Metadata-Version: 1.1
Name: cleanlab
Version: 0.0.10
Summary: The Python package for cleaning and learning with noisy labels. Works for all noisy label distributions, datasets, and models.
Home-page: https://github.com/cgnorthcutt/cleanlab
Author: Curtis G. Northcutt
Author-email: cgn@mit.edu
License: MIT
Description: .. figure:: https://raw.githubusercontent.com/cgnorthcutt/cleanlab/master/img/cleanlab_logo.png
           :align: center
           :alt: cleanlab 
        
        |  
        
        ``cleanlab`` is a machine learning python package for **learning with noisy labels** and **finding label errors in datasets**. ``cleanlab`` CLEANs LABels. It is is powered by the theory of **confident learning**.
        
        |pypi| |py_versions| |build_status| |coverage|
        
        .. |pypi| image:: https://img.shields.io/pypi/v/cleanlab.svg
            :target: https://pypi.org/pypi/cleanlab/
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            :target: https://pypi.org/pypi/cleanlab/
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            :target: https://travis-ci.com/cgnorthcutt/cleanlab
        .. |coverage| image:: https://codecov.io/gh/cgnorthcutt/cleanlab/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/cgnorthcutt/cleanlab
        
        ``cleanlab``  finds and cleans label errors in any dataset using state-of-the-art algorithms for learning with noisy labels and estimating the complete joint distribution of label noise. ``cleanlab`` is fast: its built on optimized algorithms and parallelized across all CPU threads automatically. ``cleanlab`` implements the family of theory and algorithms called **confident learning** with provable guarantees of exact noise estimation and label error finding (when model output probabilities are exact).
        
        **News! As of version 0.0.10, ``cleanlab`` now supports multi-label learning** in addition to multiclass learning :)
        
        
        Its called ``cleanlab`` because it CLEANs LABels.
        ================================================= 
        
        ``cleanlab`` is:
        
        1. fast - **runs in parallel on all CPU threads automatically**. (e.g. < 1 second to find label errors in ImageNet)
        2. robust - Provable generalization and risk minimimzation guarantees, including imperfect probability estimation.
        3. general - Works with any probablistic classifier: PyTorch, Tensorflow, MxNet, Caffe2, scikit-learn, etc.
        4. unique - The only package for multiclass learning with noisy labels or finding label errors for any dataset / classifier.
        
           
        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)
        
        
        Find label errors with PyTorch, Tensorflow, MXNet, etc. in 1 line of code!
        --------------------------------------------------------------------------
        
        .. code:: python
        
           # Compute psx (n x m matrix of predicted probabilities) on your own, with any classifier.
           # Be sure you compute probs in a holdout/out-of-sample manner (e.g. 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 estimate_py_and_noise_matrices_from_probabilities
        
           ordered_label_errors = get_noise_indices(
               s = noisy_labels,
               psx = numpy_array_of_predicted_probabilities,
               sorted_index_method='normalized_margin', # Orders label errors
            )
        
        
        Check out these `examples <https://github.com/cgnorthcutt/cleanlab/tree/master/examples>`__ and `tests <https://github.com/cgnorthcutt/cleanlab/tree/master/tests>`__ (includes how to use pyTorch, FastText, etc.).
        
        
        
        Installation
        ============
        
        Python 2.7, 3.4, 3.5, and 3.6 are supported.
        
        Stable release:
        
        .. code-block:: bash
        
           $ pip install cleanlab
        
        Developer (unstable) release:
        
        .. code-block:: bash
        
           $ pip install git+https://github.com/cgnorthcutt/cleanlab.git
        
        To install the codebase (enabling you to make modifications):
        
        .. code-block:: bash
        
           $ conda update pip # if you use conda
           $ git clone https://github.com/cgnorthcutt/cleanlab.git
           $ cd cleanlab
           $ pip install -e .
        
        
        Citations and Related Publications
        ==================================
        
        Although this package goes far beyond our 2017 publication, if you find
        this repository helpful, please cite our paper
        http://auai.org/uai2017/proceedings/papers/35.pdf. New papers will be
        posted here when they are published.
        
        ::
        
           @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},
           } 
        
        Collaboration
        -------------
        
        Most of the algorithms, theory, and results of ``cleanlab`` remain unpublished. If you'd like to work together, please reach out. 
        
        ``cleanlab`` on MNIST
        ---------------------
        
        We use ``cleanlab`` to automatically identify ~50 label errors in the MNIST dataset. 
        
        .. figure:: https://raw.githubusercontent.com/cgnorthcutt/cleanlab/master/img/mnist_training_label_errors24_prune_by_noise_rate.png
           :align: center
           :alt: Image depicting label errors in MNIST train set 
        
        Label errors of the original MNIST **train** dataset identified algorithmically using the rankpruning algorithm. Depicts the 24 least confident labels, ordered left-right, top-down by increasing self-confidence (probability of belonging to the given label), denoted conf in teal. The label with the largest predicted probability is in green. Overt errors are in red.
        
         
        ``cleanlab`` Generality: View performance across 4 distributions and 9 classifiers.
        -----------------------------------------------------------------------------------
        
        We use ``cleanlab`` to automatically learn with noisy labels regardless of dataset distribution or classifier. 
        
        .. figure:: https://raw.githubusercontent.com/cgnorthcutt/cleanlab/master/img/demo_cleanlab_across_datasets_and_classifiers.png
           :align: center
           :alt: Image depicting generality of cleanlab across datasets and classifiers 
        
        Each figure depicts the decision boundary learned using ``cleanlab.classification.LearningWithNoisyLabels`` in the presence of extreme (~35%) label errors. Label errors are circled in green. Label noise is class-conditional (not simply uniformly random). Columns are organized by the classifier used, except the left-most column which depicts the ground-truth dataset distribution. Rows are organized by dataset used. A matrix characterizing the label noise for the first row is shown below. 
        
        Each figure depicts accuracy scores on a test set 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, "A random 38% of '3' labels were flipped to '2' labels."
        
        ======  ====  ====  ====  ==== 
        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
        ======  ====  ====  ====  ====
        
        The code to reproduce this figure is available `here <https://github.com/cgnorthcutt/cleanlab/blob/master/examples/classifier_comparison.ipynb>`__.
        
        
        Get started with easy, quick examples.
        ======================================
        
        New to **cleanlab**? Start with:
        
        1. `Visualizing confident
           learning <https://github.com/cgnorthcutt/cleanlab/blob/master/examples/visualizing_confident_learning.ipynb>`__
        2. `A simple example of learning with noisy labels on the multiclass
           Iris dataset <https://github.com/cgnorthcutt/cleanlab/blob/master/examples/iris_simple_example.ipynb>`__.
        
        These examples show how easy it is to characterize label noise in
        datasets, learn with noisy labels, identify label errors, estimate
        latent priors and noisy channels, and more.
        
        .. ..
        
           <!---
        
           
        
           ![Image depicting label errors in MNIST test set.](https://raw.githubusercontent.com/cgnorthcutt/cleanlab/master/img/mnist_test_label_errors8.png)
            Selected label errors in the MNIST **test** dataset ordered by increasing self-confidence (in teal).
        
           ## Automatically identify ~5k (of 50k) validation set label errors in ImageNet. [[link]](examples/finding_ImageNet_label_errors).
           ![Image depicting label errors in ImageNet validation set.](https://raw.githubusercontent.com/cgnorthcutt/cleanlab/master/img/imagenet_validation_label_errors_96_prune_by_noise_rate.jpg)
           Label errors in the 2012 ImageNet validation dataset identified automatically with cleanlab using a pre-trained resnet18. Displayed are the 96 least confident labels. We see that ImageNet contains numerous multi-label images, although it is used widely by the machine learning and vision communities as a single-label benchmark dataset.
        
           --->
        
        Use ``cleanlab`` with any model (Tensorflow, caffe2, PyTorch, 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/cgnorthcutt/cleanlab/blob/master/cleanlab/models/mnist_pytorch.py#L28>`__
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        As you can see
        `here <https://github.com/cgnorthcutt/cleanlab/blob/master/cleanlab/models/mnist_pytorch.py#L28>`__,
        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/cgnorthcutt/cleanlab/blob/master/cleanlab/classification.py#L48>`__
        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.
        
        
        Documentation by Example
        ========================
        
        ``cleanlab`` Core Package Components
        ------------------------------------
        
        1. **cleanlab/classification.py** - The 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 indices of the examples with label errors in a dataset.
        
        Many of these methods have default parameters that won’t be covered
        here. Check out the method docstrings for full documentation.
        
        Multiclass learning with noisy labels (in **3** lines of code):
        ---------------------------------------------------------------
        
        **rankpruning** is a fast, general, robust algorithm for multiclass
        learning with noisy labels. It adds minimal overhead, needing only
        *O(nm2)* time for n training examples and m classes, works with any
        classifier, and is easy to use. Here is the example from above, with
        added commments for clarity.
        
        .. code:: python
           
           # LearningWithNoisyLabels implements a faster,
           # cross-platform and more-compatible version of the RankPruning
           # algorithm for learning with noisy labels. Unlike the original
           # algorithm which only worked for binary classification,
           # LearningWithNoisyLabels generalizes the theory and algorithms
           # of RankPruning for any number of classes.
           from cleanlab.classification import LearningWithNoisyLabels
           # LearningWithNoisyLabels uses logreg by default, so this is unnecessary. 
           # We include it here for clarity, but this step is omitted below.
           from sklearn.linear_model import LogisticRegression as logreg
        
           # 1.
           # Wrap around any classifier. Yup, neural networks work, too.
           lnl = LearningWithNoisyLabels(clf=logreg()) 
        
           # 2.
           # X_train is numpy matrix of training examples (integers for large data)
           # train_labels_with_errors is a numpy array of labels of length n (# of examples), usually denoted 's'.
           lnl.fit(X_train, train_labels_with_errors) 
        
           # 3.
           # Estimate the predictions you would have gotten by training with *no* label errors.
           predicted_test_labels = lnl.predict(X_test)
        
        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*.
        
        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,
           )
        
        Estimate label errors in a dataset:
        -----------------------------------
        
        With the ``cleanlab`` package, we can instantly fetch the indices of all
        estimated label errors, with nothing provided by the user except a
        classifier, examples, and their noisy labels. Like the previous example,
        there are various levels of granularity.
        
        .. code:: python
        
           from cleanlab.pruning import get_noise_indices
           # We computed psx, est_inv, confident_joint in the previous example.
           label_errors = get_noise_indices(
               s=train_labels_with_errors, # required
               psx=psx, # required
               inverse_noise_matrix=est_inv, # not required, include to avoid recomputing
               confident_joint=confident_joint, # not required, include to avoid recomputing
           )
        
        Estimate the latent joint probability distribution matrix of the noisy and true labels, *P(s,y)*:
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        To compute *P(s,y)*, the complete-information
        distribution matrix that captures the number of pairwise label flip
        errors when multipled by the total number of examples as *n* P(s,y)*.
        Using `cleanlab.latent_estimation.calibrate_confident_joint`, 
        this method guarantees the rows of *P(s,y)* correctly sum to *p(s)*, 
        and np.sum(confident_joint) == n (the number of labels).
        
        This method occurs when hyperparameter prune_count_method =
        ‘inverse_nm_dot_s’ in LearningWithNoisyLabels.fit() and get_noise_indices().
        
        .. code:: python
        
           from cleanlab.latent_estimation import compute_confident_joint
           joint = compute_confident_joint(s=noisy_labels, psx=probabilities)
        
        If you've already computed the confident joint, then you can
        estimate the complete joint distribution of label noise by:
        
        .. code:: python
        
           from cleanlab.latent_estimation import estimate_joint
           joint = estimate_joint(confident_joint=cj, s=noisy_labels)
        
        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)
        
        Support for numerous *weak supervision* and *learning with noisy labels* functionalities:
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. 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.
           
        
        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/cgnorthcutt/cleanlab/blob/master/examples/drawing_polyplices.ipynb>`__ 2. `Computing
        Polyplices <https://github.com/cgnorthcutt/cleanlab/blob/master/cleanlab/polyplex.py>`__
        
        License
        -------
        
        Copyright (c) 2017-2019 Curtis Northcutt. Released under the MIT License. See `LICENSE <https://github.com/cgnorthcutt/cleanlab/blob/master/LICENSE>`__ for details.
        
Keywords: machine_learning denoising classification weak_supervision learning_with_noisy_labels unsupervised_learning
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