refactor: adopt Python logging module with named loggers
Replace all print() calls with structured logging using the Python logging module. This provides: - Named loggers per subsystem: commander_ai_lab.api, .batch, .sim, .coach, .deckgen, .collection, .scanner, .ml, .cache, .pplx - Dual output: console (StreamHandler) + rotating file (RotatingFileHandler) at logs/commander-ai-lab.log (25 MB x 5 backups = 125 MB max) - Configurable via env vars: CAL_LOG_DIR, CAL_LOG_MAX_MB, CAL_LOG_BACKUP_COUNT - --verbose / -v flag for DEBUG-level output - overnight-run.py: separate rotating log at logs/overnight-run.log (10 MB x 5) - Sub-module loggers in ml/, scanner/, deck_sources/, src/ all propagate to the root commander_ai_lab logger - Stripped redundant [TAG] prefixes from messages (logger name carries context) - Format: '2026-03-13 14:00:00 INFO [commander_ai_lab.batch] message' Files changed: lab_api.py, overnight-run.py, and 11 sub-module .py files 176 total print() calls converted (101 in lab_api.py, 76 in sub-modules)
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KoalaTrapLord committed
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@@ -240,12 +240,12 @@ def print_label_distribution(labels: np.ndarray) -> Dict[str, int]:
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dist[action.value] = count
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total = len(labels)
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print(f"\n{'Action':<25} {'Count':>8} {'Pct':>8}")
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print("─" * 43)
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log.info(f"\n{'Action':<25} {'Count':>8} {'Pct':>8}")
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log.info("─" * 43)
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for action_name, count in sorted(dist.items(), key=lambda x: -x[1]):
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pct = (count / total * 100) if total > 0 else 0
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print(f" {action_name:<23} {count:>8} {pct:>7.1f}%")
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print(f"{'─' * 43}")
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print(f" {'TOTAL':<23} {total:>8}")
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log.info(f" {action_name:<23} {count:>8} {pct:>7.1f}%")
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log.info(f"{'─' * 43}")
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log.info(f" {'TOTAL':<23} {total:>8}")
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return dist
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@@ -154,17 +154,17 @@ def build_dataset(
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# Print game outcome distribution
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unique_outcomes, outcome_counts = np.unique(outcomes, return_counts=True)
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print(f"\n{'Outcome':<25} {'Count':>8}")
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print("─" * 35)
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log.info(f"\n{'Outcome':<25} {'Count':>8}")
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log.info("─" * 35)
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for o, c in sorted(zip(unique_outcomes, outcome_counts), key=lambda x: -x[1]):
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print(f" {str(o):<23} {c:>8}")
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log.info(f" {str(o):<23} {c:>8}")
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# Print playstyle distribution
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unique_styles, style_counts = np.unique(playstyles, return_counts=True)
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print(f"\n{'Playstyle':<25} {'Count':>8}")
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print("─" * 35)
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log.info(f"\n{'Playstyle':<25} {'Count':>8}")
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log.info("─" * 35)
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for s, c in sorted(zip(unique_styles, style_counts), key=lambda x: -x[1]):
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print(f" {str(s):<23} {c:>8}")
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log.info(f" {str(s):<23} {c:>8}")
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return {
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"states": states,
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@@ -288,7 +288,7 @@ def main():
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max_samples=args.max_samples,
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)
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if not dataset:
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print("\nNo data to save. Run simulations with --ml-log first.")
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log.info("\nNo data to save. Run simulations with --ml-log first.")
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sys.exit(1)
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if args.no_split:
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@@ -300,7 +300,7 @@ def main():
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save_dataset(val, f"{base}-val.npz")
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save_dataset(test, f"{base}-test.npz")
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print("\nDataset build complete.")
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log.info("\nDataset build complete.")
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if __name__ == "__main__":
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@@ -16,6 +16,10 @@ The network is designed for:
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import os
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import sys
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from pathlib import Path
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import logging
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log = logging.getLogger("commander_ai_lab.ml.training")
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# Conditional torch import — graceful failure if not installed
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try:
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@@ -25,7 +29,7 @@ try:
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TORCH_AVAILABLE = True
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except ImportError:
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TORCH_AVAILABLE = False
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print("[ML] PyTorch not installed. Install with: pip install torch")
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log.warning("PyTorch not installed. Install with: pip install torch")
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import numpy as np
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@@ -204,7 +208,7 @@ def save_checkpoint(
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"dropout": TRAINING_CONFIG.dropout,
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},
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}, path)
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print(f"[ML] Checkpoint saved: {path}")
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log.info(f"Checkpoint saved: {path}")
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def load_checkpoint(path: str, device: str = "cpu"):
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+26
-27
@@ -49,8 +49,7 @@ from ml.training.policy_network import (
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PolicyNetwork, save_checkpoint, load_checkpoint,
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)
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logging.basicConfig(level=logging.INFO, format="[%(name)s] %(message)s")
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logger = logging.getLogger("ml.trainer")
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logger = logging.getLogger("commander_ai_lab.ml.training")
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# ══════════════════════════════════════════════════════════
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@@ -502,40 +501,40 @@ def evaluate_model(
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def print_evaluation(results: Dict):
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"""Pretty-print evaluation results."""
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print()
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print("=" * 65)
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print(" Test Set Evaluation")
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print("=" * 65)
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print(f" Overall Accuracy: {results['overall_accuracy']:.1%}")
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print(f" Total Samples: {results['total_samples']:,}")
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print()
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print(f" {'Action':<20} {'Support':>8} {'Prec':>7} {'Recall':>7} {'F1':>7}")
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print(" " + "-" * 55)
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logger.info("")
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logger.info("=" * 65)
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logger.info(" Test Set Evaluation")
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logger.info("=" * 65)
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logger.info(f" Overall Accuracy: {results['overall_accuracy']:.1%}")
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logger.info(f" Total Samples: {results['total_samples']:,}")
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logger.info("")
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logger.info(f" {'Action':<20} {'Support':>8} {'Prec':>7} {'Recall':>7} {'F1':>7}")
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logger.info(" " + "-" * 55)
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for action_name, m in results["per_class"].items():
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print(
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logger.info(
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f" {action_name:<20} {m['support']:>8} "
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f"{m['precision']:>7.3f} {m['recall']:>7.3f} {m['f1']:>7.3f}"
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)
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print(" " + "-" * 55)
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logger.info(" " + "-" * 55)
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# Confusion matrix
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print()
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print(" Confusion Matrix (rows=true, cols=predicted):")
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logger.info("")
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logger.info(" Confusion Matrix (rows=true, cols=predicted):")
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header = " " + " " * 18
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for c in range(NUM_ACTIONS):
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short_name = IDX_TO_ACTION[c].value[:6]
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header += f" {short_name:>6}"
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print(header)
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logger.info(header)
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for r in range(NUM_ACTIONS):
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row_name = IDX_TO_ACTION[r].value[:16]
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row_str = f" {row_name:<18}"
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for c in range(NUM_ACTIONS):
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row_str += f" {results['confusion_matrix'][r][c]:>6}"
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print(row_str)
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print()
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logger.info(row_str)
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logger.info("")
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# ══════════════════════════════════════════════════════════
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@@ -605,8 +604,8 @@ def main():
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args = parser.parse_args()
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if not TORCH_AVAILABLE:
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print("PyTorch not installed. Install with:")
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print(" pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121")
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logger.info("PyTorch not installed. Install with:")
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logger.info(" pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121")
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sys.exit(1)
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device = args.device or get_best_device()
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@@ -621,13 +620,13 @@ def main():
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if args.eval_only:
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# Evaluation only
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if not os.path.exists(test_path):
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print(f"Test data not found: {test_path}")
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print("Run: python -m ml.scripts.ml_cli build")
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logger.info(f"Test data not found: {test_path}")
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logger.info("Run: python -m ml.scripts.ml_cli build")
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sys.exit(1)
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ckpt_path = args.checkpoint or os.path.join(ckpt_dir, "best_policy.pt")
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if not os.path.exists(ckpt_path):
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print(f"Checkpoint not found: {ckpt_path}")
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logger.info(f"Checkpoint not found: {ckpt_path}")
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sys.exit(1)
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test_states, test_labels = load_npz_split(test_path)
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@@ -641,15 +640,15 @@ def main():
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eval_path = os.path.join(ckpt_dir, "eval_results.json")
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with open(eval_path, "w") as f:
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json.dump(results, f, indent=2)
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print(f" Results saved: {eval_path}")
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logger.info(f" Results saved: {eval_path}")
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return
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# Training mode
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for path, label in [(train_path, "train"), (val_path, "val")]:
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if not os.path.exists(path):
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print(f"{label} data not found: {path}")
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print("Build the dataset first:")
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print(" python -m ml.scripts.ml_cli build --results-dir results/")
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logger.info(f"{label} data not found: {path}")
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logger.info("Build the dataset first:")
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logger.info(" python -m ml.scripts.ml_cli build --results-dir results/")
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sys.exit(1)
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train_states, train_labels = load_npz_split(train_path)
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