Positive-unlabeled learning (aka PU-learning) is a machine learning scenario for binary classification where the training set consists of a set of positively-labeled examples and an additional unlabeled set that contains positive and negative examples in unknown proportions (so no training example is explicitly labeled as negative). Positive-unlabeled learning methods aim to incorporate the unique structure of this scenario into the learning process, in a way that improves generalization of the learned notion of the positive class, when compared to simply treating all unlabaeled examples as negative examples, or alternatively discarding them and training a one-class classifier over only the positive samples.
Pulearn is a Python package that provides fully documented and tested scikit-learn wrappers to existing Python implementations of several positive-unlabeled learning methods. The familiar API and ease of installation should allow you to get going right away, and easily compare various PU-learning against both each other and naive methods.