def send_data(self):
data, graph_sel = self.data, self.graph.get_selection()
selected_data, ann_data = None, None
if data:
group_sel = np.zeros(len(data), dtype=int)
if len(graph_sel):
# we get selection by region ids so we have to map it to points
for id, s in zip(self.region_ids, graph_sel):
if s == 0:
continue
id_indices = np.where(self.data_ids == id)[0]
group_sel[id_indices] = s
else:
graph_sel = [0]
if np.sum(graph_sel) > 0:
selected_data = create_groups_table(data, group_sel, False,
"Group")
if data is not None:
if np.max(graph_sel) > 1:
ann_data = create_groups_table(data, group_sel)
else:
ann_data = create_annotated_table(data,
group_sel.astype(bool))
self.output_changed.emit(selected_data)
self.Outputs.selected_data.send(selected_data)
self.Outputs.annotated_data.send(ann_data)
def commit(self):
if self.embedding is not None:
names = get_unique_names(
[v.name for v in self.data.domain.variables],
["tsne-x", "tsne-y"])
output = embedding = Orange.data.Table.from_numpy(
Orange.data.Domain([
ContinuousVariable(names[0]),
ContinuousVariable(names[1])
]), self.embedding)
else:
output = embedding = None
if self.embedding is not None and self.data is not None:
domain = self.data.domain
domain = Orange.data.Domain(
domain.attributes, domain.class_vars,
domain.metas + embedding.domain.attributes)
output = self.data.transform(domain)
output.metas[:, -2:] = embedding.X
selection = self.graph.get_selection()
if output is not None and len(selection) > 0:
selected = create_groups_table(output, self.graph.selection, False,
"Group")
else:
selected = None
if self.graph.selection is not None and np.max(
self.graph.selection) > 1:
annotated = create_groups_table(output, self.graph.selection)
else:
annotated = create_annotated_table(output, selection)
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
def update_output(self):
if self.data is None:
self.Outputs.selected_data.send(None)
self.Outputs.annotated_data.send(None)
self._set_output_summary(None)
return
indices = np.zeros(len(self.data), dtype=int)
if self.selection is not None and np.any(self.selection):
for y in range(self.size_y):
for x in range(self.size_x):
rows = self.get_member_indices(x, y)
indices[rows] = self.selection[x, y]
if np.any(indices):
sel_data = create_groups_table(self.data, indices, False, "Group")
self.Outputs.selected_data.send(sel_data)
self._set_output_summary(sel_data)
else:
self.Outputs.selected_data.send(None)
self._set_output_summary(None)
if np.max(indices) > 1:
annotated = create_groups_table(self.data, indices)
else:
annotated = create_annotated_table(self.data,
np.flatnonzero(indices))
self.Outputs.annotated_data.send(annotated)
def apply(self):
data = self.data
selected_data = annotated_data = histogram_data = None
if self.is_valid:
if self.var.is_discrete:
group_indices, values = self._get_output_indices_disc()
else:
group_indices, values = self._get_output_indices_cont()
selected = np.nonzero(group_indices)[0]
if selected.size:
selected_data = create_groups_table(
data, group_indices,
include_unselected=False, values=values)
annotated_data = create_annotated_table(data, selected)
if self.var.is_continuous: # annotate with bins
hist_indices, hist_values = self._get_histogram_indices()
annotated_data = create_groups_table(
annotated_data, hist_indices, var_name="Bin", values=hist_values)
histogram_data = self._get_histogram_table()
summary = len(selected_data) if selected_data else self.info.NoOutput
details = format_summary_details(selected_data) if selected_data else ""
self.info.set_output_summary(summary, details)
self.Outputs.selected_data.send(selected_data)
self.Outputs.annotated_data.send(annotated_data)
self.Outputs.histogram_data.send(histogram_data)
def commit(self):
if self.embedding is not None:
names = get_unique_names([v.name for v in self.data.domain.variables],
["mds-x", "mds-y"])
output = embedding = Orange.data.Table.from_numpy(
Orange.data.Domain([ContinuousVariable(names[0]), ContinuousVariable(names[1])]),
self.embedding
)
else:
output = embedding = None
if self.embedding is not None and self.data is not None:
domain = self.data.domain
domain = Orange.data.Domain(domain.attributes,
domain.class_vars,
domain.metas + embedding.domain.attributes)
output = self.data.transform(domain)
output.metas[:, -2:] = embedding.X
selection = self.graph.get_selection()
if output is not None and len(selection) > 0:
selected = output[selection]
else:
selected = None
if self.graph.selection is not None and np.max(self.graph.selection) > 1:
annotated = create_groups_table(output, self.graph.selection)
else:
annotated = create_annotated_table(output, selection)
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
def test_create_groups_table_set_values(self):
group_indices = random.sample(range(0, len(self.zoo)), 20)
selection = np.zeros(len(self.zoo), dtype=np.uint8)
selection[group_indices[:10]] = 1
selection[group_indices[10:]] = 2
values = ("this", "that", "rest")
table = create_groups_table(self.zoo, selection, values=values)
self.assertEqual(tuple(table.domain["Selected"].values), values)
def commit(self):
def prepare_components():
if self.placement in [self.Placement.Circular, self.Placement.LDA]:
attrs = [a for a in self.model_selected[:]]
axes = self.plotdata.axes
elif self.placement == self.Placement.PCA:
axes = self._pca.components_.T
attrs = [a for a in self._pca.orig_domain.attributes]
if self.placement != self.Placement.Projection:
domain = Domain(
[
ContinuousVariable(a.name,
compute_value=lambda _: None)
for a in attrs
],
metas=[StringVariable(name="component")],
)
metas = np.array(
[[
"{}{}".format(self.Component_name[self.placement],
i + 1) for i in range(axes.shape[1])
]],
dtype=object,
).T
components = Table(domain, axes.T, metas=metas)
components.name = "components"
else:
components = self.projection
return components
selected = annotated = components = None
if self.data is not None and self.plotdata.data is not None:
components = prepare_components()
graph = self.graph
mask = self.plotdata.valid_mask.astype(int)
mask[mask == 1] = (graph.selection if graph.selection is not None
else [False * len(mask)])
selection = (np.array([], dtype=np.uint8)
if mask is None else np.flatnonzero(mask))
name = self.data.name
data = self.plotdata.data
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(data, mask)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def test_create_groups_table_include_unselected(self):
group_indices = random.sample(range(0, len(self.zoo)), 20)
selection = np.zeros(len(self.zoo), dtype=np.uint8)
selection[group_indices[:10]] = 1
selection[group_indices[10:]] = 2
table = create_groups_table(self.zoo, selection)
self.assertEqual(
len(SameValue(table.domain["Selected"], "Unselected")(table)),
len(self.zoo) - len(group_indices))
def test_create_groups_table_include_unselected(self):
group_indices = random.sample(range(0, len(self.zoo)), 20)
selection = np.zeros(len(self.zoo), dtype=np.uint8)
selection[group_indices[:10]] = 1
selection[group_indices[10:]] = 2
table = create_groups_table(self.zoo, selection)
self.assertEqual(
len(SameValue(table.domain["Selected"], "Unselected")(table)),
len(self.zoo) - len(group_indices)
)
def _get_annotated_data(data, group_sel, graph_sel):
if data is None:
return None
if graph_sel is not None and np.max(graph_sel) > 1:
return create_groups_table(data, group_sel)
else:
if group_sel is None:
mask = np.full((len(data), ), False)
else:
mask = np.nonzero(group_sel)[0]
return create_annotated_table(data, mask)
def groups_or_annotated_table(data, selection):
"""
Use either Orange's create_annotated_table (for at most 1 selected class
or create_groups_table (for more selected classes)
:param data: Orange data table
:param selection: classes for selected indices (0 for unselected)
:return: Orange data table with an added column
"""
if len(selection) and np.max(selection) > 1:
return create_groups_table(data, selection)
else:
return create_annotated_table(data, np.flatnonzero(selection))
def commit(self):
if self.data:
# add Group column (group number)
self.Outputs.selected_data.send(
create_groups_table(self.image_grid.image_list, self.selection,
False, "Group"))
# filter out empty cells - keep only indices of cells that contain images
# add Selected column (Yes/No if one group, else Unselected or group number)
if self.selection is not None and np.max(self.selection) > 1:
out_data = create_groups_table(
self.image_grid.image_list[self.nonempty],
self.selection[self.nonempty])
else:
out_data = create_annotated_table(
self.image_grid.image_list[self.nonempty],
self.selection[self.nonempty])
self.Outputs.data.send(out_data)
else:
self.Outputs.data.send(None)
self.Outputs.selected_data.send(None)
def send_data(self):
data, graph_sel = self.data, self.graph.get_selection()
selected_data, ann_data = None, None
if data:
group_sel = np.zeros(len(data), dtype=int)
if len(graph_sel):
# we get selection by region ids so we have to map it to points
for id, s in zip(self.region_ids, graph_sel):
if s == 0:
continue
id_indices = np.where(self.data_ids == id)[0]
group_sel[id_indices] = s
else:
graph_sel = [0]
if np.sum(graph_sel) > 0:
selected_data = create_groups_table(data, group_sel, False,
"Group")
if data is not None:
if np.max(graph_sel) > 1:
ann_data = create_groups_table(data, group_sel)
else:
ann_data = create_annotated_table(data,
group_sel.astype(bool))
self.output_changed.emit(selected_data)
self.Outputs.selected_data.send(selected_data)
self.Outputs.annotated_data.send(ann_data)
# Added by Jean 2020/06/20, output aggdata for future usage
agg_data = self.agg_data # type: Optional[np.ndarray]
region_ids = self.region_ids # type: Optional[np.ndarray]
if agg_data is not None:
agg_data = agg_data.reshape(agg_data.shape[0], 1)
region_ids = region_ids.reshape(region_ids.shape[0], 1)
agg_data = Table.from_numpy(None, agg_data, None, region_ids)
self.Outputs.agg_data.send(agg_data)
def apply(self):
data = self.data
selected_data = annotated_data = histogram_data = None
if self.is_valid:
if self.var.is_discrete:
group_indices, values = self._get_output_indices_disc()
else:
group_indices, values = self._get_output_indices_cont()
hist_indices, hist_values = self._get_histogram_indices()
histogram_data = create_groups_table(data,
hist_indices,
values=hist_values)
selected = np.nonzero(group_indices)[0]
if selected.size:
selected_data = create_groups_table(data,
group_indices,
include_unselected=False,
values=values)
annotated_data = create_annotated_table(data, selected)
self.Outputs.selected_data.send(selected_data)
self.Outputs.annotated_data.send(annotated_data)
self.Outputs.histogram_data.send(histogram_data)
def commit(self):
if self.data:
# add Group column (group number)
self.Outputs.selected_data.send(
create_groups_table(self.image_grid.image_list, self.selection,
False, "Group"))
# filter out empty cells - keep indices of cells that contain images
# add Selected column
# (Yes/No if one group, else Unselected or group number)
if self.selection is not None and np.max(self.selection) > 1:
out_data = create_groups_table(
self.image_grid.image_list[self.nonempty],
self.selection[self.nonempty])
else:
out_data = create_annotated_table(
self.image_grid.image_list[self.nonempty],
np.nonzero(self.selection[self.nonempty]))
self.Outputs.data.send(out_data)
else:
self.Outputs.data.send(None)
self.Outputs.selected_data.send(None)
def commit(self):
def prepare_components():
if self.placement in [self.Placement.Circular, self.Placement.LDA]:
attrs = [a for a in self.model_selected[:]]
axes = self.plotdata.axes
elif self.placement == self.Placement.PCA:
axes = self._pca.components_.T
attrs = [a for a in self._pca.orig_domain.attributes]
if self.placement != self.Placement.Projection:
domain = Domain([ContinuousVariable(a.name, compute_value=lambda _: None)
for a in attrs],
metas=[StringVariable(name='component')])
metas = np.array([["{}{}".format(self.Component_name[self.placement], i + 1)
for i in range(axes.shape[1])]],
dtype=object).T
components = Table(domain, axes.T, metas=metas)
components.name = 'components'
else:
components = self.projection
return components
selected = annotated = components = None
if self.data is not None and self.plotdata.data is not None:
components = prepare_components()
graph = self.graph
mask = self.plotdata.valid_mask.astype(int)
mask[mask == 1] = graph.selection if graph.selection is not None \
else [False * len(mask)]
selection = np.array([], dtype=np.uint8) if mask is None else np.flatnonzero(mask)
name = self.data.name
data = self.plotdata.data
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(data, mask)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def _send_selection(self, data, selection_group, no_group=False):
annotated_data = groups_or_annotated_table(data, selection_group)
self.Outputs.annotated_data.send(annotated_data)
selected = None
if data:
if no_group and data: # compatibility mode, the output used to lack the group column
selection_indices = np.flatnonzero(selection_group)
selected = data[selection_indices]
else:
selected = create_groups_table(data,
selection_group, False, "Group")
selected = selected if selected else None
self.Outputs.selected_data.send(selected if selected else None)
return annotated_data, selected
def commit(self):
selected = annotated = components = None
graph = self.graph
if self.data is not None and self.plotdata.validmask is not None:
name = self.data.name
metas = () + self.data.domain.metas + (self.variable_x,
self.variable_y)
domain = Domain(
attributes=self.data.domain.attributes,
class_vars=self.data.domain.class_vars,
metas=metas,
)
data = self.plotdata.data.transform(domain)
validmask = self.plotdata.validmask
mask = np.array(validmask, dtype=int)
mask[mask == 1] = (graph.selection if graph.selection is not None
else [False * len(mask)])
selection = (np.array([], dtype=np.uint8)
if mask is None else np.flatnonzero(mask))
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(data, mask)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
comp_domain = Domain(self.data.domain.attributes,
metas=[StringVariable(name="component")])
metas = np.array([["FreeViz 1"], ["FreeViz 2"]])
components = Table.from_numpy(comp_domain,
X=self.plotdata.anchors.T,
metas=metas)
components.name = name + ": components"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def commit(self):
selected = annotated = components = None
if self.data is not None and np.sum(self.valid_data):
name = self.data.name
domain = self.data.domain
metas = domain.metas + (self.variable_x, self.variable_y)
domain = Domain(domain.attributes, domain.class_vars, metas)
embedding_coords = np.zeros((len(self.data), 2), dtype=np.float)
embedding_coords[self.valid_data] = self._embedding_coords
data = self.data.transform(domain)
data[:, self.variable_x] = embedding_coords[:, 0][:, None]
data[:, self.variable_y] = embedding_coords[:, 1][:, None]
selection = self.graph.get_selection()
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if self.graph.selection is not None and \
np.max(self.graph.selection) > 1:
annotated = create_groups_table(data, self.graph.selection)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
points = self.graph.get_points()
comp_domain = Domain(points[:, 2],
metas=[StringVariable(name='component')])
metas = np.array([["RX"], ["RY"], ["angle"]])
angle = np.arctan2(np.array(points[:, 1].T, dtype=float),
np.array(points[:, 0].T, dtype=float))
components = Table.from_numpy(comp_domain,
X=np.row_stack(
(points[:, :2].T, angle)),
metas=metas)
components.name = name + ": components"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def commit(self):
selected = annotated = components = None
graph = self.graph
if self.plotdata.data is not None:
name = self.data.name
data = self.plotdata.data
mask = self.plotdata.valid_mask.astype(int)
mask[mask == 1] = (
graph.selection if graph.selection is not None else [False * len(mask)]
)
selection = (
np.array([], dtype=np.uint8) if mask is None else np.flatnonzero(mask)
)
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(data, mask)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
comp_domain = Domain(
self.plotdata.points[:, 2], metas=[StringVariable(name="component")]
)
metas = np.array([["RX"], ["RY"], ["angle"]])
angle = np.arctan2(
np.array(self.plotdata.points[:, 1].T, dtype=float),
np.array(self.plotdata.points[:, 0].T, dtype=float),
)
components = Table.from_numpy(
comp_domain,
X=np.row_stack((self.plotdata.points[:, :2].T, angle)),
metas=metas,
)
components.name = name + ": components"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def output_image_selection(self):
if not self.data:
self.Outputs.selected_data.send(None)
self.Outputs.annotated_data.send(None)
self.curveplot.set_data(None)
return
indices = np.flatnonzero(self.imageplot.selection_group)
annotated_data = create_groups_table(self.data, self.imageplot.selection_group)
if annotated_data is not None:
annotated_data.X = self.data.X # workaround for Orange's copying on domain conversio
self.Outputs.annotated_data.send(annotated_data)
selected = self.data[indices]
self.Outputs.selected_data.send(selected if selected else None)
if selected:
self.curveplot.set_data(selected)
else:
self.curveplot.set_data(self.data)
def commit(self):
selected = annotated = components = None
graph = self.graph
if self.data is not None and self.plotdata.validmask is not None:
name = self.data.name
metas = () + self.data.domain.metas + (self.variable_x, self.variable_y)
domain = Domain(attributes=self.data.domain.attributes,
class_vars=self.data.domain.class_vars,
metas=metas)
data = self.plotdata.data.transform(domain)
validmask = self.plotdata.validmask
mask = np.array(validmask, dtype=int)
mask[mask == 1] = graph.selection if graph.selection is not None \
else [False * len(mask)]
selection = np.array([], dtype=np.uint8) if mask is None else np.flatnonzero(mask)
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(data, mask)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
comp_domain = Domain(
self.data.domain.attributes,
metas=[StringVariable(name='component')])
metas = np.array([["FreeViz 1"], ["FreeViz 2"]])
components = Table.from_numpy(
comp_domain,
X=self.plotdata.anchors.T,
metas=metas)
components.name = name + ": components"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def commit(self):
selected = annotated = components = None
graph = self.graph
if self.plotdata.data is not None:
name = self.data.name
data = self.plotdata.data
mask = self.plotdata.valid_mask.astype(int)
mask[mask == 1] = graph.selection if graph.selection is not None \
else [False * len(mask)]
selection = np.array([], dtype=np.uint8) if mask is None else np.flatnonzero(mask)
if len(selection):
selected = data[selection]
selected.name = name + ": selected"
selected.attributes = self.data.attributes
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(data, mask)
else:
annotated = create_annotated_table(data, selection)
annotated.attributes = self.data.attributes
annotated.name = name + ": annotated"
comp_domain = Domain(
self.plotdata.points[:, 2],
metas=[StringVariable(name='component')])
metas = np.array([["RX"], ["RY"], ["angle"]])
angle = np.arctan2(np.array(self.plotdata.points[:, 1].T, dtype=float),
np.array(self.plotdata.points[:, 0].T, dtype=float))
components = Table.from_numpy(
comp_domain,
X=np.row_stack((self.plotdata.points[:, :2].T, angle)),
metas=metas)
components.name = name + ": components"
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
self.Outputs.components.send(components)
def send_data(self):
selected = None
selection = None
# TODO: Implement selection for sql data
graph = self.graph
if isinstance(self.data, SqlTable):
selected = self.data
elif self.data is not None:
selection = graph.get_selection()
if len(selection) > 0:
selected = self.data[selection]
if graph.selection is not None and np.max(graph.selection) > 1:
annotated = create_groups_table(self.data, graph.selection)
else:
annotated = create_annotated_table(self.data, selection)
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(annotated)
# Store current selection in a setting that is stored in workflow
if selection is not None and len(selection):
self.selection_group = list(
zip(selection, graph.selection[selection]))
else:
self.selection_group = None
def _get_selected():
if not len(selection):
return None
return create_groups_table(data, graph.selection, False, "Group")
def _get_annotated():
if graph.selection is not None and np.max(graph.selection) > 1:
return create_groups_table(data, graph.selection)
else:
return create_annotated_table(data, selection)
def _get_selected_data(data, selection, group_sel):
return create_groups_table(data, group_sel, False, "Group") \
if len(selection) else None
def _get_annotated_data(data, group_sel, graph_sel):
if graph_sel is not None and np.max(graph_sel) > 1:
return create_groups_table(data, group_sel)
else:
return create_annotated_table(data, np.nonzero(group_sel)[0])
def _get_annotated_data(data, selection, group_sel, graph_sel):
if graph_sel is not None and np.max(graph_sel) > 1:
return create_groups_table(data, group_sel)
else:
return create_annotated_table(data, selection)
def _get_selected_data(data, selection, group_sel):
return create_groups_table(data, group_sel, False, "Group") \
if len(selection) else None
def _get_annotated():
if graph.selection is not None and np.max(graph.selection) > 1:
return create_groups_table(data, graph.selection)
else:
return create_annotated_table(data, selection)
def _get_selected():
if not len(selection):
return None
return create_groups_table(data, graph.selection, False, "Group")
