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Updated sampling and plots to be correct shape, and changed plotting of sampling to be posterior samples p(f*|f), like it used to be, and samples_y to plot samples of p(y*|y)

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Alan Saul 2015-09-10 11:08:28 +01:00
parent 6acf5dcfac
commit ef778f75d4
5 changed files with 41 additions and 29 deletions
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46
GPy/plotting/matplot_dep/models_plots.py
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@ -75,7 +75,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
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',
apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
apply_link=False, samples_y=0, plot_uncertain_inputs=True, predict_kw=None, plot_training_data=True):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -93,24 +93,30 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
:type which_data_rows: 'all' or a list of integers
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
:type fixed_inputs: a list of tuples
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
:type resolution: int
:param levels: number of levels to plot in a contour plot.
:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
:type levels: int
:param samples: the number of a posteriori samples to plot p(y*|y)
:param samples: the number of a posteriori samples to plot p(f*|y)
:type samples: int
:param fignum: figure to plot on.
:type fignum: figure number
:param ax: axes to plot on.
:type ax: axes handle
:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
:type resolution: int
:param plot_raw: Whether to plot the raw function p(f|y)
:type plot_raw: boolean
:param linecol: color of line to plot.
:type linecol:
:type linecol: hex or color
: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 fillcol: hex or color
:param apply_link: apply the link function if plotting f (default false), as well as posterior samples if requested
:type apply_link: boolean
:param samples_f: the number of posteriori f samples to plot p(f*|y)
:type samples_f: int
:param samples_y: the number of posteriori f samples to plot p(y*|y)
:type samples_y: int
:param plot_uncertain_inputs: plot the uncertainty of the inputs as error bars if they have uncertainty (BGPLVM etc.)
:type plot_uncertain_inputs: boolean
:param predict_kw: keyword args for _raw_predict and predict functions if required
:type predict_kw: dict
:param plot_training_data: whether or not to plot the training points
:type plot_training_data: boolean
"""
@ -185,17 +191,17 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples, Y_metadata=Y_metadata)
print(Ysim.shape)
print(Xnew.shape)
for yi in Ysim.T:
plots['posterior_samples'] = ax.plot(Xnew, yi[:,None], '#3300FF', 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)
Fsim = model.posterior_samples_f(Xgrid, samples)
if apply_link:
Fsim = model.likelihood.gp_link.transf(Fsim)
for fi in Fsim.T:
plots['posterior_samples_f'] = ax.plot(Xnew, fi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
plots['posterior_samples'] = ax.plot(Xnew, fi[:,None], '#3300FF', linewidth=0.25)
#ax.plot(Xnew, fi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
if samples_y: #NOTE not tested with fixed_inputs
Ysim = model.posterior_samples(Xgrid, samples_y, Y_metadata=Y_metadata)
for yi in Ysim.T:
plots['posterior_samples_y'] = ax.scatter(Xnew, yi[:,None], s=5, c=Tango.colorsHex['darkBlue'], marker='o', alpha=0.5)
#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.