mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-02 14:45:15 +02:00
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
|
|
@ -9,7 +9,9 @@ from ..likelihoods import Gaussian, Gaussian_Mixed_Noise
|
|||
class GPBase(Model):
|
||||
"""
|
||||
Gaussian process base model for holding shared behaviour between
|
||||
sparse_GP and GP models.
|
||||
sparse_GP and GP models, and potentially other models in the future.
|
||||
|
||||
Here we define some functions that are use
|
||||
"""
|
||||
def __init__(self, X, likelihood, kernel, normalize_X=False):
|
||||
self.X = X
|
||||
|
|
@ -34,29 +36,6 @@ class GPBase(Model):
|
|||
# All leaf nodes should call self._set_params(self._get_params()) at
|
||||
# the end
|
||||
|
||||
def getstate(self):
|
||||
"""
|
||||
Get the current state of the class, here we return everything that is needed to recompute the model.
|
||||
"""
|
||||
return Model.getstate(self) + [self.X,
|
||||
self.num_data,
|
||||
self.input_dim,
|
||||
self.kern,
|
||||
self.likelihood,
|
||||
self.output_dim,
|
||||
self._Xoffset,
|
||||
self._Xscale]
|
||||
|
||||
def setstate(self, state):
|
||||
self._Xscale = state.pop()
|
||||
self._Xoffset = state.pop()
|
||||
self.output_dim = state.pop()
|
||||
self.likelihood = state.pop()
|
||||
self.kern = state.pop()
|
||||
self.input_dim = state.pop()
|
||||
self.num_data = state.pop()
|
||||
self.X = state.pop()
|
||||
Model.setstate(self, state)
|
||||
|
||||
def posterior_samples_f(self,X,size=10,which_parts='all',full_cov=True):
|
||||
"""
|
||||
|
|
@ -269,152 +248,32 @@ class GPBase(Model):
|
|||
else:
|
||||
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
|
||||
|
||||
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):
|
||||
def getstate(self):
|
||||
"""
|
||||
For a specific output, in a multioutput model, this function works just as plot_f on single output models.
|
||||
|
||||
:param output: which output to plot (for multiple output models only)
|
||||
:type output: integer (first output is 0)
|
||||
:param samples: the number of a posteriori samples to plot
|
||||
:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
|
||||
:param which_data: which if the training data to plot (default all)
|
||||
:type which_data: 'all' or a slice object to slice self.X, self.Y
|
||||
:param which_parts: which of the kernel functions to plot (additively)
|
||||
:type which_parts: 'all', or list of bools
|
||||
: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 full_cov:
|
||||
:type full_cov: bool
|
||||
:param fignum: figure to plot on.
|
||||
:type fignum: figure number
|
||||
:param ax: axes to plot on.
|
||||
:type ax: axes handle
|
||||
Get the curent state of the class. This is only used to efficiently
|
||||
pickle the model. See also self.setstate
|
||||
"""
|
||||
assert output is not None, "An output must be specified."
|
||||
assert len(self.likelihood.noise_model_list) > output, "The model has only %s outputs." %(self.output_dim + 1)
|
||||
return Model.getstate(self) + [self.X,
|
||||
self.num_data,
|
||||
self.input_dim,
|
||||
self.kern,
|
||||
self.likelihood,
|
||||
self.output_dim,
|
||||
self._Xoffset,
|
||||
self._Xscale]
|
||||
|
||||
if which_data == 'all':
|
||||
which_data = slice(None)
|
||||
|
||||
if ax is None:
|
||||
fig = pb.figure(num=fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
if self.X.shape[1] == 2:
|
||||
Xu = self.X[self.X[:,-1]==output ,0:1]
|
||||
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
|
||||
Xnew_indexed = self._add_output_index(Xnew,output)
|
||||
|
||||
m, v = self._raw_predict(Xnew_indexed, which_parts=which_parts)
|
||||
|
||||
if samples:
|
||||
Ysim = self.posterior_samples_f(Xnew_indexed, samples, which_parts=which_parts, full_cov=True)
|
||||
for yi in Ysim.T:
|
||||
ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
|
||||
|
||||
gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
|
||||
ax.plot(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
|
||||
ax.set_xlim(xmin, xmax)
|
||||
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])))
|
||||
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
|
||||
ax.set_ylim(ymin, ymax)
|
||||
|
||||
elif self.X.shape[1] == 3:
|
||||
raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
|
||||
#if samples:
|
||||
# warnings.warn("Samples only implemented for 1 dimensional inputs.")
|
||||
|
||||
else:
|
||||
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
|
||||
|
||||
|
||||
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']):
|
||||
def setstate(self, state):
|
||||
"""
|
||||
For a specific output, in a multioutput model, this function works just as plot_f on single output models.
|
||||
|
||||
:param output: which output to plot (for multiple output models only)
|
||||
:type output: integer (first output is 0)
|
||||
: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: which if the training data to plot (default all)
|
||||
:type which_data: 'all' or a slice object to slice self.X, self.Y
|
||||
:param which_parts: which of the kernel functions to plot (additively)
|
||||
:type which_parts: 'all', or list of bools
|
||||
: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 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 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
|
||||
Set the state of the model. Used for efficient pickling
|
||||
"""
|
||||
assert output is not None, "An output must be specified."
|
||||
assert len(self.likelihood.noise_model_list) > output, "The model has only %s outputs." %(self.output_dim + 1)
|
||||
if which_data == 'all':
|
||||
which_data = slice(None)
|
||||
|
||||
if ax is None:
|
||||
fig = pb.figure(num=fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
if self.X.shape[1] == 2:
|
||||
resolution = resolution or 200
|
||||
|
||||
Xu = self.X[self.X[:,-1]==output,:] #keep the output of interest
|
||||
Xu = self.X * self._Xscale + self._Xoffset
|
||||
Xu = self.X[self.X[:,-1]==output ,0:1] #get rid of the index column
|
||||
|
||||
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
|
||||
Xnew_indexed = self._add_output_index(Xnew,output)
|
||||
self._Xscale = state.pop()
|
||||
self._Xoffset = state.pop()
|
||||
self.output_dim = state.pop()
|
||||
self.likelihood = state.pop()
|
||||
self.kern = state.pop()
|
||||
self.input_dim = state.pop()
|
||||
self.num_data = state.pop()
|
||||
self.X = state.pop()
|
||||
Model.setstate(self, state)
|
||||
|
||||
|
||||
m, v, lower, upper = self.predict(Xnew_indexed, which_parts=which_parts,noise_model=output)
|
||||
|
||||
if samples: #NOTE not tested with fixed_inputs
|
||||
Ysim = self.posterior_samples(Xnew_indexed, samples, which_parts=which_parts, full_cov=True,noise_model=output)
|
||||
for yi in Ysim.T:
|
||||
ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
|
||||
|
||||
for d in range(m.shape[1]):
|
||||
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
|
||||
ax.plot(Xu[which_data], self.likelihood.noise_model_list[output].data, 'kx', mew=1.5)
|
||||
ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
|
||||
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
|
||||
ax.set_xlim(xmin, xmax)
|
||||
ax.set_ylim(ymin, ymax)
|
||||
|
||||
elif self.X.shape[1] == 3:
|
||||
raise NotImplementedError, "Plots not implemented for multioutput models with 2D inputs...yet"
|
||||
#if samples:
|
||||
# warnings.warn("Samples only implemented for 1 dimensional inputs.")
|
||||
|
||||
else:
|
||||
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
|
||||
|
||||
|
||||
def _add_output_index(self,X,output):
|
||||
"""
|
||||
In a multioutput model, appends an index column to X to specify the output it is related to.
|
||||
|
||||
:param X: Input data
|
||||
:type X: np.ndarray, N x self.input_dim
|
||||
:param output: output X is related to
|
||||
:type output: integer in {0,..., output_dim-1}
|
||||
|
||||
.. Note:: For multiple non-independent outputs models only.
|
||||
"""
|
||||
|
||||
assert hasattr(self,'multioutput'), 'This function is for multiple output models only.'
|
||||
|
||||
index = np.ones((X.shape[0],1))*output
|
||||
return np.hstack((X,index))
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue