scikit-clean 0.1.2

Example Usage

A typical label noise research workflow begins with clean labels, simulates label noise into training set, and then evaluates how a model handles that noise using clean test set. In scikit-clean, this looks like:

from skclean.simulate_noise import flip_labels_uniform
from skclean.models import RobustLR   # Robust Logistic Regression

X, y = make_classification(n_samples=200,n_features=4)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20)

y_train_noisy = flip_labels_uniform(y_train, .3)  # Flip labels of 30% samples
clf = RobustLR().fit(X_train,y_train_noisy)
print(clf.score(X_test, y_test))

scikit-clean provides a customized Pipeline for more complex workflow. Many noise robust algorithms can be broken down into two steps: detecting noise likelihood for each sample in the dataset, and train robust classifiers by using that information. This fits nicely with Pipeline’s API:

# ---Import scikit-learn stuff----
from skclean.simulate_noise import UniformNoise
from skclean.detectors import KDN
from skclean.handlers import Filter
from skclean.pipeline import Pipeline, make_pipeline  # Importing from skclean, not sklearn


clf = Pipeline([
        ('scale', StandardScaler()),          # Scale features
        ('feat_sel', VarianceThreshold(.2)),  # Feature selection
        ('detector', KDN()),                  # Detect mislabeled samples
        ('handler', Filter(SVC())),           # Filter out likely mislabeled samples and then train a SVM
])

clf_g = GridSearchCV(clf,{'detector__n_neighbors':[2,5,10]})
n_clf_g = make_pipeline(UniformNoise(.3),clf_g)  # Create label noise at the very first step

print(cross_val_score(n_clf_g, X, y, cv=5).mean())  # 5-fold cross validation

Please see this notebook before you begin for a more detailed introduction, and this for complete API.

Installation

Simplest option is probably using pip:

pip install scikit-clean

If you intend to modify the code, install in editable mode:

git clone https://github.com/Shihab-Shahriar/scikit-clean.git
cd scikit-clean
pip install -e .

If you’re only interested in small part of this library, say one or two algorithms, feel free to simply copy/paste relevant code into your project.

Alternatives

There are several open source tools to handle label noise, some of them are:

1. Cleanlab

2. Snorkel

3. NoiseFiltersR

NoiseFiltersR is closest in objective as ours, though it’s implemented in R, and doesn’t appear to be actively maintained.

Cleanlab and Snorkel are both in Python, though they have somewhat different priorities than us. While our goal is to implement as many algorithms as possible, these tools usually focus on one or few related papers. They have also been developed for some time- meaning they are more stable, well-optimized and better suited for practitioners/ engineers than scikit-clean.

Credits

We want to scikit-learn, imbalance-learn and Cleanlab, these implemntations are inspired by, and dircetly borrows code from these libraries.

We also want to thank the authors of original papers. Here is a list of papers partially or fully implemented by scikit-clean:

• Taghi M Khoshgoftaar and Pierre Rebours. Improving software quality prediction by noise filtering techniques. Journal of Computer Science and Technology, 22(3):387–396, 2007.

• Sunghun Kim, Hongyu Zhang, Rongxin Wu, and Liang Gong. Dealing with noise in defect prediction. In 2011 33rd International Conference on Software Engineering (ICSE), 481–490. IEEE, 2011.

• Alexander Hanbo Li and Andrew Martin. Forest-type regression with general losses and robust forest. In International Conference on Machine Learning, 2091–2100. 2017.

• Aditya Krishna Menon, Brendan Van Rooyen, and Nagarajan Natarajan. Learning from binary labels with instance-dependent noise. Machine Learning, 107(8-10):1561–1595, 2018.

• Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. In Advances in neural information processing systems, 1196–1204. 2013.

• Maryam Sabzevari, Gonzalo Martínez-Muñoz, and Alberto Suárez. A two-stage ensemble method for the detection of class-label noise. Neurocomputing, 275:2374–2383, 2018.

• Michael R Smith, Tony Martinez, and Christophe Giraud-Carrier. An instance level analysis of data complexity. Machine learning, 95(2):225–256, 2014.

• Felipe N Walmsley, George DC Cavalcanti, Dayvid VR Oliveira, Rafael MO Cruz, and Robert Sabourin. An ensemble generation method based on instance hardness. In 2018 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2018.

• Bianca Zadrozny, John Langford, and Naoki Abe. Cost-sensitive learning by cost-proportionate example weighting. In Third IEEE international conference on data mining, 435–442. IEEE, 2003.

• Zijin Zhao, Lingyang Chu, Dacheng Tao, and Jian Pei. Classification with label noise: a markov chain sampling framework. Data Mining and Knowledge Discovery, 33(5):1468–1504, 2019.