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"""
Utility functions for formatting output
"""
from typing import Any, Dict
def format_metrics_safe(metrics: Dict[str, Any]) -> str:
"""
Safely format metrics dictionary for logging, handling both numeric and string values.
Args:
metrics: Dictionary of metric names to values
Returns:
Formatted string representation of metrics
"""
if not metrics:
return ""
formatted_parts = []
for name, value in metrics.items():
# Check if value is numeric (int, float)
if isinstance(value, (int, float)):
try:
# Only apply float formatting to numeric values
formatted_parts.append(f"{name}={value:.4f}")
except (ValueError, TypeError):
# Fallback to string representation if formatting fails
formatted_parts.append(f"{name}={value}")
else:
# For non-numeric values (strings, etc.), just convert to string
formatted_parts.append(f"{name}={value}")
return ", ".join(formatted_parts)
def format_improvement_safe(parent_metrics: Dict[str, Any], child_metrics: Dict[str, Any]) -> str:
"""
Safely format improvement metrics for logging.
Args:
parent_metrics: Parent program metrics
child_metrics: Child program metrics
Returns:
Formatted string representation of improvements
"""
if not parent_metrics or not child_metrics:
return ""
improvement_parts = []
for metric, child_value in child_metrics.items():
if metric in parent_metrics:
parent_value = parent_metrics[metric]
# Only calculate improvement for numeric values
if isinstance(child_value, (int, float)) and isinstance(parent_value, (int, float)):
try:
diff = child_value - parent_value
improvement_parts.append(f"{metric}={diff:+.4f}")
except (ValueError, TypeError):
# Skip non-numeric comparisons
continue
return ", ".join(improvement_parts)
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