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| import pandas as pd | |
| from sklearn.metrics import f1_score, precision_score, recall_score | |
| from scipy.stats import pearsonr | |
| import torch | |
| import time | |
| def calculate_classification_performance(y_true, y_pred): | |
| """ | |
| Calculates classification performance metrics. | |
| """ | |
| macro_f1 = f1_score(y_true, y_pred, average='macro') | |
| precision = precision_score(y_true, y_pred, average='macro') | |
| recall = recall_score(y_true, y_pred, average='macro') | |
| return { | |
| "Macro-F1": macro_f1, | |
| "Precision": precision, | |
| "Recall": recall | |
| } | |
| def calculate_hierarchical_coherence(section_sentiments, doc_sentiments): | |
| """ | |
| Calculates the Hierarchical Coherence Metric (HCM). | |
| """ | |
| # This is a simplified example. A real implementation would need to align | |
| # section-level and document-level sentiments for the same aspects. | |
| if not section_sentiments or not doc_sentiments: | |
| return 0.0 | |
| # For simplicity, let's assume we have paired lists of sentiment scores | |
| # (e.g., [-1, 0, 1] for negative, neutral, positive) | |
| correlation, _ = pearsonr(section_sentiments, doc_sentiments) | |
| return correlation | |
| def calculate_attention_faithfulness(model, tokenizer, text, sentiment): | |
| """ | |
| Calculates the Attention Faithfulness Score (AFS). | |
| This is a placeholder and requires a more complex implementation. | |
| """ | |
| # This would involve getting attention weights from the model and | |
| # comparing them to sentiment-bearing words. | |
| return 0.75 # Placeholder value | |
| def calculate_bias_adjusted_sentiment_delta(promise_sentiments, performance_sentiments): | |
| """ | |
| Calculates the Bias-Adjusted Sentiment Delta (BASD). | |
| """ | |
| if not promise_sentiments or not performance_sentiments: | |
| return 0.0 | |
| mean_promise = sum(promise_sentiments) / len(promise_sentiments) | |
| mean_performance = sum(performance_sentiments) / len(performance_sentiments) | |
| # A simple delta. The confidence adjustment would require confidence scores. | |
| return mean_promise - mean_performance | |
| def measure_efficiency(model, tokenizer, text): | |
| """ | |
| Measures inference time and memory usage. | |
| """ | |
| start_time = time.time() | |
| # A simple inference pass | |
| _ = model.encode(text) | |
| end_time = time.time() | |
| inference_time = end_time - start_time | |
| # Memory usage is more complex to measure accurately and may require | |
| # a library like memory-profiler. | |
| memory_usage = 0.0 # Placeholder | |
| return { | |
| "Inference Time (s)": inference_time, | |
| "Memory Usage (MB)": memory_usage | |
| } | |
| def run_advanced_evaluation(): | |
| """ | |
| Runs all advanced evaluation metrics and returns a report. | |
| """ | |
| # This is a placeholder for where you would get your actual data | |
| y_true = [1, 0, 1, 1, 0] | |
| y_pred = [1, 1, 1, 0, 0] | |
| section_sentiments = [-0.5, 0.8, 0.2, -0.9] | |
| doc_sentiments = [-0.4, 0.7, 0.3, -0.8] | |
| promise_sentiments = [0.8, 0.9, 0.7] | |
| performance_sentiments = [0.6, 0.5, 0.7] | |
| classification_metrics = calculate_classification_performance(y_true, y_pred) | |
| hcm = calculate_hierarchical_coherence(section_sentiments, doc_sentiments) | |
| basd = calculate_bias_adjusted_sentiment_delta(promise_sentiments, performance_sentiments) | |
| report = { | |
| "Classification Performance": classification_metrics, | |
| "Hierarchical Coherence Metric (HCM)": hcm, | |
| "Bias-Adjusted Sentiment Delta (BASD)": basd, | |
| # Placeholders for metrics that need more complex implementation | |
| "Attention Faithfulness Score (AFS)": 0.75, | |
| "Efficiency": { | |
| "Inference Time (s)": 0.123, | |
| "Memory Usage (MB)": 512 | |
| } | |
| } | |
| return report | |
| if __name__ == "__main__": | |
| report = run_advanced_evaluation() | |
| print(report) |