def proofValidity(self, val=False, desc="valid"):
validity= widgets.Valid(
value=val,
description=str(desc),
style = self.style
)
return validity
def on_field_filled(self, *args):
for i, value in enumerate(self.part_info.participant_info_name):
if self.part_info.participant_info_name.get(
self.participant[i].description
)[0] > self.participant[i].value:
widgets.Valid(value=False,
description='Der eingegebene Wert ist zu tief',
style={'description_width': 'initial'})
elif self.part_info.participant_info_name.get(
self.participant[i].description
)[1] < self.participant[i].value:
widgets.Valid(value=False,
description='Der eingegebene Wert ist zu hoch',
style={'description_width': 'initial'})
else:
widgets.Valid(value=True,
description='',
style={'description_width': 'initial'})
def valid(boolVal):
# If the boolean is True
if boolVal == True:
valid_widget = widgets.Valid(
value=boolVal,
description='Valid!',
)
display(valid_widget)
print("Congrats!!")
return True
# If the boolean is false
elif boolVal == False:
valid_widget = widgets.Valid(
value=boolVal,
description='',
)
display(valid_widget)
print("Try Again!")
def __init__(self, *args):
"""
Constructs a Trial object.
"""
inc = args[0].validate()
ex = args[1].validate()
wizard_page = args[3]
## Validate In-/Exclusion criteria ##
if wizard_page == 4:
if inc and ex == True:
self.validator = widgets.Valid(
value=True,
description='Subject ist geeignet für die Studie',
style={'description_width': 'initial'})
else:
self.validator = widgets.Valid(
value=False,
description='Subject ist nicht geeignet für die Studie',
style={'description_width': 'initial'})
self.wizard_page = 100
def __init__(self, on_true=None, on_false=None, on_true_output=None, on_false_output=None, \
on_true_overwrite_previous_output=True, on_false_overwrite_previous_output=True, on_true_feedback=False, \
on_false_feedback=False, run=True, layout={'width':'max-content'}, indent=False, on_true_action_kws={}, on_false_action_kws={}, *args, **kwargs):
super().__init__(on_true=on_true, on_false=on_false, on_true_output=on_true_output, on_false_output=on_false_output, \
on_true_overwrite_previous_output=on_true_overwrite_previous_output, on_true_action_kws=on_true_action_kws, on_false_overwrite_previous_output=on_false_overwrite_previous_output, on_true_feedback=on_true_feedback, \
on_false_feedback=on_false_feedback, on_false_action_kws=on_false_action_kws)
self.widget = widgets.Valid(layout=layout,
indent=indent,
*args,
**kwargs)
if run:
self.run()
def old_initiate(self):
tab_children = []
###########################
# data 1 box
d1_vbox_childs = []
##
###
d1_button_next = widgets.Button(description='next measurement')
d1_button_prev = widgets.Button(description='prev measurement')
d1_button_next.on_click(self.on_d1_botton_next)
d1_button_prev.on_click(self.on_d1_botton_prev)
d1_box_h_1 = widgets.HBox([d1_button_prev, d1_button_next])
###
d1_vbox_childs.append(d1_box_h_1)
##
###
d1_text_path = widgets.Text(placeholder='path name', disabled=False)
self.d1_text_path = d1_text_path
d1_vbox_childs.append(d1_text_path)
##
d1_vbox = widgets.VBox(d1_vbox_childs)
tab_children.append({'element': d1_vbox, 'title': 'iMet'})
############################
# data 2 box
d2_vbox_childs = []
##
###
d2_button_next = widgets.Button(description='next measurement')
d2_button_prev = widgets.Button(description='prev measurement')
self.d2_dropdown_fnames = widgets.Dropdown(
options=[
1
], #[i.name for i in self.controller.data.dataset2.path2data_list],
value=1, #self.controller.data.dataset2.path2active.name,
# description='N',
disabled=False,
)
d2_button_next.on_click(self.on_d2_botton_next)
d2_button_prev.on_click(self.on_d2_botton_prev)
self.d2_dropdown_fnames.observe(self.on_change_d2_dropdown_fnames)
d2_box_h_1 = widgets.HBox(
[d2_button_prev, d2_button_next, self.d2_dropdown_fnames])
###
d2_vbox_childs.append(d2_box_h_1)
##
###
# text field showing the path
d2_text_path = widgets.Text(placeholder='path name', disabled=False)
self.d2_text_path = d2_text_path
d2_vbox_childs.append(d2_text_path)
##
d2_vbox = widgets.VBox(d2_vbox_childs)
tab_children.append({'element': d2_vbox, 'title': 'POPS'})
# others box
# Tab
tab = widgets.Tab([child['element'] for child in tab_children])
for e, child in enumerate(tab_children):
tab.set_title(e, child['title'])
# accordeon
self.accordeon_assigned = widgets.Valid(
value=False,
description='bound?',
)
self.dropdown_popssn = widgets.Dropdown(
options=['00', '14', '18'],
# value='2',
description='popssn',
disabled=False,
)
self.inttext_deltat = widgets.IntText(value=0,
description='deltat',
disabled=False)
self.inttext_deltat.observe(self.on_inttext_deltat)
self.dropdown_gps_bar_bad = widgets.Dropdown(
options=[
'gps', 'baro', 'bad', 'bad_but_usable_gps',
'bad_but_usable_baro'
],
value='gps',
description='which alt to use:',
disabled=False,
)
self.button_bind_measurements = widgets.ToggleButton(
description='bind/unbind measurements')
# button_bind_measurements.on_click(self.deprecated_on_button_bind_measurements)
self.button_bind_measurements.observe(self.on_button_bind_measurements)
accordon_box = widgets.VBox([
self.accordeon_assigned, self.dropdown_popssn, self.inttext_deltat,
self.dropdown_gps_bar_bad, self.button_bind_measurements
])
accordion_children = [accordon_box]
accordion = widgets.Accordion(children=accordion_children)
accordion.set_title(0, 'do_stuff')
# messages
self.messages = widgets.Textarea('\n'.join(self.controller._message),
layout={'width': '100%'})
# message_box = widgets.HBox([self.messages])
# OverVbox
overVbox = widgets.VBox([tab, accordion, self.messages])
display(overVbox)
####################
self.update_d1()
self.update_d2()
self.update_accordeon()
def plot_interactive_mapper_graph(pipeline, data, layout='kamada_kawai',
layout_dim=2, color_variable=None,
node_color_statistic=None,
color_by_columns_dropdown=False,
plotly_kwargs=None):
"""Plotting function for interactive Mapper graphs.
Parameters
----------
pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object
Mapper pipeline to act on to data.
data : array-like of shape (n_samples, n_features)
Data used to generate the Mapper graph. Can be a pandas dataframe.
layout : None, str or callable, optional, default: ``'kamada-kawai'``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph`. [1]_
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
color_variable : object or None, optional, default: ``None``
Specifies which quantity is to be used for node coloring.
1. If a numpy ndarray or pandas dataframe, `color_variable`
must have the same length as `data` and is interpreted as
a quantity of interest according to which node of the Mapper
graph is to be colored (see `node_color_statistic`).
2. If ``None`` then equivalent to passing `data`.
3. If an object implementing :meth:`transform` or
:meth:`fit_transform`, e.g. a scikit-learn estimator or
pipeline, it is applied to `data` to generate the quantity
of interest.
4. If an index or string, or list of indices / strings, equivalent
to selecting a column or subset of columns from `data`.
node_color_statistic :None, callable, or ndarray of shape (n_nodes,) or \
(n_nodes, 1), optional, default: ``None``
Specifies how to determine the colors of each node. If a
numpy array, it must have the same length as the number of nodes in
the Mapper graph, and its values are used directly for node
coloring, ignoring `color_variable`. Otherwise, it can be a
callable object which is used to obtain a summary statistic, within
each Mapper node, of the quantity specified by `color_variable`. The
default value ``None`` is equivalent to passing ``numpy.mean``.
color_by_columns_dropdown : bool, optional, default: ``False``
If ``True``, a dropdown widget is generated which allows the user to
color Mapper nodes according to any column in `data`.
plotly_kwargs : dict, optional, default: ``None``
Keyword arguments to configure the Plotly Figure.
Returns
-------
box : :class:`ipywidgets.VBox` object
A box containing the following widgets: parameters of the clustering
algorithm, parameters for the covering scheme, a Mapper graph arising
from those parameters, a validation box, and logs.
References
----------
.. [1] `igraph.Graph.layout
<https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_
documentation.
"""
# clone pipeline to avoid side effects from in-place parameter changes
pipe = clone(pipeline)
if node_color_statistic is not None:
_node_color_statistic = node_color_statistic
else:
_node_color_statistic = np.mean
def get_widgets_per_param(param, value):
if isinstance(value, float):
return (param, widgets.FloatText(
value=value,
step=0.05,
description=param.split('__')[1],
continuous_update=False,
disabled=False
))
elif isinstance(value, int):
return (param, widgets.IntText(
value=value,
step=1,
description=param.split('__')[1],
continuous_update=False,
disabled=False
))
elif isinstance(value, str):
return (param, widgets.Text(
value=value,
description=param.split('__')[1],
continuous_update=False,
disabled=False
))
else:
return None
def update_figure(figure, edge_trace, node_trace, layout_dim):
figure.data[0].x = edge_trace.x
figure.data[0].y = edge_trace.y
figure.data[1].x = node_trace.x
figure.data[1].y = node_trace.y
if layout_dim == 3:
figure.data[0].z = edge_trace.z
figure.data[1].z = node_trace.z
figure.data[1].marker.size = node_trace.marker.size
figure.data[1].marker.color = node_trace.marker.color
figure.data[1].marker.cmin = node_trace.marker.cmin
figure.data[1].marker.cmax = node_trace.marker.cmax
figure.data[1].marker.sizeref = node_trace.marker.sizeref
figure.data[1].hoverlabel = node_trace.hoverlabel
figure.data[1].hovertext = node_trace.hovertext
def on_parameter_change(change):
handler.clear_logs()
try:
for param, value in cover_params.items():
if isinstance(value, (int, float, str)):
pipe.set_params(
**{param: cover_params_widgets[param].value})
for param, value in cluster_params.items():
if isinstance(value, (int, float, str)):
pipe.set_params(
**{param: cluster_params_widgets[param].value})
logger.info("Updating figure ...")
with fig.batch_update():
(node_trace, edge_trace, node_elements, node_colors,
plot_options) = _calculate_graph_data(
pipe, data, layout, layout_dim,
color_variable, _node_color_statistic, plotly_kwargs
)
update_figure(fig, edge_trace, node_trace, layout_dim)
# Update color by column buttons
is_data_dataframe = hasattr(data, 'columns')
if color_by_columns_dropdown:
column_color_buttons = _get_column_color_buttons(
data, is_data_dataframe, node_elements, node_colors,
plot_options['node_trace_marker_colorscale'])
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(
updatemenus=[
go.layout.Updatemenu(
buttons=column_color_buttons,
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.11,
xanchor='left',
y=button_height,
yanchor="top"
),
])
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[-1])
valid.value = False
def observe_widgets(params, widgets):
for param, value in params.items():
if isinstance(value, (int, float, str)):
widgets[param].observe(on_parameter_change, names='value')
# define output widget to capture logs
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
# initialise logging
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(logging.Formatter(
'%(asctime)s - [%(levelname)s] %(message)s'))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# initialise cover and cluster dictionaries of parameters and widgets
cover_params = dict(filter(lambda x: x[0].startswith('cover'),
pipe.get_mapper_params().items()))
cover_params_widgets = dict(
filter(None, map(lambda x: get_widgets_per_param(*x),
cover_params.items())))
cluster_params = dict(filter(lambda x: x[0].startswith('clusterer'),
pipe.get_mapper_params().items()))
cluster_params_widgets = dict(
filter(None, map(lambda x: get_widgets_per_param(*x),
cluster_params.items())))
# initialise widgets for validating input parameters of pipeline
valid = widgets.Valid(
value=True,
description='Valid parameters',
style={'description_width': '100px'},
)
# initialise widget for showing the logs
logs_box = widgets.Checkbox(
description='Show logs: ',
value=False,
indent=False
)
# initialise figure with initial pipeline and config
if plotly_kwargs is None:
plotly_kwargs = dict()
fig = plot_static_mapper_graph(
pipe, data, layout, layout_dim, color_variable, _node_color_statistic,
color_by_columns_dropdown, plotly_kwargs, clone_pipeline=False)
observe_widgets(cover_params, cover_params_widgets)
observe_widgets(cluster_params, cluster_params_widgets)
logs_box.observe(click_box, names='value')
# define containers for input widgets
container_cover = widgets.HBox(
children=list(cover_params_widgets.values()))
container_cluster_layout = Layout(display='flex', flex_flow='row wrap')
container_cluster = widgets.HBox(
children=list(cluster_params_widgets.values()),
layout=container_cluster_layout)
box = widgets.VBox(
[container_cover, container_cluster, fig, valid, logs_box, out])
return box
def _create(self):
default_layout = widgets.Layout(width='auto', height='auto')
self.wg['organization'] = widgets.Text(value='',
description='Organization',
layout=default_layout)
self.wg['filter'] = widgets.Text(
description='Filter',
placeholder='Repository names must contain',
layout=default_layout)
self.wg['filename'] = widgets.Text(value='exercice.py',
description='Filename',
layout=default_layout)
self.wg['request_url'] = widgets.Text(
description='Request URL',
value=
f'https://raw.githubusercontent.com/{self.wg["organization"].value}/%RepositoryName%/master/{self.wg["filename"].value}',
layout=default_layout,
disabled=True)
self.wg['get_files'] = widgets.Button(description='Fetch submissions')
self.wg['get_files_status'] = widgets.Valid(value=True,
description='Ready',
layout=default_layout)
self.wg['previous_button'] = widgets.Button(description='Previous')
self.wg['next_button'] = widgets.Button(description='Next')
self.wg['open_in_browser'] = widgets.Button(
description='Open in GitHub', layout=default_layout)
self.wg['open_file'] = widgets.Checkbox(description='File only',
layout=default_layout)
self.wg['repository_select'] = widgets.Dropdown(
description='Select', layout=widgets.Layout(width='600px'))
self.wg['max_preview_lines'] = widgets.IntText(
value=100, disabled=False, layout=widgets.Layout(width='50px'))
self.wg['preview_lines_range'] = widgets.IntRangeSlider(
value=[0, 20],
min=0,
max=self.wg['max_preview_lines'].value,
step=1,
description='Lines range:',
continuous_update=True,
orientation='horizontal',
readout=True,
readout_format='d',
layout=widgets.Layout(width='500px'))
self.wg['repository_grading'] = widgets.HTML(
layout=widgets.Layout(width='auto',
height='auto',
border='solid 1px',
padding='2px 10px 2px 10px'))
html_layout = widgets.Layout(width='auto',
height='auto',
padding='20px 100px 0px 20px')
self.wg['file_preview_stats'] = widgets.HTML(layout=html_layout)
self.wg['file_preview'] = widgets.HTML(layout=html_layout)
self.wg['file_view_stats'] = widgets.HTML(layout=html_layout)
self.wg['file_view'] = widgets.HTML(layout=html_layout)
file_preview_box = widgets.HBox(
(self.wg['file_preview'], self.wg['file_preview_stats']))
file_view_box = widgets.HBox(
(self.wg['file_view'], self.wg['file_view_stats']))
lines_range_box = widgets.HBox(
(self.wg['preview_lines_range'], self.wg['max_preview_lines']))
self.wg['accordion'] = widgets.Accordion(children=[
widgets.VBox((lines_range_box, file_preview_box)), file_view_box
])
self.wg['accordion'].set_title(0, 'Preview')
self.wg['accordion'].set_title(1, 'File')
def plot(self,
color_data=None,
color_features=None,
node_color_statistic=None,
layout="kamada_kawai",
layout_dim=2,
n_sig_figs=3,
node_scale=12,
plotly_params=None):
""" Produce the interactive Mapper widget.
Parameters
----------
color_data : array-like of length n_samples, or None, optional, \
default: ``None``
Data to be used to construct node colors in the Mapper graph
(according to `color_features` and `node_color_statistic`). Must
have the same length as `data`. ``None`` is the same as passing
``numpy.arange(len(data))``.
color_features : object or None, optional, default: ``None``
Specifies one or more feature of interest from `color_data` to be
used, together with `node_color_statistic`, to determine node
colors.
1. ``None`` is equivalent to passing `color_data`.
2. If an object implementing :meth:`transform` or
:meth:`fit_transform`, or a callable, it is applied to
`color_data` to generate the features of interest.
3. If an index or string, or list of indices/strings, it is
equivalent to selecting a column or subset of columns from
`color_data`.
node_color_statistic : None or callable, optional, default: ``None``
If a callable, node colors will be computed as summary statistics
from the feature array ``y`` determined by `color_data` and
`color_features`. Let ``y`` have ``n`` columns (note: 1d feature
arrays are converted to column vectors). Then, for a node
representing a list ``I`` of row indices, there will be ``n``
colors, each computed as ``node_color_statistic(y[I, i])`` for
``i`` between ``0`` and ``n``.
layout : None, str or callable, optional, default: ``"kamada-kawai"``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph` [1]_.
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
n_sig_figs : int or None, optional, default: ``3``
If not ``None``, number of significant figures to which to round
node summary statistics. If ``None``, no rounding is performed.
node_scale : int or float, optional, default: ``12``
Sets the scale factor used to determine the rendered size of the
nodes. Increase for larger nodes. Implements a formula in the
`Plotly documentation \
<plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.
plotly_params : dict or None, optional, default: ``None``
Custom parameters to configure the plotly figure. Allowed keys are
``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the
corresponding values should be dictionaries containing keyword
arguments as would be fed to the :meth:`update_traces` and
:meth:`update_layout` methods of
:class:`plotly.graph_objects.Figure`.
Returns
-------
box : :class:`ipywidgets.VBox` object
A box containing the following widgets: parameters of the
clustering algorithm, parameters for the covering scheme, a Mapper
graph arising from those parameters, a validation box, and logs.
"""
# Clone pipeline to avoid side effects from in-place parameter changes
if self.clone_pipeline:
self._pipeline = clone(self.pipeline)
else:
self._pipeline = self.pipeline
def get_widgets_per_param(params):
for key, value in params.items():
style = {'description_width': 'initial'}
description = key.split("__")[1] if "__" in key else key
if isinstance(value, float):
yield (key,
widgets.FloatText(value=value,
step=0.05,
description=description,
continuous_update=False,
disabled=False,
layout=Layout(width="90%"),
style=style))
elif isinstance(value, bool):
yield (key,
widgets.ToggleButton(value=value,
description=description,
disabled=False,
layout=Layout(width="90%"),
style=style))
elif isinstance(value, int):
yield (key,
widgets.IntText(value=value,
step=1,
description=description,
continuous_update=False,
disabled=False,
layout=Layout(width="90%"),
style=style))
elif isinstance(value, str):
yield (key,
widgets.Text(value=value,
description=description,
continuous_update=False,
disabled=False,
layout=Layout(width="90%"),
style=style))
def on_parameter_change(change):
handler.clear_logs()
try:
for param, value in cover_params.items():
if isinstance(value, (int, float, str)):
self._pipeline.set_params(
**{param: cover_params_widgets[param].value})
for param, value in cluster_params.items():
if isinstance(value, (int, float, str)):
self._pipeline.set_params(
**{param: cluster_params_widgets[param].value})
for param, value in nerve_params.items():
if isinstance(value, (int, bool)):
self._pipeline.set_params(
**{param: nerve_params_widgets[param].value})
logger.info("Updating figure...")
with self._figure.batch_update():
self._graph = self._pipeline.fit_transform(self.data)
(edge_trace, node_trace,
self._node_colors_color_features) = \
_calculate_graph_data(
self._graph, self._color_data_transformed,
node_color_statistic, layout, layout_dim,
n_sig_figs, node_scale
)
if colorscale_for_hoverlabel is not None:
min_col, max_col = \
np.min(self._node_colors_color_features[:, 0]), \
np.max(self._node_colors_color_features[:, 0])
hoverlabel_bgcolor = _get_colors_for_vals(
self._node_colors_color_features[:, 0], min_col,
max_col, colorscale_for_hoverlabel)
self._figure.update_traces(
hoverlabel_bgcolor=hoverlabel_bgcolor,
selector={"name": "node_trace"})
self._figure.update_traces(
x=node_trace.x,
y=node_trace.y,
marker_color=node_trace.marker.color,
marker_size=node_trace.marker.size,
marker_sizeref=node_trace.marker.sizeref,
hovertext=node_trace.hovertext,
**({
"z": node_trace.z
} if layout_dim == 3 else dict()),
selector={"name": "node_trace"})
self._figure.update_traces(
x=edge_trace.x,
y=edge_trace.y,
**({
"z": edge_trace.z
} if layout_dim == 3 else dict()),
selector={"name": "edge_trace"})
# Update color by column buttons if relevant
if self._node_colors_color_features.shape[1] > 1:
hovertext_color_features = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(
self._node_colors_color_features,
hovertext_color_features,
colorscale_for_hoverlabel, n_sig_figs,
column_names_dropdown)
button_height = 1.1
self._figure.update_layout(updatemenus=[
go.layout.Updatemenu(buttons=column_color_buttons,
direction="down",
pad={
"r": 10,
"t": 10
},
showactive=True,
x=0.11,
xanchor="left",
y=button_height,
yanchor="top")
])
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[-1])
valid.value = False
def observe_widgets(params, widgets):
for param, value in params.items():
if isinstance(value, (int, float, str)):
widgets[param].observe(on_parameter_change, names="value")
# Define output widget to capture logs
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
# Initialise logging
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(
logging.Formatter("%(asctime)s - [%(levelname)s] %(message)s"))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# Initialise cover, cluster and nerve dictionaries of parameters and
# widgets
mapper_params_items = self._pipeline.get_mapper_params().items()
cover_params = {
key: value
for key, value in mapper_params_items if key.startswith("cover__")
}
cover_params_widgets = dict(get_widgets_per_param(cover_params))
cluster_params = {
key: value
for key, value in mapper_params_items
if key.startswith("clusterer__")
}
cluster_params_widgets = dict(get_widgets_per_param(cluster_params))
nerve_params = {
key: value
for key, value in mapper_params_items
if key in ["min_intersection", "contract_nodes"]
}
nerve_params_widgets = dict(get_widgets_per_param(nerve_params))
# Initialise widgets for validating input parameters of pipeline
valid = widgets.Valid(
value=True,
description="Valid parameters",
style={"description_width": "100px"},
)
# Initialise widget for showing the logs
logs_box = widgets.Checkbox(description="Show logs: ",
value=False,
indent=False)
# Initialise figure with initial pipeline and config
self._graph = self._pipeline.fit_transform(self.data)
(self._color_data_transformed, column_names_dropdown,
node_color_statistic) = \
_validate_color_kwargs(self._graph, self.data, color_data,
color_features, node_color_statistic,
interactive=True)
edge_trace, node_trace, self._node_colors_color_features = \
_calculate_graph_data(
self._graph, self._color_data_transformed,
node_color_statistic, layout, layout_dim, n_sig_figs,
node_scale
)
self._figure = _produce_static_figure(edge_trace, node_trace,
self._node_colors_color_features,
column_names_dropdown,
layout_dim, n_sig_figs,
plotly_params)
colorscale_for_hoverlabel = None
if layout_dim == 3:
# In plot_static_mapper_graph, hoverlabel bgcolors are set to white
# if something goes wrong in computing them according to the
# colorscale.
is_bgcolor_not_white = \
self._figure.data[1].hoverlabel.bgcolor != "white"
user_hoverlabel_bgcolor = False
if plotly_params:
if "node_trace" in plotly_params:
if "hoverlabel_bgcolor" in plotly_params["node_trace"]:
user_hoverlabel_bgcolor = True
if is_bgcolor_not_white and not user_hoverlabel_bgcolor:
colorscale_for_hoverlabel = \
self._figure.data[1].marker.colorscale
observe_widgets(cover_params, cover_params_widgets)
observe_widgets(cluster_params, cluster_params_widgets)
observe_widgets(nerve_params, nerve_params_widgets)
logs_box.observe(click_box, names="value")
# Define containers for input widgets
cover_title = HTML(value="<b>Cover parameters</b>")
container_cover = widgets.VBox(children=[cover_title] +
list(cover_params_widgets.values()))
container_cover.layout.align_items = 'center'
cluster_title = HTML(value="<b>Clusterer parameters</b>")
container_cluster = widgets.VBox(
children=[cluster_title] + list(cluster_params_widgets.values()), )
container_cluster.layout.align_items = 'center'
nerve_title = HTML(value="<b>Nerve parameters</b>")
container_nerve = widgets.VBox(children=[nerve_title] +
list(nerve_params_widgets.values()), )
container_nerve.layout.align_items = 'center'
container_parameters = widgets.HBox(
children=[container_cover, container_cluster, container_nerve])
box = widgets.VBox(
[container_parameters, self._figure, valid, logs_box, out])
return box
def create_interactive_network(pipeline, data, node_pos=None, node_color=None,
columns_to_color=None, plotly_kwargs=None,
summary_stat=np.mean):
"""
Parameters
----------
pipeline : MapperPipeline
The nerve of the refined pullback cover. Nodes correspond to cluster
sets, and an edge exists between two nodes if they share at least one
point in common.
data : ndarray, shape (n_samples, n_features)
The point cloud data used to generate the nerve.
node_pos : igraph.Graph.layout or ndarray, shape (n_nodes, n_dims)
Encodes the layout of the graph according to a layout algorithm or
pre-defined array of coordinates in an n-dimensional space.
node_color : ndarray, shape (n_nodes,)
The numerical values to color each node of the graph by.
columns_to_color : dict, optional, default: ``None``
Key-value pairs (column_name, column_index) to specify which columns of
:attr:`data` to color the graph by. Generates a dropdown widget to
select the columns to color by.
plotly_kwargs : dict, optional, default: ``None``
Keyword arguments to configure the Plotly Figure.
summary_stat : callable, default ``np.mean``
Summary statistic to apply to the elements in each node of the
topological graph.
"""
# TODO could abstract away common patterns in get_cover_params_widgets and
# get_cluster_params_widgets
# TODO allow dimension to be passed as either 2 or 3 as an arg or kwarg
# clone input pipeline to catch scenarios where user selects invalid
# configuration of parameters and re-executes cell in Jupyter notebook
pipe = clone(pipeline)
def get_cover_params_widgets(param, value):
if isinstance(value, float):
return (param, widgets.FloatSlider(
value=value,
step=0.05,
min=0.05,
max=1.0,
description=param.split('__')[1],
disabled=False
))
elif isinstance(value, int):
return (param, widgets.IntSlider(
value=value,
min=1,
max=100,
step=1,
description=param.split('__')[1],
disabled=False
))
else:
return None
def get_cluster_params_widgets(param, value):
if isinstance(value, float):
return (param, widgets.FloatText(
value=value,
step=0.1,
description=param.split('__')[1],
disabled=False
))
elif isinstance(value, int):
return (param, widgets.IntText(
value=value,
step=1,
description=param.split('__')[1],
disabled=False
))
elif isinstance(value, str):
return (param, widgets.Text(
value=value,
description=param.split('__')[1],
disabled=False
))
else:
return None
def update_figure(old_figure, new_figure):
# TODO could this be abstracted to node and edge traces and metadata
# information without need for creating a full new figure object
old_figure.data[0].x = new_figure.data[0].x
old_figure.data[0].y = new_figure.data[0].y
old_figure.data[1].x = new_figure.data[1].x
old_figure.data[1].y = new_figure.data[1].y
old_figure.data[1].marker.size = new_figure.data[1].marker.size
old_figure.data[1].marker.color = new_figure.data[1].marker.color
old_figure.data[1].marker.sizeref = new_figure.data[1].marker.sizeref
def get_figure(pipe, data, node_pos, node_color, columns_to_color,
summary_stat):
graph = pipe.fit_transform(data)
node_elements = graph['node_metadata']['node_elements']
if node_pos is None:
node_pos = graph.layout('kamada_kawai')
if node_color is None:
node_color = get_node_summary(node_elements, data,
summary_stat=summary_stat)
return create_network_2d(graph, data, node_pos, node_color,
columns_to_color, plotly_kwargs)
def response_numeric(change):
# TODO: remove hardcoding of keys and mimic what is done with clusterer
handler.clear_logs()
try:
pipe.set_mapper_params(
cover__n_intervals=cover_params_widgets['cover__n_intervals']
.value)
pipe.set_mapper_params(
cover__overlap_frac=cover_params_widgets['cover__overlap_frac']
.value)
for param, value in cluster_params.items():
if isinstance(value, (int, float)):
pipe.set_mapper_params(
**{param: cluster_params_widgets[param].value}
)
# TODO check this alternative:
#
# num_params = {param: value for param, value in
# cluster_params.items()
# if isinstance(value, (int, float))}
#
# pipe.set_mapper_params(
# **{param: cluster_params_widgets[param].value for param in
# num_params}
# )
new_fig = get_figure(pipe, data, node_pos,
node_color, columns_to_color, summary_stat)
logger.info("Updating figure ...")
with fig.batch_update():
update_figure(fig, new_fig)
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[-1])
valid.value = False
def response_text(text):
handler.clear_logs()
try:
for param, value in cluster_params.items():
if isinstance(value, str):
pipe.set_mapper_params(
**{param: cluster_params_widgets[param].value}
)
new_fig = get_figure(pipe, data, node_pos,
node_color, columns_to_color, summary_stat)
logger.info("Updating figure ...")
with fig.batch_update():
update_figure(fig, new_fig)
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[-1])
valid.value = False
def observe_numeric_widgets(params, widgets):
for param, value in params.items():
if isinstance(value, (int, float)):
widgets[param].observe(response_numeric, names='value')
# define output widget to capture logs
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
# initialise logging
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(logging.Formatter(
'%(asctime)s - [%(levelname)s] %(message)s'))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# initialise cover and cluster dictionaries of parameters and widgets
cover_params = dict(filter(lambda x: x[0].startswith('cover'),
pipe.get_mapper_params().items()))
cover_params_widgets = dict(
filter(None, map(lambda x: get_cover_params_widgets(*x),
cover_params.items())))
cluster_params = dict(filter(lambda x: x[0].startswith('clusterer'),
pipe.get_mapper_params().items()))
cluster_params_widgets = dict(
filter(None, map(lambda x: get_cluster_params_widgets(*x),
cluster_params.items())))
# create button for text inputs
submit_button = widgets.Button(description="Submit")
submit_button.on_click(response_text)
# initialise widgets for validating input parameters of pipeline
valid = widgets.Valid(
value=True,
description='Valid parameters',
style={'description_width': '100px'},
)
# initialise widget for showing the logs
logs_box = widgets.Checkbox(
description='Show logs: ',
value=False,
indent=False
)
# initialise figure with initial pipeline and config
if plotly_kwargs is None:
plotly_kwargs = dict()
fig = get_figure(pipe, data, node_pos, node_color,
columns_to_color, summary_stat)
observe_numeric_widgets(cover_params, cover_params_widgets)
observe_numeric_widgets(cluster_params, cluster_params_widgets)
logs_box.observe(click_box, names='value')
# define containers for input widgets
container_cover = widgets.HBox(children=list(
cover_params_widgets.values()))
container_cluster_text = widgets.HBox(
children=list(v for k, v in cluster_params_widgets.items()
if isinstance(cluster_params[k], str)) + [submit_button])
container_cluster_layout = Layout(display='flex', flex_flow='row wrap')
container_cluster_numeric = widgets.HBox(
children=list(v for k, v in cluster_params_widgets.items()
if isinstance(cluster_params[k], (int, float))
), layout=container_cluster_layout
)
box = widgets.VBox([container_cover, container_cluster_text,
container_cluster_numeric, fig,
valid, logs_box])
display(box, out)
def plot_interactive_mapper_graph(pipeline,
data,
layout="kamada_kawai",
layout_dim=2,
color_variable=None,
node_color_statistic=None,
clone_pipeline=True,
color_by_columns_dropdown=False,
n_sig_figs=3,
node_scale=12,
plotly_params=None):
"""Plotting function for interactive Mapper graphs.
Provides functionality to interactively update parameters from the cover
and clustering steps defined in `pipeline`. Nodes are colored according to
`color_variable` and `node_color_statistic`. By default, the hovertext on
each node displays a globally unique ID for the node, the number of data
points associated with the node, and the summary statistic which determines
its color.
Parameters
----------
pipeline : :class:`~gtda.mapper.pipeline.MapperPipeline` object
Mapper pipeline to act on to data.
data : array-like of shape (n_samples, n_features)
Data used to generate the Mapper graph. Can be a pandas dataframe.
layout : None, str or callable, optional, default: ``"kamada-kawai"``
Layout algorithm for the graph. Can be any accepted value for the
``layout`` parameter in the :meth:`layout` method of
:class:`igraph.Graph`. [1]_
layout_dim : int, default: ``2``
The number of dimensions for the layout. Can be 2 or 3.
color_variable : object or None, optional, default: ``None``
Specifies a feature of interest to be used, together with
`node_color_statistic`, to determine node colors.
1. If a numpy array or pandas dataframe, it must have the same
length as `data`.
2. ``None`` is equivalent to passing `data`.
3. If an object implementing :meth:`transform` or
:meth:`fit_transform`, it is applied to `data` to generate the
feature of interest.
4. If an index or string, or list of indices/strings, it is
equivalent to selecting a column or subset of columns from
`data`.
node_color_statistic : callable or None, optional, default: ``None``
If a callable, node colors will be computed as summary statistics from
the feature array ``Y`` determined by `color_variable` – specifically,
the color of a node representing the entries of `data` whose row
indices are in ``I`` will be ``node_color_statistic(Y[I])``. ``None``
is equivalent to passing :func:`numpy.mean`.
color_by_columns_dropdown : bool, optional, default: ``False``
If ``True``, a dropdown widget is generated which allows the user to
color Mapper nodes according to any column in `data` (still using
`node_color_statistic`) in addition to `color_variable`.
clone_pipeline : bool, optional, default: ``True``
If ``True``, the input `pipeline` is cloned before computing the
Mapper graph to prevent unexpected side effects from in-place
parameter updates.
n_sig_figs : int or None, optional, default: ``3``
If not ``None``, number of significant figures to which to round node
summary statistics. If ``None``, no rounding is performed.
node_scale : int or float, optional, default: ``12``
Sets the scale factor used to determine the rendered size of the
nodes. Increase for larger nodes. Implements a formula in the
`Plotly documentation \
<plotly.com/python/bubble-charts/#scaling-the-size-of-bubble-charts>`_.
plotly_params : dict or None, optional, default: ``None``
Custom parameters to configure the plotly figure. Allowed keys are
``"node_trace"``, ``"edge_trace"`` and ``"layout"``, and the
corresponding values should be dictionaries containing keyword
arguments as would be fed to the :meth:`update_traces` and
:meth:`update_layout` methods of :class:`plotly.graph_objects.Figure`.
Returns
-------
box : :class:`ipywidgets.VBox` object
A box containing the following widgets: parameters of the clustering
algorithm, parameters for the covering scheme, a Mapper graph arising
from those parameters, a validation box, and logs.
See also
--------
:func:`~gtda.mapper.visualization.plot_static_mapper_graph`,
:func:`~gtda.mapper.pipeline.make_mapper_pipeline`
References
----------
.. [1] `igraph.Graph.layout
<https://igraph.org/python/doc/igraph.Graph-class.html#layout>`_
documentation.
"""
# Clone pipeline to avoid side effects from in-place parameter changes
_pipeline = clone(pipeline) if clone_pipeline else pipeline
_node_color_statistic = node_color_statistic or np.mean
def get_widgets_per_param(param, value):
if isinstance(value, float):
return (param,
widgets.FloatText(value=value,
step=0.05,
description=param.split("__")[1],
continuous_update=False,
disabled=False))
elif isinstance(value, int):
return (param,
widgets.IntText(value=value,
step=1,
description=param.split("__")[1],
continuous_update=False,
disabled=False))
elif isinstance(value, str):
return (param,
widgets.Text(value=value,
description=param.split("__")[1],
continuous_update=False,
disabled=False))
else:
return None
def on_parameter_change(change):
handler.clear_logs()
try:
for param, value in cover_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(
**{param: cover_params_widgets[param].value})
for param, value in cluster_params.items():
if isinstance(value, (int, float, str)):
_pipeline.set_params(
**{param: cluster_params_widgets[param].value})
logger.info("Updating figure...")
with fig.batch_update():
(edge_trace, node_trace, node_elements,
node_colors_color_variable) = _calculate_graph_data(
_pipeline, data, is_data_dataframe, layout, layout_dim,
color_variable, _node_color_statistic, n_sig_figs,
node_scale)
if colorscale_for_hoverlabel is not None:
node_colors_color_variable = np.asarray(
node_colors_color_variable)
min_col = np.min(node_colors_color_variable)
max_col = np.max(node_colors_color_variable)
hoverlabel_bgcolor = _get_colors_for_vals(
node_colors_color_variable, min_col, max_col,
colorscale_for_hoverlabel)
fig.update_traces(hoverlabel_bgcolor=hoverlabel_bgcolor,
selector={"name": "node_trace"})
fig.update_traces(x=node_trace.x,
y=node_trace.y,
marker_color=node_trace.marker.color,
marker_size=node_trace.marker.size,
marker_sizeref=node_trace.marker.sizeref,
hovertext=node_trace.hovertext,
**({
"z": node_trace.z
} if layout_dim == 3 else dict()),
selector={"name": "node_trace"})
fig.update_traces(x=edge_trace.x,
y=edge_trace.y,
**({
"z": edge_trace.z
} if layout_dim == 3 else dict()),
selector={"name": "edge_trace"})
# Update color by column buttons
if color_by_columns_dropdown:
hovertext_color_variable = node_trace.hovertext
column_color_buttons = _get_column_color_buttons(
data, is_data_dataframe, node_elements,
node_colors_color_variable, _node_color_statistic,
hovertext_color_variable, colorscale_for_hoverlabel,
n_sig_figs)
# Avoid recomputing hoverlabel bgcolor for top button
if colorscale_for_hoverlabel is not None:
column_color_buttons[0]["args"][0][
"hoverlabel.bgcolor"] = [None, hoverlabel_bgcolor]
else:
column_color_buttons = None
button_height = 1.1
fig.update_layout(updatemenus=[
go.layout.Updatemenu(buttons=column_color_buttons,
direction="down",
pad={
"r": 10,
"t": 10
},
showactive=True,
x=0.11,
xanchor="left",
y=button_height,
yanchor="top"),
])
valid.value = True
except Exception:
exception_data = traceback.format_exc().splitlines()
logger.exception(exception_data[-1])
valid.value = False
def observe_widgets(params, widgets):
for param, value in params.items():
if isinstance(value, (int, float, str)):
widgets[param].observe(on_parameter_change, names="value")
# Define output widget to capture logs
out = widgets.Output()
@out.capture()
def click_box(change):
if logs_box.value:
out.clear_output()
handler.show_logs()
else:
out.clear_output()
# Initialise logging
logger = logging.getLogger(__name__)
handler = OutputWidgetHandler()
handler.setFormatter(
logging.Formatter("%(asctime)s - [%(levelname)s] %(message)s"))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# Initialise cover and cluster dictionaries of parameters and widgets
cover_params = dict(
filter(lambda x: x[0].startswith("cover"),
_pipeline.get_mapper_params().items()))
cover_params_widgets = dict(
filter(None,
map(lambda x: get_widgets_per_param(*x), cover_params.items())))
cluster_params = dict(
filter(lambda x: x[0].startswith("clusterer"),
_pipeline.get_mapper_params().items()))
cluster_params_widgets = dict(
filter(
None,
map(lambda x: get_widgets_per_param(*x), cluster_params.items())))
# Initialise widgets for validating input parameters of pipeline
valid = widgets.Valid(
value=True,
description="Valid parameters",
style={"description_width": "100px"},
)
# Initialise widget for showing the logs
logs_box = widgets.Checkbox(description="Show logs: ",
value=False,
indent=False)
# Initialise figure with initial pipeline and config
fig = plot_static_mapper_graph(
_pipeline,
data,
layout=layout,
layout_dim=layout_dim,
color_variable=color_variable,
node_color_statistic=_node_color_statistic,
color_by_columns_dropdown=color_by_columns_dropdown,
clone_pipeline=False,
n_sig_figs=n_sig_figs,
node_scale=node_scale,
plotly_params=plotly_params)
# Store variables for later updates
is_data_dataframe = hasattr(data, "columns")
colorscale_for_hoverlabel = None
if layout_dim == 3:
# In plot_static_mapper_graph, hoverlabel bgcolors are set to white if
# something goes wrong computing them according to the colorscale.
is_bgcolor_not_white = fig.data[1].hoverlabel.bgcolor != "white"
user_hoverlabel_bgcolor = False
if plotly_params:
if "node_trace" in plotly_params:
if "hoverlabel_bgcolor" in plotly_params["node_trace"]:
user_hoverlabel_bgcolor = True
if is_bgcolor_not_white and not user_hoverlabel_bgcolor:
colorscale_for_hoverlabel = fig.data[1].marker.colorscale
observe_widgets(cover_params, cover_params_widgets)
observe_widgets(cluster_params, cluster_params_widgets)
logs_box.observe(click_box, names="value")
# Define containers for input widgets
container_cover = widgets.HBox(
children=list(cover_params_widgets.values()))
container_cluster_layout = Layout(display="flex", flex_flow="row wrap")
container_cluster = widgets.HBox(children=list(
cluster_params_widgets.values()),
layout=container_cluster_layout)
box = widgets.VBox(
[container_cover, container_cluster, fig, valid, logs_box, out])
return box
def addValidMark(self, name, value):
self.pushControl(name, widgets.Valid(
value=value,
description=name,
))
