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[MRD] init and sim nicer now
This commit is contained in:
Max Zwiessele
parent
9b2e49c949
commit
a5c3e88f83
4 changed files with 67 additions and 66 deletions
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@ -2,7 +2,7 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as _np
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#default_seed = _np.random.seed(123344)
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# default_seed = _np.random.seed(123344)
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def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
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"""
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@ -28,7 +28,7 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
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Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T
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# k = GPy.kern.RBF_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
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#k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.RBF(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
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# k = GPy.kern.RBF(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.RBF(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
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# k = GPy.kern.RBF(input_dim, .5, 2., ARD=0) + GPy.kern.RBF(input_dim, .3, .2, ARD=0)
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@ -40,30 +40,30 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
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if nan:
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m.inference_method = GPy.inference.latent_function_inference.var_dtc.VarDTCMissingData()
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m.Y[_np.random.binomial(1,p,size=(Y.shape)).astype(bool)] = _np.nan
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m.Y[_np.random.binomial(1, p, size=(Y.shape)).astype(bool)] = _np.nan
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m.parameters_changed()
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#===========================================================================
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# randomly obstruct data with percentage p
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#===========================================================================
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#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
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#m.lengthscales = lengthscales
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# m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
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# m.lengthscales = lengthscales
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if plot:
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import matplotlib.pyplot as pb
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m.plot()
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pb.title('PCA initialisation')
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#m2.plot()
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#pb.title('PCA initialisation')
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# m2.plot()
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# pb.title('PCA initialisation')
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if optimize:
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m.optimize('scg', messages=verbose)
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#m2.optimize('scg', messages=verbose)
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# m2.optimize('scg', messages=verbose)
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if plot:
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m.plot()
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pb.title('After optimisation')
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#m2.plot()
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#pb.title('After optimisation')
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# m2.plot()
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# pb.title('After optimisation')
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return m
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@ -169,7 +169,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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data = GPy.util.datasets.oil()
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kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
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kernel = GPy.kern.RBF(Q, 1., 1. / _np.random.uniform(0, 1, (Q,)), ARD=True) # + GPy.kern.Bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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@ -180,8 +180,8 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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if plot:
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes, labels=m.data_labels)
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0, :]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1, :], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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plt.close(fig)
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@ -195,7 +195,7 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
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_np.random.seed(0)
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data = pods.datasets.oil()
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kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
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kernel = GPy.kern.RBF(Q, 1., 1. / _np.random.uniform(0, 1, (Q,)), ARD=True) # + GPy.kern.Bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.SSGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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@ -206,8 +206,8 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
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if plot:
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes, labels=m.data_labels)
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0, :]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1, :], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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plt.close(fig)
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@ -219,10 +219,12 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
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import numpy as np
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np.random.seed(3000)
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k = GPy.kern.Matern32(Q_signal, 10., lengthscale=1+(np.random.uniform(1,6,Q_signal)), ARD=1)
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t = np.c_[[np.linspace(-1,5,N) for _ in range(Q_signal)]].T
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k = GPy.kern.Matern32(Q_signal, 1., lengthscale=(np.random.uniform(1, 6, Q_signal)), ARD=1)
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for i in range(Q_signal):
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k += GPy.kern.PeriodicExponential(1, variance=1., active_dims=[i], period=3., lower=-2, upper=6)
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t = np.c_[[np.linspace(-1, 5, N) for _ in range(Q_signal)]].T
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K = k.K(t)
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s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
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s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:, :, None]
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Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)
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@ -243,7 +245,7 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
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ax.legend()
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for i, Y in enumerate(Ylist):
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ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.set_title("Y{}".format(i + 1))
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plt.draw()
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plt.tight_layout()
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@ -255,7 +257,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
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x = _np.linspace(0, 4 * _np.pi, N)[:, None]
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s1 = _np.vectorize(lambda x: _np.sin(x))
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s2 = _np.vectorize(lambda x: _np.cos(x)**2)
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s2 = _np.vectorize(lambda x: _np.cos(x) ** 2)
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s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
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sS = _np.vectorize(lambda x: _np.cos(x))
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@ -288,7 +290,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
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ax.legend()
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for i, Y in enumerate(Ylist):
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ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.set_title("Y{}".format(i + 1))
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plt.draw()
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plt.tight_layout()
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@ -323,10 +325,10 @@ def bgplvm_simulation(optimize=True, verbose=1,
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D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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Y = Ylist[0]
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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#k = kern.RBF(Q, ARD=True, lengthscale=10.)
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k = kern.Linear(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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# k = kern.RBF(Q, ARD=True, lengthscale=10.)
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m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
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m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
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m.X.variance[:] = _np.random.uniform(0, .01, m.X.shape)
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m.likelihood.variance = .1
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if optimize:
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@ -348,10 +350,10 @@ def ssgplvm_simulation(optimize=True, verbose=1,
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D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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Y = Ylist[0]
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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#k = kern.RBF(Q, ARD=True, lengthscale=10.)
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k = kern.Linear(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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# k = kern.RBF(Q, ARD=True, lengthscale=10.)
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m = SSGPLVM(Y, Q, init="pca", num_inducing=num_inducing, kernel=k)
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m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
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m.X.variance[:] = _np.random.uniform(0, .01, m.X.shape)
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m.likelihood.variance = .1
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if optimize:
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@ -365,7 +367,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
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def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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plot=True, plot_sim=False,
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max_iters=2e4,
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max_iters=2e4, percent_missing=.1,
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):
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from GPy import kern
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from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
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@ -373,9 +375,9 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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Y = Ylist[0]
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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k = kern.Linear(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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inan = _np.random.binomial(1, .2, size=Y.shape).astype(bool) # 80% missing data
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inan = _np.random.binomial(1, percent_missing, size=Y.shape).astype(bool) # 80% missing data
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Ymissing = Y.copy()
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Ymissing[inan] = _np.nan
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@ -401,11 +403,11 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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#Ylist = [Ylist[0]]
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# Ylist = [Ylist[0]]
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k = kern.Linear(Q, ARD=True)
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m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="PCA_concat", initz='permute', **kw)
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m['.*noise'] = [Y.var()/40. for Y in Ylist]
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m['.*noise'] = [Y.var() / 40. for Y in Ylist]
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if optimize:
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print "Optimizing Model:"
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@ -422,7 +424,7 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
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D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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#Ylist = [Ylist[0]]
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# Ylist = [Ylist[0]]
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k = kern.Linear(Q, ARD=True)
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inanlist = []
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@ -553,7 +555,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
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data = pods.datasets.osu_run1()
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# optimize
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back_kernel=GPy.kern.RBF(data['Y'].shape[1], lengthscale=5.)
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back_kernel = GPy.kern.RBF(data['Y'].shape[1], lengthscale=5.)
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mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
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m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
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if optimize: m.optimize(messages=verbose, max_f_eval=10000)
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@ -563,7 +565,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
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y = m.likelihood.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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#raw_input('Press enter to finish')
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# raw_input('Press enter to finish')
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return m
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@ -627,7 +629,7 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
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Y = data['Y']
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Y_mean = Y.mean(0)
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Y_std = Y.std(0)
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m = GPy.models.GPLVM((Y-Y_mean)/Y_std, 2)
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m = GPy.models.GPLVM((Y - Y_mean) / Y_std, 2)
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if optimize: m.optimize(messages=verbose, max_f_eval=10000)
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if plot:
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@ -651,17 +653,17 @@ def ssgplvm_simulation_linear():
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x = np.empty(Q)
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dies = np.random.rand(Q)
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for q in xrange(Q):
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if dies[q]<pi:
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if dies[q] < pi:
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x[q] = np.random.randn()
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else:
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x[q] = 0.
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return x
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Y = np.empty((N,D))
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X = np.empty((N,Q))
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Y = np.empty((N, D))
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X = np.empty((N, Q))
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# Generate data from random sampled weight matrices
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for n in xrange(N):
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X[n] = sample_X(Q,pi)
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w = np.random.randn(D,Q)
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Y[n] = np.dot(w,X[n])
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X[n] = sample_X(Q, pi)
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w = np.random.randn(D, Q)
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Y[n] = np.dot(w, X[n])
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