Feature engineering library that helps you keep track of feature dependencies, documentation and schema

Overview

featureclass

Feature engineering library that helps you keep track of feature dependencies, documentation and schema

This library helps define a featureclass.
featureclass is inspired by dataclass, and is meant to provide alternative way to define features engineering classes.

I have noticed that the below code is pretty common when doing feature engineering:

from statistics import variance
from math import sqrt
class MyFeatures:
    def calc_all(self, datapoint):
        out = {}
        out['var'] = self.calc_var(datapoint),
        out['stdev'] = self.calc_std(out['var'])
        return out
        
    def calc_var(self, data) -> float:
        return variance(data)

    def calc_stdev(self, var) -> float:
        return sqrt(var)

Some things were missing for me from this type of implementation:

  1. Implicit dependencies between features
  2. No simple schema
  3. No documentation for features
  4. Duplicate declaration of the same feature - once as a function and one as a dict key

This is why I created this library.
I turned the above code into this:

float: """Calc stdev""" return sqrt(self.var) print(feature_names(MyFeatures)) # ('var', 'stdev') print(feature_annotations(MyFeatures)) # {'var': float, 'stdev': float} print(asDict(MyFeatures([1,2,3,4,5]))) # {'var': 2.5, 'stdev': 1.5811388300841898} print(asDataclass(MyFeatures([1,2,3,4,5]))) # MyFeatures(stdev=1.5811388300841898, var=2.5) ">
from featureclass import feature, featureclass, feature_names, feature_annotations, asDict, asDataclass
from statistics import variance
from math import sqrt

@featureclass
class MyFeatures:
    def __init__(self, datapoint):
        self.datapoint = datapoint
    
    @feature()
    def var(self) -> float:
        """Calc variance"""
        return variance(self.datapoint)

    @feature()
    def stdev(self) -> float:
        """Calc stdev"""
        return sqrt(self.var)

print(feature_names(MyFeatures)) # ('var', 'stdev')
print(feature_annotations(MyFeatures)) # {'var': float, 'stdev': float}
print(asDict(MyFeatures([1,2,3,4,5]))) # {'var': 2.5, 'stdev': 1.5811388300841898}
print(asDataclass(MyFeatures([1,2,3,4,5]))) # MyFeatures(stdev=1.5811388300841898, var=2.5)

The feature decorator is using cached_property to cache the feature calculation,
making sure that each feature is calculated once per datapoint

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Releases(0.3.0)
  • 0.3.0(Jan 19, 2022)

    What's Changed

      • rename asDict to as_dict and asDataclass to as_dataclass by @Itayazolay in https://github.com/Itayazolay/featureclass/pull/4

    Full Changelog: https://github.com/Itayazolay/featureclass/compare/0.2.1...0.3.0

    Source code(tar.gz)
    Source code(zip)
  • 0.2.1(Jan 11, 2022)

    What's Changed

    • docs, formatting and tests by @Itayazolay in https://github.com/Itayazolay/featureclass/pull/1
    • fine-tune mypy type ignore by @Itayazolay in https://github.com/Itayazolay/featureclass/pull/3

    New Contributors

    • @Itayazolay made their first contribution in https://github.com/Itayazolay/featureclass/pull/1

    Full Changelog: https://github.com/Itayazolay/featureclass/compare/0.2.0...0.2.1

    Source code(tar.gz)
    Source code(zip)
  • 0.2.0(Jan 9, 2022)

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