def _create_resizable_view(self):
# resizable_view factory, as I need to compute the height of the plot
# from the number of annotations, and I couldn't find any other way to
# do that
# "touch" posterior_plot to have it initialize
self.plot_container
if is_display_small():
height = 760
else:
height = 800
resizable_plot_item = (
Item(
'plot_container',
editor=ComponentEditor(),
resizable=True,
show_label=False,
width = self.plot_width,
height = self.plot_height,
)
)
resizable_view = View(
VGroup(
Include('instructions_group'),
resizable_plot_item,
),
width = 450,
height = height,
resizable = True
)
return resizable_view
def _traits_plot_item_default(self):
height = -220 if is_display_small() else -280
return Item('theta_plot',
editor=ComponentEditor(),
resizable=False,
show_label=False,
height=height)
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 _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 traits_view(self):
w_view = 350 if is_display_small() else 480
parameters_group = VGroup(
Item('_'),
HGroup(
VGroup(
Spring(),
Item('handler.omega_hinton_diagram',
style='custom',
resizable=False,
show_label=False,
width=w_view), Spring()), Spring(),
VGroup(Spring(), Item('handler.view_omega', show_label=False),
Spring())), Spring(), Item('_'), Spring(),
HGroup(
VGroup(
UItem('handler.theta_views'),
UItem('handler.theta_view',
style='custom',
resizable=False,
width=w_view), Spring()), Spring(),
VGroup(Spring(), UItem('handler.view_theta'), Spring())))
body = VGroup(Include('info_group'), parameters_group)
traits_view = View(body, buttons=[OKButton], resizable=True)
return traits_view
def _create_resizable_view(self):
# resizable_view factory, as I need to compute the height of the plot
# from the number of annotations, and I couldn't find any other way to
# do that
# "touch" posterior_plot to have it initialize
self.plot_container
if is_display_small():
height = 760
else:
height = 800
resizable_plot_item = (Item(
'plot_container',
editor=ComponentEditor(),
resizable=True,
show_label=False,
width=self.plot_width,
height=self.plot_height,
))
resizable_view = View(VGroup(
Include('instructions_group'),
resizable_plot_item,
),
width=450,
height=height,
resizable=True)
return resizable_view
def _traits_plot_item_default(self):
height = -220 if is_display_small() else -280
return Item('theta_plot',
editor=ComponentEditor(),
resizable=False,
show_label=False,
height=height
)
def traits_view(self):
if is_display_small():
w_view = 350
else:
w_view = 480
parameters_group = VGroup(
Item('_'),
HGroup(
VGroup(
Spring(),
Item('handler.gamma_hinton',
style='custom',
resizable=False,
show_label=False,
width=w_view
),
Spring()
),
Spring(),
VGroup(
Spring(),
Item('handler.view_gamma', show_label=False),
Spring()
)
),
Spring(),
Item('_'),
Spring(),
HGroup(
VGroup(
UItem('handler.theta_views'),
UItem('handler.theta_view',
style='custom',
resizable=False,
width=w_view
),
Spring()
),
Spring(),
VGroup(
Spring(),
UItem('handler.view_theta'),
Spring()
)
)
)
body = VGroup(
Include('info_group'),
parameters_group,
)
traits_view = View(body, buttons=[OKButton], resizable=True)
return traits_view
def traits_view(self):
if is_display_small():
w_view = 350
else:
w_view = 450
info_group = VGroup(Item('_name',
label='Annotations name:',
style='readonly',
padding=0),
VGrid(Item('_nclasses',
label='Number of classes:',
style='readonly',
width=10),
Item('_labels',
label='Labels:',
style='readonly'),
Item('_nannotators',
label='Number of annotators:',
style='readonly',
width=10),
Item('_nitems',
label='Number of items:',
style='readonly'),
padding=0),
padding=0)
body = VGroup(
info_group,
Item('_'),
HGroup(
VGroup(
Spring(),
Item('frequency_plot',
style='custom',
resizable=False,
show_label=False,
width=w_view), Spring()), Spring(),
VGroup(
Spring(),
Item('edit_data',
enabled_when='annotations_are_defined',
show_label=False),
Item('save_data',
enabled_when='annotations_are_defined',
show_label=False), Spring())),
Spring(),
Item('_'),
)
traits_view = View(body)
return traits_view
def traits_view(self):
height = 760 if is_display_small() else 900
traits_view = View(VGroup(VGroup(
Item('show_maximum', label='Show MAP estimate (circle)'),
Item('show_majority_vote', label='Show majority vote (triangle)'),
),
VGroup(
Item('posterior_plot',
editor=InstanceEditor(),
style='custom',
show_label=False), ),
padding=0),
height=height,
scrollable=True,
resizable=True)
return traits_view
def traits_view(self):
height = 760 if is_display_small() else 900
traits_view = View(
VGroup(
VGroup(
Item('show_maximum',
label='Show MAP estimate (circle)'),
Item('show_majority_vote',
label='Show majority vote (triangle)'),
),
VGroup(
Item('posterior_plot',
editor=InstanceEditor(),
style='custom',
show_label=False),
),
padding=0
),
height = height,
scrollable=True,
resizable=True
)
return traits_view
def _plot_width_default(self):
return 450 if is_display_small() else 500
def traits_view(self):
if is_display_small():
w_view = 350
else:
w_view = 450
info_group = VGroup(
Item('_name',
label='Annotations name:',
style='readonly',
padding=0),
VGrid(
Item('_nclasses',
label='Number of classes:',
style='readonly',
width=10),
Item('_labels',
label='Labels:',
style='readonly'),
Item('_nannotators',
label='Number of annotators:',
style='readonly', width=10),
Item('_nitems',
label='Number of items:',
style='readonly'),
padding=0
),
padding=0
)
body = VGroup(
info_group,
Item('_'),
HGroup(
VGroup(
Spring(),
Item('frequency_plot',
style='custom',
resizable=False,
show_label=False,
width=w_view
),
Spring()
),
Spring(),
VGroup(
Spring(),
Item('edit_data',
enabled_when='annotations_are_defined',
show_label=False),
Item('save_data',
enabled_when='annotations_are_defined',
show_label=False),
Spring()
)
),
Spring(),
Item('_'),
)
traits_view = View(body)
return traits_view
def traits_view(self):
## Model view
# adjust sizes to display size
if is_display_small():
# full view size
w_view, h_view = 1024, 768
w_data_create_group = 350
w_data_info_group = 500
h_annotations_stats = 270
else:
w_view, h_view = 1300, 850
w_data_create_group = 400
w_data_info_group = 700
h_annotations_stats = 330
model_create_group = (
VGroup(
HGroup(
UItem(name='model_name',width=200),
UItem(name='new_model', width=100),
UItem(name='get_info_on_model', width=100, height=25),
),
label = 'Create new model'
)
)
model_group = (
VGroup (
model_create_group,
VGroup(
Item(
'model_view',
style='custom',
show_label=False,
width=400
),
label = 'Model view',
),
),
)
## Data view
data_create_group = VGroup(
#Label('Open annotation file:', width=800),
HGroup(
Item('annotations_file', style='simple', label='Open file:',
width=w_data_create_group, height=25),
UItem('new_annotations', height=25)
),
label = 'Load/create annotations',
show_border = False,
)
data_info_group = VGroup(
Item('annotations_view',
style='custom',
show_label=False,
visible_when='annotations_are_defined',
width=w_data_info_group,
),
Item('annotations_stats_view',
style='custom',
show_label=False,
visible_when='annotations_are_defined',
height=h_annotations_stats),
label = 'Data view',
)
data_group = (
VGroup (
data_create_group,
data_info_group,
),
)
## (Model,Data) view
model_data_group = (
VGroup(
#Item('info_string', show_label=False, style='readonly'),
Item('log_likelihood', label='Log likelihood', style='readonly'),
HGroup(
Item('ml_estimate',
enabled_when='annotations_are_defined'),
Item('map_estimate',
enabled_when='annotations_are_defined'),
Item('sample_posterior_over_accuracy',
enabled_when='annotations_are_defined'),
Item('estimate_labels',
enabled_when='annotations_are_defined'),
Spring(),
Item('add_to_database',
enabled_when='annotations_are_defined'),
Item('open_database'),
show_labels=False,
),
label = 'Model-data view',
)
)
## Full view
full_view = View(
VGroup(
HGroup(
model_group,
data_group
),
model_data_group,
),
title='PyAnno - Models of data annotations by multiple curators',
width = w_view,
height = h_view,
resizable = False
)
return full_view
def traits_view(self):
if is_display_small():
w_view = 400
else:
w_view = 510
parameters_group = VGroup(
Item('_'),
HGroup(
Item('handler.edit_prior',
show_label=False,
width=100),
Spring(),
),
Item('_'),
HGroup(
VGroup(
Spring(),
Item('handler.pi_hinton_diagram',
style='custom',
resizable=False,
show_label=False,
width=w_view),
Spring(),
),
Spring(),
VGroup(
Spring(),
Item('handler.view_pi', show_label=False),
Spring()
),
),
Item('_'),
HGroup(
VGroup(
Item('handler.theta_views',
show_label=False),
Item('handler.theta_view',
style='custom',
resizable=False,
show_label=False,
width = w_view),
Spring()
),
VGroup(
Spring(),
Item('handler.view_theta', show_label=False),
Spring()
)
),
)
body = VGroup(
Include('info_group'),
parameters_group
)
traits_view = View(body, buttons=[OKButton], resizable=True)
return traits_view
def _plot_width_default(self):
return 450 if is_display_small() else 500
def _theta_plot_default(self):
theta = self.theta
nannotators = theta.shape[0]
samples = self.theta_samples
# plot data object
plot_data = ArrayPlotData()
# create the plot
plot = Plot(plot_data)
# --- plot theta as vertical dashed lines
# add vertical lines extremes
plot_data.set_data('line_extr', [0., 1.])
for k in range(nannotators):
name = self._theta_name(k)
plot_data.set_data(name, [theta[k], theta[k]])
plots = {}
for k in range(nannotators):
name = self._theta_name(k)
line_plot = plot.plot(
(name, 'line_extr'),
line_width = 2.,
color = get_annotator_color(k),
line_style = 'dash',
name = name
)
plots[name] = line_plot
# --- plot samples as distributions
if samples is not None:
bins = np.linspace(0., 1., 100)
max_hist = 0.
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
hist, x = np.histogram(samples[:,k], bins=bins)
hist = hist / float(hist.sum())
max_hist = max(max_hist, hist.max())
# make "bars" out of histogram values
y = np.concatenate(([0], np.repeat(hist, 2), [0]))
plot_data.set_data(name+'x', np.repeat(x, 2))
plot_data.set_data(name+'y', y)
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
plot.plot((name+'x', name+'y'),
line_width = 2.,
color = get_annotator_color(k)
)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
plot.padding = [20,0,10,40]
# adjust axis bounds
x_low, x_high = theta.min(), theta.max()
y_low, y_high = 0., 1.
if samples is not None:
x_high = max(x_high, samples.max())
x_low = min(x_low, samples.min())
y_high = max_hist
plot.range2d = DataRange2D(
low = (max(x_low-0.05, 0.), y_low),
high = (min(x_high*1.1, 1.), min(y_high*1.1, 1.))
)
# label axes
plot.value_axis.title = 'Probability'
plot.index_axis.title = 'Theta'
# add legend
legend = Legend(component=plot, plots=plots,
align="ul", padding=5)
legend.tools.append(LegendTool(legend, drag_button="left"))
plot.overlays.append(legend)
container = VPlotContainer()
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
self._set_title(plot)
return container
def _plot_height_default(self):
return 600 if is_display_small() else 750
def _plot_height_default(self):
return 600 if is_display_small() else 750
def _theta_plot_default(self):
theta = self.theta
nannotators = theta.shape[0]
samples = self.theta_samples
# plot data object
plot_data = ArrayPlotData()
# create the plot
plot = Plot(plot_data)
# --- plot theta as vertical dashed lines
# add vertical lines extremes
plot_data.set_data('line_extr', [0., 1.])
for k in range(nannotators):
name = self._theta_name(k)
plot_data.set_data(name, [theta[k], theta[k]])
plots = {}
for k in range(nannotators):
name = self._theta_name(k)
line_plot = plot.plot(
(name, 'line_extr'),
line_width = 2.,
color = get_annotator_color(k),
line_style = 'dash',
name = name
)
plots[name] = line_plot
# --- plot samples as distributions
if samples is not None:
bins = np.linspace(0., 1., 100)
max_hist = 0.
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
hist, x = np.histogram(samples[:,k], bins=bins)
hist = hist / float(hist.sum())
max_hist = max(max_hist, hist.max())
# make "bars" out of histogram values
y = np.concatenate(([0], np.repeat(hist, 2), [0]))
plot_data.set_data(name+'x', np.repeat(x, 2))
plot_data.set_data(name+'y', y)
for k in range(nannotators):
name = self._theta_name(k) + '_distr_'
plot.plot((name+'x', name+'y'),
line_width = 2.,
color = get_annotator_color(k)
)
# --- adjust plot appearance
plot.aspect_ratio = 1.6 if is_display_small() else 1.7
plot.padding = [20,0,10,40]
# adjust axis bounds
x_low, x_high = theta.min(), theta.max()
y_low, y_high = 0., 1.
if samples is not None:
x_high = max(x_high, samples.max())
x_low = min(x_low, samples.min())
y_high = max_hist
plot.range2d = DataRange2D(
low = (max(x_low-0.05, 0.), y_low),
high = (min(x_high*1.1, 1.), min(y_high*1.1, 1.))
)
# label axes
plot.value_axis.title = 'Probability'
plot.index_axis.title = 'Theta'
# add legend
legend = Legend(component=plot, plots=plots,
align="ul", padding=5)
legend.tools.append(LegendTool(legend, drag_button="left"))
plot.overlays.append(legend)
container = VPlotContainer()
container.add(plot)
container.bgcolor = 0xFFFFFF
self.decorate_plot(container, theta)
self._set_title(plot)
return container
def traits_view(self):
## Model view
# adjust sizes to display size
if is_display_small():
# full view size
w_view, h_view = 1024, 768
w_data_create_group = 350
w_data_info_group = 500
h_annotations_stats = 270
else:
w_view, h_view = 1300, 850
w_data_create_group = 400
w_data_info_group = 700
h_annotations_stats = 330
model_create_group = (VGroup(HGroup(
UItem(name='model_name', width=200),
UItem(name='new_model', width=100),
UItem(name='get_info_on_model', width=100, height=25),
),
label='Create new model'))
model_group = (VGroup(
model_create_group,
VGroup(
Item('model_view', style='custom', show_label=False,
width=400),
label='Model view',
),
), )
## Data view
data_create_group = VGroup(
#Label('Open annotation file:', width=800),
HGroup(
Item('annotations_file',
style='simple',
label='Open file:',
width=w_data_create_group,
height=25), UItem('new_annotations', height=25)),
label='Load/create annotations',
show_border=False,
)
data_info_group = VGroup(
Item(
'annotations_view',
style='custom',
show_label=False,
visible_when='annotations_are_defined',
width=w_data_info_group,
),
Item('annotations_stats_view',
style='custom',
show_label=False,
visible_when='annotations_are_defined',
height=h_annotations_stats),
label='Data view',
)
data_group = (VGroup(
data_create_group,
data_info_group,
), )
## (Model,Data) view
model_data_group = (
VGroup(
#Item('info_string', show_label=False, style='readonly'),
Item('log_likelihood',
label='Log likelihood',
style='readonly'),
HGroup(
Item('ml_estimate',
enabled_when='annotations_are_defined'),
Item('map_estimate',
enabled_when='annotations_are_defined'),
Item('sample_posterior_over_accuracy',
enabled_when='annotations_are_defined'),
Item('estimate_labels',
enabled_when='annotations_are_defined'),
Spring(),
Item('add_to_database',
enabled_when='annotations_are_defined'),
Item('open_database'),
show_labels=False,
),
label='Model-data view',
))
## Full view
full_view = View(
VGroup(
HGroup(model_group, data_group),
model_data_group,
),
title='PyAnno - Models of data annotations by multiple curators',
width=w_view,
height=h_view,
resizable=False)
return full_view
