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Improve the performance of bayesian bootstrap by 10x #177

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Aug 1, 2023
Merged

Improve the performance of bayesian bootstrap by 10x #177

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15 changes: 6 additions & 9 deletions src/mozanalysis/bayesian_stats/bayesian_bootstrap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,13 +294,10 @@ def get_bootstrap_samples(
if seed_start is None:
seed_start = np.random.randint(np.iinfo(np.uint32).max)

# Deterministic "randomness" requires careful state handling :(
# Need to ensure every call has a unique, deterministic seed.
seed_range = range(seed_start, seed_start + num_samples)

random_state = np.random.RandomState(seed_start)
summary_stat_samples = [
_resample_and_agg_once(data_values, data_counts, stat_fn, unique_seed)
for unique_seed in seed_range
_resample_and_agg_once(data_values, data_counts, stat_fn, random_state)
for _ in range(0, num_samples)
]

summary_df = pd.DataFrame(summary_stat_samples)
Expand All @@ -312,9 +309,9 @@ def get_bootstrap_samples(
return summary_df


def _resample_and_agg_once(data_values, data_counts, stat_fn, unique_seed=None):
random_state = np.random.RandomState(unique_seed)

def _resample_and_agg_once(
data_values, data_counts, stat_fn, random_state=np.random.RandomState(None)
):
prob_weights = random_state.dirichlet(data_counts)

return stat_fn(data_values, prob_weights)