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I'm using PyGAD for cross-validation hyper-parameter tuning. I sample train/test data in the fitness function; I'm unclear on whether every strain i in iteration x will have access to the same sampled data or if sampling will differ across strains given the same iteration?
from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.utils.random import sample_without_replacement import numpy as np gene_space = [ # n_estimators np.linspace(50,200,25, dtype='int'), # min_samples_split, np.linspace(2,10,5, dtype='int'), # min_samples_leaf, np.linspace(1,10,5, dtype='int'), # min_impurity_decrease np.linspace(0,1,10, dtype='float') ] def fitness_function_factory(hyperparameters, data, y_name, sample_size): def fitness_function(solution, solution_idx): model = RandomForestClassifier( n_estimators=solution[0], min_samples_split=solution[1], min_samples_leaf=solution[2], min_impurity_decrease=solution[3] ) X = data.drop(columns=[y_name]) y = data[y_name] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5) train_idx = sample_without_replacement(n_population=len(X_train), n_samples=sample_size) test_idx = sample_without_replacement(n_population=len(X_test), n_samples=sample_size) model.fit(X_train[train_idx], y_train[train_idx]) fitness = model.score(X_test[test_idx], y_test[test_idx]) return fitness return fitness_function cross_validation = pygad.GA(gene_space=gene_space, fitness_func=fitness_function_factory(), num_generations=100, num_parents_mating=4, sol_per_pop=8, # num_genes=5, parent_selection_type='sss', keep_parents=2, crossover_type="single_point", mutation_type="random", mutation_percent_genes=10)
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Sorry for the getting back late!
The fitness function is called for each individual solution (or strain as you said). This means the data sampling will differ from one solution to another.
If you want to do sampling once for all solutions (strains) in the same generation (iteration), then you can:
- For the first generation, do sampling in a code outside the fitness function.
- For all other generations, use the
on_generation()callback function to do sampling.
from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.utils.random import sample_without_replacement import numpy as np gene_space = [ # n_estimators np.linspace(50,200,25, dtype='int'), # min_samples_split, np.linspace(2,10,5, dtype='int'), # min_samples_leaf, np.linspace(1,10,5, dtype='int'), # min_impurity_decrease np.linspace(0,1,10, dtype='float') ] X = data.drop(columns=[y_name]) y = data[y_name] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5) train_idx = sample_without_replacement(n_population=len(X_train), n_samples=sample_size) test_idx = sample_without_replacement(n_population=len(X_test), n_samples=sample_size) def on_generation(ga_instance): global X, y, X_train, X_test, y_train, y_test, train_idx, test_idx X = data.drop(columns=[y_name]) y = data[y_name] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5) train_idx = sample_without_replacement(n_population=len(X_train), n_samples=sample_size) test_idx = sample_without_replacement(n_population=len(X_test), n_samples=sample_size) def fitness_function_factory(hyperparameters, data, y_name, sample_size): def fitness_function(solution, solution_idx): model = RandomForestClassifier( n_estimators=solution[0], min_samples_split=solution[1], min_samples_leaf=solution[2], min_impurity_decrease=solution[3] ) global X, y, X_train, X_test, y_train, y_test, train_idx, test_idx model.fit(X_train[train_idx], y_train[train_idx]) fitness = model.score(X_test[test_idx], y_test[test_idx]) return fitness return fitness_function cross_validation = pygad.GA(gene_space=gene_space, fitness_func=fitness_function_factory, num_generations=100, num_parents_mating=4, sol_per_pop=8, # num_genes=5, parent_selection_type='sss', keep_parents=2, crossover_type="single_point", mutation_type="random", mutation_percent_genes=10)
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