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[testing] BGPLVM

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This commit is contained in:
mzwiessele 2015-10-06 14:48:52 +01:00
parent 298893d65f
commit 116ad8762c
6 changed files with 62 additions and 32 deletions
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31
GPy/kern/_src/kern.py
View file

@ -217,6 +217,12 @@ class Kern(Parameterized):
"""
Determine which dimensions should be plotted
Returns the top three most signification input dimensions
if less then three dimensions, the non existing dimensions are
labeled as None, so for a 1 dimensional input this returns
(0, None, None).
:param which_indices: force the indices to be the given indices.
:type which_indices: int or tuple(int,int)
"""
@ -224,23 +230,32 @@ class Kern(Parameterized):
if self.input_dim == 1:
input_1 = 0
input_2 = None
input_3 = None
if self.input_dim == 2:
input_1, input_2 = 0, 1
input_3 = None
if self.input_dim == 3:
input_1, input_2, input_3 = 0, 1, 2
else:
try:
which_indices = np.argsort(self.input_sensitivity())[::-1][:2]
which_indices = np.argsort(self.input_sensitivity())[::-1][:3]
except:
raise ValueError("cannot automatically determine which dimensions to plot, please pass 'which_indices'")
try:
input_1, input_2 = which_indices
input_1, input_2, input_3 = which_indices
except TypeError:
# which_indices was an int
input_1, input_2 = which_indices, None
except ValueError:
# which_indices was a list or array like with only one int
input_1, input_2 = which_indices[0], None
# which indices is tuple or int
try:
input_3 = None
input_1, input_2 = which_indices
except TypeError:
# which_indices is an int
input_1, input_2 = which_indices, None
except ValueError:
# which_indices was a list or array like with only one int
input_1, input_2 = which_indices[0], None
return input_1, input_2
return input_1, input_2, input_3
def __add__(self, other):

11
GPy/plotting/__init__.py
View file

@ -59,13 +59,4 @@ if config.get('plotting', 'library') is not 'none':
Kern.plot_covariance = gpy_plot.kernel_plots.plot_covariance
Kern.plot_covariance = gpy_plot.kernel_plots.plot_ARD
# Variational plot!
#from . import matplot_dep
# Still to convert to new style:
#GP.plot = matplot_dep.models_plots.plot_fit
#GP.plot_f = matplot_dep.models_plots.plot_fit_f
#GP.plot_magnification = matplot_dep.dim_reduction_plots.plot_magnification
# Variational plot!

3
GPy/plotting/gpy_plot/data_plots.py
View file

@ -170,6 +170,9 @@ def plot_inducing(self, visible_dims=None, projection='2d', label=None, **plot_k
return pl.show_canvas(canvas, plots)
def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs):
if visible_dims is None:
sig_dims = self.get_most_significant_input_dimensions()
visible_dims = [i for i in sig_dims if i is not None]
free_dims = get_free_dims(self, visible_dims, None)
Z = self.Z[:, free_dims]

29
GPy/plotting/gpy_plot/latent_plots.py
View file

@ -116,16 +116,16 @@ def _plot_prediction_fit(self, canvas, plot_limits=None,
raise NotImplementedError("Cannot plot in more then one dimension.")
return plots
def _plot_latent_scatter(self, canvas, X, input_1, input_2, labels, marker, num_samples, **kwargs):
def _plot_latent_scatter(self, canvas, X, visible_dims, labels, marker, num_samples, projection='2d', **kwargs):
from .. import Tango
Tango.reset()
if labels is None:
labels = np.ones(self.num_data)
X, labels = subsample_X(X, labels, num_samples)
scatters = []
for x, y, this_label, _, m in scatter_label_generator(labels, X, input_1, input_2, marker):
for x, y, z, this_label, _, m in scatter_label_generator(labels, X, visible_dims, marker):
update_not_existing_kwargs(kwargs, pl.defaults.latent_scatter)
scatters.append(pl.scatter(canvas, x, y, marker=m, color=Tango.nextMedium(), label=this_label, **kwargs))
scatters.append(pl.scatter(canvas, x, y, Z=z, marker=m, color=Tango.nextMedium(), label=this_label, **kwargs))
return scatters
def plot_latent_scatter(self, labels=None,
@ -134,6 +134,7 @@ def plot_latent_scatter(self, labels=None,
plot_limits=None,
marker='<>^vsd',
num_samples=1000,
projection='2d',
**kwargs):
"""
Plot a scatter plot of the latent space.
@ -146,13 +147,23 @@ def plot_latent_scatter(self, labels=None,
:param str marker: markers to use - cycle if more labels then markers are given
:param kwargs: the kwargs for the scatter plots
"""
input_1, input_2 = self.get_most_significant_input_dimensions(which_indices)
canvas, kwargs = pl.get_new_canvas(xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **kwargs)
sig_dims = self.get_most_significant_input_dimensions(which_indices)
input_1, input_2, input_3 = [i for i in sig_dims if i is not None]
canvas, kwargs = pl.get_new_canvas(projection=projection, **kwargs)
X, _, _ = get_x_y_var(self)
scatters = _plot_latent_scatter(self, canvas, X, input_1, input_2, labels, marker, num_samples, **kwargs)
return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend and (labels is not None))
scatters = _plot_latent_scatter(self, canvas, X, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
if projection == '3d':
return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend and (labels is not None),
xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
zlabel='latent dimension %i' % input_3)
else:
return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend and (labels is not None),
xlabel='latent dimension %i' % input_1,
ylabel='latent dimension %i' % input_2,
#zlabel='latent dimension %i' % input_3
)
def _plot_magnification(self, canvas, input_1, input_2, Xgrid,

16
GPy/plotting/gpy_plot/plot_util.py
View file

@ -129,7 +129,7 @@ def helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resoluti
Xgrid[:,i] = v
return X, Xvar, Y, fixed_dims, free_dims, Xgrid, x, y, xmin, xmax, resolution
def scatter_label_generator(labels, X, input_1, input_2=None, marker=None):
def scatter_label_generator(labels, X, visible_dims, marker=None):
ulabels = []
for lab in labels:
if not lab in ulabels:
@ -140,6 +140,8 @@ def scatter_label_generator(labels, X, input_1, input_2=None, marker=None):
else:
m = None
input_1, input_2, input_3 = visible_dims
for ul in ulabels:
if type(ul) is np.string_:
this_label = ul
@ -160,10 +162,16 @@ def scatter_label_generator(labels, X, input_1, input_2=None, marker=None):
if input_2 is None:
x = X[index, input_1]
y = np.zeros(index.size)
else:
z = None
elif input_3 is None:
x = X[index, input_1]
y = X[index, input_2]
yield x, y, this_label, index, m
z = None
else:
x = X[index, input_1]
y = X[index, input_2]
z = X[index, input_3]
yield x, y, z, this_label, index, m
def subsample_X(X, labels, num_samples=1000):
"""
@ -175,7 +183,7 @@ def subsample_X(X, labels, num_samples=1000):
print("Warning: subsampling X, as it has more samples then 1000. X.shape={!s}".format(X.shape))
if labels is not None:
subsample = []
for _, _, _, index, _ in scatter_label_generator(labels, X, 0):
for _, _, _, _, index, _ in scatter_label_generator(labels, X, (0, None, None)):
subsample.append(np.random.choice(index, size=max(2, int(index.size*(float(num_samples)/X.shape[0]))), replace=False))
subsample = np.hstack(subsample)
else:

4
GPy/plotting/matplot_dep/plot_definitions.py
View file

@ -82,7 +82,9 @@ class MatplotlibPlots(AbstractPlottingLibrary):
return ax.scatter(X, Y, c=color, zs=Z, label=label, marker=marker, **kwargs)
return ax.scatter(X, Y, c=color, label=label, marker=marker, **kwargs)
def plot(self, ax, X, Y, color=None, label=None, **kwargs):
def plot(self, ax, X, Y, Z=None, color=None, label=None, **kwargs):
if Z is not None:
return ax.plot(X, Y, color=color, zs=Z, label=label, **kwargs)
return ax.plot(X, Y, color=color, label=label, **kwargs)
def plot_axis_lines(self, ax, X, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):