python -m pip install vecstack`
+    * `C:/Python3/python -m pip install vecstack`
 * Upgrade vecstack and all dependencies:
     * `pip install --upgrade vecstack`
 * Upgrade vecstack WITHOUT upgrading dependencies:
@@ -137,6 +137,7 @@ S_test = stack.transform(X_test)
 28. [Can I use `(Randomized)GridSearchCV` to tune the whole stacking Pipeline?](https://github.com/vecxoz/vecstack#28-can-i-use-randomizedgridsearchcv-to-tune-the-whole-stacking-pipeline)
 29. [How to define custom metric, especially AUC?](https://github.com/vecxoz/vecstack#29-how-to-define-custom-metric-especially-auc)
 30. [Do folds (splits) have to be the same across estimators and stacking levels? How does `random_state` work?](https://github.com/vecxoz/vecstack#30-do-folds-splits-have-to-be-the-same-across-estimators-and-stacking-levels-how-does-random_state-work)
+31. [How does `vecstack.StackingTransformer` differ from `sklearn.ensemble.StackingClassifier`?](https://github.com/vecxoz/vecstack#31-how-does-vecstackstackingtransformer-differ-from-sklearnensemblestackingclassifier)
 
 ### 1. How can I report an issue? How can I ask a question about stacking or vecstack package?
 
@@ -167,7 +168,7 @@ Main idea is to use predictions as features.
 More specifically we predict train set (in CV-like fashion) and test set using some 1st level model(s), and then use these predictions as features for 2nd level model. You can find more details (concept, pictures, code) in [stacking tutorial](https://github.com/vecxoz/vecstack/blob/master/examples/00_stacking_concept_pictures_code.ipynb).  
 Also make sure to check out: 
 * [Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) ([Stacking](https://en.wikipedia.org/wiki/Ensemble_learning#Stacking)) in Wikipedia
-* Classical [Kaggle Ensembling Guide](https://mlwave.com/kaggle-ensembling-guide/)
+* Classical [Kaggle Ensembling Guide](https://mlwave.com/kaggle-ensembling-guide/) or try [another link](https://web.archive.org/web/20210727094233/https://mlwave.com/kaggle-ensembling-guide/)
 * [Stacked Generalization](https://www.researchgate.net/publication/222467943_Stacked_Generalization) paper by David H. Wolpert
 
 ### 5. What about stacking name?
@@ -216,7 +217,7 @@ Speaking about inner stacking mechanics, you should remember that when you have
 ### 12. What is *blending*? How is it related to stacking?
 
 Basically it is the same thing. Both approaches use predictions as features.  
-Often this terms are used interchangeably.  
+Often these terms are used interchangeably.  
 The difference is how we generate features (predictions) for the next level:  
 * *stacking*: perform cross-validation procedure and predict each part of train set (OOF)
 * *blending*: predict fixed holdout set
@@ -387,10 +388,14 @@ def auc(y_true, y_pred):
 
 To ensure better result, folds (splits) have to be the same across all estimators and all stacking levels. It means that `random_state` has to be the same in every call to `stacking` function or `StackingTransformer`. This is default behavior of `stacking` function and `StackingTransformer` (by default `random_state=0`). If you want to try different folds (splits) try to set different `random_state` values.  
 
+### 31. How does `vecstack.StackingTransformer` differ from `sklearn.ensemble.StackingClassifier`?
+
+It significantly differs. Please see a [detailed explanation](https://github.com/vecxoz/vecstack/issues/37).
+
 
 # Stacking concept
 
-1. We want to predict train set and test set with some 1st level model(s), and then use these predictions as features for 2nd level model(s).  
+1. We want to predict train set and test set with some 1st level model(s), and then use these predictions as features for 2nd level model(s).
 2. Any model can be used as 1st level model or 2nd level model.
 3. To avoid overfitting (for train set) we use cross-validation technique and in each fold we predict out-of-fold (OOF) part of train set.
 4. The common practice is to use from 3 to 10 folds.
@@ -404,6 +409,7 @@ To ensure better result, folds (splits) have to be the same across all estimator
 8. We can repeat this cycle using other 1st level models to get more features for 2nd level model.
 9. You can also look at animation of [Variant A](https://github.com/vecxoz/vecstack#variant-a-animation) and [Variant B](https://github.com/vecxoz/vecstack#variant-b-animation).
 
+
 # Variant A
 
 ![Fold 1 of 3](https://github.com/vecxoz/vecstack/raw/master/pic/dia1.png "Fold 1 of 3")
@@ -429,3 +435,10 @@ To ensure better result, folds (splits) have to be the same across all estimator
 # Variant B. Animation
 
 ![Variant B. Animation](https://github.com/vecxoz/vecstack/raw/master/pic/animation2.gif "Variant B. Animation")
+
+
+# References
+
+* [Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) ([Stacking](https://en.wikipedia.org/wiki/Ensemble_learning#Stacking)) in Wikipedia
+* Classical [Kaggle Ensembling Guide](https://mlwave.com/kaggle-ensembling-guide/) or try [another link](https://web.archive.org/web/20210727094233/https://mlwave.com/kaggle-ensembling-guide/)
+* [Stacked Generalization](https://www.researchgate.net/publication/222467943_Stacked_Generalization) paper by David H. Wolpert