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 understanding of machine learning models used with sequential data.

Features

• CBR-FoX approach implementation.
• Adaptable to various types of time series.
• Compatible with common machine learning models.
• Generates comprehensible explanations.

Installation

Clone this repository and install its dependencies:

git clone https://github.com/jerryperezperez/CBR-FoX.git
cd CBR-FoX
pip install -r requirements.txt

Usage

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

1. Retreive model's information: Obtain the inputs and outputs generated by your AI model.

2. Create CBRfox instances:

   cbr_instances = CBRfoxInstances(model_outputs)

3. Initialize Builder

    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. Use graph visualization methods:

   builder.visualize_pyplot(
       fmt = '--d',
       scatter_params={"s": 50},
       xtick_rotation=50,
       title="nombre",
       xlabel="x",
       ylabel="y"
   )

Library Usage Diagram

The following diagram illustrates the typical workflow when using the CBR-FoX library. From retrieving inputs and outputs from the AI model to generating visual explanations, each step is designed to facilitate the interpretation and explanation of time series-based predictions.

Library file relation diagram

The following diagram shows the classes involved in the basic functionality of the library. Thecci_distance file is used when creating an instance that employs the eponymous technique implemented in this script.