mirror of
https://github.com/velocitatem/PHANTOM.git
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ef98141ca8
* chore: update provider and pricing snitch with agnostic system * cloning pipelines per mode instance * updating airline hero section * fix: must keep airflow secretkey * fix: fixture update to hotel not shop * chore: refactored to factory design pattern of pipelines * chore: clean up definition of composite class of providers
82 lines
2.7 KiB
Python
Executable File
82 lines
2.7 KiB
Python
Executable File
import pandas as pd
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from procesing.steps.base import BaseContextStep
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class FetchInteractionsStep(BaseContextStep):
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"""Fetch raw interaction data from Kafka topic with optional time and store_mode filtering"""
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def __init__(self, context, lookback: str = None):
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super().__init__(context)
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self.lookback = lookback
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def transform(self, X=None):
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df = self.context.provider.fetch_kafka_topic('user-interactions')
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if df.empty:
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return df
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# Explode metadata JSON column
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if 'metadata' in df.columns:
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df = df.join(
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pd.json_normalize(df.pop('metadata'), sep='.').add_prefix('metadata_')
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)
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df = df.dropna(subset=['eventName'])
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# drop all where page has /admin/
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df = df[~df['page'].str.contains('/admin/', na=False)]
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# filter by store_mode from context
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if 'storeMode' in df.columns:
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df = df[df['storeMode'] == self.context.store_mode]
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# Remap dateIndex if present
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if 'metadata_dateIndex' in df.columns:
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df['dateIndex'] = df['metadata_dateIndex'].astype('Int64')
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# Apply time filtering if lookback specified
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if self.lookback and 'ts' in df.columns:
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df['ts'] = pd.to_datetime(df['ts'])
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cutoff = pd.Timestamp.now() - pd.Timedelta(self.lookback)
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df = df[df['ts'] >= cutoff]
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return df
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class FetchPriceLogsStep(BaseContextStep):
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"""Fetch price log data from Kafka topic with optional time and store_mode filtering"""
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def __init__(self, context, lookback: str = None):
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super().__init__(context)
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self.lookback = lookback
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def transform(self, X=None):
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df = self.context.provider.fetch_kafka_topic('price-logs')
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if df.empty:
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return df
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# filter by store_mode from context
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if 'storeMode' in df.columns:
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df = df[df['storeMode'] == self.context.store_mode]
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# Apply time filtering if lookback specified
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if self.lookback and 'ts' in df.columns:
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df['ts'] = pd.to_datetime(df['ts'])
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cutoff = pd.Timestamp.now() - pd.Timedelta(self.lookback)
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df = df[df['ts'] >= cutoff]
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return df
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class FetchExperimentsStep(BaseContextStep):
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"""Fetch experiment metadata for given interaction data"""
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def transform(self, interactions_df: pd.DataFrame):
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if interactions_df.empty or 'experimentId' not in interactions_df.columns:
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return pd.DataFrame()
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exp_ids = interactions_df['experimentId'].dropna().unique().tolist()
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if not exp_ids:
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return pd.DataFrame()
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return self.context.provider.fetch_experiments(exp_ids)
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