Bayesian Classifers for PU-learning

This module contains a custom implementation of Bayesian network for PU learning, written from scratch in Python 3, API inspired by SciKit-learn. This module implements multiple Bayesian classifiers:

• Positive Naive Bayes(PNB)
• Positive Tree-Augmented Naive Bayes(PTAN)
• Positive Simpler TAN(PSTAN)
• Positive Ensemble TAN(PETAN)
• Weighted Naive Bayes(WNB)
• Weighted Tree-Augmented Naive Bayes(WTAN)

PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples.

Note: All algorithms make the "Select Completely At Random(SCAR)" labeling assumption. We consider "case-control" and "single training" sampling scenariors.

Current features

PU Generator

Generate Positive and Unlabeled data from fully labeled data set, either follows "case-control" or "single training".

import PUgenerator

p450_pu = PUgenerator()
p450_pu.fit(X,y,nl = 400,nu = 800, case_control = True) # "case-control"
p450_pu.prevalence_ # p(y=1)
p450_pu.plot_dist() # plot all feature distributions

p450_pu.X_1abeled_  # labeled set
p450_pu.X_Unlabeled_ # Unlabeled set
p450_pu.y_Unlabeled_ # Unlabeled target values(unknown in practice)

PU Bayesian classifiers

Demo for implementation of PNB and PSTAN, from training to prediction

import PNB
pnb = PNB()
pnb.fit(p450_pu.X_1abeled_,p450_pu.X_Unlabeled_, p450_pu.prevalence_) # model fitting
pnb.predict(p450_pu.X_Unlabeled_) # prediction
pnb.predict_proba(p450_pu.X_Unlabeled_) # proba prediction

import PSTAN
pstan = PSTAN()
pstan.fit(p450_pu.X_1abeled_,p450_pu.X_Unlabeled_, p450_pu.prevalence_,M) # model fitting
pstan.plot_tree_structure() # plot learned tree-structure
pstan.predict(p450_pu.X_Unlabeled_) # prediction
pstan.predict_proba(p450_pu.X_Unlabeled_) # proba prediction

Evaluation

Evaluation under multiple runs, take average.

import get_cv

Accuracy, CLL, Precision,Recall = get_cv(PNB,X,y,400,800,M)
print(np.mean(Accuracy))
print(np.mean(CLL))
print(np.mean(Precision))
print(np.mean(Recall))

Built With

• Dropwizard - scikit-learn API
• Maven - Dependency Management
• ROME - Used to generate RSS Feeds

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

• Chengning Zhang - Initial work - Bayesian Classifers for PU-learning

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Hat tip to anyone whose code was used
• Inspiration
• etc