hyperactive 0.1.1

Meta heuristic optimization techniques for scikit-learn models

Hyperactive

A Python package for meta-heuristic hyperparameter optimization of scikit-learn models for supervised learning. Hyperactive automates the search for hyperparameters by utilizing metaheuristics to efficiently explore the search space and provide a sufficiently good solution. Its API is similar to scikit-learn and allows for parallel computation. Hyperactive offers a small collection of the following meta-heuristic optimization techniques:

• Random search
• Simulated annealing
• Particle swarm optimization

The multiprocessing will start n_jobs separate searches. These can operate independent of one another, which makes the workload perfectly parallel. In the current implementation the actual number of searches in each process is n_iter divided by n_jobs and rounded down to the next integer.

Installation

pip install hyperactive

Example

from sklearn.datasets import load_iris
from hyperactive import SimulatedAnnealing_Optimizer

iris_data = load_iris()
X_train = iris_data.data
y_train = iris_data.target

search_dict = {
    'sklearn.ensemble.RandomForestClassifier': {
        'n_estimators': [100],
        'criterion': ["gini", "entropy"],
        'min_samples_split': range(2, 21),
        'min_samples_leaf':  range(2, 21),
      }
}

Optimizer = SimulatedAnnealing_Optimizer(search_dict, n_iter=1000, scoring='accuracy', n_jobs=2)
Optimizer.fit(X_train, y_train)

Hyperactive API

RandomSearch_Optimizer(search_dict, n_iter, scoring, n_jobs=1, cv=5)

Methods:
    - fit(X_train, y_train)
    - predict(X_test)

SimulatedAnnealing_Optimizer(search_dict, n_iter, scoring, eps=1, t_rate=0.9, n_jobs=1, cv=5)

Methods:
    - fit(X_train, y_train)
    - predict(X_test)

ParticleSwarm_Optimizer(search_dict, n_iter, scoring, n_part=1, w=0.5, c_k=0.8, c_s=0.9, n_jobs=1, cv=5)

Methods:
    - fit(X_train, y_train)
    - predict(X_test)