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#!/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()
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