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Samples in plot_f fixed for sparse_models
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Ricardo
parent
b3fe19fa0b
commit
299f17ee46
1 changed files with 93 additions and 81 deletions
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@ -59,28 +59,28 @@ class GPBase(Model):
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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,output=None):
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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 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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: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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: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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: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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: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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"""
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if which_data == 'all':
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which_data = slice(None)
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@ -89,69 +89,81 @@ class GPBase(Model):
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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 and not hasattr(self,'multioutput'):
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Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
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if samples == 0:
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m, v = self._raw_predict(Xnew, which_parts=which_parts)
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gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
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if not hasattr(self,'multioutput'):
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if self.X.shape[1] == 1:
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Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
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if samples == 0:
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m, v = self._raw_predict(Xnew, which_parts=which_parts)
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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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else:
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m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True)
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v = v.reshape(m.size,-1) if len(v.shape)==3 else v
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Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
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gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
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for i in range(samples):
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ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
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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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if hasattr(self,'Z'):
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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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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else:
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m, v = self._raw_predict(Xnew, which_parts=which_parts, full_cov=True)
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Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
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gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
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for i in range(samples):
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ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
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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 and not hasattr(self,'multioutput'):
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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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elif self.X.shape[1] == 2 and hasattr(self,'multioutput'):
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output -= 1
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assert self.num_outputs >= output, 'The model has only %s outputs.' %self.num_outputs
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Xu = self.X[self.X[:,-1]==output ,0:1]
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Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
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if samples == 0:
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m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts)
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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(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
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else:
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m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts, full_cov=True)
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Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
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gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
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for i in range(samples):
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ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
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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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if hasattr(self,'Z'):
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Zu = self.Z[self.Z[:,-1]==output,:]
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = self.Z[self.Z[:,-1]==output ,0:1] #??
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 3 and hasattr(self,'multioutput'):
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raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
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output -= 1
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assert self.num_outputs >= output, 'The model has only %s outputs.' %self.num_outputs
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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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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assert self.num_outputs > output, 'The model has only %s outputs.' %self.num_outputs
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if self.X.shape[1] == 2:
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assert self.num_outputs >= output, 'The model has only %s outputs.' %self.num_outputs
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Xu = self.X[self.X[:,-1]==output ,0:1]
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Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
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if samples == 0:
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m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts)
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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(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
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else:
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m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts, full_cov=True)
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v = v.reshape(m.size,-1) if len(v.shape)==3 else v
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Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
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gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
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for i in range(samples):
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ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
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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] == 3:
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raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
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assert self.num_outputs >= output, 'The model has only %s outputs.' %self.num_outputs
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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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if hasattr(self,'Z'):
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Zu = self.Z[self.Z[:,-1]==output,:]
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = self.Z[self.Z[:,-1]==output ,0:1] #??
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ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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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, output=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
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"""
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@ -203,7 +215,7 @@ class GPBase(Model):
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if plotdims == 1:
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resolution = resolution or 200
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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Xu = self.X * self._Xscale + self._Xoffset #NOTE self.X are the normalized values now
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fixed_dims = np.array([i for i,v in fixed_inputs])
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freedim = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
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