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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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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: