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Changes in plot functions, to allow 1D multiple outputs visualization

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Ricardo 2013-09-13 12:36:32 +01:00
parent f0569d6d88
commit ea7c18fccc
1 changed files with 57 additions and 46 deletions
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41
GPy/core/gp_base.py
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@ -46,6 +46,8 @@ class GPBase(Model):
:param which_parts: which of the kernel functions to plot (additively)
:type which_parts: 'all', or list of bools
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
:param output: which output to plot (for multiple output models only)
:type output: integer (first output is 0)
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -92,13 +94,14 @@ class GPBase(Model):
elif self.X.shape[1] == 2 and hasattr(self,'multioutput'):
output -= 1
assert self.num_outputs >= output, 'The model has only %s outputs.' %self.num_outputs
Xu = self.X[self.X[:,-1]==output ,0:1]
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
if samples == 0:
m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts)
gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
#ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
ax.plot(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
else:
m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts, full_cov=True)
@ -117,6 +120,11 @@ class GPBase(Model):
Zu = self.Z[self.Z[:,-1]==output ,0:1] #??
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
elif self.X.shape[1] == 3 and hasattr(self,'multioutput'):
raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
output -= 1
assert self.num_outputs >= output, 'The model has only %s outputs.' %self.num_outputs
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
@ -126,6 +134,8 @@ class GPBase(Model):
:param levels: for 2D plotting, the number of contour levels to use
is ax is None, create a new figure
:param output: which output to plot (for multiple output models only)
:type output: integer (first output is 0)
"""
# TODO include samples
if which_data == 'all':
@ -135,7 +145,9 @@ class GPBase(Model):
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
if self.X.shape[1] == 1 and not hasattr(self,'multioutput'):
if not hasattr(self,'multioutput'):
if self.X.shape[1] == 1:
resolution = resolution or 200
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
@ -150,7 +162,7 @@ class GPBase(Model):
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
elif self.X.shape[1] == 2 and not hasattr(self,'multioutput'):
elif self.X.shape[1] == 2:
resolution = resolution or 50
Xnew, _, _, xmin, xmax = x_frame2D(self.X, plot_limits, resolution)
x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
@ -162,33 +174,32 @@ class GPBase(Model):
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1])
elif self.X.shape[1] == 2 and hasattr(self,'multioutput'):
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
else:
assert self.num_outputs > output, 'The model has only %s outputs.' %self.num_outputs
if self.X.shape[1] == 2:
resolution = resolution or 200
Xu = self.X[self.X[:,-1]==output,:] #keep the output of interest
Xu = self.X * self._Xscale + self._Xoffset
Xu = self.X[self.X[:,-1]==output ,0:1] #get rid of the index column
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
m, _, lower, upper = self.predict_single_output(Xnew, which_parts=which_parts,output=output)
#if not isinstance(self.likelihood,EP_Mixed_Noise):
# m, _, lower, upper = self.predict(np.hstack([Xnew,np.repeat(output,Xnew.size)[:,None]]), which_parts=which_parts)
#else:
# m, _, lower, upper = self.predict_single_output(Xnew, which_parts=which_parts,output=output)
for d in range(m.shape[1]):
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax)
#ax.plot(Xu[which_data], self.likelihood.data[self.likelihood.index==output][:,None], 'kx', mew=1.5)
ax.plot(Xu[which_data], self.likelihood.noise_model_list[output].data, 'kx', mew=1.5)
ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
elif self.X.shape[1] == 3:
raise NotImplementedError, "Plots not yet implemented for multioutput models with 2D inputs"
resolution = resolution or 50
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"