mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
8b76d24ade
* supabase product proxy and rendering * minor pipeline refactor * refactoring and demand estimation * trackion of date index searching * fixing changes of imports * data seeding * chore: airline basic refactor * feat: huge push of product changes and item review with cart * refactored design * chore: moving route elsewhere and align * fix: build of web/ * chore: fixing paper build * fixing chars
40 lines
1.8 KiB
Python
40 lines
1.8 KiB
Python
from sklearn.base import BaseEstimator, TransformerMixin
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import numpy as np
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import pandas as pd
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from supabase import create_client, Client
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import pandas as pd
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import os
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SUPABASE_URL = os.getenv("NEXT_PUBLIC_SUPABASE_URL")
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SUPABASE_KEY = os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
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supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
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class DemandEstimator(BaseEstimator, TransformerMixin):
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def __init__(self,
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store_mode:str='hotel',
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session_filter:str="",
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experiment_filter:str=""):
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self.store=store_mode
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self.session_filter=session_filter if len(session_filter)>0 else None
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self.experiment_filter=experiment_filter if len(experiment_filter)>0 else None
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def fit(self, X):
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return self
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def transform(self, interactions : pd.DataFrame):
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if interactions.empty:
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return pd.DataFrame(columns=["productId", "demand_score"])
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if self.session_filter:
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interactions = interactions[interactions['sessionId'] == self.session_filter]
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if self.experiment_filter:
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interactions = interactions[interactions['experimentId'] == self.experiment_filter]
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products=supabase.table(f'{self.store}_products').select("id, room_type, date_index, metadata, availability").execute()
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products = pd.DataFrame(products.data)
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unique_products = products['id'].unique()
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# TODO: improve demand score calculation rather than just counting interactions (use weights..)
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# while maintaining simplicity of a simple cross tab approach
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product_demand = pd.crosstab(interactions['productId'], "no_of_interactions")
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product_demand = product_demand.reindex(unique_products, fill_value=0).reset_index()
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product_demand.columns = ['productId', 'demand_score']
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return product_demand
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