def _plot_samples(self, plot, plot_data):
nclasses = self.theta.shape[0]
nsamples = self.theta_samples.shape[0]
for k in range(nclasses):
samples = np.sort(self.theta_samples[:,k,:], axis=0)
perc5 = samples[int(nsamples*0.05),:]
perc95 = samples[int(nsamples*0.95),:]
avg = samples.mean(0)
# build polygon
index_name = self._label_name(k) + '_confint_index'
value_name = self._label_name(k) + '_confint_value'
index_coord = []
value_coord = []
# bottom part
for i in range(nclasses):
index_coord.append(i)
value_coord.append(perc5[i])
# top part
for i in range(nclasses-1, -1, -1):
index_coord.append(i)
value_coord.append(perc95[i])
plot_data.set_data(index_name, np.array(index_coord,
dtype=float))
plot_data.set_data(value_name, np.array(value_coord,
dtype=float))
# make color lighter and more transparent
color = get_class_color(k)
for i in range(3):
color[i] = min(1.0, sigmoid(color[i]*5.))
color[-1] = 0.3
plot.plot(
(index_name, value_name),
type = 'polygon',
face_color = color,
edge_color = 'black',
edge_width = 0.5
)
# add average
avg_name = self._label_name(k) + '_avg_value'
plot_data.set_data(avg_name, avg)
plot.plot(
('classes', avg_name),
color = get_class_color(k),
line_style = 'dash'
)
def _plot_samples(self, plot, plot_data):
nclasses = self.theta.shape[0]
nsamples = self.theta_samples.shape[0]
for k in range(nclasses):
samples = np.sort(self.theta_samples[:, k, :], axis=0)
perc5 = samples[int(nsamples * 0.05), :]
perc95 = samples[int(nsamples * 0.95), :]
avg = samples.mean(0)
# build polygon
index_name = self._label_name(k) + '_confint_index'
value_name = self._label_name(k) + '_confint_value'
index_coord = []
value_coord = []
# bottom part
for i in range(nclasses):
index_coord.append(i)
value_coord.append(perc5[i])
# top part
for i in range(nclasses - 1, -1, -1):
index_coord.append(i)
value_coord.append(perc95[i])
plot_data.set_data(index_name, np.array(index_coord, dtype=float))
plot_data.set_data(value_name, np.array(value_coord, dtype=float))
# make color lighter and more transparent
color = get_class_color(k)
for i in range(3):
color[i] = min(1.0, sigmoid(color[i] * 5.))
color[-1] = 0.3
plot.plot((index_name, value_name),
type='polygon',
face_color=color,
edge_color='black',
edge_width=0.5)
# add average
avg_name = self._label_name(k) + '_avg_value'
plot_data.set_data(avg_name, avg)
plot.plot(('classes', avg_name),
color=get_class_color(k),
line_style='dash')
def _plot_theta_values(self, plot, plot_data):
theta = self.theta
nclasses = theta.shape[0]
data_names = ['classes']
for k in range(nclasses):
name = self._label_name(k)
plot_data.set_data(name, theta[k, :])
data_names.append(name)
plots = {}
for k in range(nclasses):
name = self._label_name(k)
line_plot = plot.plot(['classes', name],
line_width=2.,
color=get_class_color(k),
name=name)
plots[name] = line_plot
return plots
def _plot_default(self):
distr_len = len(self.data)
# PolygonPlot holding the circles of the Hinton diagram
polyplot = Plot(self.plot_data)
for idx in range(distr_len):
p = polyplot.plot(('x%d' % idx, 'y%d' % idx),
type="polygon",
face_color=get_class_color(idx),
edge_color='black')
self._set_title(polyplot)
self._remove_grid_and_axes(polyplot)
# create x axis for labels
axis = self._create_increment_one_axis(polyplot, 1., distr_len,
'bottom')
self._add_index_axis(polyplot, axis)
# create y axis for probability density
#prob_axis = self._create_probability_axis(polyplot)
#polyplot.value_axis = prob_axis
#polyplot.underlays.append(prob_axis)
# tweak some of the plot properties
range2d = DataRange2D(low=(0.5, 0.), high=(distr_len + 0.5, 1.))
polyplot.range2d = range2d
polyplot.aspect_ratio = ((range2d.x_range.high - range2d.x_range.low) /
(range2d.y_range.high - range2d.y_range.low))
polyplot.border_visible = False
polyplot.padding = [0, 0, 25, 25]
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True, valign='center')
container.add(polyplot)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
self.decorate_plot(container, self.data)
return container
def _plot_theta_values(self, plot, plot_data):
theta = self.theta
nclasses = theta.shape[0]
data_names = ['classes']
for k in range(nclasses):
name = self._label_name(k)
plot_data.set_data(name, theta[k,:])
data_names.append(name)
plots = {}
for k in range(nclasses):
name = self._label_name(k)
line_plot = plot.plot(
['classes', name],
line_width=2.,
color = get_class_color(k),
name=name
)
plots[name] = line_plot
return plots
def _plot_default(self):
distr_len = len(self.data)
# PolygonPlot holding the circles of the Hinton diagram
polyplot = Plot(self.plot_data)
for idx in range(distr_len):
p = polyplot.plot(('x%d' % idx, 'y%d' % idx),
type="polygon",
face_color=get_class_color(idx),
edge_color='black')
self._set_title(polyplot)
self._remove_grid_and_axes(polyplot)
# create x axis for labels
axis = self._create_increment_one_axis(polyplot, 1., distr_len, 'bottom')
self._add_index_axis(polyplot, axis)
# create y axis for probability density
#prob_axis = self._create_probability_axis(polyplot)
#polyplot.value_axis = prob_axis
#polyplot.underlays.append(prob_axis)
# tweak some of the plot properties
range2d = DataRange2D(low=(0.5, 0.), high=(distr_len+0.5, 1.))
polyplot.range2d = range2d
polyplot.aspect_ratio = ((range2d.x_range.high - range2d.x_range.low)
/ (range2d.y_range.high - range2d.y_range.low))
polyplot.border_visible = False
polyplot.padding = [0, 0, 25, 25]
# create a container to position the plot and the colorbar side-by-side
container = HPlotContainer(use_backbuffer=True, valign='center')
container.add(polyplot)
container.bgcolor = 0xFFFFFF # light gray: 0xEEEEEE
self.decorate_plot(container, self.data)
return container
