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feature: e2e intro pipline surge pricing

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This commit is contained in:
Daniel Rosel
2025-12-06 16:30:28 +01:00
parent 503c5e182d
commit e6a5b95875
6 changed files with 41 additions and 110 deletions
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17
experiments/procesing/pricers/simple.py
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@@ -67,24 +67,19 @@ class SimpleSurgePricer(PricingFunction):
self.surge_multiplier = surge_multiplier
self.discount_multiplier = discount_multiplier
def fit(self, historical_data: pd.DataFrame):
def fit(self, market_data : pd.DataFrame):
"""Extract base prices from product catalog or historical averages"""
if 'base_price' in historical_data.columns:
self.base_prices = historical_data['base_price'].values
elif 'price' in historical_data.columns:
self.base_prices = historical_data.groupby('productId')['price'].mean().values
else:
raise ValueError("historical_data must contain 'base_price' or 'price'")
return self
self.base_prices = market_data['base_price'].to_numpy() if 'base_price' in market_data.columns else market_data['price'].values
self.demand_history = market_data['demand'].to_numpy() if 'demand' in market_data.columns else np.zeros_like(self.base_prices)
def predict(self, state_space) -> np.ndarray:
def predict(self) -> np.ndarray:
"""
Adjust prices based on current demand using surge rules.
state_space.demand: demand counts per product
state_space.prices: current prices (fallback if base_prices not set)
"""
current_prices = self.base_prices if self.base_prices is not None else state_space.prices
demand = state_space.demand
current_prices = self.base_prices if self.base_prices is not None else np.ones_like(demand_vector) * 99.99
demand = self.demand_history if self.demand_history is not None else np.zeros_like(current_prices)
new_prices = current_prices.copy()
high_mask = demand >= self.high_threshold