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This commit is contained in:
Daniel Rosel
2026-01-12 20:59:09 +01:00
parent 62a4008c29
commit e89cb263d4
2 changed files with 23 additions and 16 deletions
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experiments/airflow/dags/surge_pricing_factory.py
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@@ -1,3 +1,4 @@
from pandas.core.algorithms import factorize_array
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago
@@ -208,3 +209,12 @@ def create_surge_pricing_dag(store_mode: str) -> DAG:
# instantiate DAGs for Airflow to discover
dag_airline = create_surge_pricing_dag('airline')
dag_hotel = create_surge_pricing_dag('hotel')
# TODO: Refactor this factory from a surge pricing factory to a general pricing factory
# We will do this by passing a pricing strategy class to the factory, since the generic pipeline is:
# take all interaction data, group by sessionId and assign a new price vector to each session
# in the grouping we get a subset of the interactions per sessionId and we can map that to some Features
# we define a custom _get_features(interactions .) methodin the strategy class
# we then run only the inference which is the .predict(trajectory) per-session which will give us a new price vector
# this we then publish for each sessionId group
# this might include no deleting most of the pricers we have defined and starting with a super simple surge-pricing algorithm that is no-fit only predict. This we can then test end-to-end and observe changes to prices according to a desired strategy - we have to define this one as a very short term strategy because we run sessions that take only a few minutes.