aa81d4f3d0
- Fix CCI indicator to use HLC prices instead of close only - Parse datetime column when loading enriched CSV - Strip timezone from annotation timestamps - Fix TA-Lib pattern names (CDL3WHITESOLDIERS, CDL3BLACKCROWS) - Exclude programmatic label columns from training features - Fix classification report to handle missing classes - Update MLflow tracking to use localhost:5000 - Grant PostgreSQL permissions to ml_user Pipeline now runs successfully end-to-end: - Feature engineering: 2543 rows, 31 columns - Annotation ingestion: 286 samples - Training: 89.47% test accuracy with Random Forest
111 lines
3.1 KiB
Python
111 lines
3.1 KiB
Python
"""
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Database connection and session management for the ML service.
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This module provides SQLAlchemy engine and session setup for PostgreSQL.
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Environment variables control the connection parameters.
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"""
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import os
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from contextlib import contextmanager
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from typing import Generator
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from sqlalchemy import create_engine, Column, Integer, String, DateTime, JSON
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker, Session
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from sqlalchemy.sql import func
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# CREATE DATABASE ml_service;
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# CREATE USER ml_user WITH ENCRYPTED PASSWORD 'ml_password';
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# GRANT ALL PRIVILEGES ON DATABASE ml_service TO ml_user;
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# Database connection configuration from environment
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DATABASE_URL = os.getenv(
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"DATABASE_URL",
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f"postgresql://{os.getenv('POSTGRES_USER', 'ml_user')}:"
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f"{os.getenv('POSTGRES_PASSWORD', 'ml_password')}@"
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f"{os.getenv('POSTGRES_HOST', 'localhost')}:"
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f"{os.getenv('POSTGRES_PORT', '5432')}/"
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f"{os.getenv('POSTGRES_DB', 'ml_service')}"
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)
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# Create SQLAlchemy engine
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engine = create_engine(
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DATABASE_URL,
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pool_pre_ping=True, # Verify connections before using them
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pool_size=5, # Number of connections to maintain
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max_overflow=10, # Max connections beyond pool_size
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)
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# Create session factory
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SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
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# Base class for declarative models
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Base = declarative_base()
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# Training runs model
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class TrainingRun(Base):
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"""Model for tracking ML training runs."""
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__tablename__ = "training_runs"
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id = Column(Integer, primary_key=True, index=True)
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run_id = Column(String(255), unique=True, nullable=False, index=True)
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model_type = Column(String(100), nullable=False)
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experiment_name = Column(String(255), nullable=False, index=True)
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pipeline_config_hash = Column(String(64), nullable=False)
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dataset_version = Column(String(100))
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metrics_summary = Column(JSON)
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status = Column(String(50), nullable=False, default="running", index=True)
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created_at = Column(DateTime(timezone=True), server_default=func.now(), index=True)
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completed_at = Column(DateTime(timezone=True))
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def __repr__(self):
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return f"<TrainingRun(run_id='{self.run_id}', status='{self.status}')>"
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def init_db():
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"""
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Initialize the database schema.
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Creates all tables defined by Base.metadata.
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"""
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Base.metadata.create_all(bind=engine)
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@contextmanager
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def get_db() -> Generator[Session, None, None]:
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"""
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Context manager for database sessions.
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Usage:
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with get_db() as db:
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# Use db session here
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training_run = TrainingRun(run_id="123", ...)
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db.add(training_run)
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db.commit()
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Yields:
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Database session
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"""
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db = SessionLocal()
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try:
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yield db
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finally:
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db.close()
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def get_db_session() -> Session:
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"""
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Get a database session (for dependency injection).
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Usage with FastAPI:
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@app.get("/")
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def endpoint(db: Session = Depends(get_db_session)):
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# Use db here
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Returns:
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Database session (caller must close it)
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"""
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return SessionLocal()
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