from pathlib import Path

import sqlalchemy as sql

# ** meta
metadata = sql.MetaData()


def get_engine(
    db_path: Path,
    echo: bool = False,
) -> sql.Engine:
    path = db_path.resolve()
    connection_str: str = f"sqlite:///{str(path)}"
    engine = sql.create_engine(connection_str, echo=echo)
    return engine


# ** table declarations
# ** ---- common
perf_meas = sql.Table(
    "performance_measurement",
    metadata,
    sql.Column("id", sql.Integer, primary_key=True),
    sql.Column("pipeline_name", sql.String(length=30)),
    sql.Column("execution_duration", sql.Float),
)
# ** ---- forecasts
sf_stats = sql.Table(
    "sales_forecast_statistics",
    metadata,
    sql.Column("id", sql.Integer, primary_key=True),
    sql.Column("status_code", sql.Integer),
    sql.Column("status_dscr", sql.String(length=200)),
    sql.Column("length_dataset", sql.Integer),
    sql.Column("score_mae", sql.Float, nullable=True),
    sql.Column("score_r2", sql.Float, nullable=True),
    sql.Column("best_start_year", sql.Integer, nullable=True),
)
sf_XGB = sql.Table(
    "sales_forecast_XGB_parameters",
    metadata,
    sql.Column("id", sql.Integer, primary_key=True),
    sql.Column(
        "forecast_id",
        sql.Integer,
        sql.ForeignKey(
            "sales_forecast_statistics.id", onupdate="CASCADE", ondelete="CASCADE"
        ),
        unique=True,
    ),
    sql.Column("n_estimators", sql.Integer),
    sql.Column("learning_rate", sql.Float),
    sql.Column("max_depth", sql.Integer),
    sql.Column("min_child_weight", sql.Integer),
    sql.Column("gamma", sql.Float),
    sql.Column("subsample", sql.Float),
    sql.Column("colsample_bytree", sql.Float),
    sql.Column("early_stopping_rounds", sql.Integer),
)