kts 0.0.46

Competition-oriented framework for interactive feature engineering and building reproducible pipelines

Kaggle Tool Set

kts is a working title, highly likely it will be changed to avoid legal consequences.

Getting started

To install the package, just clone the repo to a directory included in PYTHONPATH.

What works by now

• Base of feature engineering submodule

How it works

First of all, you need to import the module:

import kts
from kts import *

Then you should define a function to make new features based on your input dataframe:

def make_new_features(df):
    ...

To test it out, use @test decorator from kts or kts.feature:

@test
def make_new_features(df):
    ...

When you're sure that your function works fine, @register it:

@register
def make_new_features(df):
    ...

Since registering source of the function is stored in storage/features and calls are cached unless no_cache=True is used.

The function will also be contained in kts.storage.feature_constructors. If you want to separate feature engineering from other steps of your pipeline, you can easily define all registered functions in a new notebook via

kts.storage.feature_constructors.define_in_scope(globals())

To learn more, read source and example notebook.