FeatureVector/Classifier/MLogit: Rewrite to avoid creating non-sparse matrix in memory (use views), remove non-choice-specific features directly in aggregate feature vectors, create TrainingData object to wrap labeled instances + removed (non-choice-specific) features, create FeatureLabelMapper to wrap FeatureMapper and LabelMapper and include in all feature vectors
FeatureVector/etc: get_labeled_instance(s) -> import_labeled_instance(s)