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"""
Main Integration Module for Agent Tuning Optimization Framework
This module provides functionality for integrating all components of the framework
and running end-to-end experiments.
"""
import os
import json
import argparse
from typing import List, Dict, Any, Union, Optional, Tuple
from models.llm_interface import LLMInterface
from data.trajectory_data import Trajectory, TrajectoryDataset, create_synthetic_dataset
from training.negative_samples import create_negative_sample_generator
from training.synthetic_trajectories import create_synthetic_trajectory_generator
from training.agent_tuner import create_agent_tuner
from evaluation.evaluators import create_agent_evaluator
def run_experiment(
experiment_config: Dict[str, Any],
output_dir: str
) -> Dict[str, Any]:
"""
Run an end-to-end experiment with the framework.
Args:
experiment_config: Experiment configuration
output_dir: Directory to save results
Returns:
Dictionary of experiment results
"""
print(f"Starting experiment: {experiment_config['name']}")
# Create output directory
os.makedirs(output_dir, exist_ok=True)
# Save experiment configuration
with open(f"{output_dir}/experiment_config.json", "w") as f:
json.dump(experiment_config, f, indent=2)
# Initialize LLM interface
print("Initializing LLM interface...")
llm_config = experiment_config.get("llm", {})
llm_interface = LLMInterface(
model_name=llm_config.get("model_name", "gpt2"),
model_type=llm_config.get("model_type", "causal"),
device=llm_config.get("device", "cpu"),
max_length=llm_config.get("max_length", 512),
temperature=llm_config.get("temperature", 0.7)
)
# Load or create dataset
print("Preparing dataset...")
dataset_config = experiment_config.get("dataset", {})
if dataset_config.get("path"):
# Load existing dataset
dataset = TrajectoryDataset(dataset_config.get("name", "experiment_dataset"))
dataset.load_from_json(dataset_config["path"])
else:
# Create synthetic dataset
dataset = create_synthetic_dataset(dataset_config.get("num_trajectories", 20))
print(f"Dataset loaded with {len(dataset.trajectories)} trajectories")
# Generate negative samples
print("Generating negative samples...")
negative_config = experiment_config.get("negative_samples", {})
if negative_config.get("enabled", True):
negative_generator = create_negative_sample_generator(
negative_config.get("method", "response_degradation")
)
positive_trajectories = dataset.get_trajectories(positive_only=True)
negative_trajectories = negative_generator.batch_generate(
positive_trajectories,
**negative_config.get("params", {})
)
# Add negative trajectories to dataset
for trajectory in negative_trajectories:
dataset.add_trajectory(trajectory)
print(f"Added {len(negative_trajectories)} negative trajectories")
# Generate synthetic trajectories
print("Generating synthetic trajectories...")
synthetic_config = experiment_config.get("synthetic_trajectories", {})
if synthetic_config.get("enabled", True):
synthetic_generator = create_synthetic_trajectory_generator(
synthetic_config.get("method", "template"),
llm_interface if synthetic_config.get("method") in ["llm", "hybrid"] else None
)
# Generate from task descriptions
task_descriptions = [t.task_description for t in dataset.get_trajectories(positive_only=True)]
task_descriptions = list(set(task_descriptions)) # Remove duplicates
synthetic_trajectories = synthetic_generator.batch_generate(
task_descriptions,
**synthetic_config.get("params", {})
)
# Add synthetic trajectories to dataset
for trajectory in synthetic_trajectories:
dataset.add_trajectory(trajectory)
print(f"Added {len(synthetic_trajectories)} synthetic trajectories")
# Save the enhanced dataset
dataset.save_to_json(f"{output_dir}/enhanced_dataset.json")
# Analyze dataset
dataset_stats = dataset.analyze_dataset()
with open(f"{output_dir}/dataset_stats.json", "w") as f:
json.dump(dataset_stats, f, indent=2)
# Split dataset for training and evaluation
all_trajectories = dataset.get_trajectories()
split_idx = int(len(all_trajectories) * 0.8) # 80% for training
train_trajectories = all_trajectories[:split_idx]
eval_trajectories = all_trajectories[split_idx:]
print(f"Split dataset: {len(train_trajectories)} for training, {len(eval_trajectories)} for evaluation")
# Tune agent
print("Tuning agent...")
tuning_config = experiment_config.get("tuning", {})
tuner = create_agent_tuner(tuning_config.get("method", "supervised"))
tuned_model, tuning_metrics = tuner.tune(
model_name=llm_config.get("model_name", "gpt2"),
trajectories=train_trajectories,
output_dir=f"{output_dir}/tuned_model",
**tuning_config.get("params", {})
)
# Save tuning metrics
with open(f"{output_dir}/tuning_metrics.json", "w") as f:
# Convert any non-serializable values to strings
serializable_metrics = {}
for k, v in tuning_metrics.items():
if isinstance(v, (int, float, str, bool, list, dict)) or v is None:
serializable_metrics[k] = v
else:
serializable_metrics[k] = str(v)
json.dump(serializable_metrics, f, indent=2)
# Create tuned model interface
tuned_llm_interface = LLMInterface(
model_name=f"{output_dir}/tuned_model",
model_type=llm_config.get("model_type", "causal"),
device=llm_config.get("device", "cpu"),
max_length=llm_config.get("max_length", 512),
temperature=llm_config.get("temperature", 0.7)
)
# Evaluate agent
print("Evaluating agent...")
eval_config = experiment_config.get("evaluation", {})
evaluator = create_agent_evaluator(eval_config.get("method", "quality"))
eval_results = evaluator.evaluate(
llm_interface=tuned_llm_interface,
test_trajectories=eval_trajectories,
**eval_config.get("params", {})
)
# Visualize evaluation results
evaluator.visualize_results(
results=eval_results,
output_dir=f"{output_dir}/evaluation"
)
# Save evaluation results
with open(f"{output_dir}/evaluation_results.json", "w") as f:
# Create a simplified version without large data
simplified_results = {}
if "aggregated" in eval_results:
simplified_results["aggregated"] = eval_results["aggregated"]
if "metrics" in eval_results:
# Include only essential metrics
simplified_results["metrics"] = [
{k: v for k, v in m.items() if k not in ["generated_responses"]}
for m in eval_results["metrics"]
]
json.dump(simplified_results, f, indent=2)
# Comparative evaluation (if configured)
if eval_config.get("comparative", {}).get("enabled", False):
print("Performing comparative evaluation...")
# Create baseline model interface
baseline_llm_interface = LLMInterface(
model_name=llm_config.get("model_name", "gpt2"),
model_type=llm_config.get("model_type", "causal"),
device=llm_config.get("device", "cpu"),
max_length=llm_config.get("max_length", 512),
temperature=llm_config.get("temperature", 0.7)
)
# Create comparative evaluator
comparative_evaluator = create_agent_evaluator("comparative")
# Evaluate and compare
comparative_results = comparative_evaluator.evaluate(
llm_interfaces={
"baseline": baseline_llm_interface,
"tuned": tuned_llm_interface
},
test_trajectories=eval_trajectories,
**eval_config.get("comparative", {}).get("params", {})
)
# Visualize comparative results
comparative_evaluator.visualize_results(
results=comparative_results,
output_dir=f"{output_dir}/comparative"
)
# Save comparative results
with open(f"{output_dir}/comparative_results.json", "w") as f:
# Create a simplified version
simplified_comparative = {
"comparative": comparative_results.get("comparative", {})
}
json.dump(simplified_comparative, f, indent=2)
print(f"Experiment completed. Results saved to {output_dir}")
return {
"dataset_stats": dataset_stats,
"tuning_metrics": tuning_metrics,
"evaluation_results": eval_results
}
def main():
"""Main function for running the framework from command line."""
parser = argparse.ArgumentParser(description="Agent Tuning Optimization Framework")
parser.add_argument("--config", type=str, required=True, help="Path to experiment configuration file")
parser.add_argument("--output", type=str, default="./experiment_results", help="Directory to save results")
args = parser.parse_args()
# Load experiment configuration
with open(args.config, "r") as f:
experiment_config = json.load(f)
# Run experiment
run_experiment(experiment_config, args.output)
if __name__ == "__main__":
main()
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