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Plots tidied up.
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Ricardo
parent
b20ea09f89
commit
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2 changed files with 391 additions and 183 deletions
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@ -34,7 +34,6 @@ class SparseGP(GPBase):
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self.Z = Z
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self.num_inducing = Z.shape[0]
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# self.likelihood = likelihood
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if X_variance is None:
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self.has_uncertain_inputs = False
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@ -305,9 +304,8 @@ class SparseGP(GPBase):
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return mu, var[:, None]
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def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
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def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False, **likelihood_args):
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"""
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Predict the function(s) at the new point(s) Xnew.
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**Arguments**
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@ -338,56 +336,90 @@ class SparseGP(GPBase):
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mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
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# now push through likelihood
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mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
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mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, **likelihood_args)
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return mean, var, _025pm, _975pm
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def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None, output=None):
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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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"""
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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 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 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 fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot(self, samples=0, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=None, levels=20, ax=ax, output=output)
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GPBase.plot_f(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
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if not hasattr(self,'multioutput'):
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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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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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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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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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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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elif self.X.shape[1] == 2:
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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else:
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if self.X.shape[1] == 2 and hasattr(self,'multioutput'):
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"""
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Xu = self.X[self.X[:,-1]==output,:]
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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Xu = self.X[self.X[:,-1]==output ,0:1] #??
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def plot(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=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 fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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GPBase.plot(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax)
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"""
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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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#ax.set_ylim(ax.get_ylim()[0],)
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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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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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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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elif self.X.shape[1] == 2:
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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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 predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
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"""
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@ -470,3 +502,64 @@ class SparseGP(GPBase):
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var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
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return mu, var[:, None]
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def plot_single_output_f(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=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 fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot_single_output_f(self, output=output, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
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if self.X.shape[1] == 2:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:1]
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ax.plot(Zu[:,0], np.zeros_like(Zu[:,0]) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
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elif self.X.shape[1] == 2:
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:2]
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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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_single_output(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=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 fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot_single_output(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax, output=output)
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if self.X.shape[1] == 2:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,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:
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Zu = self.Z * self._Xscale + self._Xoffset
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Zu = Zu[Zu[:,1]==output,0:1]
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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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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