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Implemented plot_latents as an external function in util
This commit is contained in:
Andreas
parent
e0fe988d81
commit
bd1e98f564
3 changed files with 98 additions and 84 deletions
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@ -14,6 +14,7 @@ import itertools
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from matplotlib.colors import colorConverter
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from matplotlib.colors import colorConverter
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from matplotlib.figure import SubplotParams
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from matplotlib.figure import SubplotParams
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from GPy.inference.optimization import SCG
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from GPy.inference.optimization import SCG
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from GPy.util import plot_latent
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class Bayesian_GPLVM(sparse_GP, GPLVM):
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class Bayesian_GPLVM(sparse_GP, GPLVM):
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"""
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"""
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@ -178,18 +179,8 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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self.dbound_dZtheta = sparse_GP._log_likelihood_gradients(self)
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self.dbound_dZtheta = sparse_GP._log_likelihood_gradients(self)
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return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
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return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
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def plot_latent(self, which_indices=None, *args, **kwargs):
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def plot_latent(self, *args, **kwargs):
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util.plot_latent_indices(self, *args, **kwargs)
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if which_indices is None:
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try:
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input_1, input_2 = np.argsort(self.input_sensitivity())[:2]
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except:
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raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
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else:
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input_1, input_2 = which_indices
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ax = GPLVM.plot_latent(self, which_indices=[input_1, input_2], *args, **kwargs)
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ax.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
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return ax
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def do_test_latents(self, Y):
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def do_test_latents(self, Y):
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"""
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"""
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@ -11,6 +11,8 @@ from ..util.linalg import pdinv, PCA
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from GP import GP
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from GP import GP
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from ..likelihoods import Gaussian
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from ..likelihoods import Gaussian
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from .. import util
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from .. import util
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from GPy.util import plot_latent
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class GPLVM(GP):
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class GPLVM(GP):
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"""
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"""
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@ -60,75 +62,5 @@ class GPLVM(GP):
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mu, var, upper, lower = self.predict(Xnew)
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mu, var, upper, lower = self.predict(Xnew)
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pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
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pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
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def plot_latent(self, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
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def plot_latent(self, *args, **kwargs):
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"""
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util.plot_latent.plot_latent(self, *args, **kwargs)
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:param labels: a np.array of size self.N containing labels for the points (can be number, strings, etc)
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:param resolution: the resolution of the grid on which to evaluate the predictive variance
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"""
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if ax is None:
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ax = pb.gca()
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util.plot.Tango.reset()
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if labels is None:
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labels = np.ones(self.N)
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if which_indices is None:
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if self.Q==1:
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input_1 = 0
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input_2 = None
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if self.Q==2:
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input_1, input_2 = 0,1
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else:
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try:
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input_1, input_2 = np.argsort(self.input_sensitivity())[:2]
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except:
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raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
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else:
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input_1, input_2 = which_indices
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#first, plot the output variance as a function of the latent space
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Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(self.X[:,[input_1, input_2]],resolution=resolution)
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Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
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Xtest_full[:, :2] = Xtest
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mu, var, low, up = self.predict(Xtest_full)
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var = var[:, :1]
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ax.imshow(var.reshape(resolution, resolution).T,
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extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
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# make sure labels are in order of input:
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ulabels = []
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for lab in labels:
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if not lab in ulabels:
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ulabels.append(lab)
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for i, ul in enumerate(ulabels):
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if type(ul) is np.string_:
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this_label = ul
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elif type(ul) is np.int64:
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this_label = 'class %i'%ul
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else:
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this_label = 'class %i'%i
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if len(marker) == len(ulabels):
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m = marker[i]
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else:
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m = marker
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index = np.nonzero(labels==ul)[0]
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if self.Q==1:
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x = self.X[index,input_1]
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y = np.zeros(index.size)
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else:
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x = self.X[index,input_1]
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y = self.X[index,input_2]
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ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
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ax.set_xlabel('latent dimension %i'%input_1)
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ax.set_ylabel('latent dimension %i'%input_2)
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if not np.all(labels==1.):
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ax.legend(loc=0,numpoints=1)
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ax.set_xlim(xmin[0],xmax[0])
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ax.set_ylim(xmin[1],xmax[1])
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ax.grid(b=False) # remove the grid if present, it doesn't look good
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ax.set_aspect('auto') # set a nice aspect ratio
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return ax
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91
GPy/util/plot_latent.py
Normal file
91
GPy/util/plot_latent.py
Normal file
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@ -0,0 +1,91 @@
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import pylab as pb
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import numpy as np
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from .. import util
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def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40):
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"""
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:param labels: a np.array of size model.N containing labels for the points (can be number, strings, etc)
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:param resolution: the resolution of the grid on which to evaluate the predictive variance
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"""
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if ax is None:
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ax = pb.gca()
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util.plot.Tango.reset()
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if labels is None:
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labels = np.ones(model.N)
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if which_indices is None:
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if model.Q==1:
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input_1 = 0
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input_2 = None
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if model.Q==2:
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input_1, input_2 = 0,1
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else:
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try:
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input_1, input_2 = np.argsort(model.input_sensitivity())[:2]
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except:
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raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
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else:
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input_1, input_2 = which_indices
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#first, plot the output variance as a function of the latent space
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Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(model.X[:,[input_1, input_2]],resolution=resolution)
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Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
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Xtest_full[:, :2] = Xtest
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mu, var, low, up = model.predict(Xtest_full)
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var = var[:, :1]
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ax.imshow(var.reshape(resolution, resolution).T,
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extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
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# make sure labels are in order of input:
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ulabels = []
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for lab in labels:
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if not lab in ulabels:
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ulabels.append(lab)
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for i, ul in enumerate(ulabels):
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if type(ul) is np.string_:
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this_label = ul
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elif type(ul) is np.int64:
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this_label = 'class %i'%ul
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else:
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this_label = 'class %i'%i
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if len(marker) == len(ulabels):
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m = marker[i]
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else:
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m = marker
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index = np.nonzero(labels==ul)[0]
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if model.Q==1:
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x = model.X[index,input_1]
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y = np.zeros(index.size)
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else:
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x = model.X[index,input_1]
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y = model.X[index,input_2]
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ax.scatter(x, y, marker=m, s=s, color=util.plot.Tango.nextMedium(), label=this_label)
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ax.set_xlabel('latent dimension %i'%input_1)
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ax.set_ylabel('latent dimension %i'%input_2)
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if not np.all(labels==1.):
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ax.legend(loc=0,numpoints=1)
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ax.set_xlim(xmin[0],xmax[0])
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ax.set_ylim(xmin[1],xmax[1])
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ax.grid(b=False) # remove the grid if present, it doesn't look good
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ax.set_aspect('auto') # set a nice aspect ratio
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return ax
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def plot_latent_indices(model, which_indices=None, *args, **kwargs):
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if which_indices is None:
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try:
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input_1, input_2 = np.argsort(model.input_sensitivity())[:2]
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except:
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raise ValueError, "cannot Automatically determine which dimensions to plot, please pass 'which_indices'"
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else:
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input_1, input_2 = which_indices
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ax = plot_latent(model, which_indices=[input_1, input_2], *args, **kwargs)
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# TODO: Here test if there are inducing points...
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ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
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return ax
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