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| import os | |
| import glob | |
| import yaml | |
| import pandas as pd | |
| import json | |
| from datetime import datetime | |
| from loguru import logger | |
| def multirun_artifact_producer(base_path: str, output_path: str): | |
| """Aggregate data from the latest run's csv folder and save to a JSON file.""" | |
| # Find the latest top-level run folder | |
| latest_folder = max(glob.glob(os.path.join(base_path, "*")), key=os.path.getmtime) | |
| if not os.path.isdir(latest_folder): | |
| logger.error("No valid run folders found!") | |
| return | |
| # Initialize JSON structure | |
| output_data = {} | |
| # Process each sub-run directory within the latest run folder | |
| for run_dir in os.listdir(latest_folder): | |
| run_path = os.path.join(latest_folder, run_dir) | |
| if os.path.isdir(run_path): | |
| # Look for the latest folder in the csv subdirectory | |
| csv_base_path = os.path.join(run_path, "csv") | |
| if not os.path.isdir(csv_base_path): | |
| logger.warning(f"No csv directory found in {run_path}. Skipping.") | |
| continue | |
| # Find the latest version folder in csv | |
| latest_csv_folder = max( | |
| glob.glob(os.path.join(csv_base_path, "version_*")), | |
| key=os.path.getmtime, | |
| ) | |
| if not os.path.isdir(latest_csv_folder): | |
| logger.warning( | |
| f"No valid version folder found in {csv_base_path}. Skipping." | |
| ) | |
| continue | |
| # Paths to files in the latest csv version folder | |
| hparams_path = os.path.join(latest_csv_folder, "hparams.yaml") | |
| metrics_path = os.path.join(latest_csv_folder, "metrics.csv") | |
| # Check if necessary files exist | |
| if not os.path.isfile(hparams_path) or not os.path.isfile(metrics_path): | |
| logger.warning( | |
| f"Missing hparams.yaml or metrics.csv in {latest_csv_folder}. Skipping." | |
| ) | |
| continue | |
| # Read hparams.yaml | |
| with open(hparams_path, "r") as file: | |
| hparams = yaml.safe_load(file) | |
| # Read metrics.csv and calculate averages | |
| metrics_df = pd.read_csv(metrics_path) | |
| avg_train_acc = metrics_df["train_acc"].dropna().mean() | |
| avg_val_acc = metrics_df["val_acc"].dropna().mean() | |
| # Create JSON structure for this run | |
| output_data[f"run_{run_dir}"] = { | |
| "hparams": hparams, | |
| "metrics": {"avg_train_acc": avg_train_acc, "avg_val_acc": avg_val_acc}, | |
| } | |
| # Save aggregated data to JSON | |
| os.makedirs(output_path, exist_ok=True) | |
| output_file = os.path.join( | |
| output_path, f"aggregated_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" | |
| ) | |
| logger.info(f"Saving aggregated data to {output_file}") | |
| with open(output_file, "w") as json_file: | |
| json.dump(output_data, json_file, indent=4) | |
| if __name__ == "__main__": | |
| # Paths | |
| base_path = "./logs/train/runs" | |
| output_path = "./artifacts" | |
| multirun_artifact_producer(base_path, output_path) | |