Metadata-Version: 2.1 Name: GAparsimony Version: 0.0.13 Summary: Combines feature selection, model tuning, and parsimonious model selection with GA optimization. GA selection procedure is based on separate cost and complexity evaluations. Therefore, the best individuals are initially sorted by an error fitness function, and afterwards, models with similar costs are rearranged according to modelcomplexity measurement so as to foster models of lesser complexity. The algorithm can be run sequentially or in parallel. Home-page: https://github.com/misantam/GAparsimony Author: Millán Santamaría Author-email: GAparsimony@gmail.com Maintainer: Millán Santamaría Maintainer-email: GAparsimony@gmail.com License: MIT License Platform: UNKNOWN Classifier: Development Status :: 3 - Alpha Classifier: Intended Audience :: Science/Research Classifier: Intended Audience :: Developers Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3 Requires-Python: >=3.7 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: numpy Requires-Dist: pandas Requires-Dist: seaborn Requires-Dist: matplotlib Requires-Dist: sklearn # GAparsimony [![PyPI version](https://badge.fury.io/py/GAparsimony.svg)](https://badge.fury.io/py/GAparsimony) [![Documentation Status](https://readthedocs.org/projects/gaparsimony/badge/?version=latest)](https://gaparsimony.readthedocs.io/en/latest/?badge=latest) [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=GAparsimony&metric=alert_status)](https://sonarcloud.io/dashboard?id=GAparsimony) [![Bugs](https://sonarcloud.io/api/project_badges/measure?project=GAparsimony&metric=bugs)](https://sonarcloud.io/dashboard?id=GAparsimony) [![Code Smells](https://sonarcloud.io/api/project_badges/measure?project=GAparsimony&metric=code_smells)](https://sonarcloud.io/dashboard?id=GAparsimony) GAparsimony =========== [Documentation](https://gaparsimony.readthedocs.io/en/latest/index.html) GAparsimony for Python is a package for searching with genetic algorithms (GA) accurate parsimonious models by combining feature selection (FS), model hyperparameter optimization (HO), and parsimonious model selection (PMS). It has R implementation [R GAparsimony](https://github.com/jpison/GAparsimony) PMS is based on separate cost and complexity evaluations. The best individuals are initially sorted by an error fitness function, and afterwards, models with similar costs are rearranged according to model complexity measurement so as to foster models of lesser complexity. The algorithm can be run sequentially or in parallel. Installation ------------ Install these packages, [pip](https://pypi.org/project/GAparsimony/): ``` {.bash} pip install GAparsimony ``` To install the current development version, you need to clone the repository and run : ``` {.bash} python -m pip install << path to cloned repository >> ``` How to use this package ----------------------- ### Example 1: Classification This example shows how to search, for the *Sonar* database, a parsimony SVM classificator with **GAparsimony** package. In the next step, a fitness function is created using getFitness. This function return a fitness function for the `SVC` model, the `cohen_kappa_score` metric and the predefined `svm` complexity function for SVC models. We set regression to `False` beacause is classification example. A SVM model is trained with these parameters and the selected input features. Finally, *fitness()* returns a vector with three values: the *kappa* statistic obtained with the mean of 10 runs of a 10-fold cross-validation process, the *kappa* measured with the test database to check the model generalization capability, and the model complexity. And the trained model. The GA-PARSIMONY process begins defining the range of the SVM parameters and their names. Also, *rerank\_error* can be tuned with different *ga\_parsimony* runs to improve the **model generalization capability**. In this example, *rerank\_error* has been fixed to 0.001 but other values could improve the trade-off between model complexity and model accuracy. For example, with *rerank\_error=0.01*, we can be interested in obtaining models with a smaller number of inputs with a *gamma* rounded to two decimals. ``` {.Python} from sklearn.svm import SVC from sklearn.metrics import cohen_kappa_score from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_wine from GAparsimony import GAparsimony, Population, getFitness from GAparsimony.util import svm_complexity wine = load_wine() X, y = wine.data, wine.target X = StandardScaler().fit_transform(X) rerank_error = 0.001 params = {"C":{"range": (00.0001, 99.9999), "type": Population.FLOAT}, "gamma":{"range": (0.00001,0.99999), "type": Population.FLOAT}, "kernel": {"value": "poly", "type": Population.CONSTANT}} fitness = getFitness(SVC, cohen_kappa_score, svm_complexity, minimize=False, test_size=0.2, random_state=42, n_jobs=-1) GAparsimony_model = GAparsimony(fitness=fitness, params=params, features=wine.feature_names, keep_history = True, rerank_error = rerank_error, popSize = 40, maxiter = 50, early_stop=10, feat_thres=0.90, # Perc selected features in first generation feat_mut_thres=0.10, # Prob of a feature to be one in mutation seed_ini = 1234) ``` With small databases, it is highly recommended to execute **GAparsimony** with different seeds in order to find the most important input features and model parameters. In this example, one GA optimization is presented with a training database composed of 60 input features and 167 instances, and a test database with only 41 instances. Hence, a robust validation metric is necessary. Thus, a repeated cross-validation is performed. Starts the GA optimizaton process with 40 individuals per generation and a maximum number of 5 iterations with an early stopping when validation measure does not increase significantly in 3 generations. Parallel is activated. In addition, history of each iteration is saved in order to use *plot* and *parsimony\_importance* methods. ``` {.python} GAparsimony_model.fit(X, y) ``` ``` #output GA-PARSIMONY | iter = 0 MeanVal = 0.8797661 | ValBest = 0.9410622 | TstBest = 0.9574468 |ComplexBest = 10000000045.0| Time(min) = 0.1504835 GA-PARSIMONY | iter = 1 MeanVal = 0.9049894 | ValBest = 0.9456775 | TstBest = 1.0 |ComplexBest = 11000000044.0| Time(min) = 0.0590165 GA-PARSIMONY | iter = 2 MeanVal = 0.9189347 | ValBest = 0.9456775 | TstBest = 1.0 |ComplexBest = 11000000044.0| Time(min) = 0.0520666 GA-PARSIMONY | iter = 3 MeanVal = 0.9270711 | ValBest = 0.952701 | TstBest = 0.9568345 |ComplexBest = 10000000043.0| Time(min) = 0.0494999 ... GA-PARSIMONY | iter = 28 MeanVal = 0.9370426 | ValBest = 0.9840488 | TstBest = 0.9574468 |ComplexBest = 9000000052.0| Time(min) = 0.0497332 GA-PARSIMONY | iter = 29 MeanVal = 0.9363377 | ValBest = 0.9840488 | TstBest = 0.9574468 |ComplexBest = 9000000052.0| Time(min) = 0.0467499 GA-PARSIMONY | iter = 30 MeanVal = 0.9204895 | ValBest = 0.9840488 | TstBest = 0.9574468 |ComplexBest = 9000000052.0| Time(min) = 0.0500166 GA-PARSIMONY | iter = 31 MeanVal = 0.9466802 | ValBest = 0.9840488 | TstBest = 0.9574468 |ComplexBest = 9000000052.0| Time(min) = 0.0481334 ``` summary() shows the GA initial settings and two solutions: the solution with the best validation score in the whole GA optimization process, and finally, the best parsimonious individual at the last generation. ``` {.python} GAparsimony_model.summary() ``` ``` +------------------------------------+ | GA-PARSIMONY | +------------------------------------+ GA-PARSIMONY settings: Number of Parameters = 2 Number of Features = 13 Population size = 40 Maximum of generations = 50 Number of early-stop gen. = 10 Elitism = 8 Crossover probability = 0.8 Mutation probability = 0.1 Max diff(error) to ReRank = 0.001 Perc. of 1s in first popu.= 0.9 Prob. to be 1 in mutation = 0.1 Search domain = C gamma alcohol malic_acid ash alcalinity_of_ash \ Min_param 0.0001 0.00001 0.0 0.0 0.0 0.0 Max_param 99.9999 0.99999 1.0 1.0 1.0 1.0 magnesium total_phenols flavanoids nonflavanoid_phenols \ Min_param 0.0 0.0 0.0 0.0 Max_param 1.0 1.0 1.0 1.0 proanthocyanins color_intensity hue \ Min_param 0.0 0.0 0.0 Max_param 1.0 1.0 1.0 od280/od315_of_diluted_wines proline Min_param 0.0 0.0 Max_param 1.0 1.0 GA-PARSIMONY results: Iterations = 32 Best validation score = 0.9840488232315704 Solution with the best validation score in the whole GA process = fitnessVal fitnessTst complexity C gamma alcohol malic_acid ash \ 0 0.984049 0.957447 9e+09 0.527497 0.225906 1 1 1 alcalinity_of_ash magnesium total_phenols flavanoids nonflavanoid_phenols \ 0 1 0 0 1 0 proanthocyanins color_intensity hue od280/od315_of_diluted_wines proline 0 1 0 1 1 1 Results of the best individual at the last generation = Best indiv's validat.cost = 0.9840488232315704 Best indiv's testing cost = 0.9574468085106383 Best indiv's complexity = 9000000052.0 Elapsed time in minutes = 1.705049173037211 BEST SOLUTION = fitnessVal fitnessTst complexity C gamma alcohol malic_acid ash \ 0 0.984049 0.957447 9e+09 0.527497 0.225906 1 1 1 alcalinity_of_ash magnesium total_phenols flavanoids nonflavanoid_phenols \ 0 1 0 0 1 0 proanthocyanins color_intensity hue od280/od315_of_diluted_wines proline 0 1 0 1 1 1 ``` Plot GA evolution. ``` {.python} GAparsimony_model.plot() ``` ![GA-PARSIMONY Evolution](https://raw.githubusercontent.com/misantam/GAparsimony/main/docs/img/classification_readme.png) GA-PARSIMONY evolution Show percentage of appearance for each feature in elitists ``` {.python} # Percentage of appearance for each feature in elitists GAparsimony_model.importance() ``` ``` +--------------------------------------------+ | GA-PARSIMONY | +--------------------------------------------+ Percentage of appearance of each feature in elitists: alcohol ash proline flavanoids alcalinity_of_ash malic_acid \ 0 100 100 100 100 99.5968 98.7903 od280/od315_of_diluted_wines proanthocyanins hue nonflavanoid_phenols \ 0 98.3871 92.7419 86.6935 28.629 color_intensity total_phenols magnesium 0 22.1774 2.41935 2.01613 ``` ### Example 2: Regression This example shows how to search, for the *Boston* database, a parsimonious ANN model for regression and with **GAparsimony** package. In the next step, a fitness function is created using getFitness. This function return a fitness function for the `Lasso` model, the `mean_squared_error`(RMSE) metric and the predefined `linearModels` complexity function for SVC models. We set regression to `True` beacause is classification example. A Lasso model is trained with these parameters and the selected input features. Finally, *fitness()* returns a vector with three negatives values: the *RMSE* statistic obtained with the mean of 10 runs of a 10-fold cross-validation process, the *RMSE* measured with the test database to check the model generalization capability, and the model complexity. And the trained model. The GA-PARSIMONY process begins defining the range of the SVM parameters and their names. Also, *rerank\_error* can be tuned with different *ga\_parsimony* runs to improve the **model generalization capability**. In this example, *rerank\_error* has been fixed to 0.01 but other values could improve the trade-off between model complexity and model accuracy. Therefore, PMS considers the most parsimonious model with the lower number of features. Between two models with the same number of features, the lower sum of the squared network weights will determine the most parsimonious model (smaller weights reduce the propagation of disturbances). ``` {.python} from sklearn.linear_model import Lasso from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error from sklearn.datasets import load_boston from GAparsimony import GAparsimony, Population, getFitness from GAparsimony.util import linearModels_complexity boston = load_boston() X, y = boston.data, boston.target X = StandardScaler().fit_transform(X) # ga_parsimony can be executed with a different set of 'rerank_error' values rerank_error = 0.01 params = {"alpha":{"range": (1., 25.9), "type": Population.FLOAT}, "tol":{"range": (0.0001,0.9999), "type": Population.FLOAT}} fitness = getFitness(Lasso, mean_squared_error, linearModels_complexity, minimize=True, test_size=0.2, random_state=42, n_jobs=-1) GAparsimony_model = GAparsimony(fitness=fitness, params = params, features = boston.feature_names, keep_history = True, rerank_error = rerank_error, popSize = 40, maxiter = 5, early_stop=3, feat_thres=0.90, # Perc selected features in first generation feat_mut_thres=0.10, # Prob of a feature to be one in mutation seed_ini = 1234) ``` ``` {.python} GAparsimony_model.fit(X, y) ``` ``` #output GA-PARSIMONY | iter = 0 MeanVal = -79.1715225 | ValBest = -30.3297649 | TstBest = -29.2466835 |ComplexBest = 13000000021.927263| Time(min) = 0.1092269 GA-PARSIMONY | iter = 1 MeanVal = -55.1072918 | ValBest = -30.3251321 | TstBest = -29.2267507 |ComplexBest = 12000000022.088743| Time(min) = 0.0523999 GA-PARSIMONY | iter = 2 MeanVal = -34.9396425 | ValBest = -30.3166673 | TstBest = -28.8701544 |ComplexBest = 10000000021.774683| Time(min) = 0.0484501 GA-PARSIMONY | iter = 3 MeanVal = -38.6590874 | ValBest = -30.144799 | TstBest = -29.321512 |ComplexBest = 11000000022.865057| Time(min) = 0.0440666 ... GA-PARSIMONY | iter = 21 MeanVal = -40.5599677 | ValBest = -29.6343625 | TstBest = -29.3245345 |ComplexBest = 5000000023.114235| Time(min) = 0.0442333 GA-PARSIMONY | iter = 22 MeanVal = -36.0291598 | ValBest = -29.6343625 | TstBest = -29.3245345 |ComplexBest = 5000000023.114235| Time(min) = 0.0433499 GA-PARSIMONY | iter = 23 MeanVal = -36.6950374 | ValBest = -29.6343625 | TstBest = -29.3245345 |ComplexBest = 5000000023.114235| Time(min) = 0.0441 GA-PARSIMONY | iter = 24 MeanVal = -37.4263523 | ValBest = -29.6343625 | TstBest = -29.3245345 |ComplexBest = 5000000023.114235| Time(min) = 0.0420333 ``` summary() shows the GA initial settings and two solutions: the solution with the best validation score in the whole GA optimization process, and finally, the best parsimonious individual at the last generation. ``` {.python} GAparsimony_model.summary() ``` ``` +------------------------------------+ | GA-PARSIMONY | +------------------------------------+ GA-PARSIMONY settings: Number of Parameters = 2 Number of Features = 13 Population size = 40 Maximum of generations = 50 Number of early-stop gen. = 10 Elitism = 8 Crossover probability = 0.8 Mutation probability = 0.1 Max diff(error) to ReRank = 0.01 Perc. of 1s in first popu.= 0.9 Prob. to be 1 in mutation = 0.1 Search domain = alpha tol CRIM ZN INDUS CHAS NOX RM AGE DIS RAD \ Min_param 1.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Max_param 25.9 0.9999 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 TAX PTRATIO B LSTAT Min_param 0.0 0.0 0.0 0.0 Max_param 1.0 1.0 1.0 1.0 GA-PARSIMONY results: Iterations = 25 Best validation score = -29.634144915265725 Solution with the best validation score in the whole GA process = fitnessVal fitnessTst complexity alpha tol CRIM ZN INDUS CHAS NOX \ 0 -29.6341 -29.3465 6e+09 1.33747 0.523279 0 0 0 1 0 RM AGE DIS RAD TAX PTRATIO B LSTAT 0 1 1 0 0 0 1 1 1 Results of the best individual at the last generation = Best indiv's validat.cost = -29.634362465548378 Best indiv's testing cost = -29.324534451958808 Best indiv's complexity = 5000000023.114235 Elapsed time in minutes = 1.167609703540802 BEST SOLUTION = fitnessVal fitnessTst complexity alpha tol CRIM ZN INDUS CHAS NOX \ 0 -29.6344 -29.3245 5e+09 1.33756 0.530282 0 0 0 0 0 RM AGE DIS RAD TAX PTRATIO B LSTAT 0 1 1 0 0 0 1 1 1 ``` Plot GA evolution. ``` {.python} GAparsimony_model.plot() ``` ![GA-PARSIMONY Evolution](https://raw.githubusercontent.com/misantam/GAparsimony/main/docs/img/regression_readme.png) GA-PARSIMONY evolution Show percentage of appearance for each feature in elitists ``` {.python} # Percentage of appearance for each feature in elitists GAparsimony_model.importance() ``` ``` +--------------------------------------------+ | GA-PARSIMONY | +--------------------------------------------+ Percentage of appearance of each feature in elitists: PTRATIO LSTAT RM B AGE CHAS NOX CRIM ZN DIS \ 0 100 100 100 100 93.2292 48.9583 48.9583 43.75 28.125 26.5625 RAD INDUS TAX 0 13.5417 13.0208 8.33333 ``` References ---------- F.J. Martinez-de-Pison, J. Ferreiro, E. Fraile, A. Pernia-Espinoza, A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package, Neurocomputing, Volume 452, 2021, Pages 317-332, ISSN 0925-2312, [https://doi.org/10.1016/j.neucom.2020.02.135](https://doi.org/10.1016/j.neucom.2020.02.135). Martinez-de-Pison, F.J., Gonzalez-Sendino, R., Aldama, A., Ferreiro-Cabello, J., Fraile-Garcia, E. Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection (2019) Neurocomputing, 354, pp. 20-26. [https://doi.org/10.1016/j.neucom.2018.05.136](https://doi.org/10.1016/j.neucom.2018.05.136). Urraca R., Sodupe-Ortega E., Antonanzas E., Antonanzas-Torres F., Martinez-de-Pison, F.J. (2017). Evaluation of a novel GA-based methodology for model structure selection: The GA-PARSIMONY. Neurocomputing, Online July 2017. [https://doi.org/10.1016/j.neucom.2016.08.154](https://doi.org/10.1016/j.neucom.2016.08.154). Martinez-De-Pison, F.J., Gonzalez-Sendino, R., Ferreiro, J., Fraile, E., Pernia-Espinoza, A. GAparsimony: An R package for searching parsimonious models by combining hyperparameter optimization and feature selection (2018) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10870 LNAI, pp. 62-73. [https://doi.org/10.1007/978-3-319-92639-1_6](https://doi.org/10.1007/978-3-319-92639-1_6).