mirror of
https://github.com/velocitatem/PHANTOM.git
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ad9423bf59
* introducing airflow to run pipeline * chore: updating dag with upload to registry * introducing complete provider (non refactored and noisy) * chore: removing old shit * generic pricing baselines * feature: super simple model registry (to be updated maybe third party OS software) * chore: refactoring the providers docker config and requirements * chore: refactored and broke down components (braking * exporting all * local pipeline excution working * fix: fixing import structures from nonrelativistic * chore: enables cross comm pickling with fully e2e pipeline compilation * docs: what the pipeline is like now * pipelines local running and pipeline high level definition * cleaning old pipeline and vectorization * leaked but fixing, not so important * test: started with pipeline step testing * chore: cleaning up provider of prices * test: extra tests wit hsemantic meaning checks * migrating pricers * feature: introducing pricing predictors (pricers) * chore: e2e is done with new pipeline * extra session feature extraction * feature: experiemntal sessin pricer and metrics(vibe) * chore: redefined and connected pricers (#29)
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
import numpy as np
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import pandas as pd
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from procesing.pricers.base import PricingFunction
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class ElasticityBasedPricer(PricingFunction):
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"""
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Pricing based on demand elasticity estimates.
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f(Q, S) = base_price * (1 + alpha * elasticity * demand_deviation)
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"""
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def __init__(self, alpha: float = 0.1, price_floor: float = 0.0, price_ceil: float = np.inf):
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self.alpha = alpha
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self.price_floor = price_floor
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self.price_ceil = price_ceil
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self.elasticity = None
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self.base_prices = None
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self.mean_demand = None
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def fit(self, historical_data: pd.DataFrame):
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"""
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Calibrate from historical elasticity estimates.
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Expects: [productId, elasticity, base_price, mean_demand]
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"""
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if 'elasticity' not in historical_data.columns:
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raise ValueError("historical_data must contain 'elasticity' column")
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self.elasticity = historical_data['elasticity'].values
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self.base_prices = (historical_data['base_price'].values
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if 'base_price' in historical_data.columns
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else np.ones(len(historical_data)) * 100)
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self.mean_demand = (historical_data['mean_demand'].values
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if 'mean_demand' in historical_data.columns
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else np.ones(len(historical_data)) * 10)
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return self
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def predict(self, state_space) -> np.ndarray:
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"""
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Adjust prices based on demand deviation and elasticity.
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Higher demand -> increase price (but less for elastic goods)
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"""
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if self.elasticity is None:
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raise ValueError("Must call fit() before predict()")
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demand = np.asarray(state_space.demand)
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if len(demand) != len(self.elasticity):
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raise ValueError(f"Demand vector size {len(demand)} != elasticity size {len(self.elasticity)}")
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# compute demand deviation from mean
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demand_dev = (demand - self.mean_demand) / (self.mean_demand + 1e-6)
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# adjust price: if demand high and elastic, don't increase much
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# if demand high and inelastic, increase more
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price_multiplier = 1 + self.alpha * np.abs(self.elasticity) * demand_dev
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prices = self.base_prices * price_multiplier
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# enforce bounds
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prices = np.clip(prices, self.price_floor, self.price_ceil)
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return prices
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