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32 refine data pipeline training data construction (#37)

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* feature: modularized feature engineering for ml setup (new pipeline)

* chore: updating imports properly

* test: updating fixtures with ua and meta

* chore: migrating code ignore groups

* chore: syntax cleaning and code quality

* chore: fixing pipeline data compatability

* Update experiments/procesing/steps/session.py

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

* chore: refactoring and dixing path joining

* chore: refactoring function definition to avoid reinit

---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
Daniel Alves Rösel
2025-12-12 12:15:15 +01:00
committed by GitHub
parent a2a443c027
commit a1916c966c
6 changed files with 316 additions and 159 deletions
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4
experiments/procesing/steps/elasticity.py
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@@ -7,12 +7,12 @@ class AggregatePriceLogsStep(BaseContextStep):
"""
Aggregate price logs into time windows using VECTORIZED operations.
Input: price_logs_df
Output: list of price chunks with [productId, price]
Output: DataFrame with columns [productId, price]
"""
def transform(self, price_logs_df: pd.DataFrame):
if price_logs_df.empty:
return []
return pd.DataFrame(columns=['productId', 'price'])
df = price_logs_df.copy()
ts_col = self.context.config.get('ts_col', 'ts')