SKLearn XAI Components Library

This library provides a collection of components for working with scikit-learn models, datasets, and evaluation tools within the XAI framework. It includes components for loading datasets, data preprocessing, model training, evaluation, and various machine learning algorithms.

Prerequisites

• Python 3.8 or higher
• scikit-learn
• pandas (for CSV data handling)

Installation

You can install the required libraries using pip:

pip install -r requirements.txt

To use this component library, simply copy the directory / clone or submodule the repository to your working Xircuits project directory.

Components Overview

The library includes a variety of components categorized into dataset handling, data preprocessing, model training, and model evaluation.

Dataset Handling

• SKLearnLoadDataset: Fetches datasets from scikit-learn's dataset collection.
• CSVToSKLearnDataset: Converts a CSV file into a format compatible with scikit-learn datasets.

Data Preprocessing

• SKLearnTrainTestSplit: Splits datasets into training and testing sets.

Model Training

• SKLearnModelTraining: Trains a specified scikit-learn model using provided training data.
• SKLearnRandomForestClassifier: Initializes a RandomForestClassifier model.
• SKLearnLogisticRegression: Initializes a LogisticRegression model.
• SKLearnSVC: Initializes a Support Vector Classifier (SVC) model.
• SKLearnKNeighborsClassifier: Initializes a KNeighborsClassifier model.
• SKLearnDecisionTreeClassifier: Initializes a DecisionTreeClassifier model.
• SKLearnGradientBoostingClassifier: Initializes a GradientBoostingClassifier model.
• SKLearnSVR: Initializes a Support Vector Regression (SVR) model.
• SKLearnMultinomialNB: Initializes a Multinomial Naive Bayes (MultinomialNB) model.
• SKLearnRidgeRegression: Initializes a Ridge Regression model.
• SKLearnKMeans: Initializes a KMeans clustering model.

Model Evaluation

• SKLearnClassificationEvaluation: Evaluates a trained scikit-learn classification model using testing data.

Usage

Each component can be integrated into your XAI workflows as needed. For instance, to train a RandomForestClassifier model:

1. Load your dataset using SKLearnLoadDataset or CSVToSKLearnDataset.
2. Split the dataset into training and testing sets with SKLearnTrainTestSplit.
3. Initialize the RandomForestClassifier model using SKLearnRandomForestClassifier.
4. Train the model with SKLearnModelTraining using the training data.
5. Evaluate the model's performance on the test set using SKLearnClassificationEvaluation.

Refer to the component documentation for detailed usage instructions and parameter explanations.

Contributing

We welcome contributions to this library. If you have suggestions for new components or improvements to existing ones, please open an issue or submit a pull request.