cleanlab 0.0.3

The Python package for cleaning and learning with noisy labels. Works for all noisy label distributions, datasets, and models.

cleanlab is a machine learning python package for learning with noisy labels and cleaning label errors in datasets. It

• cleans/finds label errors in massive (and small) datasets.

• contains state-of-the-art algorithms for

  • learning with noisy labels .

  • estimating the latent noisy-channel, latent class prior distribution, and more.

cleanlab implements a family of theory and algorithms known as confident learning.

cleanlab is:

1. fast - only two hours (on cpu-based laptop) to find the label errors in the 2012 ImageNet Validation set

2. robust - provable generalization and risk minimimzation guarantees even for imperfect probability estimation

3. general - works with any probablistic classifier, LSTM, Mask-RCNN, logistic regression, etc.

4. unique - the only general implementation for multiclass learning with noisy lables for ANY classifier (deep or not), for ANY amount/distribution of label noise distribution, with no information about the label noise needed a priori.

Check out these examples.

Installation

Python 2.7 is supported. Python 3.4, 3.5, and 3.6 will be supported soon (some things work already).

Stable release:

$ pip install cleanlab

Developer (unstable) release:

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

To install the codebase (enabling you to make modifications):

$ conda update pip # if you use conda
$ git clone https://github.com/cgnorthcutt/cleanlab.git
$ cd hypopt
$ 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},
}

Core cleanlab Package Descriptions

1. cleanlab/classification.py - The RankPruning() 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.

Get started with easy, quick examples.

New to cleanlab? Start with:

1. Visualizing confident learning

2. 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.

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 inherets 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 RankPruning
rp = RankPruning(clf=YourFavoriteModel())
rp.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 RankPruning() 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.

Documentation by Example - Quick Tutorials

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.

from cleanlab.classification import RankPruning
# RankPruning 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.
rp = RankPruning(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'.
rp.fit(X_train, train_labels_with_errors)

# 3.
# Estimate the predictions you would have gotten by training with *no* label errors.
predicted_test_labels = rp.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.:

where 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.

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.

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.

# 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.

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):

There are two methods 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)*.

Method 1: Guarantees the rows of P(s,y) correctly sum to p(s), by first computing P(y | s).

This method occurs when hyperparameter prune_count_method = ‘inverse_nm_dot_s’ in RankPruning.fit() and get_noise_indices().

from cleanlab.util import value_counts
# *p(s)* is the prior of the observed, noisy labels and an array of length m (# of classes)
ps = value_counts(s) / float(len(s))
# We computed est_inv (estimated inverse noise matrix) in the previous example (two above).
psy = np.transpose(est_inv * ps) # Matrix of prob(s=l and y=k)

Method 2: Simplest. Compute by re-normalizing the confident joint. Rows won’t sum to p(s).

This method occurs when hyperparameter prune_count_method = ‘calibrate_confident_joint’ in RankPruning.fit() and get_noise_indices().

from cleanlab.util import value_counts
# *p(s)* is the prior of the observed, noisy labels and an array of length m (# of classes)
ps = value_counts(s) / float(len(s))
# We computed confident_joint in the previous example (two above).
psy = confident_joint / float(confident_joint.sum()) # calibration, i.e. re-normalization

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)

Support for numerous weak supervision and learning with noisy labels functionalities:

# 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 2. Computing Polyplices