Python API

Models

models.PLT(output[, tree_type, arity, …])	Probabilistic Labels Trees (PLTs) model with linear node estimators, using CPP core.
models.HSM(output[, tree_type, arity, …])	Hierarchical Softmax model with linear node estimators, using CPP core.
models.BR(output[, hash, …])	Binary Relevance model with linear node estimators, using CPP core
models.OVR(output[, hash, …])	One Versus Rest model with linear node estimators, using CPP core.

Datasets

datasets.download_dataset(dataset[, subset, …])	Downloads the dataset from the internet and puts it in root directory.
datasets.load_dataset(dataset[, subset, …])	Downloads the dataset from the internet and puts it in root directory.
datasets.load_libsvm_file(file)	Load data in the libsvm format into sparse CSR matrix.

Measures

measures.precision_at_k(Y_true, Y_pred[, k])	Calculate precision at 1-k places.
measures.recall_at_k(Y_true, Y_pred[, k, …])	Calculate recall at 1-k places.
measures.coverage_at_k(Y_true, Y_pred[, k])	Calculate coverage at 1-k places.
measures.dcg_at_k(Y_true, Y_pred[, k])	Calculate Discounted Cumulative Gain (DCG) at 1-k places.
measures.ndcg_at_k(Y_true, Y_pred[, k, …])	Calculate normalized Discounted Cumulative Gain (nDCG) at 1-k places.
measures.inverse_propensity(Y[, A, B])	Calculate inverse propensity as proposed in Jain et al.
measures.psprecision_at_k(Y_true, Y_pred, inv_ps)	Calculate Propensity Scored Precision (PSP) at 1-k places.
measures.psrecall_at_k(Y_true, Y_pred, inv_ps)	Calculate Propensity Scored Recall (PSR) at 1-k places.
measures.psdcg_at_k(Y_true, Y_pred, inv_ps)	Calculate Propensity Scored Discounted Cumulative Gain (PSDCG) at 1-k places.
measures.psndcg_at_k(Y_true, Y_pred, inv_ps)	Calculate Propensity normalized Scored Discounted Cumulative Gain (PSnDCG) at 1-k places.
measures.hamming_loss(Y_true, Y_pred)	Calculate unnormalized hamming loss - average number of misclassified labels.
measures.f1_measure(Y_true, Y_pred[, …])	Calculate F1 measure, also known as balanced F-score or F-measure.