evaluate_explanation
Assess AI explanation quality using OpenXAI metrics like PGI, PGU, and RIS to measure faithfulness, robustness, and completeness for model transparency.
Instructions
Evaluate explanation quality using OpenXAI metrics
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | Evaluation metric to use (PGI, PGU, RIS, RRS, ROS, etc.) | |
| explanation | Yes | JSON string of the explanation to evaluate | |
| model_info | Yes | Information about the model |
Implementation Reference
- index.js:768-815 (handler)The core handler function for the 'evaluate_explanation' tool. It validates the metric, constructs a Python code example for evaluating explanations using OpenXAI's Evaluator class, and returns formatted response with evaluation details.
async evaluateExplanation(metric, explanation, modelInfo) { const validMetrics = ['PGI', 'PGU', 'RIS', 'RRS', 'ROS', 'FA', 'RA', 'SA', 'SRA', 'RC', 'PRA']; if (!validMetrics.includes(metric)) { throw new Error(`Metric '${metric}' not supported. Available metrics: ${validMetrics.join(', ')}`); } const codeExample = ` # Example usage with OpenXAI: from openxai import Evaluator from openxai import LoadModel from openxai import Explainer from openxai.dataloader import ReturnLoaders # Load model and data model = LoadModel(data_name='${modelInfo.data_name}', ml_model='${modelInfo.ml_model}', pretrained=True) trainloader, testloader = ReturnLoaders(data_name='${modelInfo.data_name}', download=True) # Generate explanations explainer = Explainer(method='lime', model=model) inputs, labels = next(iter(testloader)) explanations = explainer.get_explanations(inputs) # Evaluate explanations evaluator = Evaluator(model, metric='${metric}') score = evaluator.evaluate( inputs=inputs, labels=labels, explanations=explanations ) print(f"${metric} score: {score}") `; return { content: [ { type: 'text', text: `Evaluated explanation using ${metric} metric\n\n` + `Metric: ${metric}\n` + `Dataset: ${modelInfo.data_name}\n` + `Model: ${modelInfo.ml_model}\n` + `Explanation: ${explanation}\n\n` + `Python code example:\n\`\`\`python${codeExample}\`\`\`` } ] }; } - index.js:172-198 (registration)The tool registration entry in the list_tools response, including name, description, and input schema definition.
{ name: 'evaluate_explanation', description: 'Evaluate explanation quality using OpenXAI metrics', inputSchema: { type: 'object', properties: { metric: { type: 'string', description: 'Evaluation metric to use (PGI, PGU, RIS, RRS, ROS, etc.)', enum: ['PGI', 'PGU', 'RIS', 'RRS', 'ROS', 'FA', 'RA', 'SA', 'SRA', 'RC', 'PRA'] }, explanation: { type: 'string', description: 'JSON string of the explanation to evaluate' }, model_info: { type: 'object', description: 'Information about the model', properties: { data_name: { type: 'string' }, ml_model: { type: 'string' } } } }, required: ['metric', 'explanation', 'model_info'] } }, - index.js:175-197 (schema)The input schema defining parameters for the evaluate_explanation tool: metric (enum), explanation (string), model_info (object).
inputSchema: { type: 'object', properties: { metric: { type: 'string', description: 'Evaluation metric to use (PGI, PGU, RIS, RRS, ROS, etc.)', enum: ['PGI', 'PGU', 'RIS', 'RRS', 'ROS', 'FA', 'RA', 'SA', 'SRA', 'RC', 'PRA'] }, explanation: { type: 'string', description: 'JSON string of the explanation to evaluate' }, model_info: { type: 'object', description: 'Information about the model', properties: { data_name: { type: 'string' }, ml_model: { type: 'string' } } } }, required: ['metric', 'explanation', 'model_info'] }