cleanlab is a machine learning python package for learning with noisy labels and finding label errors in datasets. cleanlab CLEANs LABels. It is powered by the theory of confident learning, published in this paper and explained in this blog. Using the confidentlearning-reproduce repo, cleanlab v0.1.0 reproduces results in the CL paper.
cleanlab documentation is available in this blog post.
cleanlab finds and cleans label errors in any dataset using state-of-the-art algorithms 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 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 finds and cleans label errors in any dataset using state-of-the-art algorithms for learning with noisy labels by characterizing label noise. cleanlab is fast: its built on optimized algorithms and parallelized across 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 (even when model output probabilities are noisy/imperfect).
How does confident learning work? See: TUTORIAL: confident learning with just numpy and for-loops.
cleanlab supports multi-label, multiclass, sparse matrices, and more.
cleanlab is:
- fast - Single-shot, non-iterative, parallelized algorithms (e.g. < 1 second to find label errors in ImageNet)
- robust - Provable generalization and risk minimimzation guarantees, including imperfect probability estimation.
- general - Works with any probablistic classifier: PyTorch, Tensorflow, MxNet, Caffe2, scikit-learn, etc.
- unique - The only package for multiclass learning with noisy labels or finding label errors for any dataset / classifier.
# 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 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
)Pre-computed out-of-sample predicted probabilities for CIFAR-10 train set are available here: [LINK].
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 and tests (includes how to use pyTorch, FastText, etc.).
Python 2.7, 3.4, 3.5, and 3.6 are supported.
Stable release:
$ pip install cleanlabDeveloper (unstable) release:
$ pip install git+https://github.com/cgnorthcutt/cleanlab.gitTo install the codebase (enabling you to make modifications):
$ conda update pip # if you use conda
$ git clone https://github.com/cgnorthcutt/cleanlab.git
$ cd cleanlab
$ pip install -e .If you use this package in your work, please cite the confident learning paper:
@misc{northcutt2019confidentlearning,
title={Confident Learning: Estimating Uncertainty in Dataset Labels},
author={Curtis G. Northcutt and Lu Jiang and Isaac L. Chuang},
year={2019},
eprint={1911.00068},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
and the cleanlab code base here:
@misc{northcutt2019cleanlab,
author = {Curtis Northcutt},
title = {Clean Lab},
year = {2019},
howpublished = {\url{https://github.com/cgnorthcutt/cleanlab}},
note = {commit xxxxxxx, version xxxx}
}
If used for binary classification, cleanlab also implements this paper:
@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},
}
Reproducing Results in confident learning paper
See cleanlab/examples. You'll need to git clone confidentlearning-reproduce which contains the data and files needed to reproduce the CIFAR-10 results.
Use cleanlab to identify ~100,000 label errors in the 2012 ImageNet training dataset.
Top label issues in the 2012 ILSVRC ImageNet train set identified using cleanlab. Label Errors are boxed in red. Ontological issues in green. Multi-label images in blue.
Use cleanlab to identify ~50 label errors in the MNIST dataset.
Label errors of the original MNIST train dataset identified algorithmically using cleanlab. 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.
Use cleanlab to learn with noisy labels regardless of dataset distribution or classifier.
Each sub-figure in the figure above 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.
The code to reproduce this figure is available here.
Each figure depicts accuracy scores on a test set as decimal values:
- LEFT (in black): The classifier test accuracy trained with perfect labels (no label errors).
- MIDDLE (in blue): The classifier test accuracy trained with noisy labels using
cleanlab. - 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 |
New to cleanlab? Start with:
- Visualizing confident learning
- A simple example of learning with noisy labels on the multiclass Iris dataset.
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.
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:
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
As you can see
here,
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
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.
- cleanlab/classification.py - The LearningWithNoisyLabels() class for learning with noisy labels.
- cleanlab/latent_algebra.py - Equalities when noise information is known.
- cleanlab/latent_estimation.py - Estimates and fully characterizes all variants of label noise.
- cleanlab/noise_generation.py - Generate mathematically valid synthetic noise matrices.
- cleanlab/polyplex.py - Characterizes joint distribution of label noise EXACTLY from noisy channel.
- 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.
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.
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)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,
)# 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,
)The joint probability distribution of noisy and true labels, P(s,y), completely characterizes label noise with a class-conditional m x m matrix.
from cleanlab.latent_estimation import estimate_joint
joint = compute_confident_joint(
s=noisy_labels,
psx=probabilities,
confident_joint=None, # Provide if you have it already
)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:
# 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.
# 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 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 2. Computing Polyplices
Copyright (c) 2017-2019 Curtis Northcutt. Released under the MIT License. See LICENSE for details.