psi=np.eye(target_dim) * psi_mnw) aux = MatrixNormalWishart(**models_hypparams) models_prior.append(aux) gating_hypparams = dict(K=args.nb_models, gammas=np.ones((args.nb_models, )), deltas=np.ones((args.nb_models, )) * args.alpha) gating_prior = TruncatedStickBreaking(**gating_hypparams) ilr = BayesianMixtureOfLinearGaussians( gating=CategoricalWithStickBreaking(gating_prior), basis=[ GaussianWithNormalWishart(basis_prior[i]) for i in range(args.nb_models) ], models=[ LinearGaussianWithMatrixNormalWishart(models_prior[i], affine=args.affine) for i in range(args.nb_models) ]) import copy from sklearn.utils import shuffle from sklearn.metrics import mean_squared_error, r2_score anim = [] split_size = int(nb_train / args.nb_splits) mse = np.zeros((args.nb_splits, ))
def _job(kwargs): args = kwargs.pop('arguments') seed = kwargs.pop('seed') input = kwargs.pop('train_input') target = kwargs.pop('train_target') input_dim = input.shape[-1] target_dim = target.shape[-1] # set random seed np.random.seed(seed) nb_params = input_dim if args.affine: nb_params += 1 basis_prior = [] models_prior = [] # initialize Normal psi_nw = 1e0 kappa = 1e-2 # initialize Matrix-Normal psi_mnw = 1e0 K = 1e-3 for n in range(args.nb_models): basis_hypparams = dict(mu=np.zeros((input_dim, )), psi=np.eye(input_dim) * psi_nw, kappa=kappa, nu=input_dim + 1) aux = NormalWishart(**basis_hypparams) basis_prior.append(aux) models_hypparams = dict(M=np.zeros((target_dim, nb_params)), K=K * np.eye(nb_params), nu=target_dim + 1, psi=np.eye(target_dim) * psi_mnw) aux = MatrixNormalWishart(**models_hypparams) models_prior.append(aux) # define gating if args.prior == 'stick-breaking': gating_hypparams = dict(K=args.nb_models, gammas=np.ones((args.nb_models, )), deltas=np.ones( (args.nb_models, )) * args.alpha) gating_prior = TruncatedStickBreaking(**gating_hypparams) ilr = BayesianMixtureOfLinearGaussians( gating=CategoricalWithStickBreaking(gating_prior), basis=[ GaussianWithNormalWishart(basis_prior[i]) for i in range(args.nb_models) ], models=[ LinearGaussianWithMatrixNormalWishart(models_prior[i], affine=args.affine) for i in range(args.nb_models) ]) else: gating_hypparams = dict(K=args.nb_models, alphas=np.ones( (args.nb_models, )) * args.alpha) gating_prior = Dirichlet(**gating_hypparams) ilr = BayesianMixtureOfLinearGaussians( gating=CategoricalWithDirichlet(gating_prior), basis=[ GaussianWithNormalWishart(basis_prior[i]) for i in range(args.nb_models) ], models=[ LinearGaussianWithMatrixNormalWishart(models_prior[i], affine=args.affine) for i in range(args.nb_models) ]) ilr.add_data(target, input, whiten=True) # Gibbs sampling ilr.resample(maxiter=args.gibbs_iters, progprint=args.verbose) for _ in range(args.super_iters): if args.stochastic: # Stochastic meanfield VI ilr.meanfield_stochastic_descent(maxiter=args.svi_iters, stepsize=args.svi_stepsize, batchsize=args.svi_batchsize) if args.deterministic: # Meanfield VI ilr.meanfield_coordinate_descent(tol=args.earlystop, maxiter=args.meanfield_iters, progprint=args.verbose) ilr.gating.prior = ilr.gating.posterior for i in range(ilr.likelihood.size): ilr.basis[i].prior = ilr.basis[i].posterior ilr.models[i].prior = ilr.models[i].posterior return ilr
aux = MatrixNormalWishart(**models_hypparams) models_prior.append(aux) gating_hypparams = dict(K=args.nb_models, gammas=np.ones((args.nb_models, )), deltas=np.ones( (args.nb_models, )) * args.alpha) gating_prior = TruncatedStickBreaking(**gating_hypparams) ilrs.append( BayesianMixtureOfLinearGaussians( gating=CategoricalWithStickBreaking(gating_prior), basis=[ GaussianWithNormalWishart(basis_prior[i]) for i in range(args.nb_models) ], models=[ LinearGaussianWithMatrixNormalWishart(models_prior[i], affine=args.affine) for i in range(args.nb_models) ])) import copy from sklearn.utils import shuffle from sklearn.metrics import mean_squared_error, r2_score # split data for n sequential updates and k models data_size = input.shape[0] batch_size = int(data_size / args.nb_splits) mse = np.zeros((args.nb_seeds, args.nb_splits))