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[plotting] latent plotting had dimension mix up in it

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mzwiessele 2015-11-30 16:35:30 +00:00
parent 8878353fdb
commit cb3b4ca08d
14 changed files with 49 additions and 25 deletions
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59
GPy/testing/plotting_tests.py
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@ -311,26 +311,35 @@ def test_gplvm():
from ..examples.dimensionality_reduction import _simulate_matern
from ..kern import RBF
from ..models import GPLVM
np.random.seed(11111)
import matplotlib
np.random.seed(12345)
matplotlib.rcParams.update(matplotlib.rcParamsDefault)
matplotlib.rcParams[u'figure.figsize'] = (4,3)
matplotlib.rcParams[u'text.usetex'] = False
Q = 3
_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
Y = Ylist[0]
k = RBF(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
# Define dataset
N = 10
k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
k3 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[0.1,0.1,10,10,10]), ARD=True)
X = np.random.normal(0, 1, (N, 5))
A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
Y = np.vstack((A,B,C))
labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
k = RBF(Q, ARD=True, lengthscale=2) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = GPLVM(Y, Q, init="PCA", kernel=k)
m.likelihood.variance = .1
m.likelihood.variance = .001
#m.optimize(messages=0)
labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
np.random.seed(111)
m.plot_latent()
m.plot_latent(labels=labels)
np.random.seed(111)
m.plot_scatter(projection='3d', labels=labels)
np.random.seed(111)
m.plot_magnification(labels=labels)
m.plot_steepest_gradient_map(resolution=7)
m.plot_steepest_gradient_map(resolution=7, data_labels=labels)
for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
yield (do_test, )
@ -338,31 +347,41 @@ def test_bayesian_gplvm():
from ..examples.dimensionality_reduction import _simulate_matern
from ..kern import RBF
from ..models import BayesianGPLVM
import matplotlib
np.random.seed(12345)
matplotlib.rcParams.update(matplotlib.rcParamsDefault)
matplotlib.rcParams[u'figure.figsize'] = (4,3)
matplotlib.rcParams[u'text.usetex'] = False
np.random.seed(111)
Q = 3
_, _, Ylist = _simulate_matern(5, 1, 1, 100, num_inducing=5, plot_sim=False)
Y = Ylist[0]
k = RBF(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
# k = kern.RBF(Q, ARD=True, lengthscale=10.)
# Define dataset
N = 10
k1 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,10,10,0.1,0.1]), ARD=True)
k2 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[10,0.1,10,0.1,10]), ARD=True)
k3 = GPy.kern.RBF(5, variance=1, lengthscale=1./np.random.dirichlet(np.r_[0.1,0.1,10,10,10]), ARD=True)
X = np.random.normal(0, 1, (N, 5))
A = np.random.multivariate_normal(np.zeros(N), k1.K(X), Q).T
B = np.random.multivariate_normal(np.zeros(N), k2.K(X), Q).T
C = np.random.multivariate_normal(np.zeros(N), k3.K(X), Q).T
Y = np.vstack((A,B,C))
labels = np.hstack((np.zeros(A.shape[0]), np.ones(B.shape[0]), np.ones(C.shape[0])*2))
k = RBF(Q, ARD=True, lengthscale=2) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", kernel=k)
m.likelihood.variance = .1
m.likelihood.variance = .001
#m.optimize(messages=0)
labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
np.random.seed(111)
m.plot_inducing(projection='2d')
np.random.seed(111)
m.plot_inducing(projection='3d')
np.random.seed(111)
m.plot_scatter(projection='3d')
m.plot_latent(projection='2d', labels=labels)
np.random.seed(111)
m.plot_scatter(projection='3d', labels=labels)
np.random.seed(111)
m.plot_magnification(labels=labels)
np.random.seed(111)
m.plot_steepest_gradient_map(resolution=7)
for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):
m.plot_steepest_gradient_map(resolution=7, data_labels=labels)
for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
yield (do_test, )
if __name__ == '__main__':