61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
from pathlib import Path
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import sqlalchemy as sql
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# ** meta
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metadata = sql.MetaData()
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def get_engine(
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db_path: Path,
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echo: bool = False,
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) -> sql.Engine:
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path = db_path.resolve()
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connection_str: str = f"sqlite:///{str(path)}"
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engine = sql.create_engine(connection_str, echo=echo)
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return engine
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# ** table declarations
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# ** ---- common
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perf_meas = sql.Table(
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"performance_measurement",
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metadata,
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sql.Column("id", sql.Integer, primary_key=True),
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sql.Column("pipeline_name", sql.String(length=30)),
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sql.Column("execution_duration", sql.Float),
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)
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# ** ---- forecasts
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sf_stats = sql.Table(
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"sales_forecast_statistics",
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metadata,
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sql.Column("id", sql.Integer, primary_key=True),
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sql.Column("status_code", sql.Integer),
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sql.Column("status_dscr", sql.String(length=200)),
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sql.Column("length_dataset", sql.Integer),
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sql.Column("score_mae", sql.Float, nullable=True),
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sql.Column("score_r2", sql.Float, nullable=True),
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sql.Column("best_start_year", sql.Integer, nullable=True),
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)
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sf_XGB = sql.Table(
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"sales_forecast_XGB_parameters",
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metadata,
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sql.Column("id", sql.Integer, primary_key=True),
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sql.Column(
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"forecast_id",
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sql.Integer,
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sql.ForeignKey(
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"sales_forecast_statistics.id", onupdate="CASCADE", ondelete="CASCADE"
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),
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unique=True,
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),
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sql.Column("n_estimators", sql.Integer),
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sql.Column("learning_rate", sql.Float),
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sql.Column("max_depth", sql.Integer),
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sql.Column("min_child_weight", sql.Integer),
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sql.Column("gamma", sql.Float),
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sql.Column("subsample", sql.Float),
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sql.Column("colsample_bytree", sql.Float),
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sql.Column("early_stopping_rounds", sql.Integer),
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)
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