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:

from featureclass import feature, featureclass, feature_names, feature_annotations, asDict, asDataclass
from statistics import variance
from math import sqrt

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

    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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