ps-optimize 2.0.4

PSO meet hyperparameter tuning.

ps-opt

This Python package provides a tool for hyperparameter tuning and feature selection in machine learning models using Particle Swarm Optimization (PSO) techniques. The package includes a vanilla PSO for feature selection, a vanilla PSO for hyperparameter tuning, and two different PSO variants for machine learning hyperparameter tuning.

Features

Vanilla PSO for Feature Selection: This feature uses the standard PSO algorithm to select the most optimal features for a given machine learning model. The algorithm works by searching the feature space and identifying a subset of features that maximizes the performance of the model.

Vanilla PSO for Hyperparameter Tuning: This feature uses the standard PSO algorithm to tune the hyperparameters of a given machine learning model. The algorithm works by searching the hyperparameter space and identifying a set of parameters that maximize the performance of the model.

PSO Variants for Hyperparameter Tuning: This package includes two different PSO variants for hyperparameter tuning. These variants offer different search strategies and can provide better results depending on the specific characteristics of the problem.

Cross-Validation: The package uses cross-validation in the hyperparameter tuning process to avoid overfitting and to ensure that the tuned parameters generalize well to unseen data.

Example Usage

The following is a basic example of how to use this package:

Installation

Install it using pip:

pip install ps-optimize

Hyperparameters Tuning

import pandas as pd
from sklearn.datasets import make_classification
from ps_opt.hyperparameters_tuning import ParticleSwarmSearchCV
from ps_opt.search_space_characteristics import Categorical, Real, Integer
from sklearn.linear_model import LogisticRegression


X, y = make_classification()
X = pd.DataFrame(X)
y = pd.Series(y)

search_space = {
    "penalty": Categorical("l1", "l2", "elasticnet", None),
    "max_iter": Integer(10, 20, "exponential"),
    "tol": Real(.0001, .1, "uniform"),
    "solver": Categorical('lbfgs', 'liblinear', 'newton-cg', 'sag', 'saga')
}

psocv = ParticleSwarmSearchCV(
    search_space=search_space,
    n_particles=30,
    estimator=LogisticRegression,
    cv=5,
    scoring="accuracy",
    max_iter=10,
    n_jobs=1,
    verbosity=0
)

psocv.fit(X, y)

print(f"Best Score: {psocv.best_score_}")
print(f"Best Parameters: {psocv.best_params_}")
print(f"Best Probabilities: {psocv.best_proba_}")

Feature Selection

import pandas as pd
from sklearn.datasets import make_classification
from ps_opt.feature_selection import ParticleSwarmFeatureSelectionCV
from ps_opt.search_space_characteristics import Categorical, Real, Integer
from sklearn.linear_model import LogisticRegression


X, y = make_classification()
X = pd.DataFrame(X)
y = pd.Series(y)

psocv = ParticleSwarmFeatureSelectionCV(
    n_particles=30,
    estimator=LogisticRegression,
    cv=5,
    scoring="accuracy",
    max_iter=10,
    n_jobs=-1,
    verbosity=0
)

psocv.fit(X, y)

print(f"Best Score: {psocv.best_score_}")
print(f"Best Features: {psocv.best_features_}")
print(f"Best Probabilities: {psocv.best_proba_}")