Optimize _geometric_adstock for 99% function speedup, reducing total model train time by ~40% - Python version #1217
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…~40% - Replaced loop-based implementation with `scipy.signal.lfilter` - Improved `_geometric_adstock` execution time from **108.9s → 0.495s** (~99% faster) per trial - Reduced overall app runtime from **~277.9s → ~166.7s** (~40% speedup) per trial - Added unit test to ensure numerical correctness (`np.allclose`)
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Project Robyn
Summary
This PR optimizes
_geometric_adstock, the main performance bottleneck, reducing its execution time by ~99% and improving the total model train time by ~40% on Python version.Fixes
This PR does not address an existing GitHub issue but significantly improves performance.
Type of change
Motivation & Context
_geometric_adstockpreviously accounted for 39% of the total model train time, significantly slowing down model execution.scipy.signal.lfilter, replacing a Python loop with a highly efficient SciPy-based implementation (built on NumPy).Performance Benchmark (tutorial1.ipynb, Trial=1, iterations=2000)
Before Optimization:
_geometric_adstock: 108.9s (39% of total model train time)After Optimization:
_geometric_adstock: 0.495s (99% faster)