Datasets:

nvidia
/

ChatRAG-Hi

#!/usr/bin/env python3
"""
This script provides a unified interface to:
1. Run inference for all datasets using HuggingFace models
2. Evaluate all predictions and generate scores
"""

import os
import sys
import argparse
import subprocess
from typing import List, Optional
import pandas as pd

ALL_DATASETS = [
    'doc2dial', 'quac', 'qrecc', 'inscit',
    'hybridial', 
    'doqa_cooking', 'doqa_travel', 'doqa_movies', 
    'convfinqa'
]

def run_inference_for_dataset(
    model_id: str,
    dataset: str,
    data_folder: str,
    output_folder: str,
    device: str = 'cuda',
    num_ctx: int = 5,
    max_tokens: int = 64,
    expected_samples: int = 500,
    limit: Optional[int] = None
) -> bool:
    """
    Run inference for a single dataset
    
    Args:
        model_id: Model identifier or path
        dataset: Dataset name
        data_folder: Path to data folder
        output_folder: Path to output folder
        device: Device to run on (cuda/cpu)
        num_ctx: Number of contexts
        max_tokens: Maximum number of tokens to generate
        expected_samples: Expected number of samples
        limit: Limit number of samples to process
        
    Returns:
        bool: True if successful, False otherwise
    """
    print(f"\n{'='*80}")
    print(f"Running inference for dataset: {dataset}")
    print(f"{'='*80}\n")
    
    cmd = [
        'python', 'run_generation_hf.py',
        '--model-id', model_id,
        '--data-folder', data_folder,
        '--output-folder', output_folder,
        '--eval-dataset', dataset,
        '--device', device,
        '--num-ctx', str(num_ctx),
        '--max-tokens', str(max_tokens),
        '--expected-samples', str(expected_samples)
    ]
    
    if limit is not None:
        cmd.extend(['--limit', str(limit)])
    
    try:
        result = subprocess.run(cmd, check=True, capture_output=False, text=True)
        print(f"✓ Inference completed for {dataset}")
        return True
    except subprocess.CalledProcessError as e:
        print(f"✗ Error running inference for {dataset}: {e}")
        return False
    except Exception as e:
        print(f"✗ Unexpected error for {dataset}: {e}")
        return False


def run_inference_for_all_datasets(
    model_id: str,
    datasets: List[str],
    data_folder: str,
    output_folder: str,
    device: str = 'cuda',
    num_ctx: int = 5,
    max_tokens: int = 64,
    expected_samples: int = 500,
    limit: Optional[int] = None
) -> dict:
    """
    Run inference for all specified datasets
    
    Args:
        model_id: Model identifier or path
        datasets: List of dataset names
        data_folder: Path to data folder
        output_folder: Path to output folder
        device: Device to run on (cuda/cpu)
        num_ctx: Number of contexts
        max_tokens: Maximum number of tokens to generate
        expected_samples: Expected number of samples
        limit: Limit number of samples to process
        
    Returns:
        dict: Dictionary mapping dataset names to success status
    """
    print(f"\n{'#'*80}")
    print(f"# Running Inference for Model: {model_id}")
    print(f"# Total Datasets: {len(datasets)}")
    print(f"{'#'*80}\n")
    
    results = {}
    for dataset in datasets:
        success = run_inference_for_dataset(
            model_id=model_id,
            dataset=dataset,
            data_folder=data_folder,
            output_folder=output_folder,
            device=device,
            num_ctx=num_ctx,
            max_tokens=max_tokens,
            expected_samples=expected_samples,
            limit=limit
        )
        results[dataset] = success
    
    # Print summary
    print(f"\n{'='*80}")
    print("Inference Summary:")
    print(f"{'='*80}")
    successful = sum(1 for v in results.values() if v)
    print(f"✓ Successful: {successful}/{len(datasets)}")
    print(f"✗ Failed: {len(datasets) - successful}/{len(datasets)}")
    
    if successful < len(datasets):
        print("\nFailed datasets:")
        for dataset, success in results.items():
            if not success:
                print(f"  - {dataset}")
    
    return results


def run_evaluation(
    results_dir: str,
    data_path: str,
    datasets: List[str],
    output_csv: Optional[str] = None
) -> pd.DataFrame:
    """
    Run evaluation for all models and datasets
    
    Args:
        results_dir: Directory containing model results
        data_path: Path to ground truth data
        datasets: List of dataset names to evaluate
        output_csv: Path to output CSV file
        
    Returns:
        pd.DataFrame: Evaluation results
    """
    print(f"\n{'#'*80}")
    print(f"# Running Evaluation")
    print(f"# Results Directory: {results_dir}")
    print(f"# Data Path: {data_path}")
    print(f"{'#'*80}\n")
    
    cmd = [
        'python', 'get_scores.py',
        '--results-dir', results_dir,
        '--data-path', data_path,
        '--datasets'
    ] + datasets
    
    if output_csv:
        cmd.extend(['--output-csv', output_csv])
    
    try:
        result = subprocess.run(cmd, check=True, capture_output=False, text=True)
        print(f"\n✓ Evaluation completed successfully")
        
        # Load and return the results
        if output_csv:
            csv_path = output_csv
        else:
            csv_path = os.path.join(results_dir, 'scores.csv')
        
        if os.path.exists(csv_path):
            df = pd.read_csv(csv_path)
            return df
        else:
            print(f"Warning: Output CSV not found at {csv_path}")
            return pd.DataFrame()
            
    except subprocess.CalledProcessError as e:
        print(f"✗ Error running evaluation: {e}")
        return pd.DataFrame()
    except Exception as e:
        print(f"✗ Unexpected error during evaluation: {e}")
        return pd.DataFrame()

def run_full_pipeline(
    model_id: str,
    data_folder: str,
    output_folder: str,
    datasets: List[str] = ALL_DATASETS,
    device: str = 'cuda',
    num_ctx: int = 5,
    max_tokens: int = 64,
    expected_samples: int = 500,
    limit: Optional[int] = None,
    skip_inference: bool = False,
    skip_evaluation: bool = False,
    output_csv: Optional[str] = None
) -> pd.DataFrame:
    """
    Run the complete pipeline: inference + evaluation
    
    Args:
        model_id: Model identifier or path
        data_folder: Path to data folder
        output_folder: Path to output folder
        datasets: List of dataset names
        device: Device to run on (cuda/cpu)
        num_ctx: Number of contexts
        max_tokens: Maximum number of tokens to generate
        expected_samples: Expected number of samples
        limit: Limit number of samples to process
        skip_inference: Skip inference step
        skip_evaluation: Skip evaluation step
        output_csv: Path to output CSV file
        
    Returns:
        pd.DataFrame: Evaluation results
    """
    print(f"\n{'#'*80}")
    print(f"# ChatRAG-Hi Full Evaluation Pipeline")
    print(f"{'#'*80}\n")
    print(f"Model: {model_id}")
    print(f"Datasets: {', '.join(datasets)}")
    print(f"Device: {device}")
    print(f"Skip Inference: {skip_inference}")
    print(f"Skip Evaluation: {skip_evaluation}")
    
    # Step 1: Run inference
    if not skip_inference:
        inference_results = run_inference_for_all_datasets(
            model_id=model_id,
            datasets=datasets,
            data_folder=data_folder,
            output_folder=output_folder,
            device=device,
            num_ctx=num_ctx,
            max_tokens=max_tokens,
            expected_samples=expected_samples,
            limit=limit
        )
    else:
        print("\n⊘ Skipping inference step")
    
    # Step 2: Run evaluation
    if not skip_evaluation:
        eval_results = run_evaluation(
            results_dir=output_folder,
            data_path=data_folder,
            datasets=datasets,
            output_csv=output_csv
        )
        return eval_results
    else:
        print("\n⊘ Skipping evaluation step")
        return pd.DataFrame()

def get_args():
    """Parse command line arguments"""
    parser = argparse.ArgumentParser(
        description="Comprehensive wrapper for ChatRAG-Hi inference and evaluation"
    )
    
    # Pipeline control
    parser.add_argument('--mode', type=str, choices=['inference', 'evaluation', 'full'],
                        default='full',
                        help='Pipeline mode: inference only, evaluation only, or full pipeline')
    
    # Model configuration
    parser.add_argument('--model-id', type=str, required=True,
                        help='Model identifier or path')
    
    # Data paths
    parser.add_argument('--data-folder', type=str, required=True,
                        help='Path to data folder containing ground truth JSON files')
    parser.add_argument('--output-folder', type=str, required=True,
                        help='Path to output folder for predictions and scores')
    
    # Dataset selection
    parser.add_argument('--datasets', type=str, nargs='+',
                        default=ALL_DATASETS,
                        help='List of datasets to process')
    parser.add_argument('--all-datasets', action='store_true',
                        help='Process all available datasets')
    
    # Inference parameters
    parser.add_argument('--device', type=str, default='cuda',
                        help='Device to run on: cpu or cuda')
    parser.add_argument('--num-ctx', type=int, default=5,
                        help='Number of contexts')
    parser.add_argument('--max-tokens', type=int, default=64,
                        help='Maximum number of tokens to generate')
    parser.add_argument('--expected-samples', type=int, default=500,
                        help='Expected number of samples per dataset')
    parser.add_argument('--limit', type=int, default=None,
                        help='Limit number of samples to process (for testing)')
    
    # Output options
    parser.add_argument('--output-csv', type=str, default=None,
                        help='Path to output CSV file for scores')
    
    args = parser.parse_args()
    
    # Use all datasets if specified
    if args.all_datasets:
        args.datasets = ALL_DATASETS
    
    return args


def main():
    """Main entry point"""
    args = get_args()
    
    # Create output directory if it doesn't exist
    os.makedirs(args.output_folder, exist_ok=True)
    
    # Determine what to skip based on mode
    skip_inference = (args.mode == 'evaluation')
    skip_evaluation = (args.mode == 'inference')
    
    # Run the pipeline
    results = run_full_pipeline(
        model_id=args.model_id,
        data_folder=args.data_folder,
        output_folder=args.output_folder,
        datasets=args.datasets,
        device=args.device,
        num_ctx=args.num_ctx,
        max_tokens=args.max_tokens,
        expected_samples=args.expected_samples,
        limit=args.limit,
        skip_inference=skip_inference,
        skip_evaluation=skip_evaluation,
        output_csv=args.output_csv
    )
    
    if not results.empty and args.mode != 'inference':
        print(f"\n{'='*80}")
        print("Final Evaluation Results:")
        print(f"{'='*80}\n")
        print(results.to_string(index=False))
        print(f"\n{'='*80}\n")


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""
	This script provides a unified interface to:
	1. Run inference for all datasets using HuggingFace models
	2. Evaluate all predictions and generate scores
	"""

	import os
	import sys
	import argparse
	import subprocess
	from typing import List, Optional
	import pandas as pd

	ALL_DATASETS = [
	'doc2dial', 'quac', 'qrecc', 'inscit',
	'hybridial',
	'doqa_cooking', 'doqa_travel', 'doqa_movies',
	'convfinqa'
	]

	def run_inference_for_dataset(
	model_id: str,
	dataset: str,
	data_folder: str,
	output_folder: str,
	device: str = 'cuda',
	num_ctx: int = 5,
	max_tokens: int = 64,
	expected_samples: int = 500,
	limit: Optional[int] = None
	) -> bool:
	"""
	Run inference for a single dataset

	Args:
	model_id: Model identifier or path
	dataset: Dataset name
	data_folder: Path to data folder
	output_folder: Path to output folder
	device: Device to run on (cuda/cpu)
	num_ctx: Number of contexts
	max_tokens: Maximum number of tokens to generate
	expected_samples: Expected number of samples
	limit: Limit number of samples to process

	Returns:
	bool: True if successful, False otherwise
	"""
	print(f"\n{'='*80}")
	print(f"Running inference for dataset: {dataset}")
	print(f"{'='*80}\n")

	cmd = [
	'python', 'run_generation_hf.py',
	'--model-id', model_id,
	'--data-folder', data_folder,
	'--output-folder', output_folder,
	'--eval-dataset', dataset,
	'--device', device,
	'--num-ctx', str(num_ctx),
	'--max-tokens', str(max_tokens),
	'--expected-samples', str(expected_samples)
	]

	if limit is not None:
	cmd.extend(['--limit', str(limit)])

	try:
	result = subprocess.run(cmd, check=True, capture_output=False, text=True)
	print(f"✓ Inference completed for {dataset}")
	return True
	except subprocess.CalledProcessError as e:
	print(f"✗ Error running inference for {dataset}: {e}")
	return False
	except Exception as e:
	print(f"✗ Unexpected error for {dataset}: {e}")
	return False


	def run_inference_for_all_datasets(
	model_id: str,
	datasets: List[str],
	data_folder: str,
	output_folder: str,
	device: str = 'cuda',
	num_ctx: int = 5,
	max_tokens: int = 64,
	expected_samples: int = 500,
	limit: Optional[int] = None
	) -> dict:
	"""
	Run inference for all specified datasets

	Args:
	model_id: Model identifier or path
	datasets: List of dataset names
	data_folder: Path to data folder
	output_folder: Path to output folder
	device: Device to run on (cuda/cpu)
	num_ctx: Number of contexts
	max_tokens: Maximum number of tokens to generate
	expected_samples: Expected number of samples
	limit: Limit number of samples to process

	Returns:
	dict: Dictionary mapping dataset names to success status
	"""
	print(f"\n{'#'*80}")
	print(f"# Running Inference for Model: {model_id}")
	print(f"# Total Datasets: {len(datasets)}")
	print(f"{'#'*80}\n")

	results = {}
	for dataset in datasets:
	success = run_inference_for_dataset(
	model_id=model_id,
	dataset=dataset,
	data_folder=data_folder,
	output_folder=output_folder,
	device=device,
	num_ctx=num_ctx,
	max_tokens=max_tokens,
	expected_samples=expected_samples,
	limit=limit
	)
	results[dataset] = success

	# Print summary
	print(f"\n{'='*80}")
	print("Inference Summary:")
	print(f"{'='*80}")
	successful = sum(1 for v in results.values() if v)
	print(f"✓ Successful: {successful}/{len(datasets)}")
	print(f"✗ Failed: {len(datasets) - successful}/{len(datasets)}")

	if successful < len(datasets):
	print("\nFailed datasets:")
	for dataset, success in results.items():
	if not success:
	print(f" - {dataset}")

	return results


	def run_evaluation(
	results_dir: str,
	data_path: str,
	datasets: List[str],
	output_csv: Optional[str] = None
	) -> pd.DataFrame:
	"""
	Run evaluation for all models and datasets

	Args:
	results_dir: Directory containing model results
	data_path: Path to ground truth data
	datasets: List of dataset names to evaluate
	output_csv: Path to output CSV file

	Returns:
	pd.DataFrame: Evaluation results
	"""
	print(f"\n{'#'*80}")
	print(f"# Running Evaluation")
	print(f"# Results Directory: {results_dir}")
	print(f"# Data Path: {data_path}")
	print(f"{'#'*80}\n")

	cmd = [
	'python', 'get_scores.py',
	'--results-dir', results_dir,
	'--data-path', data_path,
	'--datasets'
	] + datasets

	if output_csv:
	cmd.extend(['--output-csv', output_csv])

	try:
	result = subprocess.run(cmd, check=True, capture_output=False, text=True)
	print(f"\n✓ Evaluation completed successfully")

	# Load and return the results
	if output_csv:
	csv_path = output_csv
	else:
	csv_path = os.path.join(results_dir, 'scores.csv')

	if os.path.exists(csv_path):
	df = pd.read_csv(csv_path)
	return df
	else:
	print(f"Warning: Output CSV not found at {csv_path}")
	return pd.DataFrame()

	except subprocess.CalledProcessError as e:
	print(f"✗ Error running evaluation: {e}")
	return pd.DataFrame()
	except Exception as e:
	print(f"✗ Unexpected error during evaluation: {e}")
	return pd.DataFrame()

	def run_full_pipeline(
	model_id: str,
	data_folder: str,
	output_folder: str,
	datasets: List[str] = ALL_DATASETS,
	device: str = 'cuda',
	num_ctx: int = 5,
	max_tokens: int = 64,
	expected_samples: int = 500,
	limit: Optional[int] = None,
	skip_inference: bool = False,
	skip_evaluation: bool = False,
	output_csv: Optional[str] = None
	) -> pd.DataFrame:
	"""
	Run the complete pipeline: inference + evaluation

	Args:
	model_id: Model identifier or path
	data_folder: Path to data folder
	output_folder: Path to output folder
	datasets: List of dataset names
	device: Device to run on (cuda/cpu)
	num_ctx: Number of contexts
	max_tokens: Maximum number of tokens to generate
	expected_samples: Expected number of samples
	limit: Limit number of samples to process
	skip_inference: Skip inference step
	skip_evaluation: Skip evaluation step
	output_csv: Path to output CSV file

	Returns:
	pd.DataFrame: Evaluation results
	"""
	print(f"\n{'#'*80}")
	print(f"# ChatRAG-Hi Full Evaluation Pipeline")
	print(f"{'#'*80}\n")
	print(f"Model: {model_id}")
	print(f"Datasets: {', '.join(datasets)}")
	print(f"Device: {device}")
	print(f"Skip Inference: {skip_inference}")
	print(f"Skip Evaluation: {skip_evaluation}")

	# Step 1: Run inference
	if not skip_inference:
	inference_results = run_inference_for_all_datasets(
	model_id=model_id,
	datasets=datasets,
	data_folder=data_folder,
	output_folder=output_folder,
	device=device,
	num_ctx=num_ctx,
	max_tokens=max_tokens,
	expected_samples=expected_samples,
	limit=limit
	)
	else:
	print("\n⊘ Skipping inference step")

	# Step 2: Run evaluation
	if not skip_evaluation:
	eval_results = run_evaluation(
	results_dir=output_folder,
	data_path=data_folder,
	datasets=datasets,
	output_csv=output_csv
	)
	return eval_results
	else:
	print("\n⊘ Skipping evaluation step")
	return pd.DataFrame()

	def get_args():
	"""Parse command line arguments"""
	parser = argparse.ArgumentParser(
	description="Comprehensive wrapper for ChatRAG-Hi inference and evaluation"
	)

	# Pipeline control
	parser.add_argument('--mode', type=str, choices=['inference', 'evaluation', 'full'],
	default='full',
	help='Pipeline mode: inference only, evaluation only, or full pipeline')

	# Model configuration
	parser.add_argument('--model-id', type=str, required=True,
	help='Model identifier or path')

	# Data paths
	parser.add_argument('--data-folder', type=str, required=True,
	help='Path to data folder containing ground truth JSON files')
	parser.add_argument('--output-folder', type=str, required=True,
	help='Path to output folder for predictions and scores')

	# Dataset selection
	parser.add_argument('--datasets', type=str, nargs='+',
	default=ALL_DATASETS,
	help='List of datasets to process')
	parser.add_argument('--all-datasets', action='store_true',
	help='Process all available datasets')

	# Inference parameters
	parser.add_argument('--device', type=str, default='cuda',
	help='Device to run on: cpu or cuda')
	parser.add_argument('--num-ctx', type=int, default=5,
	help='Number of contexts')
	parser.add_argument('--max-tokens', type=int, default=64,
	help='Maximum number of tokens to generate')
	parser.add_argument('--expected-samples', type=int, default=500,
	help='Expected number of samples per dataset')
	parser.add_argument('--limit', type=int, default=None,
	help='Limit number of samples to process (for testing)')

	# Output options
	parser.add_argument('--output-csv', type=str, default=None,
	help='Path to output CSV file for scores')

	args = parser.parse_args()

	# Use all datasets if specified
	if args.all_datasets:
	args.datasets = ALL_DATASETS

	return args


	def main():
	"""Main entry point"""
	args = get_args()

	# Create output directory if it doesn't exist
	os.makedirs(args.output_folder, exist_ok=True)

	# Determine what to skip based on mode
	skip_inference = (args.mode == 'evaluation')
	skip_evaluation = (args.mode == 'inference')

	# Run the pipeline
	results = run_full_pipeline(
	model_id=args.model_id,
	data_folder=args.data_folder,
	output_folder=args.output_folder,
	datasets=args.datasets,
	device=args.device,
	num_ctx=args.num_ctx,
	max_tokens=args.max_tokens,
	expected_samples=args.expected_samples,
	limit=args.limit,
	skip_inference=skip_inference,
	skip_evaluation=skip_evaluation,
	output_csv=args.output_csv
	)

	if not results.empty and args.mode != 'inference':
	print(f"\n{'='*80}")
	print("Final Evaluation Results:")
	print(f"{'='*80}\n")
	print(results.to_string(index=False))
	print(f"\n{'='*80}\n")


	if __name__ == "__main__":
	main()