fuzzic 0.4.15

FuzzIC is a Python library for evaluating the interpretability of fuzzy rule bases.

FuzzIC

FuzzIC is a Python library for evaluating the interpretability of fuzzy rule bases.
It computes up to 19 different interpretability criteria, and provides configurable, flexible, and customizable evaluation tools.

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📑 Table of Contents

• Features
• Installation
• Quickstart
• Input / Output
• Configuration
• Add a New Criterion
• Acknowledgement
• License

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✨ Features

• Evaluation of up to 19 interpretability criteria.
• Configurable via a central config object.
• Works with XML or FisPro rule base formats.
• Outputs results in JSON, easy to reuse for visualization or comparison.
• Allows adding custom interpretability criteria.

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🛠 Installation

Clone and install locally:

git clone https://gitlab.lip6.fr/pontoizeau/fuzzic.git
cd fuzzic
pip install -e .

✅ Requirements: Python ≥ 3.8, numpy, matplotlib, etc.
(Dependencies are installed automatically via pip.)

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🚀 Quickstart

Quick example on a A/C controller rule base :

from fuzzic.study.study import Study

S = Study("climatiseur") # the folder can be found in <working_dir>/study

S.display()
S.evaluate()

Setting your own rule bases.

from fuzzic.study.study import Study, create_project

# 1. Create a new project
study_name = "MyStudy"
create_project(study_name)

# 2. Initialize the study
S = Study(study_name)

# 3. Display and evaluate rule bases
S.display()
S.evaluate()

• A new folder study/MyStudy is created.
• Put your rule bases in rulebases/.
• Optionally, put a dataset in dataset/ (CSV format).
• Results will be generated in the results/ folder.

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📥 Input / 📤 Output

Input

• Rule bases in XML or FisPro format (templates provided).
• Fuzzy sets must be trapezoidal or Gaussian.
• Dataset (optional) in CSV:
  • First line = labels
  • Following lines = instances (comma-separated)

Output

• A JSON file containing all computed criteria values for the rule bases.

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🔧 Configuration

All configuration parameters are managed in:

from fuzzic.configuration.config import config
print(config.reminder())

Example:

config.sample_size = 800
print(config.reminder())

Main parameters

• Criteria configuration
  alpha_coverage, rounding, similarity, sample_size, etc.
• Aggregators
  criteria_aggregation, t_norm, t_conorm, etc.
• Plotting
  size_of_plot_x, size_of_plot_y
• User-defined
  Add your own parameters with:

  config.add_param('my_param', [1, 2, 3], 'Example custom parameter')
  

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➕ Add a New Criterion

Define a function taking a rulebase and returning a dict:

def interpretability(rulebase):
    return {"warning": "Nothing to say", "score": 1.0}

Register it in the global CRITERIA list:

from fuzzic.interpretability.criteria import CRITERIA, criterion

CRITERIA.append(criterion(
    name="example",
    category="linguistic variables",
    direction="max",
    active=True,
    func_interpretability=interpretability
))

Set the criterion parameter:

• criterion_name: The name of your criterion
• category: The object which the criterion applies on. Current are ['linguistic variables', 'fuzzy rule', 'fuzzy set', 'fuzzy rule base', 'fuzzy sets'].
• direction : if the criterion must be maximized, minimized
• active: if you wish to evaluate this criterion or not during evaluation
• func_interpretability: the reference to the interpretability evaluation function of this criterion

Manage active criteria:

import fuzzic.interpretability.criteria as ic

ic.activate("normality")
ic.deactivate("coverage")
print(ic.status())

Here is a full example:

from fuzzic.study.study import Study, create_project
from fuzzic.interpretability.interpretability_manager import  status, deactivate
from fuzzic.configuration.config import config

#### IF A NEW INTERPRETABILITY CRITERION HAS TO BE DEFINED IN ADDITION
from fuzzic.interpretability.interpretability_manager import CRITERIA, criterion

def interpretability(rulebase):
    ...
    return {"warning" : 'Nothing to say', "score" : 1.}

CRITERIA.append(criterion(name="example", category="linguistic variables", direction="max",
          active=True, func_interpretability=interpretability))
#########

config.add_param('additional_param', [1, 2, 3, 4], 'A example of user criteria')
print(config.reminder())
config.additional_param = [3, 2]

study_name = "climatiseur" # the folder name in working_dir/study where the rule-base and all the results are/will be stored

create_project(study_name)

deactivate("coverage")
print(status())

S = Study(study_name)

S.display()
S.evaluate()

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🙏 Acknowledgement

This work has been funded by the project IFP-in-RL, ANR-22-ASTR-0032.

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📜 License

Distributed under the MIT License.