apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-09 03:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[magnification] mostly plotting and some model corrections for _predictive_variable

Browse source
This commit is contained in:
Max Zwiessele 2015-09-02 15:46:40 +01:00
parent 26bdcfa82e
commit ca60ad3195
7 changed files with 54 additions and 33 deletions
Download patch file Download diff file Expand all files Collapse all files

21
GPy/core/gp.py
View file

@ -405,9 +405,10 @@ class GP(Model):
for each point N in Xnew
"""
from ..util.linalg import jitchol
G = self.predict_wishard_embedding(Xnew, kern)
return np.array([2*np.sqrt(np.exp(np.sum(np.log(np.diag(jitchol(G[n, :, :])))))) for n in range(Xnew.shape[0])])
from ..util.linalg import jitchol
return np.array([np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :])))))) for n in range(Xnew.shape[0])])
#return np.array([np.sqrt(np.linalg.det(G[n, :, :])) for n in range(Xnew.shape[0])])
def posterior_samples_f(self,X,size=10, full_cov=True):
"""
@ -564,6 +565,22 @@ class GP(Model):
plot_raw=plot_raw, Y_metadata=Y_metadata,
data_symbol=data_symbol, predict_kw=predict_kw, **kw)
def plot_magnification(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)
def input_sensitivity(self, summarize=True):
"""
Returns the sensitivity for each dimension of this model

15
GPy/core/sparse_gp.py
View file

@ -181,3 +181,18 @@ class SparseGP(GP):
var[i] = np.diag(var_)+p0-t2
return mu, var
def plot_magnification(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, plot_inducing=True, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
resolution, ax, marker, s,
fignum, plot_inducing, legend,
plot_limits, aspect, updates, **kwargs)

10
GPy/core/verbose_optimization.py
View file

@ -24,7 +24,6 @@ class VerboseOptimization(object):
self.model.add_observer(self, self.print_status)
self.status = 'running'
self.clear = clear_after_finish
self.deltat = .2
self.update()
@ -80,6 +79,7 @@ class VerboseOptimization(object):
def __enter__(self):
self.start = time.time()
self._time = self.start
return self
def print_out(self, seconds):
@ -143,12 +143,12 @@ class VerboseOptimization(object):
def print_status(self, me, which=None):
self.update()
seconds = time.time()-self.start
t = time.time()
seconds = t-self.start
#sys.stdout.write(" "*len(self.message))
self.deltat += seconds
if self.deltat > .3 or seconds < .3:
if t-self._time > .3 or seconds < .3:
self.print_out(seconds)
self.deltat = 0
self._time = t
self.iteration += 1

4
GPy/kern/_src/kern.py
View file

@ -59,7 +59,7 @@ class Kern(Parameterized):
self._sliced_X = 0
self.useGPU = self._support_GPU and useGPU
self._return_psi2_n_flag = ObsAr(np.zeros(1)).astype(bool)
from .psi_comp import PSICOMP_GH
self.psicomp = PSICOMP_GH()
@ -102,6 +102,8 @@ class Kern(Parameterized):
raise NotImplementedError
def gradients_XX(self, dL_dK, X, X2):
raise(NotImplementedError, "This is the second derivative of K wrt X and X2, and not implemented for this kernel")
def gradients_XX_diag(self, dL_dK, X, X2):
raise(NotImplementedError, "This is the diagonal of the second derivative of K wrt X and X2, and not implemented for this kernel")
def gradients_X_diag(self, dL_dKdiag, X):
raise NotImplementedError

15
GPy/models/gplvm.py
View file

@ -81,18 +81,3 @@ class GPLVM(GP):
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)
def plot_magnification(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import dim_reduction_plots
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
plot_limits, aspect, updates, **kwargs)

3
GPy/models/sparse_gp_minibatch.py
View file

@ -44,7 +44,7 @@ class SparseGPMiniBatch(SparseGP):
def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None,
name='sparse gp', Y_metadata=None, normalizer=False,
missing_data=False, stochastic=False, batchsize=1):
# pick a sensible inference method
if inference_method is None:
if isinstance(likelihood, likelihoods.Gaussian):
@ -76,6 +76,7 @@ class SparseGPMiniBatch(SparseGP):
logger.info("Adding Z as parameter")
self.link_parameter(self.Z, index=0)
self.posterior = None
self._predictive_variable = self.Z
def has_uncertain_inputs(self):
return isinstance(self.X, VariationalPosterior)

19
GPy/plotting/matplot_dep/dim_reduction_plots.py
View file

@ -322,11 +322,11 @@ def plot_magnification(model, labels=None, which_indices=None,
index = np.nonzero(labels == ul)[0]
if model.input_dim == 1:
x = model.X[index, input_1]
x = X[index, input_1]
y = np.zeros(index.size)
else:
x = model.X[index, input_1]
y = model.X[index, input_2]
x = X[index, input_1]
y = X[index, input_2]
ax.scatter(x, y, marker=m, s=s, color=Tango.nextMedium(), label=this_label)
ax.set_xlabel('latent dimension %i' % input_1)
@ -335,13 +335,14 @@ def plot_magnification(model, labels=None, which_indices=None,
if not np.all(labels == 1.) and legend:
ax.legend(loc=0, numpoints=1)
ax.set_xlim(xmin[0], xmax[0])
ax.set_ylim(xmin[1], xmax[1])
ax.set_xlim((xmin, xmax))
ax.set_ylim((ymin, ymax))
ax.grid(b=False) # remove the grid if present, it doesn't look good
ax.set_aspect('auto') # set a nice aspect ratio
if plot_inducing:
ax.plot(model.Z[:, input_1], model.Z[:, input_2], '^w')
Z = model.Z
ax.scatter(Z[:, input_1], Z[:, input_2], c='w', s=18, marker="^", edgecolor='k', linewidth=.3, alpha=.7)
if updates:
fig.canvas.show()
@ -386,8 +387,8 @@ def plot_steepest_gradient_map(model, fignum=None, ax=None, which_indices=None,
this_label = 'class %i' % i
m = marker.next()
index = np.nonzero(data_labels == ul)[0]
x = model.X[index, input_1]
y = model.X[index, input_2]
x = X[index, input_1]
y = X[index, input_2]
ax.scatter(x, y, marker=m, s=data_s, color=Tango.nextMedium(), label=this_label)
ax.set_xlabel('latent dimension %i' % input_1)
@ -395,7 +396,7 @@ def plot_steepest_gradient_map(model, fignum=None, ax=None, which_indices=None,
controller = ImAnnotateController(ax,
plot_function,
tuple(model.X.min(0)[:, significant_dims]) + tuple(model.X.max(0)[:, significant_dims]),
tuple(X.min(0)[:, significant_dims]) + tuple(X.max(0)[:, significant_dims]),
resolution=resolution,
aspect=aspect,
cmap=get_cmap('jet'),