.. figure:: https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/cleanlab_logo.png
   :target: https://github.com/cleanlab/cleanlab/
   :align: center
   :alt: cleanlab 

|  

``cleanlab`` is the data-centric ML ops package for **machine learning with noisy labels**. ``cleanlab`` ``clean``\s ``lab``\els and supports **finding, quantifying, and learning** with label errors in datasets. See datasets cleaned with ``cleanlab`` at `labelerrors.com <https://labelerrors.com>`__.

Check out the: `documentation <https://cleanlab.readthedocs.io/>`__, `examples <https://github.com/cleanlab/examples>`__, and `installation instructions <https://github.com/cleanlab/cleanlab#installation>`__

``cleanlab`` is powered by **confident learning**, published in this `paper <https://jair.org/index.php/jair/article/view/12125>`__ | `blog <https://l7.curtisnorthcutt.com/confident-learning>`__. 


|pypi| |os| |py_versions| |build_status| |coverage| |docs|

.. |pypi| image:: https://img.shields.io/pypi/v/cleanlab.svg
    :target: https://pypi.org/pypi/cleanlab/
.. |os| image:: https://img.shields.io/badge/platform-windows%20%7C%20macos%20%7C%20linux-lightgrey
    :target: https://pypi.org/pypi/cleanlab/
.. |py_versions| image:: https://img.shields.io/badge/python-2.7%20%7C%203.6%2B-blue
    :target: https://pypi.org/pypi/cleanlab/
.. |build_status| image:: https://github.com/cleanlab/cleanlab/workflows/CI/badge.svg
    :target: https://github.com/cleanlab/cleanlab/actions?query=workflow%3ACI
.. |coverage| image:: https://codecov.io/gh/cleanlab/cleanlab/branch/master/graph/badge.svg
    :target: https://app.codecov.io/gh/cleanlab/cleanlab
.. |docs| image:: https://readthedocs.org/projects/cleanlab/badge/?version=latest
    :target: https://cleanlab.readthedocs.io/en/latest/?badge=latest


Get started with tutorials
==========================

* (Easiest) Improve a simple classifier from 60% to 80% accuracy on the Iris dataset:
  
  * `TUTORIAL: simple cleanlab on Iris <https://github.com/cleanlab/examples/blob/master/iris_simple_example.ipynb>`__

* (Comprehensive) Image classification with noisy labels

  * `TUTORIAL: learning with noisy labels on CIFAR <https://github.com/cleanlab/examples/tree/master/cifar10>`__

* Run Cleanlab on 4 datasets using 9 different classifiers/models:
  
  * `TUTORIAL: classifier comparison <https://github.com/cleanlab/examples/blob/master/classifier_comparison.ipynb>`__

* Find `label errors <https://arxiv.org/abs/2103.14749>`_ in MNIST, ImageNet, CIFAR-10/100, Caltech-256, QuickDraw, Amazon Reviews, IMDB, 20 Newsgroups, AudioSet:

  * `TUTORIAL: Find Label Errors in the 10 most common ML benchmark test datasets with cleanlab <https://github.com/cleanlab/label-errors/blob/main/examples/Tutorial%20-%20How%20To%20Find%20Label%20Errors%20With%20CleanLab.ipynb>`__

* Demystifying `Confident Learning <https://www.jair.org/index.php/jair/article/view/12125>`_:

  * `TUTORIAL: confident learning with just numpy and for-loops <https://github.com/cleanlab/examples/blob/master/simplifying_confident_learning_tutorial.ipynb>`__
 
  * `TUTORIAL: visualizing confident learning <https://github.com/cleanlab/examples/blob/master/visualizing_confident_learning.ipynb>`__

****

.. raw:: html

    <details><summary><b>News! (2021) </b> -- <code>cleanlab</code> finds pervasive label errors in the most common ML test sets (<b>click to learn more</b>) </summary>
      <ul>
        <li> <b>Apr 2021 🎉</b>  Journal of AI Research published the <a href="https://jair.org/index.php/jair/article/view/12125">confident learning paper (Northcutt, Jiang, & Chuang, 2021)</a>.</li>
        <li><b>Mar 2021 😲</b>  <code>cleanlab</code> used to find and fix label errors in 10 of the most common ML benchmark datasets, published in: <a href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/f2217062e9a397a1dca429e7d70bc6ca-Abstract-round1.html">NeurIPS 2021</a>. Along with <a href="https://arxiv.org/abs/2103.14749">the paper (Northcutt, Athalye, & Mueller, 2021)</a>, the authors launched <a href="https://labelerrors.com">labelerrors.com</a> where you can view the label errors in these datasets.</li>
      </ul>
    </details>
 
  <details><summary><b>News! (2020) </b> -- <code>cleanlab</code> adds support for all OS, achieves state-of-the-art, supports co-teaching, and more (<b>click to learn more</b>) </summary>
   <p>
   
   <ul>
      <li> <b>Dec 2020 🎉</b>  <code>cleanlab</code> supports NeurIPS workshop paper <a href="http://securedata.lol/camera_ready/28.pdf">(Northcutt, Athalye, & Lin, 2020)</a>.</li>
      <li> <b>Dec 2020 🤖</b>  <code>cleanlab</code>` supports <a href="https://github.com/cleanlab/cleanlab#pu-learning-with-cleanlab">PU learning</a>.</li>
      <li> <b>Feb 2020 🤖</b>  <code>cleanlab</code> now natively supports Mac, Linux, and Windows.</li>
      <li> <b>Feb 2020 🤖</b>  <code>cleanlab</code> now supports <a href="https://github.com/cleanlab/cleanlab/blob/master/cleanlab/coteaching.py">Co-Teaching</a> <a href="https://arxiv.org/abs/1804.06872">(Han et al., 2018)</a>.</li>
      <li> <b>Jan 2020 🎉</b> <code>cleanlab</code> achieves state-of-the-art on CIFAR-10 with noisy labels. Code to reproduce:  <a href="https://github.com/cleanlab/examples/tree/master/cifar10">examples/cifar10</a>. This is a great place to see how to use cleanlab on real datasets (with   predicted_probas already precomputed for you).</li>
   </ul>


   </p>
   </details>

Past release notes and **future features planned**  is available `here <https://github.com/cleanlab/cleanlab/blob/master/cleanlab/version.py>`__.

****

So fresh, so ``cleanlab`` 
=========================

``cleanlab`` finds and cleans label errors in any dataset using `state-of-the-art algorithms <https://arxiv.org/abs/1911.00068>`__ to find label errors, characterize noise, and learn in spite of it. ``cleanlab`` is fast: its built on optimized algorithms and parallelized across CPU threads automatically. ``cleanlab`` is powered by `provable guarantees <https://arxiv.org/abs/1911.00068>`__ of exact noise estimation and label error finding in realistic cases when model output probabilities are erroneous. ``cleanlab`` supports multi-label, multiclass, sparse matrices, etc. By default, ``cleanlab`` requires no hyper-parameters.

``cleanlab`` implements the family of theory and algorithms called `confident learning <https://arxiv.org/abs/1911.00068>`__ with provable guarantees of exact noise estimation and label error finding (even when model output probabilities are noisy/imperfect). 

``cleanlab`` supports most weak supervision tasks: multi-label, multiclass, sparse matrices, etc. 

``cleanlab`` is:

1. backed-by-theory - Provable perfect label error finding in realistic conditions.
2. fast - Non-iterative, parallelized algorithms (e.g. < 1 second to find label errors in ImageNet)
3. general - Works with any ML or deep learning framework: Tensorflow, PyTorch, sklearn, xgboost, etc.
4. unique - The only package for weak supervion with any dataset / classifier.


Find label errors with PyTorch, Tensorflow, sklearn, xgboost, etc. in 1 line of code
------------------------------------------------------------------------------------

.. code:: python

   # Compute psx (n x m matrix of predicted probabilities) on your own, with any classifier.
   # Here is an example that shows in detail how to compute psx on CIFAR-10:
   #    https://github.com/cleanlab/examples/tree/master/cifar10
   # Be sure you compute probs in a holdout/out-of-sample manner (e.g. via cross-validation)
   # Now getting label errors is trivial with cleanlab... its one line of code.
   # Label errors are ordered by likelihood of being an error. First index is most likely error.
   from cleanlab.pruning import get_noise_indices

   ordered_label_errors = get_noise_indices(
       s=numpy_array_of_noisy_labels,
       psx=numpy_array_of_predicted_probabilities,
       sorted_index_method='normalized_margin', # Orders label errors
    )

**CAUTION:** Predicted probabilities from your model must be out-of-sample! You should never provide predictions on the same datapoints used to train the model, as these will be overfit and unsuitable for finding label-errors. To obtain out-of-sample predicted probabilities for every datapoint in your dataset, you can use `cross-validation <https://machinelearningmastery.com/out-of-fold-predictions-in-machine-learning/>`__. Alternatively it is ok if your model was trained on a separate dataset and you are only evaluating labels in data that was previously held-out.

Pre-computed **out-of-sample** predicted probabilities for CIFAR-10 train set are available: `here <https://github.com/cleanlab/examples/tree/master/cifar10#pre-computed-psx-for-every-noise--sparsity-condition>`__


Learning with noisy labels in 3 lines of code
---------------------------------------------
   
.. code:: python
   
   from cleanlab.classification import LearningWithNoisyLabels
   from sklearn.linear_model import LogisticRegression

   # Wrap around any classifier. Yup, you can use sklearn/pyTorch/Tensorflow/FastText/etc.
   lnl = LearningWithNoisyLabels(clf=LogisticRegression()) 
   lnl.fit(X=X_train_data, s=train_noisy_labels) 
   # Estimate the predictions you would have gotten by training with *no* label errors.
   predicted_test_labels = lnl.predict(X_test)


Check out these `examples <https://github.com/cleanlab/examples>`__ and `tests <https://github.com/cleanlab/cleanlab/tree/master/tests>`__ (includes how to use other types of models).

Learn cleanlab in 5min
----------------------

New to ``cleanlab``?  Try out these easy tutorials:

1. `Simple example of learning with noisy labels on the 
   Iris dataset (multiclass classification) <https://github.com/cleanlab/examples/blob/master/iris_simple_example.ipynb>`__.
2. `Learning with noisy labels on CIFAR <https://github.com/cleanlab/examples/tree/master/cifar10>`__

Use ``cleanlab`` with any model (Tensorflow, PyTorch, sklearn, xgboost, etc.)
-----------------------------------------------------------------------------

All of the features of the ``cleanlab`` package work with **any model**.
Yes, any model. Feel free to use PyTorch, Tensorflow, caffe2,
scikit-learn, mxnet, etc. If you use a scikit-learn classifier, all
``cleanlab`` methods will work out-of-the-box. It’s also easy to use
your favorite model from a non-scikit-learn package, just wrap your
model into a Python class that inherits the
``sklearn.base.BaseEstimator``:

.. code:: python

   from sklearn.base import BaseEstimator
   class YourFavoriteModel(BaseEstimator): # Inherits sklearn base classifier
       def __init__(self, ):
           pass
       def fit(self, X, y, sample_weight=None):
           pass
       def predict(self, X):
           pass
       def predict_proba(self, X):
           pass
       def score(self, X, y, sample_weight=None):
           pass
           
   # Now you can use your model with `cleanlab`. Here's one example:
   from cleanlab.classification import LearningWithNoisyLabels
   lnl = LearningWithNoisyLabels(clf=YourFavoriteModel())
   lnl.fit(train_data, train_labels_with_errors)

Want to see a working example? `Here’s a compliant PyTorch MNIST CNN class <https://github.com/cleanlab/cleanlab/blob/master/cleanlab/models/mnist_pytorch.py>`__
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

As you can see
`here <https://github.com/cleanlab/cleanlab/blob/master/cleanlab/models/mnist_pytorch.py>`__,
technically you don’t actually need to inherit from
``sklearn.base.BaseEstimator``, as you can just create a class that
defines `.fit()`, `.predict()`, and `.predict_proba()`, but inheriting makes
downstream scikit-learn applications like hyper-parameter optimization
work seamlessly. For example, the `LearningWithNoisyLabels()
model <https://github.com/cleanlab/cleanlab/blob/master/cleanlab/classification.py>`__
is fully compliant.

Note, some libraries exists to do this for you. For PyTorch, check out
the ``skorch`` Python library which will wrap your ``pytorch`` model
into a ``scikit-learn`` compliant model.


Installation
============

Python 3.6+ are supported. Linux, macOS, and Windows are supported.

Stable release (pip):

.. code-block:: bash

   $ pip install cleanlab  # Using pip

Stable release (conda):

.. code-block:: bash

   $ conda install -c conda-forge cleanlab  # Using conda

Developer release:

.. code-block:: bash

   $ pip install git+https://github.com/cleanlab/cleanlab.git

To install from the source code (enabling you to make modifications locally):

.. code-block:: bash

   $ conda update pip # if you use conda
   $ git clone https://github.com/cleanlab/cleanlab.git
   $ cd cleanlab
   $ pip install -e .


Reproducing Results in  `Confident Learning paper <https://arxiv.org/abs/1911.00068>`__ 
=======================================================================================

State of the Art Learning with Noisy Labels in CIFAR
----------------------------------------------------

A step-by-step guide to reproduce these results is available `here <https://github.com/cleanlab/examples/tree/master/cifar10>`__. This guide is also a good tutorial for using cleanlab on any large dataset. You'll need to ``git clone`` `confidentlearning-reproduce <https://github.com/cgnorthcutt/confidentlearning-reproduce>`__  which contains the data and files needed to reproduce the CIFAR-10 results.

.. figure:: https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/cifar10_benchmarks.png
   :align: center
   :alt: Image depicting CIFAR10 benchmarks 

Comparison of confident learning (CL), as implemented in `cleanlab`, versus seven recent methods for learning with noisy labels in CIFAR-10. Highlighted cells show CL robustness to sparsity. The five CL methods estimate label errors, remove them, then train on the cleaned data using `Co-Teaching <https://github.com/cleanlab/cleanlab/blob/master/cleanlab/coteaching.py>`__.

Observe how cleanlab (i.e. the CL method) is robust to large sparsity in label noise whereas prior art tends to reduce in performance for increased sparsity, as shown by the red highlighted regions. This is important because real-world label noise is often sparse, e.g. a tiger is likely to be mislabeled as a lion, but not as most other classes like airplane, bathtub, and microwave.

Find Label Errors in ImageNet
-----------------------------

Use ``cleanlab`` to identify ~100,000 label errors in the 2012 ILSVRC ImageNet training dataset: `examples/imagenet <https://github.com/cleanlab/examples/tree/master/imagenet>`__ 

.. figure:: https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/imagenet_train_label_errors_32.jpg
   :align: center
   :alt: Image depicting label errors in ImageNet train set 

Label issues in ImageNet train set found via ``cleanlab``. Label Errors are boxed in red. Ontological issues in green. Multi-label images in blue.

Find Label Errors in MNIST
--------------------------

Use ``cleanlab`` to identify ~50 label errors in the MNIST dataset: `examples/mnist <https://github.com/cleanlab/examples/tree/master/mnist>`__

.. figure:: https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/mnist_training_label_errors24_prune_by_noise_rate.png
   :align: center
   :alt: Image depicting label errors in MNIST train set 

Top 24 least-confident labels in the original MNIST **train** dataset, algorithmically identified via ``cleanlab``. Examples are ordered left-right, top-down by increasing self-confidence (predicted probability that the **given** label is correct), denoted **conf** in teal. The most-likely correct label (with largest predicted probability) is in green. Overt label errors highlighted in red.

 
``cleanlab`` Performance across 4 Data Distributions and 9 Classifiers
----------------------------------------------------------------------

``cleanlab`` is a general tool that can learn with noisy labels regardless of dataset distribution or classifier type: `examples/classifier_comparison <https://github.com/cleanlab/examples/blob/master/classifier_comparison.ipynb>`__ 

.. figure:: https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/demo_cleanlab_across_datasets_and_classifiers.png
   :align: center
   :alt: Image depicting generality of cleanlab across datasets and classifiers 

Each sub-figure above depicts the decision boundary learned using ``cleanlab.classification.LearningWithNoisyLabels`` in the presence of extreme (\~35%) label errors (circled in green). Label noise is class-conditional (not uniformly random). Columns are organized by the classifier used, except the left-most column which depicts the ground-truth data distribution. Rows are organized by dataset.

Each sub-figure depicts accuracy scores on a test set (with correct non-noisy labels) as decimal values: 

1. LEFT (in black): The classifier test accuracy trained with perfect labels (no label errors). 
2. MIDDLE (in blue): The classifier test accuracy trained with noisy labels using ``cleanlab``. 
3. RIGHT (in white): The baseline classifier test accuracy trained with noisy labels.

As an example, this is the noise matrix (noisy channel) *P(s \| y)* characterizing the label noise for the first dataset row in the figure. *s* represents the observed noisy labels and *y* represents the latent, true labels. The trace of this matrix is 2.6. A trace of 4 implies no label noise. A cell in this matrix is read like: "Around 38% of true underlying '3' labels were randomly flipped to '2' labels in the observed dataset."

======  ====  ====  ====  ==== 
p(s|y)   y=0   y=1   y=2   y=3
======  ====  ====  ====  ==== 
s=0     0.55  0.01  0.07  0.06
s=1     0.22  0.87  0.24  0.02
s=2     0.12  0.04  0.64  0.38
s=3     0.11  0.08  0.05  0.54
======  ====  ====  ====  ====


ML Research with cleanlab
=========================

``cleanlab`` Core Package Components
------------------------------------

1. **cleanlab/classification.py** - `LearningWithNoisyLabels()` class for learning with noisy labels.
2. **cleanlab/latent_algebra.py** -	Equalities when noise information is known.
3. **cleanlab/latent_estimation.py** -	Estimates and fully characterizes all variants of label noise.
4. **cleanlab/noise_generation.py** - Generate mathematically valid synthetic noise matrices.
5. **cleanlab/polyplex.py** -	Characterizes joint distribution of label noise EXACTLY from noisy channel.
6. **cleanlab/pruning.py** - Finds the examples with label errors in a dataset.

Many methods have default parameters not covered here. Check out the method docstrings and our `full documentation <https://cleanlab.readthedocs.io/>`__.

For additional details/notation, refer to `the Confident Learning paper <https://jair.org/index.php/jair/article/view/12125>`__.


Methods to Standardize Research with Noisy Labels
-------------------------------------------------

``cleanlab`` supports a number of functions to generate noise for benchmarking and standardization in research. This next example shows how to generate valid, class-conditional, unformly random noisy channel matrices:

.. code:: python

    # Generate a valid (necessary conditions for learnability are met) noise matrix for any trace > 1
    from cleanlab.noise_generation import generate_noise_matrix_from_trace
    noise_matrix=generate_noise_matrix_from_trace(
        K=number_of_classes, 
        trace=float_value_greater_than_1_and_leq_K,
        py=prior_of_y_actual_labels_which_is_just_an_array_of_length_K,
        frac_zero_noise_rates=float_from_0_to_1_controlling_sparsity,
    )

    # Check if a noise matrix is valid (necessary conditions for learnability are met)
    from cleanlab.noise_generation import noise_matrix_is_valid
    is_valid=noise_matrix_is_valid(noise_matrix, prior_of_y_which_is_just_an_array_of_length_K)

For a given noise matrix, this example shows how to generate noisy labels. Methods can be seeded for reproducibility.

.. code:: python

    # Generate noisy labels using the noise_marix. Guarantees exact amount of noise in labels.
    from cleanlab.noise_generation import generate_noisy_labels
    s_noisy_labels = generate_noisy_labels(y_hidden_actual_labels, noise_matrix)
   
    # This package is a full of other useful methods for learning with noisy labels.
    # The tutorial stops here, but you don't have to. Inspect method docstrings for full docs.


Estimate the confident joint, the latent noisy channel matrix, *P(s \| y)* and inverse, *P(y \| s)*, the latent prior of the unobserved, actual true labels, *p(y)*, and the predicted probabilities.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

*s* denotes a random variable that represents the observed, noisy
label and *y* denotes a random variable representing the hidden, actual
labels. Both *s* and *y* take any of the m classes as values. The
``cleanlab`` package supports different levels of granularity for
computation depending on the needs of the user. Because of this, we
support multiple alternatives, all no more than a few lines, to estimate
these latent distribution arrays, enabling the user to reduce
computation time by only computing what they need to compute, as seen in
the examples below.

Throughout these examples, you’ll see a variable called
*confident_joint*. The confident joint is an m x m matrix (m is the
number of classes) that counts, for every observed, noisy class, the
number of examples that confidently belong to every latent, hidden
class. It counts the number of examples that we are confident are
labeled correctly or incorrectly for every pair of obseved and
unobserved classes. The confident joint is an unnormalized estimate of
the complete-information latent joint distribution, *Ps,y*. Most of the
methods in the **cleanlab** package start by first estimating the
*confident_joint*. You can learn more about this in the `confident learning paper <https://arxiv.org/abs/1911.00068>`__.

Option 1: Compute the confident joint and predicted probs first. Stop if that’s all you need.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: python

   from cleanlab.latent_estimation import estimate_latent
   from cleanlab.latent_estimation import estimate_confident_joint_and_cv_pred_proba

   # Compute the confident joint and the n x m predicted probabilities matrix (psx),
   # for n examples, m classes. Stop here if all you need is the confident joint.
   confident_joint, psx = estimate_confident_joint_and_cv_pred_proba(
       X=X_train, 
       s=train_labels_with_errors,
       clf=logreg(), # default, you can use any classifier
   )

   # Estimate latent distributions: p(y) as est_py, P(s|y) as est_nm, and P(y|s) as est_inv
   est_py, est_nm, est_inv = estimate_latent(confident_joint, s=train_labels_with_errors)

Option 2: Estimate the latent distribution matrices in a single line of code.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: python

   from cleanlab.latent_estimation import estimate_py_noise_matrices_and_cv_pred_proba
   est_py, est_nm, est_inv, confident_joint, psx = estimate_py_noise_matrices_and_cv_pred_proba(
       X=X_train,
       s=train_labels_with_errors,
   )

Option 3: Skip computing the predicted probabilities if you already have them.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code:: python

   # Already have psx? (n x m matrix of predicted probabilities)
   # For example, you might get them from a pre-trained model (like resnet on ImageNet)
   # With the cleanlab package, you estimate directly with psx.
   from cleanlab.latent_estimation import estimate_py_and_noise_matrices_from_probabilities
   est_py, est_nm, est_inv, confident_joint = estimate_py_and_noise_matrices_from_probabilities(
       s=train_labels_with_errors, 
       psx=psx,
   )


Completely characterize label noise in a dataset:
-------------------------------------------------

The joint probability distribution of noisy and true labels, *P(s,y)*, completely characterizes label noise with a class-conditional *m x m* matrix. 

.. code:: python

    from cleanlab.latent_estimation import estimate_joint
    joint = estimate_joint(
        s=noisy_labels,
        psx=probabilities,
        confident_joint=None,  # Provide if you have it already
    )


The Polyplex
------------

The key to learning in the presence of label errors is estimating the joint distribution between the actual, hidden labels ‘*y*’ and the observed, noisy labels ‘*s*’. Using ``cleanlab`` and the theory of confident learning, we can completely characterize the trace of the latent joint distribution, *trace(P(s,y))*, given *p(y)*, for any fraction of label errors, i.e. for any trace of the noisy channel, *trace(P(s|y))*.

You can check out how to do this yourself here: 1. `Drawing
Polyplices <https://github.com/cleanlab/examples/blob/master/drawing_polyplices.ipynb>`__ 2. `Computing
Polyplices <https://github.com/cleanlab/cleanlab/blob/master/cleanlab/polyplex.py>`__


PU learning with cleanlab:
--------------------------

Positive-Unlabeled learning (in which your data only contains a few positively labeled examples with the rest unlabeled) is just a special case of LearningWithNoisyLabels when one of the classes has no error. P stands for the positive class and **is assumed to have zero label errors** and U stands for unlabeled data, but in practice, we just assume the U class is a noisy negative class that actually contains some positive examples. Thus, the goal of PU learning is to (1) estimate the proportion of negatively labeled datapoints that actually belong to the positive class (see `fraction_noise_in_unlabeled_class` in the last example), (2) find the errors (see last example), and (3) train on clean data (see first example below). `cleanlab` does all three, taking into account that there are no label errors in whichever class you specify as positive.

There are two ways to use `cleanlab` for PU learning. We'll look at each here.

Method 1. If you are using the cleanlab classifier `LearningWithNoisyLabels()`, and your dataset has exactly two classes (positive = 1, and negative = 0), PU learning is supported directly in `cleanlab`. You can perform PU learning like this:

.. code:: python

   from cleanlab.classification import LearningWithNoisyLabels
   from sklearn.linear_model import LogisticRegression
   # Wrap around any classifier. Yup, you can use sklearn/pyTorch/Tensorflow/FastText/etc.
   pu_class = 0 # Should be 0 or 1. Label of class with NO ERRORS. (e.g., P class in PU)
   lnl = LearningWithNoisyLabels(clf=LogisticRegression(), pulearning=pu_class)
   lnl.fit(X=X_train_data, s=train_noisy_labels)
   # Estimate the predictions you would have gotten by training with *no* label errors.
   predicted_test_labels = lnl.predict(X_test)


Method 2. However, you might be using a more complicated classifier that doesn't work well with LearningWithNoisyLabels (see this example for CIFAR-10). Or you might have 3 or more classes. Here's how to use cleanlab for PU learning in this situation.
To let cleanlab know which class has no error (in standard PU learning, this is the P class), you need to set the threshold for that class to 1 (1 means the probability that the labels of that class are correct is 1, i.e. that class has no error). Here's the code:

.. code:: python

   import numpy as np
   # K is the number of classes in your dataset
   # psx are the cross-validated predicted probabilities.
   # s is the array/list/iterable of noisy labels
   # pu_class is a 0-based integer for the class that has no label errors.
   thresholds = np.asarray([np.mean(psx[:, k][s == k]) for k in range(K)])
   thresholds[pu_class] = 1.0


Now you can use cleanlab however you were before.
Just be sure to pass in this thresholds parameter wherever it applies. For example:
 
.. code:: python

   # Uncertainty quantification (characterize the label noise
   # by estimating the joint distribution of noisy and true labels)
   cj = compute_confident_joint(s, psx, thresholds=thresholds, )
   # Now the noise (cj) has been estimated taking into account that some class(es) have no error.
   # We can use cj to find label errors like this:
   indices_of_label_errors = get_noise_indices(s, psx, confident_joint=cj, )
   
   # In addition to label errors, we can find the fraction of noise in the unlabeled class.
   # First we need the inv_noise_matrix which contains P(y|s) (proportion of mislabeling).
   _, _, inv_noise_matrix = estimate_latent(confident_joint=cj, s=s, )
   # Because inv_noise_matrix contains P(y|s), p (y = anything | s = pu_class) should be 0
   # because the prob(true label is something else | example is in pu_class) is 0.
   # What's more interesting is p(y = anything | s is not put_class), or in the binary case
   # this translates to p(y = pu_class | s = 1 - pu_class) because pu_class is 0 or 1.
   # So, to find the fraction_noise_in_unlabeled_class, for binary, you just compute:
   fraction_noise_in_unlabeled_class = inv_noise_matrix[pu_class][1 - pu_class] 


Now that you have `indices_of_label_errors`, you can remove those label errors and train on clean data (or only remove some of the label errors and iteratively use confident learning / cleanlab to improve results).


Citation and Related Publications
=================================

`cleanlab` isn't just a github, it's based on peer-reviewed research. Here are the relevant papers to cite if you use this package:

The `confident learning paper <https://arxiv.org/abs/1911.00068>`__:

::

  @article{northcutt2021confidentlearning,
     title={Confident Learning: Estimating Uncertainty in Dataset Labels},
     author={Curtis G. Northcutt and Lu Jiang and Isaac L. Chuang},
     journal={Journal of Artificial Intelligence Research (JAIR)},
     volume={70},
     pages={1373--1411},
     year={2021}
   }

If you use this package for binary classification or PU learning, please also cite the `rankpruning paper <https://arxiv.org/abs/1705.01936>`__:

::

   @inproceedings{northcutt2017rankpruning,
    author={Northcutt, Curtis G. and Wu, Tailin and Chuang, Isaac L.},
    title={Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels},
    booktitle = {Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence},
    series = {UAI'17},
    year = {2017},
    location = {Sydney, Australia},
    numpages = {10},
    url = {http://auai.org/uai2017/proceedings/papers/35.pdf},
    publisher = {AUAI Press},
   }

Other Resources
---------------

- `Blogpost: Introduction to Confident Learning <https://l7.curtisnorthcutt.com/confident-learning>`__

- `NeurIPS 2021 paper: Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks <https://arxiv.org/abs/2103.14749>`__


Join our Community
==================

Have ideas for the future of cleanlab? How are you using cleanlab?  `Join the discussion <https://github.com/cleanlab/cleanlab/discussions>`__.

Have code improvements for cleanlab?  `Submit a code pull request <https://github.com/cleanlab/cleanlab/issues/new>`__.

Do you have an issue with cleanlab?  `Submit an issue <https://github.com/cleanlab/cleanlab/issues/new>`__.


License
=======

Copyright (c) 2017-2022 Cleanlab Inc.

cleanlab is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

cleanlab is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  

See `GNU Affero General Public LICENSE <https://github.com/cleanlab/cleanlab/blob/master/LICENSE>`__ for details.