featkit 0.4.2

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

Feature naming anatomy

Every feature produced by featkit has a deterministic, human-readable name built from fixed segments separated by __ (double underscore). Understanding the segments lets you decode any feature name without looking at the code.

There are four families of features, each with its own naming pattern.

---

Layer 2A — Pivot features

Pattern: {AGG}__{MEASUREMENT}[__{FIELD}_{VALUE}…]

Segment	Source	Example
AGG	Layer2Aggregator enum	SUM, COUNT, AVG, MIN, MAX
MEASUREMENT	MeasurementField.name	MTO, TRX
FIELD_VALUE	CategoricalField.name + _ + value, one per non-marginal field, sorted alphabetically by field name	CANAL_DIGITAL, SECTOR_RETAIL

The valid aggregators for each MEASUREMENT depend on its MeasurementType. Only contract-permitted aggregator–measurement combinations are generated.

Measurement type	Semantic meaning	Valid AGG values
MONTO	Monetary amount	SUM, MAX, MIN, AVG
CANTIDAD	Count / quantity	SUM
TICKET	Average ticket size	AVG
FLAG	Binary indicator	MAX
FECHA	Date / timestamp	MAX, MIN
BALANCE	Point-in-time balance	MAX, MIN, AVG
TIME_DIFF	Duration / elapsed time	SUM, AVG, MAX, MIN
ESTADISTICO	Generic statistic	SUM, AVG, MAX, MIN, COUNT

Categorical fields set to the ∅ marginal (no filter on that dimension) are omitted from the name entirely, so the name implicitly aggregates over all values of that dimension.

SUM__MTO                                  # global — all sectors, all channels
SUM__MTO__CANAL_DIGITAL                   # CANAL=DIGITAL, marginal over SECTOR
SUM__MTO__SECTOR_RETAIL                   # SECTOR=RETAIL, marginal over CANAL
SUM__MTO__CANAL_DIGITAL__SECTOR_RETAIL    # CANAL=DIGITAL and SECTOR=RETAIL (alphabetical order)
SUM__TRX__CANAL_PRESENCIAL                # sum of TRX (CANTIDAD → only SUM is valid) for PRESENCIAL channel

---

Layer 2B — Distributional features

Pattern: {CATEGORICAL}__{MEASUREMENT}__{AGG}__{METRIC}

Segment	Source	Example
CATEGORICAL	CategoricalField.name	CANAL, SECTOR
MEASUREMENT	MeasurementField.name	MTO
AGG	Layer2Aggregator enum	SUM
METRIC	DistributionalMetric enum	ENTROPY, HHI, DOMINANT_PROPORTION, MODE, COUNT

These columns capture the shape of the value distribution of a categorical field, weighted by the aggregated measurement.

Metric	What it measures
ENTROPY	Shannon entropy of the category distribution — higher means more uniform spread
HHI	Herfindahl-Hirschman Index — concentration; higher means more dominated by one value
DOMINANT_PROPORTION	Share of the most common category value
MODE	The most frequent category value (output type: categorical)
COUNT	Number of distinct observed values

CANAL__MTO__SUM__ENTROPY            # entropy of channel distribution by amount
SECTOR__TRX__SUM__HHI               # HHI of sector distribution by transaction count (CANTIDAD → only SUM)
CANAL__MTO__SUM__MODE               # dominant channel by amount (categorical output)

---

Layer 2C — Ratio features

Pattern: {NUMERATOR}__over__{DENOMINATOR}

where NUMERATOR and DENOMINATOR are full Layer 2A pivot feature names. The denominator is always a proper marginal projection of the numerator: it has at least one categorical dimension set to ∅ that is non-∅ in the numerator, and no contradicting values.

The underlying value is numerator / NULLIF(denominator, 0) computed per entity per period.

# Numerator: DIGITAL channel + RETAIL sector
# Denominator: RETAIL sector only (CANAL marginalized → share of DIGITAL within RETAIL)
SUM__MTO__CANAL_DIGITAL__SECTOR_RETAIL__over__SUM__MTO__SECTOR_RETAIL

# Denominator: DIGITAL channel only (SECTOR marginalized → share of RETAIL within DIGITAL)
SUM__MTO__CANAL_DIGITAL__SECTOR_RETAIL__over__SUM__MTO__CANAL_DIGITAL

# Denominator: global total (both marginalized → share of DIGITAL/RETAIL in total portfolio)
SUM__MTO__CANAL_DIGITAL__SECTOR_RETAIL__over__SUM__MTO

---

Layer 3 — Temporal features

Pattern: {L2_NAME}__{OPERATOR}__{DIRECTION}[__{WINDOW}]

L2_NAME is the full name of any Layer 2A, 2B, or 2C feature. The temporal segments are appended at the end.

Segment	Source	Notes
OPERATOR	TemporalOperator enum	See table below
DIRECTION	TimeWindowDirection enum	BACKWARD or FORWARD
WINDOW	window_size (integer, number of periods)	Omitted for point-in-time operators

Temporal operators

Operator	Type	Description
PROM_U	Windowed	Arithmetic mean of the monthly values over the window — each period contributes equally regardless of its volume
PROM_P	Windowed	Volume-proportional weighted mean — each period's contribution is weighted by its share of the total aggregated value across the window; weights are derived automatically from the data, no user configuration required
SUM_U	Windowed	Unweighted sum of the monthly values over the window
SUM_P	Windowed	Volume-weighted sum over the window (analogous weighting to PROM_P)
MIN_U	Windowed	Minimum value observed in the window
MAX_U	Windowed	Maximum value observed in the window
CREC	Windowed	Growth rate across the window
FREQ	Windowed	Count of periods in the window where the value was non-null and strictly greater than 0
XM	Windowed	1 if every period in the window had a non-null and strictly positive value, 0 otherwise — an all-or-nothing activity indicator (e.g. 1 means the customer was active on every single month in the window)
MEDIA_ABS	Windowed (composed)	Mean absolute deviation over the window
RATIO	Windowed (composed)	Ratio of two sub-windows
ULT_MES	Point-in-time	Value at the most recent period (no window suffix)
PREV_MES	Point-in-time	Value at the immediately preceding period (no window suffix)
REC	Point-in-time	Recency — periods elapsed since last non-null / non-zero observation (no window suffix)

Valid operators per Layer 2 output type

Output type	Valid operators
NUMERIC	PROM_U, PROM_P, SUM_U, SUM_P, MIN_U, MAX_U, CREC, FREQ, XM, ULT_MES, PREV_MES, MEDIA_ABS, RATIO
FLAG	ULT_MES, PREV_MES, FREQ, XM, REC
CATEGORICAL	ULT_MES, PREV_MES, REC
TEMPORAL	ULT_MES, PREV_MES, REC, MIN_U, MAX_U, CREC

Examples

# Average amount (DIGITAL + RETAIL) over the last 6 months
SUM__MTO__CANAL_DIGITAL__SECTOR_RETAIL__PROM_U__BACKWARD__6

# Total transaction sum for RETAIL sector in the last 3 months (CANTIDAD → only SUM valid)
SUM__TRX__SECTOR_RETAIL__SUM_U__BACKWARD__3

# Most recent value of the CANAL entropy (by amount)
CANAL__MTO__SUM__ENTROPY__ULT_MES__BACKWARD

# Share of DIGITAL/RETAIL in total portfolio, averaged over last 12 months
SUM__MTO__CANAL_DIGITAL__SECTOR_RETAIL__over__SUM__MTO__PROM_U__BACKWARD__12

# Recency of the dominant channel (MODE is categorical → only REC/ULT_MES/PREV_MES valid)
CANAL__MTO__SUM__MODE__REC__BACKWARD

---

Quick-reference: full name structure

┌─ Layer 2A pivot ──────────────────────────────────────────────────┐
│  AGG  __  MEASUREMENT  [__  FIELD_VALUE  …]                       │
└───────────────────────────────────────────────────────────────────┘

┌─ Layer 2B distributional ─────────────────────────────────────────┐
│  CATEGORICAL  __  MEASUREMENT  __  AGG  __  METRIC                │
└───────────────────────────────────────────────────────────────────┘

┌─ Layer 2C ratio ──────────────────────────────────────────────────┐
│  {Layer 2A name}  __over__  {Layer 2A name}                       │
└───────────────────────────────────────────────────────────────────┘

┌─ Layer 3 temporal (windowed) ─────────────────────────────────────┐
│  {Layer 2A/2B/2C name}  __  OPERATOR  __  DIRECTION  __  WINDOW   │
└───────────────────────────────────────────────────────────────────┘

┌─ Layer 3 temporal (point-in-time) ────────────────────────────────┐
│  {Layer 2A/2B/2C name}  __  OPERATOR  __  DIRECTION               │
└───────────────────────────────────────────────────────────────────┘

Architecture

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

License

MIT