def test_with_samples(self): from mxfusion.common import config config.DEFAULT_DTYPE = 'float64' dtype = 'float64' D, X, Y, noise_var, lengthscale, variance = self.gen_data() m = Model() m.N = Variable() m.X = Normal.define_variable(mean=0, variance=1, shape=(m.N, 3)) m.noise_var = Variable(transformation=PositiveTransformation(), initial_value=mx.nd.array(noise_var, dtype=dtype)) kernel = RBF(input_dim=3, ARD=True, variance=mx.nd.array(variance, dtype=dtype), lengthscale=mx.nd.array(lengthscale, dtype=dtype), dtype=dtype) m.Y = GPRegression.define_variable(X=m.X, kernel=kernel, noise_var=m.noise_var, shape=(m.N, D)) q = create_Gaussian_meanfield(model=m, observed=[m.Y]) infr = GradBasedInference( inference_algorithm=StochasticVariationalInference( model=m, posterior=q, num_samples=10, observed=[m.Y])) infr.run(Y=mx.nd.array(Y, dtype='float64'), max_iter=2, learning_rate=0.1, verbose=True) infr2 = Inference( ForwardSamplingAlgorithm(model=m, observed=[m.X], num_samples=5)) infr2.run(X=mx.nd.array(X, dtype='float64')) infr_pred = TransferInference(ModulePredictionAlgorithm( model=m, observed=[m.X], target_variables=[m.Y]), infr_params=infr.params) xt = np.random.rand(13, 3) res = infr_pred.run(X=mx.nd.array(xt, dtype=dtype))[0] gp = m.Y.factor gp.attach_prediction_algorithms( targets=gp.output_names, conditionals=gp.input_names, algorithm=GPRegressionSamplingPrediction(gp._module_graph, gp._extra_graphs[0], [gp._module_graph.X]), alg_name='gp_predict') gp.gp_predict.diagonal_variance = False gp.gp_predict.jitter = 1e-6 infr_pred2 = TransferInference(ModulePredictionAlgorithm( model=m, observed=[m.X], target_variables=[m.Y]), infr_params=infr.params) xt = np.random.rand(13, 3) res = infr_pred2.run(X=mx.nd.array(xt, dtype=dtype))[0]
def test_gluon_func_save_and_load(self): m = self.make_simple_gluon_model() infr = Inference(ForwardSamplingAlgorithm(m, observed=[m.x])) infr.run(x=mx.nd.ones((1, 1))) infr.save(self.ZIPNAME) m2 = self.make_simple_gluon_model() infr2 = Inference(ForwardSamplingAlgorithm(m2, observed=[m2.x])) infr2.run(x=mx.nd.ones((1, 1))) infr2.load(self.ZIPNAME) infr2.run(x=mx.nd.ones((1, 1))) for n in m.f.parameter_names: assert np.allclose(infr.params[getattr(m.y.factor, n)].asnumpy(), infr2.params[getattr(m2.y.factor, n)].asnumpy()) os.remove(self.ZIPNAME)
def test_draw_samples(self): D, X, Y, Z, noise_var, lengthscale, variance = self.gen_data() dtype = 'float64' m = self.gen_mxfusion_model(dtype, D, Z, noise_var, lengthscale, variance) observed = [m.X] infr = Inference(ForwardSamplingAlgorithm( m, observed, num_samples=2, target_variables=[m.Y]), dtype=dtype) samples = infr.run(X=mx.nd.array(X, dtype=dtype))[0] assert samples.shape == (2,) + Y.shape
def test_gluon_parameters(self): self.setUp() m = Model() m.x = Variable(shape=(1, 1)) m.f = MXFusionGluonFunction(self.net, num_outputs=1) m.y = m.f(m.x) infr = Inference(ForwardSamplingAlgorithm(m, observed=[m.x])) infr.run(x=mx.nd.ones((1, 1))) assert all([ v.uuid in infr.params.param_dict for v in m.f.parameters.values() ])
def test_draw_samples_w_mean(self): D, X, Y, noise_var, lengthscale, variance = self.gen_data() dtype = 'float64' rand_gen = MockMXNetRandomGenerator( mx.nd.array(np.random.rand(20 * D), dtype=dtype)) m, net = self.gen_mxfusion_model_w_mean(dtype, D, noise_var, lengthscale, variance, rand_gen) observed = [m.X] infr = Inference(ForwardSamplingAlgorithm(m, observed, num_samples=2, target_variables=[m.Y]), dtype=dtype) samples = infr.run(X=mx.nd.array(X, dtype=dtype), Y=mx.nd.array(Y, dtype=dtype))[0].asnumpy() kern = RBF(3, True, name='rbf', dtype=dtype) + White(3, dtype=dtype) X_var = Variable(shape=(10, 3)) mean_func = MXFusionGluonFunction(net, num_outputs=1, broadcastable=True) mean_var = mean_func(X_var) gp = GaussianProcess.define_variable(X=X_var, kernel=kern, mean=mean_var, shape=(10, D), dtype=dtype, rand_gen=rand_gen).factor variables = { gp.X.uuid: mx.nd.expand_dims(mx.nd.array(X, dtype=dtype), axis=0), gp.add_rbf_lengthscale.uuid: mx.nd.expand_dims(mx.nd.array(lengthscale, dtype=dtype), axis=0), gp.add_rbf_variance.uuid: mx.nd.expand_dims(mx.nd.array(variance, dtype=dtype), axis=0), gp.add_white_variance.uuid: mx.nd.expand_dims(mx.nd.array(noise_var, dtype=dtype), axis=0), mean_var.uuid: mx.nd.expand_dims(net(mx.nd.array(X, dtype=dtype)), axis=0) } samples_2 = gp.draw_samples(F=mx.nd, variables=variables, num_samples=2).asnumpy() assert np.allclose(samples, samples_2), (samples, samples_2)