Add XAI

xai_utils.py

@@ -0,0 +1,52 @@
+import shap
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+def get_token_importance(model, tokenizer, text):
+    """
+    Calculates token importance using SHAP.
+    """
+    # Placeholder implementation
+    return {"token_importance": "Not yet implemented."}
+
+def get_attention_maps(model, tokenizer, text):
+    """
+    Generates attention heatmaps for a given text.
+    """
+    # Placeholder implementation
+    return {"attention_maps": "Not yet implemented."}
+
+def get_aspect_wise_explanation(shap_values, aspects):
+    """
+    Aggregates SHAP values by ontology aspect.
+    """
+    # Placeholder implementation
+    return {"aspect_wise_explanation": "Not yet implemented."}
+
+def get_confidence_calibration_diagram(model, texts, labels):
+    """
+    Creates a confidence calibration diagram.
+    """
+    # Placeholder implementation
+    return {"confidence_calibration": "Not yet implemented."}
+
+def run_xai_analysis(model_path, text1, text2):
+    """
+    Runs the full XAI analysis pipeline.
+    """
+    # tokenizer = AutoTokenizer.from_pretrained(model_path)
+    # model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+    # token_importance = get_token_importance(model, tokenizer, text1)
+    # attention_maps = get_attention_maps(model, tokenizer, text1)
+
+    # For now, returning placeholders
+    analysis_results = {
+        "token_importance": "Not yet implemented",
+        "attention_maps": "Not yet implemented",
+        "aspect_wise_explanation": "Not yet implemented.",
+        "confidence_calibration": "Not yet implemented."
+    }
+    return analysis_results