model_evaluation.py

# /// script
# dependencies = [
#     "transformers>=4.40.0",
#     "datasets>=2.18.0",
#     "torch>=2.0.0",
#     "rouge-score>=0.1.2",
#     "evaluate>=0.4.0",
#     "numpy>=1.24.0",
#     "pandas>=2.0.0",
#     "scikit-learn>=1.3.0",
#     "huggingface-hub>=0.20.0",
#     "accelerate>=0.27.0",
#     "trackio"
# ]
# ///

import os
import json
import pandas as pd
import numpy as np
from datetime import datetime
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from rouge_score import rouge_scorer
from sklearn.metrics import f1_score
import re
import trackio
from huggingface_hub import HfApi, upload_file
import torch

def normalize_text(text):
    """Normalize text for comparison"""
    if not isinstance(text, str):
        return ""
    # Remove extra whitespace and normalize
    text = re.sub(r'\s+', ' ', text.strip())
    return text.lower()

def compute_exact_match(pred, true):
    """Compute exact match score"""
    return float(normalize_text(pred) == normalize_text(true))

def compute_f1_score(pred, true):
    """Compute token-level F1 score"""
    pred_tokens = normalize_text(pred).split()
    true_tokens = normalize_text(true).split()

    if len(pred_tokens) == 0 and len(true_tokens) == 0:
        return 1.0
    if len(pred_tokens) == 0 or len(true_tokens) == 0:
        return 0.0

    # Convert to sets for intersection
    pred_set = set(pred_tokens)
    true_set = set(true_tokens)

    if len(pred_set) == 0 and len(true_set) == 0:
        return 1.0

    intersection = pred_set.intersection(true_set)
    precision = len(intersection) / len(pred_set) if pred_set else 0
    recall = len(intersection) / len(true_set) if true_set else 0

    if precision + recall == 0:
        return 0.0

    f1 = 2 * (precision * recall) / (precision + recall)
    return f1

def compute_rouge_l(pred, true):
    """Compute ROUGE-L score"""
    scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
    scores = scorer.score(normalize_text(true), normalize_text(pred))
    return scores['rougeL'].fmeasure

def evaluate_model():
    # Initialize Trackio
    trackio.init()

    print("🚀 Starting model evaluation...")

    # Configuration
    model_name = "ligaments-enterprise/llama3.2-1b-instruct-sec-finetuned"
    dataset_name = "ligaments-enterprise/sec-data"

    print(f"📊 Loading dataset: {dataset_name}")
    try:
        # Try to load the dataset
        dataset = load_dataset(dataset_name, split="train")
        print(f"✅ Dataset loaded successfully. Size: {len(dataset)}")
    except Exception as e:
        print(f"❌ Error loading dataset: {e}")
        # Try different splits
        try:
            dataset = load_dataset(dataset_name)
            if isinstance(dataset, dict):
                # Use the first available split
                split_name = list(dataset.keys())[0]
                dataset = dataset[split_name]
                print(f"✅ Using split '{split_name}'. Size: {len(dataset)}")
        except Exception as e2:
            print(f"❌ Failed to load dataset: {e2}")
            return

    # Inspect dataset structure
    print(f"📋 Dataset columns: {dataset.column_names}")
    print(f"📋 First example: {dataset[0]}")

    # Determine input/output columns
    possible_input_cols = ['prompt', 'input', 'question', 'instruction', 'text']
    possible_output_cols = ['response', 'output', 'answer', 'completion', 'target']

    input_col = None
    output_col = None

    for col in possible_input_cols:
        if col in dataset.column_names:
            input_col = col
            break

    for col in possible_output_cols:
        if col in dataset.column_names:
            output_col = col
            break

    # Handle messages format
    if 'messages' in dataset.column_names:
        print("📋 Detected messages format, extracting prompts and responses...")
        def extract_from_messages(example):
            messages = example['messages']
            if isinstance(messages, list) and len(messages) >= 2:
                # Find the last user message and assistant response
                user_msg = None
                assistant_msg = None
                for msg in messages:
                    if msg.get('role') == 'user':
                        user_msg = msg.get('content', '')
                    elif msg.get('role') == 'assistant':
                        assistant_msg = msg.get('content', '')

                return {
                    'input_text': user_msg or '',
                    'target_text': assistant_msg or ''
                }
            return {'input_text': '', 'target_text': ''}

        dataset = dataset.map(extract_from_messages)
        input_col = 'input_text'
        output_col = 'target_text'

    if not input_col or not output_col:
        print(f"❌ Could not identify input/output columns. Available: {dataset.column_names}")
        return

    print(f"✅ Using input column: {input_col}, output column: {output_col}")

    print(f"🤖 Loading model: {model_name}")
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto",
            trust_remote_code=True
        )

        # Set pad token if not set
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        print("✅ Model loaded successfully")
    except Exception as e:
        print(f"❌ Error loading model: {e}")
        return

    # Create text generation pipeline
    generator = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.float16,
        device_map="auto"
    )

    # Limit evaluation to reasonable size for demonstration
    eval_size = min(100, len(dataset))
    eval_dataset = dataset.select(range(eval_size))
    print(f"📊 Evaluating on {eval_size} samples...")

    results = []

    for i, example in enumerate(eval_dataset):
        if i % 10 == 0:
            print(f"📈 Processing sample {i+1}/{eval_size}")

        input_text = example[input_col]
        target_text = example[output_col]

        if not input_text or not target_text:
            continue

        # Generate prediction
        try:
            # Format prompt appropriately
            if not input_text.strip().endswith(('?', '.', '!', ':')):
                formatted_prompt = f"{input_text.strip()}:"
            else:
                formatted_prompt = input_text.strip()

            generated = generator(
                formatted_prompt,
                max_new_tokens=256,
                do_sample=False,  # Deterministic for evaluation
                temperature=0.1,
                pad_token_id=tokenizer.eos_token_id,
                return_full_text=False
            )

            prediction = generated[0]['generated_text'].strip()

            # Compute metrics
            exact_match = compute_exact_match(prediction, target_text)
            f1 = compute_f1_score(prediction, target_text)
            rouge_l = compute_rouge_l(prediction, target_text)

            # Error analysis
            error_type = "correct" if exact_match == 1.0 else "incorrect"
            if exact_match == 0 and f1 > 0.5:
                error_type = "partial_match"
            elif exact_match == 0 and rouge_l > 0.3:
                error_type = "semantic_similarity"
            elif len(prediction.split()) > len(target_text.split()) * 2:
                error_type = "too_verbose"
            elif len(prediction.split()) < len(target_text.split()) * 0.5:
                error_type = "too_brief"

            result = {
                'sample_id': i,
                'input': input_text,
                'target': target_text,
                'prediction': prediction,
                'exact_match': exact_match,
                'f1_score': f1,
                'rouge_l': rouge_l,
                'error_type': error_type,
                'input_length': len(input_text.split()),
                'target_length': len(target_text.split()),
                'prediction_length': len(prediction.split())
            }

            results.append(result)

        except Exception as e:
            print(f"⚠️  Error processing sample {i}: {e}")
            continue

    if not results:
        print("❌ No results generated")
        return

    # Compute summary statistics
    df_results = pd.DataFrame(results)

    summary_metrics = {
        'evaluation_timestamp': datetime.now().isoformat(),
        'model_name': model_name,
        'dataset_name': dataset_name,
        'total_samples': len(results),
        'exact_match_avg': df_results['exact_match'].mean(),
        'f1_score_avg': df_results['f1_score'].mean(),
        'rouge_l_avg': df_results['rouge_l'].mean(),
        'exact_match_std': df_results['exact_match'].std(),
        'f1_score_std': df_results['f1_score'].std(),
        'rouge_l_std': df_results['rouge_l'].std(),
        'perfect_matches': int(df_results['exact_match'].sum()),
        'perfect_match_rate': df_results['exact_match'].mean()
    }

    # Error analysis summary
    error_analysis = df_results['error_type'].value_counts().to_dict()
    summary_metrics['error_breakdown'] = error_analysis

    # Performance by length buckets
    df_results['target_length_bucket'] = pd.cut(
        df_results['target_length'],
        bins=[0, 10, 25, 50, 100, float('inf')],
        labels=['very_short', 'short', 'medium', 'long', 'very_long']
    )

    length_performance = df_results.groupby('target_length_bucket')[['exact_match', 'f1_score', 'rouge_l']].mean().to_dict()
    summary_metrics['performance_by_length'] = length_performance

    print("\n📊 EVALUATION RESULTS:")
    print(f"Total Samples: {summary_metrics['total_samples']}")
    print(f"Exact Match: {summary_metrics['exact_match_avg']:.4f} ± {summary_metrics['exact_match_std']:.4f}")
    print(f"F1 Score: {summary_metrics['f1_score_avg']:.4f} ± {summary_metrics['f1_score_std']:.4f}")
    print(f"ROUGE-L: {summary_metrics['rouge_l_avg']:.4f} ± {summary_metrics['rouge_l_std']:.4f}")
    print(f"Perfect Matches: {summary_metrics['perfect_matches']}/{summary_metrics['total_samples']} ({summary_metrics['perfect_match_rate']:.2%})")

    print("\n🔍 Error Breakdown:")
    for error_type, count in error_analysis.items():
        print(f"  {error_type}: {count} ({count/len(results):.2%})")

    # Save results locally first
    os.makedirs('eval_results', exist_ok=True)

    # Save detailed results
    df_results.to_csv('eval_results/detailed_results.csv', index=False)

    # Save summary metrics
    with open('eval_results/summary_metrics.json', 'w') as f:
        json.dump(summary_metrics, f, indent=2, default=str)

    # Save top errors for analysis
    worst_samples = df_results.nsmallest(10, 'f1_score')[['sample_id', 'input', 'target', 'prediction', 'f1_score', 'error_type']]
    worst_samples.to_csv('eval_results/worst_predictions.csv', index=False)

    # Save best samples
    best_samples = df_results.nlargest(10, 'f1_score')[['sample_id', 'input', 'target', 'prediction', 'f1_score', 'error_type']]
    best_samples.to_csv('eval_results/best_predictions.csv', index=False)

    print("\n💾 Results saved locally to eval_results/")

    # Upload results to model repository
    try:
        print("🚀 Uploading results to model repository...")
        api = HfApi()

        # Upload all result files
        files_to_upload = [
            ('eval_results/summary_metrics.json', 'eval_results/summary_metrics.json'),
            ('eval_results/detailed_results.csv', 'eval_results/detailed_results.csv'),
            ('eval_results/worst_predictions.csv', 'eval_results/worst_predictions.csv'),
            ('eval_results/best_predictions.csv', 'eval_results/best_predictions.csv')
        ]

        for local_path, repo_path in files_to_upload:
            api.upload_file(
                path_or_fileobj=local_path,
                path_in_repo=repo_path,
                repo_id=model_name,
                commit_message=f"Add evaluation results - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
            )
            print(f"✅ Uploaded {repo_path}")

        print(f"✅ All evaluation results uploaded to {model_name}")

        # Log to Trackio
        trackio.log({
            "exact_match": summary_metrics['exact_match_avg'],
            "f1_score": summary_metrics['f1_score_avg'],
            "rouge_l": summary_metrics['rouge_l_avg'],
            "perfect_match_rate": summary_metrics['perfect_match_rate'],
            "total_samples": summary_metrics['total_samples']
        })

    except Exception as e:
        print(f"⚠️  Warning: Could not upload to repository: {e}")
        print("💾 Results are saved locally in eval_results/ directory")

    print("\n🎉 Evaluation completed successfully!")
    return summary_metrics

if __name__ == "__main__":
    evaluate_model()