https://github.com/mblondel/fenchel-young-losses

Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses
https://github.com/mblondel/fenchel-young-losses

loss-functions probabilistic-classification pytorch sklearn tensorflow

Last synced: about 11 hours ago
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Probabilistic classification in PyTorch/TensorFlow/scikit-learn with Fenchel-Young losses

• Host: GitHub
• URL: https://github.com/mblondel/fenchel-young-losses
• Owner: mblondel
• Created: 2018-10-17T13:28:51.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-09-19T22:09:22.000Z (about 1 year ago)
• Last Synced: 2024-09-18T06:16:50.231Z (7 days ago)
• Topics: loss-functions, probabilistic-classification, pytorch, sklearn, tensorflow
• Language: Python
• Homepage:
• Size: 244 KB
• Stars: 183
• Watchers: 11
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst

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README

        
.. -*- mode: rst -*-

Fenchel-Young losses

=====================

This package implements loss functions useful for **probabilistic classification**. More specifically, it provides

* drop-in replacements for PyTorch loss functions

* drop-in replacements for TensorFlow loss functions

* scikit-learn compatible classifiers

The package is based on the **Fenchel-Young loss** framework [1,2,3].

.. image:: examples/tsallis.png

   :alt: Tsallis losses

   :align: center

Notice from the center plot that sparsemax and Tsallis are able to produce **exactly zero** (sparse) probabilities unlike the logistic (softmax) loss.

Supported Fenchel-Young losses

------------------------------

* Multinomial logistic loss

* One-vs-all logistic loss

* Sparsemax loss (**sparse** probabilities!)

* Tsallis losses (**sparse** probabilities!)

Sparse means that some classes have exactly zero probability, i.e., these classes are irrelevant.

Tsallis losses are a family of losses parametrized by a positive real value α. They recover the multinomial logistic loss with α=1 and the sparsemax loss with α=2. Values of α between 1 and 2 enable to interpolate between the two losses.

In all losses above, the ground-truth can either be a n_samples 1d-array of **label integers** (each label should be between 0 and n_classes-1) or a n_samples x n_classes 2d-array of **label proportions** (each row should sum to 1).

Examples

---------

scikit-learn compatible classifier:

.. code-block:: python

  import numpy as np

  from sklearn.datasets import make_classification

  from fyl_sklearn import FYClassifier

  X, y = make_classification(n_samples=10, n_features=5, n_informative=3,

                             n_classes=3, random_state=0)

  clf = FYClassifier(loss="sparsemax")

  clf.fit(X, y)

  print(clf.predict_proba(X[:3]))

Drop-in replacement for PyTorch losses:

.. code-block:: python

  import torch

  from fyl_pytorch import SparsemaxLoss

  # integers between 0 and n_classes-1, shape = n_samples

  y_true = torch.tensor([0, 2])

  # model scores, shapes = n_samples x n_classes

  theta = torch.tensor([[-2.5, 1.2, 0.5],

                        [2.2, 0.8, -1.5]])

  loss = SparsemaxLoss()

  # loss value (caution: reversed convention compared to numpy and tensorflow)

  print(loss(theta, y_true))

  # predictions (probabilities) are stored for convenience

  print(loss.y_pred)

  # can also recompute them from theta

  print(loss.predict(theta))

  # label proportions are also allowed

  y_true = torch.tensor([[0.8, 0.2, 0],

                         [0.1, 0.2, 0.7]])

  print(loss(theta, y_true))

Drop-in replacement for tensorflow losses:

.. code-block:: python

  import tensorflow as tf

  from fyl_tensorflow import sparsemax_loss, sparsemax_predict

  # integers between 0 and n_classes-1, shape = n_samples

  y_true = tf.constant([0, 2])

  # model scores, shapes = n_samples x n_classes

  theta = tf.constant([[-2.5, 1.2, 0.5],

                       [2.2, 0.8, -1.5]])

  # loss value

  print(sparsemax_loss(y_true, theta))

  # predictions (probabilities)

  print(sparsemax_predict(theta))

  # label proportions are also allowed

  y_true = tf.constant([[0.8, 0.2, 0],

                        [0.1, 0.2, 0.7]])

  print(sparsemax_loss(y_true, theta))

Installation

------------

Simply copy relevant files to your project.

The TensorFlow implementation requires the installation of `TensorFlow Addons `_.

References

----------

.. [1] *SparseMAP: Differentiable Sparse Structured Inference.*

        Vlad Niculae, André F. T. Martins, Mathieu Blondel, Claire Cardie.

        In Proc. of ICML 2018.

        [`arXiv `_]

.. [2] *Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms.*

        Mathieu Blondel, André F. T. Martins, Vlad Niculae.

        In Proc. of AISTATS 2019.

        [`arXiv `_]

        

.. [3] *Learning with Fenchel-Young Losses.*

        Mathieu Blondel, André F. T. Martins, Vlad Niculae.

        Preprint.

        [`arXiv `_]

        

Author

------

- Mathieu Blondel, 2018