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