apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 21:12:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Plotting functions modified

Browse source
This commit is contained in:
Ricardo 2014-01-28 13:39:59 +00:00
parent 822459cdb6
commit 1654080402
1 changed files with 15 additions and 133 deletions
Download patch file Download diff file Expand all files Collapse all files

148
GPy/core/gp.py
View file

@ -2,10 +2,9 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import pylab as pb
import sys
import warnings
from .. import kern
from ..util.plot import gpplot, Tango, x_frame1D, x_frame2D
from ..util.linalg import dtrtrs
from model import Model
from parameterization import ObservableArray
@ -122,9 +121,9 @@ class GP(Model):
:param X: The points at which to take the samples.
:type X: np.ndarray, Nnew x self.input_dim.
:param size: the number of a posteriori samples to plot.
:param size: the number of a posteriori samples.
:type size: int.
:param which_parts: which of the kernel functions to plot (additively).
:param which_parts: which of the kernel functions to use (additively).
:type which_parts: 'all', or list of bools.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
@ -145,9 +144,9 @@ class GP(Model):
:param X: the points at which to take the samples.
:type X: np.ndarray, Nnew x self.input_dim.
:param size: the number of a posteriori samples to plot.
:param size: the number of a posteriori samples.
:type size: int.
:param which_parts: which of the kernel functions to plot (additively).
:param which_parts: which of the kernel functions to use (additively).
:type which_parts: 'all', or list of bools.
:param full_cov: whether to return the full covariance matrix, or just the diagonal.
:type full_cov: bool.
@ -172,20 +171,13 @@ class GP(Model):
"""
Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
This is a convenience function: we simply call self.plot with the
argument use_raw_predict set True. All args and kwargs are passed on to
plot.
see also: gp.plot
This is a convenience function: arguments are passed to GPy.plotting.matplot_dep.models_plots.plot_f_fit
"""
kwargs['plot_raw'] = True
self.plot(*args, **kwargs)
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import models_plots
models_plots.plot_fit_f(self,*args,**kwargs)
def plot(self, plot_limits=None, which_data_rows='all',
which_data_ycols='all', which_parts='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
def plot(self, *args):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -193,121 +185,13 @@ class GP(Model):
- In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.
Can plot only part of the data and part of the posterior functions
using which_data_rowsm which_data_ycols and which_parts
using which_data_rows which_data_ycols and which_parts
:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
:type plot_limits: np.array
:param which_data_rows: which of the training data to plot (default all)
:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
:param which_data_ycols: when the data has several columns (independant outputs), only plot these
:type which_data_rows: 'all' or a list of integers
:param which_parts: which of the kernel functions to plot (additively)
:type which_parts: 'all', or list of bools
: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.
:type levels: int
:param samples: the number of a posteriori samples to plot
:type samples: int
:param fignum: figure to plot on.
:type fignum: figure number
:param ax: axes to plot on.
:type ax: axes handle
:type output: integer (first output is 0)
:param linecol: color of line to plot.
: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
This is a convenience function: arguments are passed to GPy.plotting.matplot_dep.models_plots.plot_fit
"""
#deal with optional arguments
if which_data_rows == 'all':
which_data_rows = slice(None)
if which_data_ycols == 'all':
which_data_ycols = np.arange(self.output_dim)
if len(which_data_ycols)==0:
raise ValueError('No data selected for plotting')
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
#work out what the inputs are for plotting (1D or 2D)
fixed_dims = np.array([i for i,v in fixed_inputs])
free_dims = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
#one dimensional plotting
if len(free_dims) == 1:
#define the frame on which to plot
resolution = resolution or 200
Xnew, xmin, xmax = x_frame1D(self.X[:,free_dims], plot_limits=plot_limits)
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
Xgrid[:,free_dims] = Xnew
for i,v in fixed_inputs:
Xgrid[:,i] = v
#make a prediction on the frame and plot it
if plot_raw:
m, v = self._raw_predict(Xgrid, which_parts=which_parts)
lower = m - 2*np.sqrt(v)
upper = m + 2*np.sqrt(v)
Y = self.Y
else:
m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts)
Y = self.Y
for d in which_data_ycols:
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
ax.plot(self.X[which_data_rows,free_dims], Y[which_data_rows, d], 'kx', mew=1.5)
#optionally plot some samples
if samples: #NOTE not tested with fixed_inputs
Ysim = self.posterior_samples(Xgrid, samples, which_parts=which_parts)
for yi in Ysim.T:
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.
#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))
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
#2D plotting
elif len(free_dims) == 2:
#define the frame for plotting on
resolution = resolution or 50
Xnew, _, _, xmin, xmax = x_frame2D(self.X[:,free_dims], plot_limits, resolution)
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
Xgrid[:,free_dims] = Xnew
for i,v in fixed_inputs:
Xgrid[:,i] = v
x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
#predict on the frame and plot
if plot_raw:
m, _ = self._raw_predict(Xgrid, which_parts=which_parts)
Y = self.likelihood.Y
else:
m, _, _, _ = self.predict(Xgrid, which_parts=which_parts,sampling=False)
Y = self.likelihood.data
for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T
ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
ax.scatter(self.X[which_data_rows, free_dims[0]], self.X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
#set the limits of the plot to some sensible values
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1])
if samples:
warnings.warn("Samples are rather difficult to plot for 2D inputs...")
else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import models_plots
models_plots.plot_fit(self,*args)
def _getstate(self):
"""
@ -333,5 +217,3 @@ class GP(Model):
self.num_data = state.pop()
self.X = state.pop()
Model._setstate(self, state)