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BGPLVM MRD Examples and plotting adjustments
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Max Zwiessele
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2d9ecaa042
commit
65ead17ff5
3 changed files with 90 additions and 53 deletions
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@ -8,6 +8,7 @@ from matplotlib import pyplot as plt, pyplot
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import GPy
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from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
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from GPy.util.datasets import simulation_BGPLVM
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from GPy.core.transformations import square, logexp_clipped
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default_seed = np.random.seed(123344)
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@ -62,17 +63,21 @@ def GPLVM_oil_100(optimize=True):
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m.plot_latent(labels=m.data_labels)
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return m
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300, plot=False):
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data = GPy.util.datasets.oil()
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# create simple GP model
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.001)
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m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel=kernel, M=M)
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
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Y = data['X'][:N]
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=kernel, M=M)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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# optimize
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if optimize:
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m.constrain_fixed('noise', 0.05)
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m.constrain_fixed('noise', 1. / Y.var())
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m.constrain('variance', logexp_clipped())
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m['lengt'] = 1000
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m.ensure_default_constraints()
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m.optimize('scg', messages=1, max_f_eval=max(80, max_f_eval))
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m.unconstrain('noise')
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@ -81,14 +86,15 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
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else:
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m.ensure_default_constraints()
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y = m.likelihood.Y[0, :]
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fig, (latent_axes, hist_axes) = plt.subplots(1, 2)
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plt.sca(latent_axes)
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m.plot_latent()
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data_show = GPy.util.visualize.vector_show(y)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
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raw_input('Press enter to finish')
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plt.close('all')
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if plot:
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y = m.likelihood.Y[0, :]
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fig, (latent_axes, hist_axes) = plt.subplots(1, 2)
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plt.sca(latent_axes)
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m.plot_latent()
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data_show = GPy.util.visualize.vector_show(y)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes) # , sense_axes=sense_axes)
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raw_input('Press enter to finish')
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plt.close('all')
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# # plot
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# print(m)
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# m.plot_latent(labels=m.data_labels)
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@ -221,6 +227,8 @@ def bgplvm_simulation(burnin='scg', plot_sim=False,
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# k = kern.white(Q, .00001) + kern.bias(Q)
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m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
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# m.set('noise',)
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m.constrain('variance', logexp_clipped())
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m.ensure_default_constraints()
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .001
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@ -304,7 +312,7 @@ def mrd_simulation(plot_sim=False):
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# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T
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# Y2 -= Y2.mean(0)
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# make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
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D1, D2, D3, N, M, Q = 2000, 34, 8, 500, 3, 6
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D1, D2, D3, N, M, Q = 150, 250, 300, 700, 10, 7
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slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
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from GPy.models import mrd
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@ -316,18 +324,19 @@ def mrd_simulation(plot_sim=False):
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# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(S2), D2).T
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# Y3 = np.random.multivariate_normal(np.zeros(N), k.K(S3), D3).T
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Ylist = Ylist[0:2]
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# Ylist = Ylist[0:2]
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# k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
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k = kern.linear(Q, ARD=True) + kern.bias(Q, .01) + kern.white(Q, .001)
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k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
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m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute', _debug=False)
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for i, Y in enumerate(Ylist):
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m.set('{}_noise'.format(i + 1), Y.var() / 100.)
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m['{}_noise'.format(i + 1)] = Y.var() / 100.
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m.constrain('variance', logexp_clipped())
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m.ensure_default_constraints()
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m.auto_scale_factor = True
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# m.auto_scale_factor = True
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# cstr = 'variance'
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# m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)
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@ -335,19 +344,19 @@ def mrd_simulation(plot_sim=False):
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# cstr = 'linear_variance'
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# m.unconstrain(cstr), m.constrain_positive(cstr)
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# print "initializing beta"
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# cstr = "noise"
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# m.unconstrain(cstr); m.constrain_fixed(cstr)
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# m.optimize('scg', messages=1, max_f_eval=100)
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print "initializing beta"
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cstr = "noise"
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m.unconstrain(cstr); m.constrain_fixed(cstr)
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m.optimize('scg', messages=1, max_f_eval=200, gtol=11)
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# print "releasing beta"
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# cstr = "noise"
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# m.unconstrain(cstr); m.constrain_positive(cstr)
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print "releasing beta"
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cstr = "noise"
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m.unconstrain(cstr); m.constrain(cstr, logexp_clipped())
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np.seterr(all='call')
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def ipdbonerr(errtype, flags):
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import ipdb; ipdb.set_trace()
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np.seterrcall(ipdbonerr)
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# np.seterr(all='call')
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# def ipdbonerr(errtype, flags):
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# import ipdb; ipdb.set_trace()
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# np.seterrcall(ipdbonerr)
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return m # , mtest
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