sklearn_utils

Utility functions, preprocessing steps, and class I need during in my research and developement projects in scikit learn.

Installation

You can install sklearn-utils with pip:

pip install sklearn-utils

Examples

If you want to scale your data based on reference values you may use StandardScalerByLabel. For example, I scale all the blood sample by healthy samples.

from sklearn_utils.preprocessing import StandardScalerByLabel

preprocessing = StandardScalerByLabel('healthy')
X_t = preprocessing.fit_transform(X, y)

Or you may want your list of dict in the end of sklearn pipeline, after set of operations and feature selection.

from sklearn_utils.preprocessing import InverseDictVectorizer

vect = DictVectorizer(sparse=False) 
skb = SelectKBest(k=100)
pipe = Pipeline([
    ('vect', vect),
    ('skb', skb),
    ('inv_vect', InverseDictVectorizer(vect, skb))
])

X_t = pipe.fit_transform(X, y)

For more features, You can check the documentation.

Documentation

The documentation of the project avaiable in http://sklearn-utils.rtfd.io .