cleanlab documentation#

cleanlab automatically detects data and label issues in your ML datasets.

This helps you improve your data and train reliable ML models on noisy real-world datasets. cleanlab has already found thousands of label errors in ImageNet, MNIST, and other popular ML benchmarking datasets. Beyond handling label errors, this is a comprehensive open-source library implementing many data-centric AI capabilities. Start using automation to improve your data in 5 minutes!

Quickstart#

1. Install cleanlab#

pip install cleanlab

To install the package with all optional dependencies:

pip install "cleanlab[all]"

2. Find common issues in your data#

cleanlab automatically detects various issues in any dataset that a classifier can be trained on. The cleanlab package works with any ML model by operating on model outputs (predicted class probabilities or feature embeddings) – it doesn’t require that a particular model created those outputs. For any classification dataset, use your trained model to produce pred_probs (predicted class probabilities) and/or feature_embeddings (numeric vector representations of each datapoint). Then, these few lines of code can detect common real-world issues in your dataset like label errors, outliers, near duplicates, etc:

from cleanlab import Datalab

lab = Datalab(data=your_dataset, label_name="column_name_of_labels")
lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
lab.report()  # summarize issues in dataset, how severe they are, ...

3. Handle label errors and train robust models with noisy labels#

Mislabeled data is a particularly concerning issue plaguing real-world datasets. To use a scikit-learn-compatible model for classification with noisy labels, you don’t need to train a model to find label issues – you can pass the untrained model object, data, and labels into CleanLearning.find_label_issues and cleanlab will handle model training for you.

from cleanlab.classification import CleanLearning

# This works with any sklearn-compatible model - just input data + labels and cleanlab will detect label issues ツ
label_issues_info = CleanLearning(clf=sklearn_compatible_model).find_label_issues(data, labels)

CleanLearning also works with models from most standard ML frameworks by wrapping the model for scikit-learn compliance, e.g. tensorflow/keras (using our KerasWrapperModel), pytorch (using skorch package), etc.

find_label_issues returns a boolean mask flagging which examples have label issues and a numeric label quality score for each example quantifying our confidence that its label is correct.

Beyond standard classification tasks, cleanlab can also detect mislabeled examples in: multi-label data (e.g. image/document tagging), sequence prediction (e.g. entity recognition), and data labeled by multiple annotators (e.g. crowdsourcing).

Important

Cleanlab performs better if the pred_probs from your model are out-of-sample. Details on how to compute out-of-sample predicted probabilities for your entire dataset are here.

cleanlab’s CleanLearning class trains a more robust version of any existing (scikit-learn compatible) classification model, clf, by fitting it to an automatically filtered version of your dataset with low-quality data removed. It returns a model trained only on the clean data, from which you can get predictions in the same way as your existing classifier.

from sklearn.linear_model import LogisticRegression
from cleanlab.classification import CleanLearning

cl = CleanLearning(clf=LogisticRegression())  # any sklearn-compatible classifier
cl.fit(train_data, labels)

# Estimate the predictions you would have gotten if you trained without mislabeled data
predictions = cl.predict(test_data)

4. Dataset curation: fix dataset-level issues#

cleanlab’s dataset module helps you deal with dataset-level issues – find overlapping classes (classes to merge), rank class-level label quality (classes to keep/delete), and measure overall dataset health (to track dataset quality as you make adjustments).

View all dataset-level issues in one line of code with dataset.health_summary().

from cleanlab.dataset import health_summary

health_summary(labels, pred_probs, class_names=class_names)

5. Improve your data via many other techniques#

Beyond handling label errors, cleanlab supports other data-centric AI capabilities including:

  • Detecting outliers and out-of-distribution examples in both training and future test data (tutorial)

  • Analyzing data labeled by multiple annotators to estimate consensus labels and their quality (tutorial)

  • Active learning with multiple annotators to identify which data is most informative to label or re-label next (tutorial)

If you have questions, check out our FAQ and feel free to ask in Slack!

Contributing#

As cleanlab is an open-source project, we welcome contributions from the community.

Please see our contributing guidelines for more information.

Easy Mode#

While this open-source library finds data issues, its utility depends on you having a good ML model and interface to efficiently fix these issues in your dataset. Providing all these pieces, Cleanlab Studio is a no-code platform to find and fix problems in image/text/tabular datasets. Cleanlab Studio integrates the data quality algorithms from this library on top of cutting-edge AutoML & Foundation models fit to your data, and presents detected issues in a smart data editing interface. There is no easier way to turn unreliable raw data into reliable models/analytics. Try it for free!

Stages of modern AI pipeline that can now be automated with Cleanlab Studio