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[magnification] plot_magnification expanded

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This commit is contained in:
Max Zwiessele 2015-09-03 09:33:07 +01:00
parent ca60ad3195
commit 839e3dc6f0
8 changed files with 64 additions and 60 deletions
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19
GPy/core/gp.py
View file

@ -371,7 +371,7 @@ class GP(Model):
var_jac = compute_cov_inner(self.posterior.woodbury_inv)
return mean_jac, var_jac
def predict_wishard_embedding(self, Xnew, kern=None):
def predict_wishard_embedding(self, Xnew, kern=None, mean=True, covariance=True):
"""
Predict the wishard embedding G of the GP. This is the density of the
input of the GP defined by the probabilistic function mapping f.
@ -391,13 +391,16 @@ class GP(Model):
mumuT = np.einsum('iqd,ipd->iqp', mu_jac, mu_jac)
if var_jac.ndim == 3:
Sigma = np.einsum('iqd,ipd->iqp', var_jac, var_jac)
G = mumuT + Sigma
else:
Sigma = np.einsum('iq,ip->iqp', var_jac, var_jac)
G = mumuT + self.output_dim*Sigma
Sigma = self.output_dim*np.einsum('iq,ip->iqp', var_jac, var_jac)
G = 0.
if mean:
G += mumuT
if covariance:
G += Sigma
return G
def predict_magnification(self, Xnew, kern=None):
def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True):
"""
Predict the magnification factor as
@ -405,7 +408,7 @@ class GP(Model):
for each point N in Xnew
"""
G = self.predict_wishard_embedding(Xnew, kern)
G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
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])])
@ -569,7 +572,7 @@ class GP(Model):
resolution=50, ax=None, marker='o', s=40,
fignum=None, legend=True,
plot_limits=None,
aspect='auto', updates=False, **kwargs):
aspect='auto', updates=False, plot_inducing=True, kern=None, **kwargs):
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
@ -577,7 +580,7 @@ class GP(Model):
return dim_reduction_plots.plot_magnification(self, labels, which_indices,
resolution, ax, marker, s,
fignum, False, legend,
fignum, plot_inducing, legend,
plot_limits, aspect, updates, **kwargs)

15
GPy/core/sparse_gp.py
View file

@ -181,18 +181,3 @@ 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)

16
GPy/kern/_src/add.py
View file

@ -14,7 +14,7 @@ class Add(CombinationKernel):
This kernel will take over the active dims of it's subkernels passed in.
"""
def __init__(self, subkerns, name='add'):
def __init__(self, subkerns, name='sum'):
for i, kern in enumerate(subkerns[:]):
if isinstance(kern, Add):
del subkerns[i]
@ -71,11 +71,21 @@ class Add(CombinationKernel):
target = np.zeros(X.shape)
[target.__iadd__(p.gradients_X_diag(dL_dKdiag, X)) for p in self.parts]
return target
def gradients_XX(self, dL_dK, X, X2):
target = 0.
[target.__iadd__(p.gradients_XX(dL_dK, X, X2)) for p in self.parts]
return target
def gradients_XX_diag(self, dL_dKdiag, X):
target = np.zeros(X.shape)
[target.__iadd__(p.gradients_XX_diag(dL_dKdiag, X)) for p in self.parts]
return target
@Cache_this(limit=2, force_kwargs=['which_parts'])
def psi0(self, Z, variational_posterior):
return reduce(np.add, (p.psi0(Z, variational_posterior) for p in self.parts))
@Cache_this(limit=2, force_kwargs=['which_parts'])
def psi1(self, Z, variational_posterior):
return reduce(np.add, (p.psi1(Z, variational_posterior) for p in self.parts))

2
GPy/kern/_src/kern.py
View file

@ -102,7 +102,7 @@ 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):
def gradients_XX_diag(self, dL_dKdiag, X):
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

5
GPy/kern/_src/static.py
View file

@ -24,6 +24,11 @@ class Static(Kern):
def gradients_X_diag(self, dL_dKdiag, X):
return np.zeros(X.shape)
def gradients_XX(self, dL_dK, X, X2):
return np.zeros((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)
def gradients_XX_diag(self, dL_dKdiag, X):
return np.zeros(X.shape)
def gradients_Z_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
return np.zeros(Z.shape)

14
GPy/models/bayesian_gplvm.py
View file

@ -137,20 +137,6 @@ class BayesianGPLVM(SparseGP_MPI):
fignum, plot_inducing, legend,
plot_limits, aspect, updates, predict_kwargs, imshow_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)
def do_test_latents(self, Y):
"""
Compute the latent representation for a set of new points Y

46
GPy/plotting/matplot_dep/dim_reduction_plots.py
View file

@ -9,7 +9,8 @@ import itertools
try:
import Tango
from matplotlib.cm import get_cmap
import pylab as pb
from matplotlib import pyplot as pb
from matplotlib import cm
except:
pass
@ -137,7 +138,7 @@ def plot_latent(model, labels=None, which_indices=None,
view = ImshowController(ax, plot_function,
(xmin, ymin, xmax, ymax),
resolution, aspect=aspect, interpolation='bilinear',
cmap=pb.cm.binary, **imshow_kwargs)
cmap=cm.binary, **imshow_kwargs)
# make sure labels are in order of input:
labels = np.asarray(labels)
@ -192,18 +193,18 @@ def plot_latent(model, labels=None, which_indices=None,
if updates:
try:
ax.figure.canvas.show()
fig.canvas.show()
except Exception as e:
print("Could not invoke show: {}".format(e))
raw_input('Enter to continue')
view.deactivate()
#raw_input('Enter to continue')
return view
return ax
def plot_magnification(model, labels=None, which_indices=None,
resolution=60, ax=None, marker='o', s=40,
fignum=None, plot_inducing=False, legend=True,
plot_limits=None,
aspect='auto', updates=False):
aspect='auto', updates=False, mean=True, covariance=True, kern=None):
"""
:param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc)
:param resolution: the resolution of the grid on which to evaluate the predictive variance
@ -211,6 +212,8 @@ def plot_magnification(model, labels=None, which_indices=None,
if ax is None:
fig = pb.figure(num=fignum)
ax = fig.add_subplot(111)
else:
fig = ax.figure
Tango.reset()
if labels is None:
@ -295,13 +298,13 @@ def plot_magnification(model, labels=None, which_indices=None,
def plot_function(x):
Xtest_full = np.zeros((x.shape[0], X.shape[1]))
Xtest_full[:, [input_1, input_2]] = x
mf = model.predict_magnification(Xtest_full)
mf = model.predict_magnification(Xtest_full, kern=kern, mean=mean, covariance=covariance)
return mf
view = ImshowController(ax, plot_function,
(xmin, ymin, xmax, ymax),
resolution, aspect=aspect, interpolation='bilinear',
cmap=pb.cm.gray)
cmap=cm.gray)
# make sure labels are in order of input:
ulabels = []
@ -317,7 +320,7 @@ def plot_magnification(model, labels=None, which_indices=None,
elif type(ul) is np.int64:
this_label = 'class %i' % ul
else:
this_label = 'class %i' % i
this_label = unicode(ul)
m = marker.next()
index = np.nonzero(labels == ul)[0]
@ -327,7 +330,7 @@ def plot_magnification(model, labels=None, which_indices=None,
else:
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.scatter(x, y, marker=m, s=s, c=Tango.nextMedium(), label=this_label, linewidth=.2, edgecolor='k', alpha=.9)
ax.set_xlabel('latent dimension %i' % input_1)
ax.set_ylabel('latent dimension %i' % input_2)
@ -337,18 +340,27 @@ def plot_magnification(model, labels=None, which_indices=None,
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:
if plot_inducing and hasattr(model, 'Z'):
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()
raw_input('Enter to continue')
try:
fig.canvas.draw()
fig.tight_layout()
fig.canvas.draw()
except Exception as e:
print("Could not invoke tight layout: {}".format(e))
pass
pb.title('Magnification Factor')
if updates:
try:
fig.canvas.draw()
fig.canvas.show()
except Exception as e:
print("Could not invoke show: {}".format(e))
#raw_input('Enter to continue')
return view
return ax

7
GPy/plotting/matplot_dep/latent_space_visualizations/controllers/axis_event_controller.py
View file

@ -9,6 +9,9 @@ class AxisEventController(object):
def __init__(self, ax):
self.ax = ax
self.activate()
def __del__(self):
self.deactivate()
return self
def deactivate(self):
for cb_class in self.ax.callbacks.callbacks.values():
for cb_num in cb_class.keys():
@ -81,9 +84,9 @@ class BufferedAxisChangedController(AxisChangedController):
def __init__(self, ax, plot_function, plot_limits, resolution=50, update_lim=None, **kwargs):
"""
Buffered axis changed controller. Controls the buffer and handles update events for when the axes changed.
Updated plotting will be after first reload (first time will be within plot limits, after that the limits will be buffered)
:param plot_function:
function to use for creating image for plotting (return ndarray-like)
plot_function gets called with (2D!) Xtest grid if replotting required