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fixed up the plotting
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James Hensman
parent
1fe92b2515
commit
eedeaa4492
1 changed files with 43 additions and 81 deletions
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@ -89,90 +89,43 @@ class GPBase(Model):
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return Ysim
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def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
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def plot_f(self, *args, **kwargs):
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"""
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Plot the GP's view of the world, where the data is normalized and the
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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- In two dimsensions, a contour-plot shows the mean predicted function
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- Not implemented in higher dimensions
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Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
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:param samples: the number of a posteriori samples to plot
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:param which_data: which if the training data to plot (default all)
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:type which_data: 'all' or a slice object to slice self.X, self.Y
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:param which_parts: which of the kernel functions to plot (additively)
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:type which_parts: 'all', or list of bools
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param full_cov:
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:type full_cov: bool
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:param fignum: figure to plot on.
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:type fignum: figure number
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:param ax: axes to plot on.
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:type ax: axes handle
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This is a convenience function: we simply call self.plot with the
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argument use_raw_predict set True. All args and kwargs are passed on to
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plot.
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:param output: which output to plot (for multiple output models only)
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:type output: integer (first output is 0)
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see also: gp_base.plot
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"""
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if which_data == 'all':
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which_data = slice(None)
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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if self.X.shape[1] == 1:
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resolution = resolution or 200
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Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
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m, v = self._raw_predict(Xnew, which_parts=which_parts)
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if samples:
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Ysim = self.posterior_samples_f(Xnew, samples, which_parts=which_parts, full_cov=True)
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for yi in Ysim.T:
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ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
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gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
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ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
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ax.set_xlim(xmin, xmax)
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ymin, ymax = min(np.append(self.likelihood.Y, m - 2 * np.sqrt(np.diag(v)[:, None]))), max(np.append(self.likelihood.Y, m + 2 * np.sqrt(np.diag(v)[:, None])))
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ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
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ax.set_ylim(ymin, ymax)
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elif self.X.shape[1] == 2:
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resolution = resolution or 50
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Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits, resolution)
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m, v = self._raw_predict(Xnew, which_parts=which_parts)
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m = m.reshape(resolution, resolution).T
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ax.contour(xx, yy, m, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet) # @UndefinedVariable
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ax.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max()) # @UndefinedVariable
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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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if samples:
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warnings.warn("Samples only implemented for 1 dimensional inputs.")
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
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"""
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Plot the GP with noise where the likelihood is Gaussian.
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kwargs['use_raw_predict'] = True
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self.plot(*args, **kwargs)
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def plot(self, plot_limits=None, which_data_rows='all',
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which_data_ycols='all', which_parts='all', fixed_inputs=[],
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levels=20, samples=0, fignum=None, ax=None, resolution=None,
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use_raw_predict=False,
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linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
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"""
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Plot the posterior of the GP.
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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- In two dimsensions, a contour-plot shows the mean predicted function
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- In higher dimensions, use fixed_inputs to plot the GP with some of the inputs fixed.
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Can plot only part of the data and part of the posterior functions
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using which_data and which_functions
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using which_data_rowsm which_data_ycols and which_parts
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:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
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:type plot_limits: np.array
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:param which_data: which if the training data to plot (default all)
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:type which_data: 'all' or a slice object to slice self.X, self.Y
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:param which_data_rows: which of the training data to plot (default all)
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:type which_data_rows: 'all' or a slice object to slice self.X, self.Y
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:param which_data_ycols: when the data has several columns (independant outputs), only plot these
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:type which_data_rows: 'all' or a list of integers
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:param which_parts: which of the kernel functions to plot (additively)
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:type which_parts: 'all', or list of bools
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
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:type resolution: int
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:param levels: number of levels to plot in a contour plot.
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@ -184,16 +137,18 @@ class GPBase(Model):
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:param ax: axes to plot on.
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:type ax: axes handle
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:type output: integer (first output is 0)
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:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
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:type fixed_inputs: a list of tuples
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:param linecol: color of line to plot.
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:type linecol:
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:param fillcol: color of fill
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:param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
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"""
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if which_data == 'all':
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which_data = slice(None)
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#deal with optional arguments
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if which_data_rows == 'all':
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which_data_rows = slice(None)
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if which_data_ycols == 'all':
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which_data_ycols = np.arange(self.output_dim)
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if len(which_data_ycols)==0:
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raise ValueError('No data selected for plotting')
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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@ -215,10 +170,15 @@ class GPBase(Model):
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Xgrid[:,i] = v
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#make a prediction on the frame and plot it
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m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts)
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for d in range(m.shape[1]):
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if use_raw_predict:
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m, v = self._raw_predict(Xgrid, which_parts=which_parts)
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lower = m - 2*np.sqrt(v)
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upper = m + 2*np.sqrt(v)
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else:
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m, v, lower, upper = self.predict(Xgrid, which_parts=which_parts)
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for d in which_data_ycols:
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gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
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ax.plot(Xu[which_data,free_dims], self.likelihood.data[which_data, d], 'kx', mew=1.5)
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ax.plot(Xu[which_data_rows,free_dims], self.likelihood.data[which_data_rows, d], 'kx', mew=1.5)
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#optionally plot some samples
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if samples: #NOTE not tested with fixed_inputs
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@ -227,7 +187,6 @@ class GPBase(Model):
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ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
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#ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
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#set the limits of the plot to some sensible values
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ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
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ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
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@ -248,12 +207,15 @@ class GPBase(Model):
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x, y = np.linspace(xmin[0], xmax[0], resolution), np.linspace(xmin[1], xmax[1], resolution)
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#predict on the frame and plot
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m, _, _, _ = self.predict(Xgrid, which_parts=which_parts)
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for d in range(m.shape[1]):
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if use_raw_predict:
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m, _ = self._raw_predict(Xgrid, which_parts=which_parts)
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else:
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m, _, _, _ = self.predict(Xgrid, which_parts=which_parts)
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for d in which_data_ycols:
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m_d = m[:,d].reshape(resolution, resolution).T
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ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
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Y_d = self.likelihood.Y[:,d]
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ax.scatter(self.X[:, free_dims[0]], self.X[:, free_dims[1]], 40, Y_d, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
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Y_d = self.likelihood.Y[which_data_rows,d]
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ax.scatter(self.X[which_data_rows, free_dims[0]], self.X[which_data_rows, free_dims[1]], 40, Y_d, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
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#set the limits of the plot to some sensible values
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ax.set_xlim(xmin[0], xmax[0])
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