apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-03 00:32:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Added option to plot the transformed link function (posterior once the link function has been applied)

Browse source
This commit is contained in:
Alan Saul 2015-04-10 15:44:15 +01:00
parent dff9ca8e6b
commit f4cf052bce
2 changed files with 77 additions and 17 deletions
Download patch file Download diff file Expand all files Collapse all files

75
GPy/plotting/matplot_dep/models_plots.py
View file

@ -1,4 +1,4 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
try:
@ -16,7 +16,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
which_data_ycols='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx'):
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
apply_link=False, samples_f=0, plot_uncertain_inputs=True):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -38,7 +39,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
:type resolution: int
:param levels: number of levels to plot in a contour plot.
:type levels: int
:param samples: the number of a posteriori samples to plot
:param samples: the number of a posteriori samples to plot p(y*|y)
:type samples: int
:param fignum: figure to plot on.
:type fignum: figure number
@ -49,6 +50,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
:type linecol:
:param fillcol: color of fill
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
:param apply_link: apply the link function if plotting f (default false)
:type apply_link: boolean
:param samples_f: the number of posteriori f samples to plot p(f*|y)
:type samples_f: int
"""
#deal with optional arguments
if which_data_rows == 'all':
@ -88,8 +93,14 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#make a prediction on the frame and plot it
if plot_raw:
m, v = model._raw_predict(Xgrid)
lower = m - 2*np.sqrt(v)
upper = m + 2*np.sqrt(v)
if apply_link:
lower = model.likelihood.gp_link.transf(m - 2*np.sqrt(v))
upper = model.likelihood.gp_link.transf(m + 2*np.sqrt(v))
#Once transformed this is now the median of the function
m = model.likelihood.gp_link.transf(m)
else:
lower = m - 2*np.sqrt(v)
upper = m + 2*np.sqrt(v)
else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
@ -110,13 +121,31 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
if samples_f: #NOTE not tested with fixed_inputs
Fsim = model.posterior_samples_f(Xgrid, samples_f)
for fi in Fsim.T:
plots['posterior_samples_f'] = ax.plot(Xnew, fi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
#add error bars for uncertain (if input uncertainty is being modelled)
if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
plots['xerrorbar'] = ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs() and plot_uncertain_inputs:
if plot_raw:
#add error bars for uncertain (if input uncertainty is being modelled), for plot_f
#Hack to plot error bars on latent function, rather than on the data
vs = model.X.mean.values.copy()
for i,v in fixed_inputs:
vs[:,i] = v
m_X, _ = model._raw_predict(vs)
if apply_link:
m_X = model.likelihood.gp_link.transf(m_X)
plots['xerrorbar'] = ax.errorbar(X[which_data_rows, free_dims].flatten(), m_X[which_data_rows, which_data_ycols].flatten(),
xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
else:
plots['xerrorbar'] = ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
#set the limits of the plot to some sensible values
ymin, ymax = min(np.append(Y[which_data_rows, which_data_ycols].flatten(), lower)), max(np.append(Y[which_data_rows, which_data_ycols].flatten(), upper))
@ -186,3 +215,29 @@ def plot_fit_f(model, *args, **kwargs):
"""
kwargs['plot_raw'] = True
plot_fit(model,*args, **kwargs)
def fixed_inputs(model, non_fixed_inputs, fix_routine='median'):
"""
Convenience function for returning back fixed_inputs where the other inputs
are fixed using fix_routine
:param model: model
:type model: Model
:param non_fixed_inputs: dimensions of non fixed inputs
:type non_fixed_inputs: list
:param fix_routine: fixing routine to use, 'mean', 'median', 'zero'
:type fix_routine: string
"""
f_inputs = []
if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
X = model.X.mean.values.copy()
else:
X = model.X.values.copy()
for i in range(X.shape[1]):
if i not in non_fixed_inputs:
if fix_routine == 'mean':
f_inputs.append( (i, np.mean(X[:,i])) )
if fix_routine == 'median':
f_inputs.append( (i, np.median(X[:,i])) )
elif fix_routine == 'zero':
f_inputs.append( (i, 0) )
return f_inputs