NiaAML 1.1.6

Python automated machine learning framework.

NiaAML is an automated machine learning Python framework based on nature-inspired algorithms for optimization. The name comes from the automated machine learning method of the same name [1]. Its goal is to efficiently compose the best possible classification pipeline for the given task using components on the input. The components are divided into three groups: feature seletion algorithms, feature transformation algorithms and classifiers. The framework uses nature-inspired algorithms for optimization to choose the best set of components for the classification pipeline on the output and optimize their parameters. We use NiaPy framework for the optimization process which is a popular Python collection of nature-inspired algorithms. The NiaAML framework is easy to use and customize or expand to suit your needs.

The NiaAML framework allows you not only to run full pipeline optimization, but also separate implemented components such as classifiers, feature selection algorithms, etc. It supports numerical and categorical features as well as missing values in datasets.

• Documentation: https://niaaml.readthedocs.io/en/latest/,

• Tested OS: Windows, Ubuntu, Fedora, Linux Mint and CentOS. However, that does not mean it does not work on others.

Installation

pip

Install NiaAML with pip3:

pip3 install niaaml

In case you would like to try out the latest pre-release version of the framework, install it using:

pip3 install niaaml --pre

Install From Source

In case you want to install directly from the source code, use:

git clone https://github.com/lukapecnik/NiaAML.git
cd NiaAML
python3 setup.py install

Graphical User Interface

You can find a simple graphical user interface for NiaAML package here.

Usage

See the project’s repository for usage examples.

Components

In the following sections you can see a list of currently implemented components divided into groups: classifiers, feature selection algorithms and feature transformation algorithms. At the end you can also see a list of currently implemented fitness functions for the optimization process, categorical features’ encoders, and missing values’ imputers.

Classifiers

• Adaptive Boosting (AdaBoost),

• Bagging (Bagging),

• Extremely Randomized Trees (ExtremelyRandomizedTrees),

• Linear SVC (LinearSVC),

• Multi Layer Perceptron (MultiLayerPerceptron),

• Random Forest Classifier (RandomForest),

• Decision Tree Classifier (DecisionTree),

• K-Neighbors Classifier (KNeighbors),

• Gaussian Process Classifier (GaussianProcess),

• Gaussian Naive Bayes (GaussianNB),

• Quadratic Discriminant Analysis (QuadraticDiscriminantAnalysis).

Feature Selection Algorithms

• Select K Best (SelectKBest),

• Select Percentile (SelectPercentile),

• Variance Threshold (VarianceThreshold).

Nature-Inspired

• Bat Algorithm (BatAlgorithm),

• Differential Evolution (DifferentialEvolution),

• Self-Adaptive Differential Evolution (jDEFSTH),

• Grey Wolf Optimizer (GreyWolfOptimizer),

• Particle Swarm Optimization (ParticleSwarmOptimization).

Feature Transformation Algorithms

• Normalizer (Normalizer),

• Standard Scaler (StandardScaler),

• Maximum Absolute Scaler (MaxAbsScaler),

• Quantile Transformer (QuantileTransformer),

• Robust Scaler (RobustScaler).

Fitness Functions based on

• Accuracy (Accuracy),

• Cohen’s kappa (CohenKappa),

• F1-Score (F1),

• Precision (Precision).

Categorical Feature Encoders

• One-Hot Encoder (OneHotEncoder).

Feature Imputers

• Simple Imputer (SimpleImputer).

Licence

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!

References

[1] Iztok Fister Jr., Milan Zorman, Dušan Fister, Iztok Fister. Continuous optimizers for automatic design and evaluation of classification pipelines. In: Frontier applications of nature inspired computation. Springer tracts in nature-inspired computing, pp.281-301, 2020.