This is meant to be an alternative to popular methods inside scikit-learn such as Grid Search and Randomized Grid Search for hyperparameters tuning, and from RFE (Recursive Feature Elimination), Select From Model for feature selection.

Table of Contents ######################

Sklearn-genetic-opt Overview
- Main Features
- Demos on Features
Installation
- Basic Installation
- Full Installation with Extras
Usage
- Hyperparameters Tuning
- Feature Selection
Documentation
- Stable
- Latest
- Development
Changelog
Important Links
Source Code
Contributing
Testing

Sklearn-genetic-opt uses evolutionary algorithms from the DEAP <https://deap.readthedocs.io/en/master/>_ (Distributed Evolutionary Algorithms in Python) package to choose the set of hyperparameters that optimizes (max or min) the cross-validation scores, it can be used for both regression and classification problems.

Documentation is available here <https://sklearn-genetic-opt.readthedocs.io/>_

Main Features: ##############

GASearchCV: Main class of the package for hyperparameters tuning, holds the evolutionary cross-validation optimization routine.
GAFeatureSelectionCV: Main class of the package for feature selection.
Algorithms: Set of different evolutionary algorithms to use as an optimization procedure.
Callbacks: Custom evaluation strategies to generate early stopping rules, logging (into TensorBoard, .pkl files, etc) or your custom logic.
Schedulers: Adaptive methods to control learning parameters.
Plots: Generate pre-defined plots to understand the optimization process.
MLflow: Build-in integration with mlflow to log all the hyperparameters, cv-scores and the fitted models.

Demos on Features: ##################

Visualize the progress of your training:

.. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/docs/images/progress_bar.gif?raw=true

Real-time metrics visualization and comparison across runs:

.. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/docs/images/tensorboard_log.png?raw=true

Sampled distribution of hyperparameters:

.. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/docs/images/density.png?raw=true

Artifacts logging:

.. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/docs/images/mlflow_artifacts_4.png?raw=true

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

If you want to get all the features, including plotting, tensorboard and mlflow logging capabilities, install all the extra packages::

pip install sklearn-genetic-opt[all]

Example: Hyperparameters Tuning ###############################

.. code-block:: python

from sklearn_genetic import GASearchCV from sklearn_genetic.space import Continuous, Categorical, Integer from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split, StratifiedKFold from sklearn.datasets import load_digits from sklearn.metrics import accuracy_score

data = load_digits() n_samples = len(data.images) X = data.images.reshape((n_samples, -1)) y = data['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

clf = RandomForestClassifier()

Defines the possible values to search

param_grid = {'min_weight_fraction_leaf': Continuous(0.01, 0.5, distribution='log-uniform'), 'bootstrap': Categorical([True, False]), 'max_depth': Integer(2, 30), 'max_leaf_nodes': Integer(2, 35), 'n_estimators': Integer(100, 300)}

Seed solutions

warm_start_configs = [ {"min_weight_fraction_leaf": 0.02, "bootstrap": True, "max_depth": None, "n_estimators": 100}, {"min_weight_fraction_leaf": 0.4, "bootstrap": True, "max_depth": 5, "n_estimators": 200}, ]

cv = StratifiedKFold(n_splits=3, shuffle=True)

evolved_estimator = GASearchCV(estimator=clf, cv=cv, scoring='accuracy', population_size=20, generations=35, param_grid=param_grid, n_jobs=-1, verbose=True, use_cache=True, warm_start_configs=warm_start_configs, keep_top_k=4)

Train and optimize the estimator

evolved_estimator.fit(X_train, y_train)

Best parameters found

print(evolved_estimator.best_params_)

Use the model fitted with the best parameters

y_predict_ga = evolved_estimator.predict(X_test) print(accuracy_score(y_test, y_predict_ga))

Saved metadata for further analysis

print("Stats achieved in each generation: ", evolved_estimator.history) print("Best k solutions: ", evolved_estimator.hof)

Example: Feature Selection ##########################

.. code:: python3

from sklearn_genetic import GAFeatureSelectionCV, ExponentialAdapter
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
import numpy as np

data = load_iris()
X, y = data["data"], data["target"]

# Add random non-important features
noise = np.random.uniform(5, 10, size=(X.shape[0], 5))
X = np.hstack((X, noise))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)

clf = SVC(gamma='auto')
mutation_scheduler = ExponentialAdapter(0.8, 0.2, 0.01)
crossover_scheduler = ExponentialAdapter(0.2, 0.8, 0.01)

evolved_estimator = GAFeatureSelectionCV(
    estimator=clf,
    scoring="accuracy",
    population_size=30,
    generations=20,
    mutation_probability=mutation_scheduler,
    crossover_probability=crossover_scheduler,
    n_jobs=-1)

# Train and select the features
evolved_estimator.fit(X_train, y_train)

# Features selected by the algorithm
features = evolved_estimator.support_
print(features)

# Predict only with the subset of selected features
y_predict_ga = evolved_estimator.predict(X_test)
print(accuracy_score(y_test, y_predict_ga))

# Transform the original data to the selected features
X_reduced = evolved_estimator.transform(X_test)

Changelog #########

See the changelog <https://sklearn-genetic-opt.readthedocs.io/en/latest/release_notes.html>__ for notes on the changes of Sklearn-genetic-opt

Important links ###############

Official source code repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/
Download releases: https://pypi.org/project/sklearn-genetic-opt/
Issue tracker: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/issues
Stable documentation: https://sklearn-genetic-opt.readthedocs.io/en/stable/

Source code ###########

You can check the latest development version with the command::

git clone https://github.com/rodrigo-arenas/Sklearn-genetic-opt.git

Install the development dependencies::

pip install -r dev-requirements.txt

Check the latest in-development documentation: https://sklearn-genetic-opt.readthedocs.io/en/latest/

Contributing ############

Contributions are more than welcome! There are several opportunities on the ongoing project, so please get in touch if you would like to help out. Make sure to check the current issues and also the Contribution guide <https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/CONTRIBUTING.md>_.

Big thanks to the people who are helping with this project!

|Contributors|_

Testing #######

After installation, you can launch the test suite from outside the source directory::

pytest sklearn_genetic

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Install

sklearn-genetic-opt

Defines the possible values to search

Seed solutions

Train and optimize the estimator

Best parameters found

Use the model fitted with the best parameters

Saved metadata for further analysis

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