Python API

Models

models.PLT(output[, tree_type, arity, ...])	Probabilistic Labels Trees (PLTs) (multi-label) classifier with linear node estimators, using CPP core.
models.HSM(output[, tree_type, arity, ...])	Hierarchical Softmax (multi-class) classifier with linear node estimators, using CPP core.
models.BR(output[, hash, ...])	Binary Relevance (multi-label) classifier with linear estimators, using CPP core
models.OVR(output[, hash, ...])	One Versus Rest (multi-class) classifier with linear 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.
datasets.load_json_lines_file(file[, ...])	Load data in the JSON lines format into list of features and list of labels.
datasets.to_csr_matrix(X[, shape, ...])	Converts sparse matrix-like data, like list of list of tuples (idx, value), to Scipy csr_matrix.
datasets.to_np_matrix(X[, shape, dtype])	Converts sparse matrix-like data, like list of list of tuples (idx, value), to Numpy matrix (2D array).

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.Jain_et_al_inverse_propensity(Y[, A, B])	Calculate inverse propensity as proposed in Jain et al. 2016.
measures.Jain_et_al_propensity(Y[, A, B])	Calculate propensity as proposed in Jain et al. 2016.
measures.ndcg_at_k(Y_true, Y_pred[, k, ...])	Calculate normalized Discounted Cumulative Gain (nDCG) at 1-k places.
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 Scored normalized Discounted Cumulative Gain (PSnDCG) at 1-k places.
measures.hamming_loss(Y_true, Y_pred)	Calculate unnormalized (to avoid very small numbers because of large number of labels) 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.