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cleaning old pipeline and vectorization

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This commit is contained in:
Daniel Rosel
2025-11-28 14:20:05 +01:00
parent 1054fe7720
commit f3bc81e0ed
6 changed files with 17 additions and 592 deletions
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31
experiments/procesing/elasticity.py
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@@ -130,25 +130,24 @@ class TemporalElasticityEstimator(BaseEstimator, TransformerMixin):
def _build_product_timeseries(self, aligned_chunks):
"""Build time series [price, quantity] per product."""
series_by_product = {}
# vectorize chunk merging instead of iterating rows
all_merged = []
for chunk in aligned_chunks:
demand_df = chunk['demand']
price_df = chunk['prices']
merged = chunk['demand'].merge(chunk['prices'], on='productId', how='inner')
merged['timestamp'] = chunk['window_start']
all_merged.append(merged[['productId', 'timestamp', 'price', 'demand_score']])
# merge on productId
merged = demand_df.merge(price_df, on='productId', how='inner')
if not all_merged:
return {}
for _, row in merged.iterrows():
pid = row['productId']
if pid not in series_by_product:
series_by_product[pid] = []
series_by_product[pid].append({
'timestamp': chunk['window_start'],
'price': row['price'],
'quantity': row['demand_score']
})
# concat all chunks and group by productId in one pass
combined = pd.concat(all_merged, ignore_index=True)
series_by_product = {
pid: group[['timestamp', 'price', 'demand_score']].rename(
columns={'demand_score': 'quantity'}
).to_dict('records')
for pid, group in combined.groupby('productId')
}
return series_by_product