bugfix: Set requires_fit tag to False for all stateless estimators

README.md

@@ -25,7 +25,7 @@ The current version of TPOT was developed at Cedars-Sinai by:
     - Jay Moran (jay.moran@cshs.org)  
     - Nicholas Matsumoto (nicholas.matsumoto@cshs.org)  
     - Hyunjun Choi (hyunjun.choi@cshs.org)  
-    - Gabriel Ketron (gabriel.ketron@cshs.org)
+    - Gabriel Ketron (gabriel.ketron@cshs.org)  
     - Miguel E. Hernandez (miguel.e.hernandez@cshs.org)  
     - Jason Moore (moorejh28@gmail.com)  
 
@@ -83,7 +83,6 @@ scipy
 scikit-learn
 update_checker
 tqdm
-stopit
 pandas
 joblib
 xgboost
@@ -226,23 +225,36 @@ We welcome you to check the existing issues for bugs or enhancements to work on.
 
 If you use TPOT in a scientific publication, please consider citing at least one of the following papers:
 
-Trang T. Le, Weixuan Fu and Jason H. Moore (2020). [Scaling tree-based automated machine learning to biomedical big data with a feature set selector](https://academic.oup.com/bioinformatics/article/36/1/250/5511404). *Bioinformatics*.36(1): 250-256.
+Hernandez, J. G., Saini, A. K., Ghosh, A., & Moore, J. H. (2025). [The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning](https://www.cell.com/patterns/fulltext/S2666-3899(25)00162-X). Patterns, 6(7).
 
 BibTeX entry:
 
-```bibtex
-@article{le2020scaling,
-  title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
-  author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
-  journal={Bioinformatics},
-  volume={36},
-  number={1},
-  pages={250--256},
-  year={2020},
-  publisher={Oxford University Press}
+```bibtext
+@article{hernandez2025tree,
+  title={The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning},
+  author={Hernandez, Jose Guadalupe and Saini, Anil Kumar and Ghosh, Attri and Moore, Jason H},
+  journal={Patterns},
+  volume={6},
+  number={7},
+  year={2025},
+  publisher={Elsevier}
 }
 ```
 
+Ribeiro, P., Saini, A., Moran, J., Matsumoto, N., Choi, H., Hernandez, M., & Moore, J. H. (2024). [TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning](https://link.springer.com/chapter/10.1007/978-981-99-8413-8_1). In Genetic programming theory and practice XX (pp. 1-17). Singapore: Springer Nature Singapore.
+
+BitTex entry:
+
+```bibtex
+@incollection{ribeiro2024tpot2,
+  title={TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning},
+  author={Ribeiro, Pedro and Saini, Anil and Moran, Jay and Matsumoto, Nicholas and Choi, Hyunjun and Hernandez, Miguel and Moore, Jason H},
+  booktitle={Genetic programming theory and practice XX},
+  pages={1--17},
+  year={2024},
+  publisher={Springer}
+}
+```
 
 Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). [Automating biomedical data science through tree-based pipeline optimization](http://link.springer.com/chapter/10.1007/978-3-319-31204-0_9). *Applications of Evolutionary Computation*, pages 123-137.
 
@@ -286,6 +298,26 @@ BibTeX entry:
 }
 ```
 
+## Related Papers
+
+Trang T. Le, Weixuan Fu and Jason H. Moore (2020). [Scaling tree-based automated machine learning to biomedical big data with a feature set selector](https://academic.oup.com/bioinformatics/article/36/1/250/5511404). *Bioinformatics*.36(1): 250-256.
+
+BibTeX entry:
+
+```bibtex
+@article{le2020scaling,
+  title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
+  author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
+  journal={Bioinformatics},
+  volume={36},
+  number={1},
+  pages={250--256},
+  year={2020},
+  publisher={Oxford University Press}
+}
+```
+
+
 ## Support for TPOT
 
 TPOT was developed in the [Artificial Intelligence Innovation (A2I) Lab](http://epistasis.org/) at Cedars-Sinai with funding from the [NIH](http://www.nih.gov/) under grants U01 AG066833 and R01 LM010098. We are incredibly grateful for the support of the NIH and the Cedars-Sinai during the development of this project.

docs/cite.md

@@ -1,23 +1,36 @@
 # Citing TPOT
 If you use TPOT in a scientific publication, please consider citing at least one of the following papers:
 
-Trang T. Le, Weixuan Fu and Jason H. Moore (2020). [Scaling tree-based automated machine learning to biomedical big data with a feature set selector](https://academic.oup.com/bioinformatics/article/36/1/250/5511404). *Bioinformatics*.36(1): 250-256.
+Hernandez, J. G., Saini, A. K., Ghosh, A., & Moore, J. H. (2025). [The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning](https://www.cell.com/patterns/fulltext/S2666-3899(25)00162-X). Patterns, 6(7).
 
 BibTeX entry:
 
-```bibtex
-@article{le2020scaling,
-  title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
-  author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
-  journal={Bioinformatics},
-  volume={36},
-  number={1},
-  pages={250--256},
-  year={2020},
-  publisher={Oxford University Press}
+```bibtext
+@article{hernandez2025tree,
+  title={The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning},
+  author={Hernandez, Jose Guadalupe and Saini, Anil Kumar and Ghosh, Attri and Moore, Jason H},
+  journal={Patterns},
+  volume={6},
+  number={7},
+  year={2025},
+  publisher={Elsevier}
 }
 ```
 
+Ribeiro, P., Saini, A., Moran, J., Matsumoto, N., Choi, H., Hernandez, M., & Moore, J. H. (2024). [TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning](https://link.springer.com/chapter/10.1007/978-981-99-8413-8_1). In Genetic programming theory and practice XX (pp. 1-17). Singapore: Springer Nature Singapore.
+
+BitTex entry:
+
+```bibtex
+@incollection{ribeiro2024tpot2,
+  title={TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning},
+  author={Ribeiro, Pedro and Saini, Anil and Moran, Jay and Matsumoto, Nicholas and Choi, Hyunjun and Hernandez, Miguel and Moore, Jason H},
+  booktitle={Genetic programming theory and practice XX},
+  pages={1--17},
+  year={2024},
+  publisher={Springer}
+}
+```
 
 Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, and Jason H. Moore (2016). [Automating biomedical data science through tree-based pipeline optimization](http://link.springer.com/chapter/10.1007/978-3-319-31204-0_9). *Applications of Evolutionary Computation*, pages 123-137.
 
@@ -59,4 +72,4 @@ BibTeX entry:
     publisher = {ACM},
     address = {New York, NY, USA},
 }
-```
+```

pyproject.toml

@@ -33,7 +33,6 @@ dependencies = [
     "scikit-learn>=1.6",
     "update_checker>=0.16",
     "tqdm>=4.36.1",
-    "stopit>=1.1.1",
     "pandas>=2.2.0",
     "joblib>=1.1.1",
     "xgboost>=3.0.0",

tpot/builtin_modules/arithmetictransformer.py

@@ -125,6 +125,12 @@ def transform_helper(self, X):
         elif self.function ==  "1":
                 return np.ones((X.shape[0],1))
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
+
 def issorted(x, rev=False):
     if rev:
         s = sorted(x)
@@ -163,6 +169,12 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.expand_dims(np.sum(X,1),1)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
+
 class mul_neg_1_Transformer(TransformerMixin, BaseEstimator):
     def __init__(self):
         """
@@ -185,7 +197,13 @@ def transform_helper(self, X):
         if len(X.shape) == 1:
             X = np.expand_dims(X,0)
         return X*-1
-
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
+
 class MulTransformer(TransformerMixin, BaseEstimator):
 
     def __init__(self):
@@ -210,6 +228,12 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.expand_dims(np.prod(X,1),1)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
+
 class SafeReciprocalTransformer(TransformerMixin, BaseEstimator):
 
     def __init__(self):
@@ -234,6 +258,12 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.divide(1.0, X.astype(float), out=np.zeros_like(X).astype(float), where=X!=0) #TODO remove astypefloat?
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
+
 class EQTransformer(TransformerMixin, BaseEstimator):
 
     def __init__(self):
@@ -258,6 +288,12 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.expand_dims(np.all(X == X[0,:], axis = 1),1).astype(float)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
+
 class NETransformer(TransformerMixin, BaseEstimator):
 
     def __init__(self):
@@ -282,6 +318,10 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return 1- np.expand_dims(np.all(X == X[0,:], axis = 1),1).astype(float)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
 
 
 class GETransformer(TransformerMixin, BaseEstimator):
@@ -309,6 +349,11 @@ def transform_helper(self, X):
         result = X >= 0
         return  result.astype(float)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class GTTransformer(TransformerMixin, BaseEstimator):
     def __init__(self):
@@ -334,6 +379,11 @@ def transform_helper(self, X):
         result = X > 0
         return  result.astype(float)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class LETransformer(TransformerMixin, BaseEstimator):
     def __init__(self):
@@ -359,6 +409,11 @@ def transform_helper(self, X):
         result = X <= 0
         return  result.astype(float)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class LTTransformer(TransformerMixin, BaseEstimator):
     def __init__(self):
@@ -384,6 +439,11 @@ def transform_helper(self, X):
         result = X < 0
         return  result.astype(float)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class MinTransformer(TransformerMixin, BaseEstimator):
     def __init__(self):
@@ -408,6 +468,10 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.expand_dims(np.amin(X,1),1)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
 
 
 class MaxTransformer(TransformerMixin, BaseEstimator):
@@ -434,6 +498,11 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.expand_dims(np.amax(X,1),1)
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class ZeroTransformer(TransformerMixin, BaseEstimator):
 
@@ -459,6 +528,11 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.zeros((X.shape[0],1))
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class OneTransformer(TransformerMixin, BaseEstimator):
     def __init__(self):
@@ -483,6 +557,11 @@ def transform_helper(self, X):
             X = np.expand_dims(X,0)
         return np.ones((X.shape[0],1))
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+
 
 class NTransformer(TransformerMixin, BaseEstimator):
 
@@ -507,3 +586,9 @@ def transform_helper(self, X):
         if len(X.shape) == 1:
             X = np.expand_dims(X,0)
         return np.ones((X.shape[0],1))*self.n
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.requires_fit = False
+        return tags
+

tpot/builtin_modules/estimatortransformer.py

@@ -178,4 +178,5 @@ def __sklearn_is_fitted__(self):
     @property
     def classes_(self):
         """The classes labels. Only exist if the last step is a classifier."""
-        return self.estimator._classes
+        return self.estimator._classes
+