masksemi 0.0.1

Code for converting a list of labels in a scikit-like semi-supervised labels.

masksemi

Code for converting a label list in a scikit-like semi-supervised label list.

Getting Started

Dependencies

You need Python 3.7 or later to use masksemi. You can find it at python.org.

You also need numpy package, which is available from PyPI. If you have pip, just run:

pip install numpy

Installation

Clone this repo to your local machine using:

git clone https://github.com/caiocarneloz/masksemi.git

Features

Given the label list and a certain percentage, mask the amount of unlabeled data based on percentage and also encode the data for scikit usage with semi-supervised models. The percentage split is considered by class. This way, all classes will have the given percentage as labeled data.

Usage

Considering iris dataset labels:

array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
        ...,
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
        ...,
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',
       'Iris-virginica', 'Iris-virginica', ...], dtype=object)

The maskData function is called by passing labels and the percentage of labeled data:

masked_labels = maskData(labels, 0.1)

The labels are encoded and masked:

array([-1, -1, -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0, -1, -1, -1,
       -1,  0,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1,  1, -1,  1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1,  2, -1, -1, -1, -1,  2, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  2,  2, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  2])