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Experiment tracking and logging infrastructure.
Tracks:
- Run metadata (config, environment, hardware)
- Training/validation/test metrics
- Checkpoint locations
- Results summaries
"""
from __future__ import annotations
import json
import logging
from dataclasses import asdict, dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Any
import platform
import torch
log = logging.getLogger(__name__)
@dataclass
class ExperimentMetadata:
"""Metadata for an experiment run."""
run_id: str
timestamp: str
model_name: str
dataset: str = "ABIDE"
split_strategy: str = "site_holdout"
notes: str = ""
# Environment
python_version: str = ""
pytorch_version: str = ""
device: str = ""
num_gpus: int = 0
# Hyperparameters
hyperparameters: dict[str, Any] = field(default_factory=dict)
# Results
test_metrics: dict[str, float] = field(default_factory=dict)
checkpoint_path: str = ""
def to_dict(self) -> dict:
"""Convert to dictionary."""
return asdict(self)
def to_json(self) -> str:
"""Convert to JSON string."""
return json.dumps(self.to_dict(), indent=2)
@classmethod
def from_args(
cls,
run_id: str,
args,
notes: str = "",
) -> ExperimentMetadata:
"""Create metadata from training arguments.
Parameters
----------
run_id : str
Unique run identifier.
args : argparse.Namespace
Training arguments.
notes : str, optional
Additional notes.
Returns
-------
ExperimentMetadata
Metadata object.
"""
hyperparams = {
"hidden_dim": getattr(args, "hidden_dim", None),
"dropout": getattr(args, "dropout", None),
"lr": getattr(args, "lr", None),
"weight_decay": getattr(args, "weight_decay", None),
"batch_size": getattr(args, "batch_size", None),
"max_epochs": getattr(args, "max_epochs", None),
"drop_edge_p": getattr(args, "drop_edge_p", None),
"bold_noise_std": getattr(args, "bold_noise_std", None),
}
return cls(
run_id=run_id,
timestamp=datetime.now().isoformat(),
model_name=getattr(args, "model_name", "unknown"),
split_strategy=getattr(args, "split_strategy", "site_holdout"),
notes=notes,
python_version=platform.python_version(),
pytorch_version=torch.__version__,
device=str(torch.device("cuda" if torch.cuda.is_available() else "cpu")),
num_gpus=torch.cuda.device_count(),
hyperparameters=hyperparams,
)
class ExperimentTracker:
"""Tracks and logs experiment runs."""
def __init__(self, output_dir: str | Path = "experiments"):
"""Initialize tracker.
Parameters
----------
output_dir : str or Path
Directory to save experiment logs.
"""
self.output_dir = Path(output_dir)
self.output_dir.mkdir(parents=True, exist_ok=True)
self.metadata_list: list[ExperimentMetadata] = []
def add_run(
self,
metadata: ExperimentMetadata,
) -> None:
"""Record a completed run.
Parameters
----------
metadata : ExperimentMetadata
Run metadata.
"""
self.metadata_list.append(metadata)
self._save_run(metadata)
def _save_run(self, metadata: ExperimentMetadata) -> None:
"""Save individual run to JSON."""
run_dir = self.output_dir / metadata.run_id
run_dir.mkdir(parents=True, exist_ok=True)
meta_file = run_dir / "metadata.json"
with open(meta_file, "w") as f:
f.write(metadata.to_json())
log.info(f"Experiment metadata saved to {meta_file}")
def save_summary(self) -> None:
"""Save summary of all runs."""
summary_file = self.output_dir / "summary.json"
summary = {
"total_runs": len(self.metadata_list),
"runs": [m.to_dict() for m in self.metadata_list],
}
with open(summary_file, "w") as f:
json.dump(summary, f, indent=2)
log.info(f"Experiment summary saved to {summary_file}")
def load_summary(self) -> dict:
"""Load summary from disk."""
summary_file = self.output_dir / "summary.json"
if not summary_file.exists():
return {"total_runs": 0, "runs": []}
with open(summary_file) as f:
return json.load(f)
class RunLogger:
"""Context manager for logging a single run."""
def __init__(
self,
run_id: str,
args,
tracker: ExperimentTracker,
notes: str = "",
):
"""Initialize run logger.
Parameters
----------
run_id : str
Unique run ID.
args : argparse.Namespace
Training arguments.
tracker : ExperimentTracker
Parent tracker.
notes : str, optional
Notes about the run.
"""
self.run_id = run_id
self.args = args
self.tracker = tracker
self.notes = notes
self.metadata = ExperimentMetadata.from_args(run_id, args, notes)
def __enter__(self):
"""Enter context."""
log.info(f"Starting run: {self.run_id}")
return self.metadata
def __exit__(self, exc_type, exc_val, exc_tb):
"""Exit context and log results."""
if exc_type is not None:
log.error(f"Run {self.run_id} failed: {exc_val}")
return
self.tracker.add_run(self.metadata)
log.info(f"Run {self.run_id} completed and logged")
def update_metrics(self, metrics: dict) -> None:
"""Update test metrics."""
self.metadata.test_metrics.update(metrics)
def set_checkpoint_path(self, path: str | Path) -> None:
"""Record checkpoint location."""
self.metadata.checkpoint_path = str(path)
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