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chore: update provider and pricing snitch with agnostic system

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This commit is contained in:
Daniel Rosel
2025-12-06 19:06:40 +01:00
parent d45b344264
commit 4426b0ff74
3 changed files with 22 additions and 7 deletions
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2
experiments/procesing/pipelines.py
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@@ -131,7 +131,7 @@ if __name__ == '__main__':
# example run # example run
context = PipelineContext( context = PipelineContext(
provider=HistoricalProvider(), provider=HistoricalProvider(),
store_mode='hotel', store_mode='airline',
) )
product_features, prices = full_pipeline(context) product_features, prices = full_pipeline(context)

15
experiments/procesing/providers/supabase.py
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@@ -18,10 +18,17 @@ class SupabaseProvider(DataProvider):
self.supabase: Client = create_client(self.supabase_url, self.supabase_key) self.supabase: Client = create_client(self.supabase_url, self.supabase_key)
def fetch_products(self, store_mode: str) -> pd.DataFrame: def fetch_products(self, store_mode: str) -> pd.DataFrame:
resp = self.supabase.table(f'{store_mode}_products').select( # hotel uses room_type, airline uses flight_type; select all and normalize
"id, room_type, date_index, metadata, availability" resp = self.supabase.table(f'{store_mode}_products').select("*").execute()
).execute() if not resp.data:
return pd.DataFrame(resp.data) if resp.data else pd.DataFrame() return pd.DataFrame()
df = pd.DataFrame(resp.data)
# normalize type column: hotel has room_type, airline has flight_type
if 'room_type' in df.columns:
df['product_type'] = df['room_type']
elif 'flight_type' in df.columns:
df['product_type'] = df['flight_type']
return df
def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame: def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
if not experiment_ids: if not experiment_ids:

12
experiments/procesing/steps/fetch.py
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@@ -2,7 +2,7 @@ import pandas as pd
from procesing.steps.base import BaseContextStep from procesing.steps.base import BaseContextStep
class FetchInteractionsStep(BaseContextStep): class FetchInteractionsStep(BaseContextStep):
"""Fetch raw interaction data from Kafka topic with optional time filtering""" """Fetch raw interaction data from Kafka topic with optional time and store_mode filtering"""
def __init__(self, context, lookback: str = None): def __init__(self, context, lookback: str = None):
super().__init__(context) super().__init__(context)
@@ -24,6 +24,10 @@ class FetchInteractionsStep(BaseContextStep):
# drop all where page has /admin/ # drop all where page has /admin/
df = df[~df['page'].str.contains('/admin/', na=False)] df = df[~df['page'].str.contains('/admin/', na=False)]
# filter by store_mode from context
if 'storeMode' in df.columns:
df = df[df['storeMode'] == self.context.store_mode]
# Remap dateIndex if present # Remap dateIndex if present
if 'metadata_dateIndex' in df.columns: if 'metadata_dateIndex' in df.columns:
df['dateIndex'] = df['metadata_dateIndex'].astype('Int64') df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
@@ -38,7 +42,7 @@ class FetchInteractionsStep(BaseContextStep):
class FetchPriceLogsStep(BaseContextStep): class FetchPriceLogsStep(BaseContextStep):
"""Fetch price log data from Kafka topic with optional time filtering""" """Fetch price log data from Kafka topic with optional time and store_mode filtering"""
def __init__(self, context, lookback: str = None): def __init__(self, context, lookback: str = None):
super().__init__(context) super().__init__(context)
@@ -50,6 +54,10 @@ class FetchPriceLogsStep(BaseContextStep):
if df.empty: if df.empty:
return df return df
# filter by store_mode from context
if 'storeMode' in df.columns:
df = df[df['storeMode'] == self.context.store_mode]
# Apply time filtering if lookback specified # Apply time filtering if lookback specified
if self.lookback and 'ts' in df.columns: if self.lookback and 'ts' in df.columns:
df['ts'] = pd.to_datetime(df['ts']) df['ts'] = pd.to_datetime(df['ts'])