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docstrings and removal of duplicated plotting code in gp_base
This commit is contained in:
James Hensman
parent
33b6a7d24f
commit
bddb22f4af
1 changed files with 25 additions and 166 deletions
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@ -9,7 +9,9 @@ from ..likelihoods import Gaussian, Gaussian_Mixed_Noise
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class GPBase(Model):
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class GPBase(Model):
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"""
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"""
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Gaussian process base model for holding shared behaviour between
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Gaussian process base model for holding shared behaviour between
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sparse_GP and GP models.
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sparse_GP and GP models, and potentially other models in the future.
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Here we define some functions that are use
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"""
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"""
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def __init__(self, X, likelihood, kernel, normalize_X=False):
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def __init__(self, X, likelihood, kernel, normalize_X=False):
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self.X = X
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self.X = X
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@ -34,29 +36,6 @@ class GPBase(Model):
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# All leaf nodes should call self._set_params(self._get_params()) at
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# All leaf nodes should call self._set_params(self._get_params()) at
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# the end
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# the end
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def getstate(self):
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"""
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Get the current state of the class, here we return everything that is needed to recompute the model.
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"""
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return Model.getstate(self) + [self.X,
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self.num_data,
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self.input_dim,
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self.kern,
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self.likelihood,
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self.output_dim,
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self._Xoffset,
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self._Xscale]
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def setstate(self, state):
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self._Xscale = state.pop()
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self._Xoffset = state.pop()
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self.output_dim = state.pop()
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self.likelihood = state.pop()
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self.kern = state.pop()
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self.input_dim = state.pop()
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self.num_data = state.pop()
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self.X = state.pop()
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Model.setstate(self, state)
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def posterior_samples_f(self,X,size=10,which_parts='all',full_cov=True):
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def posterior_samples_f(self,X,size=10,which_parts='all',full_cov=True):
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"""
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"""
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@ -269,152 +248,32 @@ class GPBase(Model):
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else:
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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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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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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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def getstate(self):
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"""
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"""
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For a specific output, in a multioutput model, this function works just as plot_f on single output models.
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Get the curent state of the class. This is only used to efficiently
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pickle the model. See also self.setstate
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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 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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"""
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"""
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assert output is not None, "An output must be specified."
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return Model.getstate(self) + [self.X,
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assert len(self.likelihood.noise_model_list) > output, "The model has only %s outputs." %(self.output_dim + 1)
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self.num_data,
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self.input_dim,
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self.kern,
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self.likelihood,
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self.output_dim,
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self._Xoffset,
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self._Xscale]
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if which_data == 'all':
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def setstate(self, state):
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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] == 2:
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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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Xnew_indexed = self._add_output_index(Xnew,output)
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m, v = self._raw_predict(Xnew_indexed, which_parts=which_parts)
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if samples:
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Ysim = self.posterior_samples_f(Xnew_indexed, 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(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], '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] == 3:
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raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
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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_single_output(self, output=None, 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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"""
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For a specific output, in a multioutput model, this function works just as plot_f on single output models.
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Set the state of the model. Used for efficient pickling
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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 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_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 levels: number of levels to plot in a contour plot.
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:type levels: int
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:param samples: the number of a posteriori samples to plot
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:type samples: int
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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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: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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"""
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assert output is not None, "An output must be specified."
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self._Xscale = state.pop()
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assert len(self.likelihood.noise_model_list) > output, "The model has only %s outputs." %(self.output_dim + 1)
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self._Xoffset = state.pop()
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if which_data == 'all':
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self.output_dim = state.pop()
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which_data = slice(None)
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self.likelihood = state.pop()
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self.kern = state.pop()
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if ax is None:
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self.input_dim = state.pop()
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fig = pb.figure(num=fignum)
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self.num_data = state.pop()
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ax = fig.add_subplot(111)
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self.X = state.pop()
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Model.setstate(self, state)
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if self.X.shape[1] == 2:
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resolution = resolution or 200
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Xu = self.X[self.X[:,-1]==output,:] #keep the output of interest
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Xu = self.X * self._Xscale + self._Xoffset
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Xu = self.X[self.X[:,-1]==output ,0:1] #get rid of the index column
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Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
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Xnew_indexed = self._add_output_index(Xnew,output)
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m, v, lower, upper = self.predict(Xnew_indexed, which_parts=which_parts,noise_model=output)
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if samples: #NOTE not tested with fixed_inputs
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Ysim = self.posterior_samples(Xnew_indexed, samples, which_parts=which_parts, full_cov=True,noise_model=output)
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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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for d in range(m.shape[1]):
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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], self.likelihood.noise_model_list[output].data, 'kx', mew=1.5)
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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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ax.set_xlim(xmin, xmax)
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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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#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 _add_output_index(self,X,output):
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"""
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In a multioutput model, appends an index column to X to specify the output it is related to.
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:param X: Input data
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:type X: np.ndarray, N x self.input_dim
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:param output: output X is related to
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:type output: integer in {0,..., output_dim-1}
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.. Note:: For multiple non-independent outputs models only.
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"""
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assert hasattr(self,'multioutput'), 'This function is for multiple output models only.'
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index = np.ones((X.shape[0],1))*output
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return np.hstack((X,index))
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