Transformers Usage

This tutorial helps you to understand how you can transform your data using DataTransformer and MatrixTransformer classes and how to make your own classes for data transformation.

1. MatrixTransformer

import numpy as np

from reskit.normalizations import mean_norm
from reskit.core import MatrixTransformer

matrix_0 = np.random.rand(5, 5)
matrix_1 = np.random.rand(5, 5)
matrix_2 = np.random.rand(5, 5)
y = np.array([0, 0, 1])

X = np.array([matrix_0,
              matrix_1,
              matrix_2])

output = np.array([mean_norm(matrix_0),
                   mean_norm(matrix_1),
                   mean_norm(matrix_2)])

result = MatrixTransformer(
            func=mean_norm).fit_transform(X)

(output == result).all()

True

This is a simple example of MatrixTransformer usage. Input X for transformation with MatrixTransformer should be a 3 dimensional array (array of matrices). So, MatrixTransformer just transforms each matrix in X.

If you have a data with specific data structure it is useful and convenient to write your function for data processing.

2. DataTransformer

To simply write new transformers we provide DataTransformer. The main idea is to write functions which takes some X and output transformed X. Thus, you shouldn’t write a transformation class for compatibility with sklearn pipelines. So, here is example of DataTransformer usage:

from reskit.core import DataTransformer


def mean_norm_trans(X):
    X = X.copy()
    N = len(X)
    for i in range(N):
        X[i] = mean_norm(X[i])
    return X

result = DataTransformer(
            func=mean_norm_trans).fit_transform(X)

(output == result).all()

True

As you can see, we writed the same transformation, but with DataTransformer instead of MatrixTransformer.

3. Your own transformer

If you need more flexibility in transformation, you can implement your own transformer. Simplest example:

from sklearn.base import TransformerMixin
from sklearn.base import BaseEstimator

class MyTransformer(BaseEstimator, TransformerMixin):

    def __init__(self):
        pass

    def fit(self, X, y=None, **fit_params):
        #
        # Write here the code if transformer need
        # to learn anything from data.
        #
        # Usually nothing should be here,
        # just return self.
        #
        return self

    def transform(self, X):
        #
        # Write here your transformation
        #
        return X