Remove stopit imports, references, and dependency

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/utils/eval_utils.py

@@ -40,11 +40,9 @@
 import traceback
 from collections.abc import Iterable
 import warnings
-from stopit import threading_timeoutable, TimeoutException
 from tpot.selectors import survival_select_NSGA2
 import time
 import dask
-import stopit
 from dask.diagnostics import ProgressBar
 from tqdm.dask import TqdmCallback
 from dask.distributed import progress
@@ -269,47 +267,3 @@ def parallel_eval_objective_list(individual_list,
     final_scores = process_scores(final_scores, n_expected_columns)
     return final_scores, final_start_times, final_end_times, final_eval_errors
 
-
-###################
-# Parallel optimization
-#############
-
-@threading_timeoutable(np.nan) #TODO timeout behavior
-def optimize_objective(ind, objective, steps=5, verbose=0):
-
-    with warnings.catch_warnings(record=True) as w:  #catches all warnings in w so it can be supressed by verbose                
-        try:
-            value = ind.optimize(objective, steps=steps)
-            if not isinstance(value, Iterable):
-                value = [value]               
-
-            if len(w) and verbose>=2:
-                warnings.warn(w[0].message)
-            return value
-        except Exception as e:
-            if verbose >= 2:
-                print('WARNING THIS INDIVIDUAL CAUSED AND EXCEPTION')
-                print(e)
-                print()
-            if verbose >= 3:
-                print(traceback.format_exc())
-                print()
-            return [np.nan]
-
-
-
-def parallel_optimize_objective(individual_list,
-                                objective,
-                                n_jobs = 1,
-                                verbose=0,
-                                steps=5,
-                                timeout=None,
-                                **objective_kwargs,  ):
-
-    Parallel(n_jobs=n_jobs)(delayed(optimize_objective)(ind,  objective,  steps, verbose, timeout=timeout)  for ind in individual_list ) #TODO: parallelize
-
-
-
-
-
-