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from pathlib import Path
from typing import Dict, List, Optional
import matplotlib.pyplot as plt
from datetime import datetime
import logging
logger = logging.getLogger(__name__)
class TrainingPlotter:
def __init__(self, save_dir: Optional[Path] = None):
self.save_dir = save_dir
if save_dir:
self.save_dir.mkdir(parents=True, exist_ok=True)
def plot_training_history(self, history: Dict[str, List[float]], title: str = "Training History"):
"""Plot and optionally save training metrics history.
Args:
history: Dictionary containing training metrics
title: Title for the plot
"""
# Silence matplotlib debug messages
logger.setLevel(logging.WARNING)
# Create figure with subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 12))
# Plot losses
ax1.plot(history['train_loss'], label='Train Loss')
ax1.plot(history['val_loss'], label='Validation Loss')
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss')
ax1.set_title('Training and Validation Loss')
ax1.legend()
ax1.grid(True)
# Plot learning rate if available
if 'learning_rate' in history:
ax2.plot(history['learning_rate'], label='Learning Rate')
ax2.set_xlabel('Step')
ax2.set_ylabel('Learning Rate')
ax2.set_title('Learning Rate Schedule')
ax2.legend()
ax2.grid(True)
plt.suptitle(title)
plt.tight_layout()
# Reset the logger level
logger.setLevel(logging.INFO)
# Save if directory provided
if self.save_dir:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
save_path = self.save_dir / f'training_history_{timestamp}.png'
plt.savefig(save_path)
logger.info(f"Saved training history plot to {save_path}")
plt.show()
def plot_validation_metrics(self, metrics: Dict[str, float]):
"""Plot validation metrics as a bar chart.
Args:
metrics: Dictionary of validation metrics. Can handle nested dictionaries.
"""
# Silence matplotlib debug messages
logger.setLevel(logging.WARNING)
# Flatten nested metrics dictionary
flat_metrics = {}
for key, value in metrics.items():
# Skip num_queries_tested
if key == 'num_queries_tested':
continue
if isinstance(value, dict):
# If value is a dictionary, flatten it with key prefix
for subkey, subvalue in value.items():
if isinstance(subvalue, (int, float)): # Only include numeric values
flat_metrics[f"{key}_{subkey}"] = subvalue
elif isinstance(value, (int, float)): # Only include numeric values
flat_metrics[key] = value
if not flat_metrics:
logger.warning("No numeric metrics to plot")
return
plt.figure(figsize=(12, 6))
# Extract metrics and values
metric_names = list(flat_metrics.keys())
values = list(flat_metrics.values())
# Create bar chart
bars = plt.bar(range(len(metric_names)), values)
# Customize the plot
plt.title('Validation Metrics')
plt.xticks(range(len(metric_names)), metric_names, rotation=45, ha='right')
plt.ylabel('Value')
# Add value labels on top of bars
for bar in bars:
height = bar.get_height()
plt.text(bar.get_x() + bar.get_width()/2., height,
f'{height:.3f}',
ha='center', va='bottom')
# Set y-axis limits to focus on metrics between 0 and 1
plt.ylim(0, 1.1) # Slight padding above 1 for label visibility
# Adjust layout to prevent label cutoff
plt.tight_layout()
# Reset the logger level
logger.setLevel(logging.INFO)
# Save if directory provided
if self.save_dir:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
save_path = self.save_dir / f'validation_metrics_{timestamp}.png'
plt.savefig(save_path)
logger.info(f"Saved validation metrics plot to {save_path}")
plt.show()
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