It would be nice to follow a consistent naming convention for parameters and be as consistent as possible with sklearn. For instance:
In supervised versions of weakly supervised algorithms, num_constraints should be renamed n_constraints, num_chunks to n_chunks
In LMNN, the parameter k could be renamed n_neighbors like in sklearn's KNeighborsClassifier
There is also tol and convergence_threshold which are both used to refer to optimization tolerance (we should always use tol which is quite standard, cf `scipy.optimize)
It would be nice to follow a consistent naming convention for parameters and be as consistent as possible with sklearn. For instance:
num_constraintsshould be renamedn_constraints,num_chunkston_chunkskcould be renamedn_neighborslike in sklearn'sKNeighborsClassifiertolandconvergence_thresholdwhich are both used to refer to optimization tolerance (we should always usetolwhich is quite standard, cf `scipy.optimize)