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[plotting] added predict_kw to plot function

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mzwiessele 2015-04-24 11:02:01 +02:00
parent 9c19f8584e
commit 335df2942f
4 changed files with 22 additions and 12 deletions
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4
GPy/core/gp.py
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@ -395,7 +395,7 @@ class GP(Model):
which_data_ycols='all', fixed_inputs=[],
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx'):
linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx', predict_kw=None):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -444,7 +444,7 @@ class GP(Model):
which_data_ycols, fixed_inputs,
levels, samples, fignum, ax, resolution,
plot_raw=plot_raw, Y_metadata=Y_metadata,
data_symbol=data_symbol, **kw)
data_symbol=data_symbol, predict_kw=predict_kw, **kw)
def input_sensitivity(self, summarize=True):
"""

10
GPy/core/sparse_gp.py
View file

@ -137,7 +137,7 @@ class SparseGP(GP):
else:
Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0)
var = (Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0))[:,None]
elif self.posterior.woodbury_inv.ndim == 3:
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]):
@ -147,9 +147,9 @@ class SparseGP(GP):
if self.mean_function is not None:
mu += self.mean_function.f(Xnew)
else:
psi0_star = self.kern.psi0(self.Z, Xnew)
psi1_star = self.kern.psi1(self.Z, Xnew)
#psi2_star = self.kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
psi0_star = kern.psi0(self.Z, Xnew)
psi1_star = kern.psi1(self.Z, Xnew)
#psi2_star = kern.psi2(self.Z, Xnew) # Only possible if we get NxMxM psi2 out of the code.
la = self.posterior.woodbury_vector
mu = np.dot(psi1_star, la) # TODO: dimensions?
@ -161,7 +161,7 @@ class SparseGP(GP):
for i in range(Xnew.shape[0]):
_mu, _var = Xnew.mean.values[[i]], Xnew.variance.values[[i]]
psi2_star = self.kern.psi2(self.Z, NormalPosterior(_mu, _var))
psi2_star = kern.psi2(self.Z, NormalPosterior(_mu, _var))
tmp = (psi2_star[:, :] - psi1_star[[i]].T.dot(psi1_star[[i]]))
var_ = mdot(la.T, tmp, la)

7
GPy/core/verbose_optimization.py
View file

@ -141,6 +141,13 @@ class VerboseOptimization(object):
def finish(self, opt):
self.status = opt.status
if self.verbose and self.ipython_notebook:
if 'conv' in self.status.lower():
self.progress.bar_style = 'success'
elif self.iteration >= self.maxiters:
self.progress.bar_style = 'warning'
else:
self.progress.bar_style = 'danger'
def __exit__(self, type, value, traceback):
if self.verbose:

13
GPy/plotting/matplot_dep/models_plots.py
View file

@ -17,7 +17,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
levels=20, samples=0, fignum=None, ax=None, resolution=None,
plot_raw=False,
linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue'], Y_metadata=None, data_symbol='kx',
apply_link=False, samples_f=0, plot_uncertain_inputs=True):
apply_link=False, samples_f=0, plot_uncertain_inputs=True, predict_kw=None):
"""
Plot the posterior of the GP.
- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
@ -76,6 +76,9 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
if hasattr(model, 'Z'): Z = model.Z
if predict_kw is None:
predict_kw = {}
#work out what the inputs are for plotting (1D or 2D)
fixed_dims = np.array([i for i,v in fixed_inputs])
free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
@ -92,7 +95,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#make a prediction on the frame and plot it
if plot_raw:
m, v = model._raw_predict(Xgrid)
m, v = model._raw_predict(Xgrid, **predict_kw)
if apply_link:
lower = model.likelihood.gp_link.transf(m - 2*np.sqrt(v))
upper = model.likelihood.gp_link.transf(m + 2*np.sqrt(v))
@ -106,7 +109,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
else:
meta = None
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta)
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta, **predict_kw)
lower, upper = model.predict_quantiles(Xgrid, Y_metadata=meta)
@ -178,13 +181,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
#predict on the frame and plot
if plot_raw:
m, _ = model._raw_predict(Xgrid)
m, _ = model._raw_predict(Xgrid, **predict_kw)
else:
if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
else:
meta = None
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta)
m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta, **predict_kw)
for d in which_data_ycols:
m_d = m[:,d].reshape(resolution, resolution).T
plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)