def _theta_plot_default(self):
theta = self.theta
nclasses = theta.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data('classes', list(range(nclasses)))
# create the plot
plot = Plot(plot_data)
# --- plot theta samples
if self.theta_samples is not None:
self._plot_samples(plot, plot_data)
# --- plot values of theta
plots = self._plot_theta_values(plot, plot_data)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
# adjust axis bounds
y_high = theta.max()
if self.theta_samples is not None:
y_high = max(y_high, self.theta_samples.max())
plot.range2d = DataRange2D(low=(-0.2, 0.0),
high=(nclasses - 1 + 0.2, y_high * 1.1))
# create new horizontal axis
label_axis = self._create_increment_one_axis(plot, 0., nclasses,
'bottom')
label_axis.title = 'True classes'
self._add_index_axis(plot, label_axis)
# label vertical axis
plot.value_axis.title = 'Probability'
# add legend
legend = Legend(component=plot,
plots=plots,
align="ur",
border_padding=10)
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.padding_right = -100
plot.overlays.append(legend)
container = VPlotContainer(width=plot.width + 100, halign='left')
plot.padding_bottom = 50
plot.padding_top = 10
plot.padding_left = 0
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
return container
def _theta_plot_default(self):
theta = self.theta
nclasses = theta.shape[0]
# create a plot data object and give it this data
plot_data = ArrayPlotData()
plot_data.set_data('classes', range(nclasses))
# create the plot
plot = Plot(plot_data)
# --- plot theta samples
if self.theta_samples is not None:
self._plot_samples(plot, plot_data)
# --- plot values of theta
plots = self._plot_theta_values(plot, plot_data)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
# adjust axis bounds
y_high = theta.max()
if self.theta_samples is not None:
y_high = max(y_high, self.theta_samples.max())
plot.range2d = DataRange2D(
low = (-0.2, 0.0),
high = (nclasses-1+0.2, y_high*1.1)
)
# create new horizontal axis
label_axis = self._create_increment_one_axis(
plot, 0., nclasses, 'bottom')
label_axis.title = 'True classes'
self._add_index_axis(plot, label_axis)
# label vertical axis
plot.value_axis.title = 'Probability'
# add legend
legend = Legend(component=plot, plots=plots,
align="ur", border_padding=10)
legend.tools.append(LegendTool(legend, drag_button="left"))
legend.padding_right = -100
plot.overlays.append(legend)
container = VPlotContainer(width=plot.width + 100, halign='left')
plot.padding_bottom = 50
plot.padding_top = 10
plot.padding_left = 0
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
return container
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits=((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses, 'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height
* nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper = LinearMapper(range=colormap.range),
color_mapper = colormap,
plot = img_plot,
orientation = 'v',
resizable = '',
width = 15,
height = 250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(PanTool(img_plot, constrain=True,
constrain_direction="y", speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container
def _plot_container_default(self):
data = self.posterior
nannotations, nclasses = data.shape
# create a plot data object
plot_data = ArrayPlotData()
plot_data.set_data("values", data)
# create the plot
plot = Plot(plot_data, origin=self.origin)
img_plot = plot.img_plot("values",
interpolation='nearest',
xbounds=(0, nclasses),
ybounds=(0, nannotations),
colormap=self._create_colormap())[0]
ndisp = 55
img_plot.y_mapper.range.high = ndisp
img_plot.y_mapper.domain_limits = ((0, nannotations))
self._set_title(plot)
plot.padding_top = 80
# create x axis for labels
label_axis = self._create_increment_one_axis(plot, 0.5, nclasses,
'top')
label_axis.title = 'classes'
self._add_index_axis(plot, label_axis)
plot.y_axis.title = 'items'
# tweak plot aspect
goal_aspect_ratio = 2.0
plot_width = (goal_aspect_ratio * self.plot_height * nclasses / ndisp)
self.plot_width = min(max(plot_width, 200), 400)
plot.aspect_ratio = self.plot_width / self.plot_height
# add colorbar
colormap = img_plot.color_mapper
colorbar = ColorBar(index_mapper=LinearMapper(range=colormap.range),
color_mapper=colormap,
plot=img_plot,
orientation='v',
resizable='',
width=15,
height=250)
colorbar.padding_top = plot.padding_top
colorbar.padding_bottom = int(self.plot_height - colorbar.height -
plot.padding_top)
colorbar.padding_left = 0
colorbar.padding_right = 30
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True)
container.add(plot)
container.add(colorbar)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
# add pan tools
img_plot.tools.append(
PanTool(img_plot,
constrain=True,
constrain_direction="y",
speed=7.))
self.decorate_plot(container, self.posterior)
self.plot_posterior = plot
return container
