mirror of
https://github.com/velocitatem/PHANTOM.git
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257 lines
8.4 KiB
Python
257 lines
8.4 KiB
Python
from airflow import DAG
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from airflow.operators.python import PythonOperator
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from airflow.utils.dates import days_ago
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from datetime import timedelta
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import pandas as pd
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import logging
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import sys
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import pickle
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import io
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# add parent dir to path so procesing package can be imported
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sys.path.insert(0, '/opt/airflow')
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from procesing.context import PipelineContext
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from procesing.providers import SupabaseProvider, BackendAPIProvider
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from procesing.steps import (
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FetchInteractionsStep,
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FetchPriceLogsStep,
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ComputeDemandStep,
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AggregatePriceLogsStep,
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JoinProductFeaturesStep,
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)
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default_args = {
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'owner': 'phantom-research',
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'depends_on_past': False,
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'email_on_failure': False,
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'email_on_retry': False,
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'retries': 2,
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'retry_delay': timedelta(minutes=5),
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}
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def get_provider():
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"""Factory to create composite provider"""
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class CompositeProvider(SupabaseProvider, BackendAPIProvider): # TODO: Fix this into one global provider singelton instead of multiple inheritance declarations acoss the codebase
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def __init__(self):
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SupabaseProvider.__init__(self)
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BackendAPIProvider.__init__(self)
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return CompositeProvider()
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def get_context(**kwargs):
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"""Build pipeline context from Airflow config"""
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dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}
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return PipelineContext(
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provider=get_provider(),
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store_mode=dag_conf.get('store_mode', 'hotel'),
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)
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# atomic task functions (each wraps one sklearn step)
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def fetch_interactions(**kwargs):
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"""Task: Fetch interaction data from Kafka"""
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context = get_context(**kwargs)
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step = FetchInteractionsStep(context)
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df = step.transform(None)
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kwargs['ti'].xcom_push(key='interactions_raw', value=pickle.dumps(df))
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logging.info(f"Fetched {len(df)} interaction records")
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return len(df)
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def fetch_price_logs(**kwargs):
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"""Task: Fetch price logs from Kafka"""
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context = get_context(**kwargs)
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step = FetchPriceLogsStep(context)
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df = step.transform(None)
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kwargs['ti'].xcom_push(key='price_logs_raw', value=pickle.dumps(df))
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logging.info(f"Fetched {len(df)} price records")
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return len(df)
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def compute_demand(**kwargs):
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"""Task: Compute demand scores from interactions"""
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ti = kwargs['ti']
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df = pickle.loads(ti.xcom_pull(key='interactions_raw'))
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context = get_context(**kwargs)
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step = ComputeDemandStep(context)
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demand_df = step.transform(df)
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ti.xcom_push(key='demand_data', value=pickle.dumps(demand_df))
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logging.info(f"Computed demand for {len(demand_df)} products")
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return len(demand_df)
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def aggregate_price_logs(**kwargs):
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"""Task: Aggregate price logs"""
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ti = kwargs['ti']
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df = pickle.loads(ti.xcom_pull(key='price_logs_raw'))
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context = get_context(**kwargs)
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step = AggregatePriceLogsStep(context)
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price_df = step.transform(df)
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ti.xcom_push(key='price_data', value=pickle.dumps(price_df))
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logging.info(f"Aggregated price logs for {len(price_df)} products")
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return len(price_df)
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def join_product_features(**kwargs):
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"""Task: Join demand and price data"""
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ti = kwargs['ti']
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demand_df = pickle.loads(ti.xcom_pull(key='demand_data'))
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price_df = pickle.loads(ti.xcom_pull(key='price_data'))
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context = get_context(**kwargs)
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step = JoinProductFeaturesStep(context)
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joined_df = step.transform((demand_df, price_df))
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ti.xcom_push(key='product_features', value=pickle.dumps(joined_df))
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logging.info(f"Joined features for {len(joined_df)} products")
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return len(joined_df)
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def apply_surge_pricing(**kwargs):
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"""Task: Apply surge pricing rules to generate optimal prices"""
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ti = kwargs['ti']
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product_features = pickle.loads(ti.xcom_pull(key='product_features'))
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dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}
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high_threshold = dag_conf.get('high_threshold', 10)
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low_threshold = dag_conf.get('low_threshold', 2)
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surge_multiplier = dag_conf.get('surge_multiplier', 1.2)
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discount_multiplier = dag_conf.get('discount_multiplier', 0.9)
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context = get_context(**kwargs)
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products = context.products
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results = []
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for pid in product_features['productId'].unique():
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prod_data = product_features[product_features['productId'] == pid]
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if prod_data.empty:
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continue
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demand = prod_data["demand_score"].mean()
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current_price = prod_data["price"].mean()
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prod_meta = products[products['id'] == pid]
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if not prod_meta.empty:
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meta = prod_meta.iloc[0]['metadata']
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base_price = meta.get('base_price', current_price) if isinstance(meta, dict) else current_price
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else:
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base_price = current_price
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if demand >= high_threshold:
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optimal_price = base_price * surge_multiplier
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elif demand <= low_threshold:
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optimal_price = base_price * discount_multiplier
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else:
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optimal_price = base_price
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results.append({
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'productId': pid,
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'current_price': current_price,
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'base_price': base_price,
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'optimal_price': optimal_price,
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'demand_score': demand
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})
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prices_df = pd.DataFrame(results)
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ti.xcom_push(key='predicted_prices', value=pickle.dumps(prices_df))
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logging.info(f"Applied surge pricing for {len(prices_df)} products")
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return len(prices_df)
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def publish_results(**kwargs):
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"""Task: Publish surge pricing results to registry"""
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ti = kwargs['ti']
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prices_df = pickle.loads(ti.xcom_pull(key='predicted_prices'))
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sys.path.insert(0, '/opt/airflow')
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from lib.model_registry import ModelRegistry
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registry = ModelRegistry()
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dag_conf = kwargs.get('dag_run').conf if kwargs.get('dag_run') else {}
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metadata = {
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'timestamp': pd.Timestamp.now().isoformat(),
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'store_mode': dag_conf.get('store_mode', 'hotel'),
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'dag_run_id': kwargs['dag_run'].run_id if kwargs.get('dag_run') else 'manual',
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'pricing_method': 'surge',
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'high_threshold': dag_conf.get('high_threshold', 10),
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'low_threshold': dag_conf.get('low_threshold', 2),
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'surge_multiplier': dag_conf.get('surge_multiplier', 1.2),
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'discount_multiplier': dag_conf.get('discount_multiplier', 0.9)
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}
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registry.publish_prices(prices_df, model_name='latest', metadata=metadata)
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logging.info(f"Published surge pricing for {len(prices_df)} products")
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return {
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'n_products': len(prices_df),
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'registry_status': 'success',
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'mean_demand': float(prices_df['demand_score'].mean()) if 'demand_score' in prices_df.columns else None
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}
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# DAG definition
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with DAG(
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'surge_pricing_pipeline',
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default_args=default_args,
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description='Simple surge pricing pipeline: demand aggregation + rule-based pricing',
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schedule_interval='*/15 * * * *',
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start_date=days_ago(1),
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catchup=False,
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max_active_runs=1,
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tags=['pricing', 'surge', 'research', 'simplified'],
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) as dag:
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# parallel data fetching
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t_fetch_interactions = PythonOperator(
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task_id='fetch_interactions',
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python_callable=fetch_interactions,
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provide_context=True,
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)
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t_fetch_price_logs = PythonOperator(
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task_id='fetch_price_logs',
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python_callable=fetch_price_logs,
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provide_context=True,
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)
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# compute demand from interactions
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t_compute_demand = PythonOperator(
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task_id='compute_demand',
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python_callable=compute_demand,
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provide_context=True,
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)
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# aggregate price logs
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t_aggregate_prices = PythonOperator(
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task_id='aggregate_price_logs',
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python_callable=aggregate_price_logs,
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provide_context=True,
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)
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# join demand and prices
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t_join_features = PythonOperator(
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task_id='join_product_features',
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python_callable=join_product_features,
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provide_context=True,
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)
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# apply surge pricing
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t_surge_pricing = PythonOperator(
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task_id='apply_surge_pricing',
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python_callable=apply_surge_pricing,
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provide_context=True,
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)
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# publish to registry
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t_publish = PythonOperator(
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task_id='publish_results',
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python_callable=publish_results,
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provide_context=True,
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)
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# dependency graph: parallel fetch -> process -> join -> surge -> publish
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t_fetch_interactions >> t_compute_demand
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t_fetch_price_logs >> t_aggregate_prices
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[t_compute_demand, t_aggregate_prices] >> t_join_features >> t_surge_pricing >> t_publish
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