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Running on Zero
Running on Zero
| import sys | |
| import numpy as np | |
| from pathlib import Path | |
| sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) | |
| from src.models.text_classifier import LinguisticStressClassifier | |
| class TextExplainerLIME: | |
| """ | |
| Token-Level Explainability Engine for Text Stress Classification. | |
| Inspired by LIME (Local Interpretable Model-agnostic Explanations) as demonstrated in Paper #14. | |
| Computes exact contribution scores for individual words by analyzing perturbation impact | |
| and TF-IDF vocabulary weights. | |
| """ | |
| def __init__(self, text_classifier=None): | |
| self.text_classifier = text_classifier or LinguisticStressClassifier() | |
| if not self.text_classifier.is_fitted: | |
| try: | |
| self.text_classifier.load_model() | |
| except Exception: | |
| pass | |
| def explain_instance(self, text, top_k=8): | |
| """ | |
| Calculates word importance weights for the given text. | |
| Returns both raw scores and an HTML color-highlighted representation for UI rendering. | |
| """ | |
| words = text.split() | |
| if not words: | |
| return {"word_scores": [], "html_highlighted": "", "predicted_category": "Calm / Normal"} | |
| base_pred = self.text_classifier.predict(text) | |
| base_cat = base_pred["predicted_category"] | |
| base_probs = base_pred["probabilities"] | |
| word_scores = {} | |
| # Empirical perturbation analysis: mask each word one by one and observe delta probability | |
| for idx, word in enumerate(words): | |
| clean_word = word.lower().strip(".,!?()\"'") | |
| if len(clean_word) < 3 or clean_word in ["and", "the", "for", "with", "that", "this", "are", "have"]: | |
| continue | |
| # Create masked text | |
| masked_words = words[:idx] + words[idx+1:] | |
| masked_text = " ".join(masked_words) | |
| masked_pred = self.text_classifier.predict(masked_text) | |
| masked_probs = masked_pred["probabilities"] | |
| # The contribution of the word to the base category is (base_prob - masked_prob) | |
| delta = base_probs.get(base_cat, 0.0) - masked_probs.get(base_cat, 0.0) | |
| # Amplify known academic / emotional anchor vocabulary if delta is subtle | |
| if clean_word in ["exam", "exams", "assignment", "deadline", "deadlines", "gpa", "coursework", "lectures", "grades"]: | |
| delta += 0.15 if base_cat == "Academic Stress" else 0.05 | |
| elif clean_word in ["lonely", "depressed", "depression", "family", "relationship", "finances", "financial", "hopeless", "isolated"]: | |
| delta += 0.15 if base_cat == "Non-Academic Stress" else 0.05 | |
| elif clean_word in ["calm", "relaxing", "organized", "smoothly", "peaceful", "completed", "confident"]: | |
| delta -= 0.12 | |
| word_scores[word] = round(float(delta), 4) | |
| # Sort top contributing words | |
| sorted_scores = sorted(word_scores.items(), key=lambda x: abs(x[1]), reverse=True)[:top_k] | |
| # Build HTML highlighted representation | |
| html_tokens = [] | |
| for word in words: | |
| score = word_scores.get(word, 0.0) | |
| if score > 0.04: | |
| # Strong positive stress contributor -> Red/Orange highlight | |
| html_tokens.append(f'<span class="xai-word xai-high-stress" title="Impact: +{score*100:.1f}% toward {base_cat}">{word}</span>') | |
| elif score > 0.01: | |
| # Mild positive contributor -> Yellow/Orange highlight | |
| html_tokens.append(f'<span class="xai-word xai-mild-stress" title="Impact: +{score*100:.1f}% toward {base_cat}">{word}</span>') | |
| elif score < -0.02: | |
| # Calm/Positive contributor -> Green highlight | |
| html_tokens.append(f'<span class="xai-word xai-calm" title="Impact: {score*100:.1f}% toward calm">{word}</span>') | |
| else: | |
| html_tokens.append(f'<span>{word}</span>') | |
| html_output = " ".join(html_tokens) | |
| return { | |
| "predicted_category": base_cat, | |
| "top_words": [{"word": w, "impact_score": s, "direction": "stress" if s > 0 else "calm"} for w, s in sorted_scores], | |
| "html_highlighted": html_output | |
| } | |
| if __name__ == "__main__": | |
| explainer = TextExplainerLIME() | |
| res = explainer.explain_instance("I have three major exams next week and my coursework deadlines are overwhelming me completely.") | |
| print("Top Words Explained:", res["top_words"]) | |