Evaluation Tools Module

The ex_fuzzy.eval_tools module provides evaluation and analysis tools for fuzzy classification models.

Overview

This module includes performance metrics, statistical analysis, and model evaluation tools specifically designed for fuzzy rule-based systems.

Classes

Main Function

Model Evaluation

Core Class

FuzzyEvaluator

Examples

Basic Model Evaluation

import ex_fuzzy.eval_tools as et
from ex_fuzzy.classifiers import RuleMineClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data and train classifier
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

classifier = RuleMineClassifier(nRules=15, nAnts=3, verbose=True)
classifier.fit(X_train, y_train)

# Comprehensive evaluation
report = et.eval_fuzzy_model(
    fl_classifier=classifier,
    X_train=X_train,
    y_train=y_train,
    X_test=X_test,
    y_test=y_test,
    plot_rules=True,
    plot_partitions=True,
    bootstrap_results_print=True
)

print(report)

Using FuzzyEvaluator Class

# Create evaluator
evaluator = et.FuzzyEvaluator(classifier)

# Get predictions
y_pred = evaluator.predict(X_test)

# Get specific metrics
accuracy = evaluator.get_metric('accuracy_score', X_test, y_test)
f1_score = evaluator.get_metric('f1_score', X_test, y_test, average='macro')

print(f"Accuracy: {accuracy:.3f}")
print(f"F1-score: {f1_score:.3f}")

# Detailed evaluation
evaluator.eval_fuzzy_model(
    X_train, y_train, X_test, y_test,
    plot_rules=True,
    print_rules=True,
    plot_partitions=True
)

See Also

• ex_fuzzy.classifiers : Fuzzy classification algorithms

• ex_fuzzy.vis_rules : Rule visualization utilities

• sklearn.metrics : Standard classification metrics