def compute_nb(X, y, Z): labels = [int(t) for t in y] ptrain = [X[i] for i in range(len(labels)) if labels[i] == 0] ntrain = [X[i] for i in range(len(labels)) if labels[i] == 1] poscounts = nbsvm.build_dict(ptrain, [1,2]) negcounts = nbsvm.build_dict(ntrain, [1,2]) dic, r = nbsvm.compute_ratio(poscounts, negcounts) trainX = nbsvm.process_text(X, dic, r, [1,2]) devX = nbsvm.process_text(Z, dic, r, [1,2]) return trainX, devX
def compute_nb(X, y, Z): """ Compute NB features """ labels = [int(t) for t in y] ptrain = [X[i] for i in range(len(labels)) if labels[i] == 0] ntrain = [X[i] for i in range(len(labels)) if labels[i] == 1] poscounts = nbsvm.build_dict(ptrain, [1,2]) negcounts = nbsvm.build_dict(ntrain, [1,2]) dic, r = nbsvm.compute_ratio(poscounts, negcounts) trainX = nbsvm.process_text(X, dic, r, [1,2]) devX = nbsvm.process_text(Z, dic, r, [1,2]) return trainX, devX