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Implemented plot_latents as an external function in util

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Andreas 2013-05-22 16:06:35 +01:00
parent e0fe988d81
commit bd1e98f564
3 changed files with 98 additions and 84 deletions
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15
GPy/models/Bayesian_GPLVM.py
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@ -14,6 +14,7 @@ import itertools
from matplotlib.colors import colorConverter
from matplotlib.figure import SubplotParams
from GPy.inference.optimization import SCG
from GPy.util import plot_latent
class Bayesian_GPLVM(sparse_GP, GPLVM):
"""
@ -178,18 +179,8 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
self.dbound_dZtheta = sparse_GP._log_likelihood_gradients(self)
return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
def plot_latent(self, which_indices=None, *args, **kwargs):
if which_indices is None:
try:
input_1, input_2 = np.argsort(self.input_sensitivity())[:2]
except:
raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
else:
input_1, input_2 = which_indices
ax = GPLVM.plot_latent(self, which_indices=[input_1, input_2], *args, **kwargs)
ax.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
return ax
def plot_latent(self, *args, **kwargs):
util.plot_latent_indices(self, *args, **kwargs)
def do_test_latents(self, Y):
"""

76
GPy/models/GPLVM.py
View file

@ -11,6 +11,8 @@ from ..util.linalg import pdinv, PCA
from GP import GP
from ..likelihoods import Gaussian
from .. import util
from GPy.util import plot_latent
class GPLVM(GP):
"""
@ -60,75 +62,5 @@ class GPLVM(GP):
mu, var, upper, lower = self.predict(Xnew)
pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
def plot_latent(self, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
"""
:param labels: a np.array of size self.N containing labels for the points (can be number, strings, etc)
:param resolution: the resolution of the grid on which to evaluate the predictive variance
"""
if ax is None:
ax = pb.gca()
util.plot.Tango.reset()
if labels is None:
labels = np.ones(self.N)
if which_indices is None:
if self.Q==1:
input_1 = 0
input_2 = None
if self.Q==2:
input_1, input_2 = 0,1
else:
try:
input_1, input_2 = np.argsort(self.input_sensitivity())[:2]
except:
raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
else:
input_1, input_2 = which_indices
#first, plot the output variance as a function of the latent space
Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(self.X[:,[input_1, input_2]],resolution=resolution)
Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
Xtest_full[:, :2] = Xtest
mu, var, low, up = self.predict(Xtest_full)
var = var[:, :1]
ax.imshow(var.reshape(resolution, resolution).T,
extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
# make sure labels are in order of input:
ulabels = []
for lab in labels:
if not lab in ulabels:
ulabels.append(lab)
for i, ul in enumerate(ulabels):
if type(ul) is np.string_:
this_label = ul
elif type(ul) is np.int64:
this_label = 'class %i'%ul
else:
this_label = 'class %i'%i
if len(marker) == len(ulabels):
m = marker[i]
else:
m = marker
index = np.nonzero(labels==ul)[0]
if self.Q==1:
x = self.X[index,input_1]
y = np.zeros(index.size)
else:
x = self.X[index,input_1]
y = self.X[index,input_2]
ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
ax.set_xlabel('latent dimension %i'%input_1)
ax.set_ylabel('latent dimension %i'%input_2)
if not np.all(labels==1.):
ax.legend(loc=0,numpoints=1)
ax.set_xlim(xmin[0],xmax[0])
ax.set_ylim(xmin[1],xmax[1])
ax.grid(b=False) # remove the grid if present, it doesn't look good
ax.set_aspect('auto') # set a nice aspect ratio
return ax
def plot_latent(self, *args, **kwargs):
util.plot_latent.plot_latent(self, *args, **kwargs)