Contribution

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This project started becauser we saw people rewrite the same transformers and estimators at clients over and over again. Our goal is to have a place where more experimental building blocks for scikit learn pipelines might exist. This means we’re usually open to ideas to add here but there are a few things to keep in mind.

Before You Make a New Feature

  1. Discuss the feature and implementation you want to add on Github before you write a PR for it.
  2. Features need a somewhat general usecase. If the usecase is very niche it will be hard for us to consider maintaining it.
  3. If you’re going to add a feature consider if you could help out in the maintenance of it.

When Writing a New Feature

When writing a new feauture there’s some more details with regard to how scikit learn likes to have it’s parts implemented. We will display the a sample implementation of the RandomAdder below.

from sklearn.base import BaseEstimator, TransformerMixin, MetaEstimatorMixin
from sklearn.utils import check_array, check_X_y
from sklearn.utils.validation import FLOAT_DTYPES, check_random_state, check_is_fitted

from sklego.common import TrainOnlyTransformerMixin


class RandomAdder(TrainOnlyTransformerMixin, BaseEstimator):
    def __init__(self, noise=1, random_state=None):
        self.noise = noise
        self.random_state = random_state

    def fit(self, X, y):
        super().fit(X, y)
        X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES)
        self.dim_ = X.shape[1]

        return self

    def transform_train(self, X):
        rs = check_random_state(self.random_state)
        check_is_fitted(self, ['dim_'])

        X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)

        return X + rs.normal(0, self.noise, size=X.shape)

There’s a few good practices we observe here that we’d appreciate seeing in pull requests. We want to re-use features from sklearn as much as possible. In particular, for this example:

  1. We inherit from the mixins found in sklearn.
  2. We use the validation utils from sklearn in our object to confirm if the model is fitted, if the array going into the model is of the correct type and if the random state is appropriate.

Feel free to look at example implementations before writing your own from scratch.

Unit Tests

We write unit tests on these objects to make sure that they will work in a Pipeline. This must be guaranteed. To facilitate this we have some “standard” tests that will check things like “do we change the shape of the input”? If your transformer belongs here: feel free to add it.