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e027d8d1ee
_calculate_improvements() guards latency/memory/energy division against
opt_metrics[metric] <= 0 with a documented "fallback to 1.0", but
accuracy_retention = opt_metrics['accuracy'] / base_metrics['accuracy']
had no such guard on the denominator. A baseline model with 0.0 accuracy
(a broken/failing baseline -- exactly the kind of input a benchmarking
suite should handle gracefully) raises ZeroDivisionError and crashes the
whole comparison, while the sibling metrics computed two lines above
degrade gracefully.
Added the same guard pattern already used for the other three metrics.
The function's own docstring already promised "Handle division by zero
with fallback to 1.0" as a general rule -- this fix makes the accuracy
branch actually honor it.
Verified: the old code raises ZeroDivisionError for
base_metrics={'accuracy': 0.0}; the fixed code returns
accuracy_retention=1.0 instead, and the normal (non-zero) case is
unaffected (0.6/0.5 = 1.2, unchanged).