FuzzyLTN

A novel interpretable and adaptable neuro-symbolic framework that integrates Fuzzy Systems with Logical Tensor Networks

Directories

• data/: Contains everything related to data.

  • Description: This directory contains the script for loading the datasets.

• experiments/: Contains everything related to experiments.

  • Description: This directory includes configurations, results, and evaluation scripts.

• models/: Contains models and operators.

  • Description: Includes models used and operators (e.g., "and" and "or").

Running the Code

1. Set up the Project Environment

• First, ensure you have Conda installed. If not, follow the instructions here.

• Then, create the project environment using the provided fuzzyltn_env.yml file:

  conda env create -f fuzzyltn_env.yml

• And activate it:

  conda activate fuzzyltn

2. Set up Configuration

• Ensure you have a configuration file located in the experiments/configurations/<dataset>/ directory.

• This file should contain experiment settings such as number of seeds, neuron types, number of membership functions (MFs), activation function, and optimizer (see conf file inside experiments/configurations/iris/ ).

3. Command-line Arguments

• The script accepts command-line arguments to specify dataset, path to configuration file, and directory to store results.

• Use the following command-line arguments:

  • -dataset: Specify the dataset to use (default is "iris").
  • -path_to_conf: Provide path to configuration file (default is ./experiments/configurations/iris/conf-00.json).
  • -path_to_results: Define directory where results will be saved (default is ./experiments/results/iris/).

4. Run the Script

• Run the main script using Python:

  python main.py -dataset <dataset> -path_to_conf ./experiments/configurations/<dataset>/<name_of_conf>.json -path_to_results ./experiments/results/<dataset>/

5. Results

Results are stored in ./results/<dataset>/ directory. This directory contains:

• Plots, fuzzy rules and axioms generated during each experiment.
• runs_results.csv: A CSV file storing the results of each run.
• mean_std_results.csv: A CSV file storing the mean and standard deviation of grouped runs (by NeuronType and MFS).

Authors

• Paulo Vitor De Campos Souza: [email protected]
• Gianluca Apriceno: [email protected]
• Mauro Dragoni: [email protected]

License

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