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feat: integration of pipeline hooks into testing

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This commit is contained in:
Daniel Rosel
2026-01-12 13:37:48 +01:00
parent 9a8525a854
commit acf731efcb
9 changed files with 58 additions and 19 deletions
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24
experiments/airflow/dags/surge_pricing_pipeline.py
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@@ -120,15 +120,31 @@ def apply_surge_pricing(**kwargs):
# rename demand_score to demand for pricer compatibility
data = product_features.rename(columns={'demand_score': 'demand'})
high_thresh = dag_conf.get('high_threshold', 10)
low_thresh = dag_conf.get('low_threshold', 2)
surge_mult = dag_conf.get('surge_multiplier', 1.2)
discount_mult = dag_conf.get('discount_multiplier', 0.9)
logging.info(f"Surge pricing config: high_thresh={high_thresh}, low_thresh={low_thresh}, surge_mult={surge_mult}, discount_mult={discount_mult}")
logging.info(f"Demand stats: min={data['demand'].min():.2f}, max={data['demand'].max():.2f}, mean={data['demand'].mean():.2f}")
logging.info(f"Products with high demand (>={high_thresh}): {(data['demand'] >= high_thresh).sum()}")
logging.info(f"Products with low demand (<={low_thresh}): {(data['demand'] <= low_thresh).sum()}")
surge_pricer = SimpleSurgePricer(
high_threshold=dag_conf.get('high_threshold', 10),
low_threshold=dag_conf.get('low_threshold', 2),
surge_multiplier=dag_conf.get('surge_multiplier', 1.2),
discount_multiplier=dag_conf.get('discount_multiplier', 0.9)
high_threshold=high_thresh,
low_threshold=low_thresh,
surge_multiplier=surge_mult,
discount_multiplier=discount_mult
)
surge_pricer.fit(data)
data['optimal_price'] = surge_pricer.predict()
base_avg = data['base_price'].mean()
optimal_avg = data['optimal_price'].mean()
price_change_pct = ((optimal_avg - base_avg) / base_avg) * 100
logging.info(f"Price adjustment: base_avg={base_avg:.2f}, optimal_avg={optimal_avg:.2f}, change={price_change_pct:+.1f}%")
prices_df = data[['productId', 'price', 'base_price', 'optimal_price', 'demand']].rename(columns={
'price': 'current_price',
'demand': 'demand_score'

3
experiments/procesing/pricers/simple.py
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@@ -124,7 +124,8 @@ class SimpleSurgePricer(PricingFunction):
if base is None:
base = np.ones(len(demand)) * 99.99
new_prices = base.copy()
# ensure float dtype to allow multiplication by float multipliers
new_prices = base.astype(np.float64).copy()
high_mask = demand >= self.high_threshold
new_prices[high_mask] *= self.surge_multiplier