example-wise-f1-maximizer 0.1.1

A scikit-learn meta-estimator for multi-label classification that aims to maximize the example-wise F1 measure

Example-wise F1 Maximizer

Important links: Issue Tracker | Changelog | Code of Conduct

This software package provides an implementation of a meta-learning algorithm for multi-label classification that aims to maximize the example-wise F1-measure. It integrates with the popular scikit-learn machine learning framework and can also be used with frameworks for multi-label classification like scikit-multilearn.

The goal of multi-label classification is the automatic assignment of sets of labels to individual data points, for example, the annotation of text documents with topics. The example-wise F1-measure is a particularly relevant evaluation measure for this kind of predictions, as it requires a classifier to achieve a good balance between labels predicted as relevant or irrelevant for an example, i.e., it must neither be to conservative nor to aggressive when it comes to predicting labels as relevant.

Methodology

The algorithm implemented by this project transforms an original multi-label problem with n labels into a series of n * n + 1 binary classification problems. A probabilistic base estimator is then fit to each of these independent sub-problems as described in the following paper:

Mingyuan Zhan, Harish G. Ramaswamy, and Shivani Agarwal. Convex Calibrated Surrogates for the Multi-Label F-Measure. In: Proceedings of the International Conference on Machine Learning (ICML), 2020.

The probabilities predicted by the individual base estimators for unseen examples consitute a n x n probability matrix p, as well as an additional probability p_0. Whereas p_0 corresponds to the prior probability of the null vector, i.e., a label vector that does not contain any relevant labels, each probability p_ik at the i-th row and k-th column of p corresponds to the conditional probability of a label vector with k relevant labels, where the i-th label is relevant. In order to identify the label vector that maximizes the F1-measure in expectation, these probabilities are used as inputs to the "General F-Measure maximizer" (GFM), as proposed in the following paper:

Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, and Eyke Hüllermeier. An Exact Algorithm for F-Measure Maximization. In: Advances in Neural Information Processing Systems, 2011.

Please note that this implementation has not been written by any of the authors shown above.

Documentation

Installation

The software package is available at PiPy and can easily be installed via PIP using the following command:

pip install example-wise-f1-maximizer

Usage

To use the classifier in your own Python code, you need to import the class ExampleWiseF1Maximizer. It can be instantiated and used as shown below:

from example_wise_f1_maximizer import ExampleWiseF1Maximizer
from sklearn.linear_model import LogisticRegression

clf = ExampleWiseF1Maximizer(estimator=LogisticRegression())
x = [[  1,  2,  3],  # Two training examples with three features
     [ 11, 12, 13]]
y = [[1, 0],  # Ground truth labels of each training example
     [0, 1]]
clf.fit(x, y)
pred = clf.predict(x)

The fit method accepts two inputs, x and y:

• A two-dimensional feature matrix x, where each row corresponds to a training example and each column corresponds to a particular feature.
• A two-dimensional binary label matrix y, where each row corresponds to a training examples and each column corresponds to a label. If an element in the matrix is unlike zero, it indicates that respective label is relevant to an example. Elements that are equal to zero denote irrevant labels.

Both, x and y, are expected to be numpy arrays or equivalent array-like data types. In particular, the use of scipy sparse matrices is supported.

In the previous example, logistic regression as implemented by the class LogisticRegression from the scikit-learn framework is used as a base estimator. Alternatively, you can use any probabilistic estimator for binary classification that is compatible with the scikit-learn framework and implements the predict_proba function.

License

This project is open source software licensed under the terms of the MIT license. We welcome contributions to the project to enhance its functionality and make it more accessible to a broader audience.

All contributions to the project and discussions on the issue tracker are expected to follow the code of conduct.