CBR-FoX 1.0.2

A case-based reasoning python library that aims to help researchers find similar cases according to an input case with a wide range of methods that can detect similarity based on the features of each time series

CBR-FoX: Case-Based Reasoning for Time Series Prediction Explanations

CBR-FoX is a Python library designed to provide case-based reasoning explanations for time series prediction models. This approach enhances the transparency and interpretability of machine learning models applied to sequential data.

Features

• Case-Based Reasoning (CBR) Implementation: Utilizes case-based reasoning to enhance explainability in time series predictions.
• Versatile & Adaptable: Supports various types of time series data.
• ML Model Compatibility: Easily integrates with common machine learning models.
• Comprehensible Explanations: Provides clear, human-readable insights into model predictions.

Installation

To install CBR-FoX and its dependencies, either clone the repository or use the pip package manager:

Install via PyPI

pip install CBR-FoX

Install via GitHub

# Clone the repository
git clone https://github.com/aaaimx/CBR-FoX.git
cd CBR-FoX

# Install required dependencies
pip install -r requirements.txt

Usage

Follow these steps to use CBR-FoX in your projects:

1. Retrieve Model Information

Extract the relevant inputs and outputs from your AI model.

2. Create CBR-FoX Instances

from cbr_fox import CBRfoxInstances
cbr_instances = CBRfoxInstances(model_outputs)

3. Initialize the Builder

from cbr_fox import CBRfoxBuilder
builder = CBRfoxBuilder(cbr_instances)

4. Train the Instance

builder.fit(train_windows, train_targets, target_to_analyze, window_to_predict)

5. Obtain Explanations

builder.predict(prediction=prediction, num_cases=5)

6. Visualize Results

builder.visualize_pyplot(
    fmt='--d',
    scatter_params={'s': 50},
    xtick_rotation=50,
    title='Example Visualization',
    xlabel='X-axis',
    ylabel='Y-axis'
)

Library Usage Diagram

The following diagram illustrates the typical workflow of CBR-FoX, from retrieving AI model outputs to generating visual explanations.

Library File Relation Diagram

The following diagram represents the core classes and their interactions within the library. The cci_distance file is utilized when an instance is created using the custom distance metric implemented in this script.

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For further details, check out the official documentation: CBR-FoX on Read the Docs.