A Hybrid Approach for Automatic Artificial Intelligence Algorithm Selection and Hyperparameter Tuning
Project description
Introduction
This is method used for solving the problem of AI algorithm selection and hyperparameter tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between Particle Swarm Optimization and the Simulated Annealing.
Example Usage
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from AutoAIAlgorithm.ParticleSwarmOptimization import PSO
- def main():
# load the MNIST digits dataset
mnist = datasets.load_digits()
X = mnist.data
y = mnist.target
# Splitting the data into training set, test set and validation set
x_train, x_test, y_train, y_test = train_test_split(X, y)
num_particles=5
num_iterations=30
- pso = PSO(particle_count=num_particles,
distance_between_initial_particles=0.7, evaluation_metric=accuracy_score)
- best_metric, best_model = pso.fit(X_train=x_train,
X_test=x_test, Y_train=y_train, Y_test=y_test, maxiter=num_iterations, verbose=True, max_distance=0.05)
print(best_metric) print(best_model)
- if __name__ == “__main__”:
main()
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