Sklearn models hyperparameters tuning using genetic algorithms
Project description
Sklearn-genetic-opt
Sklearn models hyperparameters tuning using genetic algorithms
Usage:
Install sklearn-genetic-opt
It's advised to install sklearn-genetic using a virtual env, inside the env use:
pip install sklearn-genetic-opt
Example
from sklearn_genetic import GASearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits
from sklearn.metrics import accuracy_score
data = load_digits()
y = data['target']
X = data['data']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
clf = DecisionTreeClassifier()
evolved_estimator = GASearchCV(clf,
cv=3,
scoring='accuracy',
population_size=16,
generations=30,
tournament_size=3,
elitism=True,
crossover_probability=0.9,
mutation_probability=0.05,
continuous_parameters={'min_weight_fraction_leaf': (0, 0.5)},
categorical_parameters={'criterion': ['gini', 'entropy']},
integer_parameters={'max_depth': (2, 20), 'max_leaf_nodes': (2, 30)},
encoding_length=10,
criteria='max',
n_jobs=-1)
evolved_estimator.fit(X_train,y_train)
print(evolved_estimator.best_params_)
y_predict_ga = evolved_estimator.predict(X_test)
print(accuracy_score(y_test,y_predict_ga))
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