mirror of
https://github.com/velocitatem/PHANTOM.git
synced 2026-05-31 08:33:36 +00:00
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)
22 lines
646 B
Python
Executable File
22 lines
646 B
Python
Executable File
from abc import ABC, abstractmethod
|
|
from typing import List
|
|
import pandas as pd
|
|
|
|
class DataProvider(ABC):
|
|
"""Abstract interface for data access, enables DI and testing"""
|
|
|
|
@abstractmethod
|
|
def fetch_products(self, store_mode: str) -> pd.DataFrame:
|
|
"""Fetch product catalog for given store mode"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def fetch_experiments(self, experiment_ids: List[str]) -> pd.DataFrame:
|
|
"""Fetch experiment metadata for given IDs"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def fetch_kafka_topic(self, topic: str) -> pd.DataFrame:
|
|
"""Fetch data from Kafka topic via backend API"""
|
|
pass
|