examples/ibis/feature_engineering/column_dataflow.py

from typing import Optional

import ibis
import ibis.expr.types as ir

from hamilton.function_modifiers import extract_columns
from hamilton.plugins import ibis_extensions  # noqa: F401


# extract specific columns from the table
@extract_columns("son", "pet", "month_of_absence")
def raw_table(raw_data_path: str) -> ir.Table:
    """Load the CSV found at `raw_data_path` into a Table expression
    and format columns to snakecase
    """
    return ibis.read_csv(sources=raw_data_path, table_name="absenteism").rename("snake_case")


# accesses a single column from `raw_table`
def has_children(son: ir.Column) -> ir.BooleanColumn:
    """True if someone has any children"""
    return ibis.ifelse(son > 0, True, False)


# narrows the return type from `ir.Column` to `ir.BooleanColumn`
def has_pet(pet: ir.Column) -> ir.BooleanColumn:
    """True if someone has any pets"""
    return ibis.ifelse(pet > 0, True, False).cast(bool)


# typing and docstring provides business context to features
def is_summer_brazil(month_of_absence: ir.Column) -> ir.BooleanColumn:
    """True if it is summer in Brazil during this month

    People in the northern hemisphere are likely to take vacations
    to warm places when it's cold locally
    """
    return month_of_absence.isin([1, 2, 12])


def feature_table(
    raw_table: ir.Table,
    has_children: ir.BooleanColumn,
    has_pet: ir.BooleanColumn,
    is_summer_brazil: ir.BooleanColumn,
) -> ir.Table:
    """Join computed features to the `raw_data` table"""
    return raw_table.mutate(
        has_children=has_children,
        has_pet=has_pet,
        is_summer_brazil=is_summer_brazil,
    )


def feature_set(
    feature_table: ir.Table,
    feature_selection: list[str],
    condition: Optional[ibis.common.deferred.Deferred] = None,
) -> ir.Table:
    """Select feature columns and filter rows"""
    return feature_table[feature_selection].filter(condition)