featkit 0.4.1

featkit — automated feature store generation from relational facts tables

featkit

featkit is a Python framework for automated feature store generation from relational facts tables.

It implements a three-layer architecture:

• Layer 1 — input facts table with typed columns (ID, time, categorical, measurement)
• Layer 2 — horizontal concept table built via pivot (2A) and distributional aggregations (2B)
• Layer 3 — temporal feature table produced by sliding operators over the Layer 2 columns

The framework is engine-agnostic: the same pipeline definition produces either a standalone SQL script (Snowflake, Databricks SQL, Spark SQL) or a lazy PySpark execution plan, with the choice abstracted behind a code generator interface.

Key concepts

Layer	What it does
Layer 2A — Pivot	GROUP BY (ID, time) + CASE WHEN per categorical combination × measurement × aggregator
Layer 2B — Distributional	Per-categorical CTEs computing entropy, HHI, dominant proportion, mode, count
Layer 3 — Temporal	Sliding window operators (PROM_U, SUM_U, CREC, FREQ, REC, …) over all Layer 2 columns

Installation

pip install featkit

Quickstart

from featkit import FeatureStorePipeline, FeatureStoreConfig
from featkit.dataset import SimpleDataset
from featkit.fields import IDField, TimeField, CategoricalField, MeasurementField
from featkit.enums import MeasurementType, TimeGranularity, CategoricalTreatment
from featkit.generators.sql import SnowflakeSQLCodeGenerator

# Define schema
fields = [
    IDField(name="ID_CLIENTE"),
    TimeField(name="PERIODO",
              source_granularity=TimeGranularity.MONTHLY,
              target_granularity=TimeGranularity.MONTHLY),
    CategoricalField(name="SECTOR", treatment=CategoricalTreatment.PIVOT,
                     allowed_values=["RETAIL", "CORP", "PYME"]),
    CategoricalField(name="CANAL",  treatment=CategoricalTreatment.PIVOT,
                     allowed_values=["DIGITAL", "PRESENCIAL", "TELEFONO"]),
    MeasurementField(name="MTO", measurement_type=MeasurementType.MONTO),
    MeasurementField(name="TRX", measurement_type=MeasurementType.CANTIDAD),
]

dataset = SimpleDataset(
    source_reference="MY_DB.MY_SCHEMA.FACTS_TABLE",
    fields=fields,
)

config = FeatureStoreConfig(
    dataset=dataset,
    output_schema="MY_DB.MY_SCHEMA",
    output_table_prefix="FS",
    time_windows=[3, 6, 9, 12],
)

pipeline = FeatureStorePipeline(config).build()
output = pipeline.run(SnowflakeSQLCodeGenerator())

output.save("./output")
# Writes: output/script.sql, output/dag.json, output/diagram.md

Architecture

See docs/general_plan.md for the full implementation plan.

License

MIT