class OWLinearProjection(widget.OWWidget):
name = "Linear Projection"
description = "A multi-axis projection of data onto " \
"a two-dimensional plane."
icon = "icons/LinearProjection.svg"
priority = 240
selection_indices = settings.Setting(None, schema_only=True)
class Inputs:
data = Input("Data", Table, default=True)
data_subset = Input("Data Subset", Table)
projection = Input("Projection", Table)
class Outputs:
selected_data = Output("Selected Data", Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Table)
components = Output("Components", Table)
Placement = Enum("Placement",
dict(Circular=0,
LDA=1,
PCA=2,
Projection=3),
type=int,
qualname="OWLinearProjection.Placement")
Component_name = {Placement.Circular: "C", Placement.LDA: "LD", Placement.PCA: "PC"}
Variable_name = {Placement.Circular: "circular",
Placement.LDA: "lda",
Placement.PCA: "pca",
Placement.Projection: "projection"}
jitter_sizes = [0, 0.1, 0.5, 1.0, 2.0]
settings_version = 3
settingsHandler = settings.DomainContextHandler()
variable_state = settings.ContextSetting({})
placement = settings.Setting(Placement.Circular)
radius = settings.Setting(0)
auto_commit = settings.Setting(True)
resolution = 256
graph = settings.SettingProvider(OWLinProjGraph)
ReplotRequest = QEvent.registerEventType()
vizrank = settings.SettingProvider(LinearProjectionVizRank)
graph_name = "graph.plot_widget.plotItem"
class Warning(widget.OWWidget.Warning):
no_cont_features = widget.Msg("Plotting requires numeric features")
not_enough_components = widget.Msg("Input projection has less than 2 components")
trivial_components = widget.Msg(
"All components of the PCA are trivial (explain 0 variance). "
"Input data is constant (or near constant).")
class Error(widget.OWWidget.Error):
proj_and_domain_match = widget.Msg("Projection and Data domains do not match")
no_valid_data = widget.Msg("No projection due to invalid data")
def __init__(self):
super().__init__()
self.data = None
self.projection = None
self.subset_data = None
self._subset_mask = None
self._selection = None
self.__replot_requested = False
self.n_cont_var = 0
#: Remember the saved state to restore
self.__pending_selection_restore = self.selection_indices
self.selection_indices = None
self.variable_x = None
self.variable_y = None
box = gui.vBox(self.mainArea, True, margin=0)
self.graph = OWLinProjGraph(self, box, "Plot", view_box=LinProjInteractiveViewBox)
box.layout().addWidget(self.graph.plot_widget)
plot = self.graph.plot_widget
SIZE_POLICY = (QSizePolicy.Minimum, QSizePolicy.Maximum)
self.variables_selection = VariablesSelection()
self.model_selected = VariableListModel(enable_dnd=True)
self.model_other = VariableListModel(enable_dnd=True)
self.variables_selection(self, self.model_selected, self.model_other)
self.vizrank, self.btn_vizrank = LinearProjectionVizRank.add_vizrank(
self.controlArea, self, "Suggest Features", self._vizrank)
self.variables_selection.add_remove.layout().addWidget(self.btn_vizrank)
box = gui.widgetBox(
self.controlArea, "Placement", sizePolicy=SIZE_POLICY)
self.radio_placement = gui.radioButtonsInBox(
box, self, "placement",
btnLabels=["Circular Placement",
"Linear Discriminant Analysis",
"Principal Component Analysis",
"Use input projection"],
callback=self._change_placement
)
self.viewbox = plot.getViewBox()
self.replot = None
g = self.graph.gui
box = g.point_properties_box(self.controlArea)
self.models = g.points_models
g.add_widget(g.JitterSizeSlider, box)
box.setSizePolicy(*SIZE_POLICY)
box = gui.widgetBox(self.controlArea, "Hide axes", sizePolicy=SIZE_POLICY)
self.rslider = gui.hSlider(
box, self, "radius", minValue=0, maxValue=100,
step=5, label="Radius", createLabel=False, ticks=True,
callback=self.update_radius)
self.rslider.setTickInterval(0)
self.rslider.setPageStep(10)
box = gui.vBox(self.controlArea, "Plot Properties")
box.setSizePolicy(*SIZE_POLICY)
g.add_widgets([g.ShowLegend,
g.ToolTipShowsAll,
g.ClassDensity,
g.LabelOnlySelected], box)
box = self.graph.box_zoom_select(self.controlArea)
box.setSizePolicy(*SIZE_POLICY)
self.icons = gui.attributeIconDict
p = self.graph.plot_widget.palette()
self.graph.set_palette(p)
gui.auto_commit(self.controlArea, self, "auto_commit", "Send Selection",
auto_label="Send Automatically")
self.graph.zoom_actions(self)
self._new_plotdata()
self._change_placement()
self.graph.jitter_continuous = True
def reset_graph_data(self):
if self.data is not None:
self.graph.rescale_data()
self._update_graph(reset_view=True)
def keyPressEvent(self, event):
super().keyPressEvent(event)
self.graph.update_tooltip(event.modifiers())
def keyReleaseEvent(self, event):
super().keyReleaseEvent(event)
self.graph.update_tooltip(event.modifiers())
def _vizrank(self, attrs):
self.variables_selection.display_none()
self.model_selected[:] = attrs[:]
self.model_other[:] = [var for var in self.model_other if var not in attrs]
def _change_placement(self):
placement = self.placement
p_Circular = self.Placement.Circular
p_LDA = self.Placement.LDA
self.variables_selection.set_enabled(placement in [p_Circular, p_LDA])
self._vizrank_color_change()
self.rslider.setEnabled(placement != p_Circular)
self._setup_plot()
self.commit()
def _get_min_radius(self):
return self.radius * np.max(np.linalg.norm(self.plotdata.axes, axis=1)) / 100 + 1e-5
def update_radius(self):
# Update the anchor/axes visibility
pd = self.plotdata
assert pd is not None
if pd.hidecircle is None:
return
min_radius = self._get_min_radius()
for anchor, item in zip(pd.axes, pd.axisitems):
item.setVisible(np.linalg.norm(anchor) > min_radius)
pd.hidecircle.setRect(QRectF(-min_radius, -min_radius, 2 * min_radius, 2 * min_radius))
def _new_plotdata(self):
self.plotdata = namespace(
valid_mask=None,
embedding_coords=None,
axisitems=[],
axes=[],
variables=[],
data=None,
hidecircle=None
)
def _anchor_circle(self, variables):
# minimum visible anchor radius (radius)
min_radius = self._get_min_radius()
axisitems = []
for anchor, var in zip(self.plotdata.axes, variables[:]):
axitem = AnchorItem(line=QLineF(0, 0, *anchor), text=var.name,)
axitem.setVisible(np.linalg.norm(anchor) > min_radius)
axitem.setPen(pg.mkPen((100, 100, 100)))
axitem.setArrowVisible(True)
self.viewbox.addItem(axitem)
axisitems.append(axitem)
self.plotdata.axisitems = axisitems
if self.placement == self.Placement.Circular:
return
hidecircle = QGraphicsEllipseItem()
hidecircle.setRect(QRectF(-min_radius, -min_radius, 2 * min_radius, 2 * min_radius))
_pen = QPen(Qt.lightGray, 1)
_pen.setCosmetic(True)
hidecircle.setPen(_pen)
self.viewbox.addItem(hidecircle)
self.plotdata.hidecircle = hidecircle
def update_colors(self):
self._vizrank_color_change()
def clear(self):
# Clear/reset the widget state
self.data = None
self.model_selected.clear()
self.model_other.clear()
self._clear_plot()
self.selection_indices = None
def _clear_plot(self):
self.Warning.trivial_components.clear()
for axisitem in self.plotdata.axisitems:
self.viewbox.removeItem(axisitem)
if self.plotdata.hidecircle:
self.viewbox.removeItem(self.plotdata.hidecircle)
self._new_plotdata()
self.graph.hide_axes()
def invalidate_plot(self):
"""
Schedule a delayed replot.
"""
if not self.__replot_requested:
self.__replot_requested = True
QApplication.postEvent(self, QEvent(self.ReplotRequest), Qt.LowEventPriority - 10)
def init_attr_values(self):
self.graph.set_domain(self.data)
def _vizrank_color_change(self):
is_enabled = False
if self.data is None:
self.btn_vizrank.setToolTip("There is no data.")
return
vars = [v for v in chain(self.data.domain.variables, self.data.domain.metas) if
v.is_primitive and v is not self.graph.attr_color]
self.n_cont_var = len(vars)
if self.placement not in [self.Placement.Circular, self.Placement.LDA]:
msg = "Suggest Features works only for Circular and " \
"Linear Discriminant Analysis Projection"
elif self.graph.attr_color is None:
msg = "Color variable has to be selected"
elif self.graph.attr_color.is_continuous and self.placement == self.Placement.LDA:
msg = "Suggest Features does not work for Linear Discriminant Analysis Projection " \
"when continuous color variable is selected."
elif len(vars) < 3:
msg = "Not enough available continuous variables"
else:
is_enabled = True
msg = ""
self.btn_vizrank.setToolTip(msg)
self.btn_vizrank.setEnabled(is_enabled)
self.vizrank.stop_and_reset(is_enabled)
@Inputs.projection
def set_projection(self, projection):
self.Warning.not_enough_components.clear()
if projection and len(projection) < 2:
self.Warning.not_enough_components()
projection = None
if projection is not None:
self.placement = self.Placement.Projection
self.projection = projection
@Inputs.data
def set_data(self, data):
"""
Set the input dataset.
Args:
data (Orange.data.table): data instances
"""
def sql(data):
if isinstance(data, SqlTable):
if data.approx_len() < 4000:
data = Table(data)
else:
self.information("Data has been sampled")
data_sample = data.sample_time(1, no_cache=True)
data_sample.download_data(2000, partial=True)
data = Table(data_sample)
return data
def settings(data):
# get the default encoded state, replacing the position with Inf
state = VariablesSelection.encode_var_state(
[list(self.model_selected), list(self.model_other)]
)
state = {key: (source_ind, np.inf) for key, (source_ind, _) in state.items()}
self.openContext(data.domain)
selected_keys = [key for key, (sind, _) in self.variable_state.items() if sind == 0]
if set(selected_keys).issubset(set(state.keys())):
pass
if self.__pending_selection_restore is not None:
self._selection = np.array(self.__pending_selection_restore, dtype=int)
self.__pending_selection_restore = None
# update the defaults state (the encoded state must contain
# all variables in the input domain)
state.update(self.variable_state)
# ... and restore it with saved positions taking precedence over
# the defaults
selected, other = VariablesSelection.decode_var_state(
state, [list(self.model_selected), list(self.model_other)])
return selected, other
self.closeContext()
self.clear()
self.Warning.no_cont_features.clear()
self.information()
data = sql(data)
if data is not None:
domain = data.domain
vars = [var for var in chain(domain.variables, domain.metas) if var.is_continuous]
if not len(vars):
self.Warning.no_cont_features()
data = None
self.data = data
self.init_attr_values()
if data is not None and len(data):
self._initialize(data)
self.model_selected[:], self.model_other[:] = settings(data)
self.vizrank.stop_and_reset()
self.vizrank.attrs = self.data.domain.attributes if self.data is not None else []
def _check_possible_opt(self):
def set_enabled(is_enabled):
for btn in self.radio_placement.buttons:
btn.setEnabled(is_enabled)
self.variables_selection.set_enabled(is_enabled)
p_Circular = self.Placement.Circular
p_LDA = self.Placement.LDA
p_Input = self.Placement.Projection
if self.data:
set_enabled(True)
domain = self.data.domain
if not domain.has_discrete_class or len(domain.class_var.values) < 2:
self.radio_placement.buttons[p_LDA].setEnabled(False)
if self.placement == p_LDA:
self.placement = p_Circular
if not self.projection:
self.radio_placement.buttons[p_Input].setEnabled(False)
if self.placement == p_Input:
self.placement = p_Circular
self._setup_plot()
else:
self.graph.new_data(None)
self.rslider.setEnabled(False)
set_enabled(False)
self.commit()
@Inputs.data_subset
def set_subset_data(self, subset):
"""
Set the supplementary input subset dataset.
Args:
subset (Orange.data.table): subset of data instances
"""
self.subset_data = subset
self._subset_mask = None
self.controls.graph.alpha_value.setEnabled(subset is None)
def handleNewSignals(self):
if self.data is not None and self.subset_data is not None:
# Update the plot's highlight items
dataids = self.data.ids.ravel()
subsetids = np.unique(self.subset_data.ids)
self._subset_mask = np.in1d(dataids, subsetids, assume_unique=True)
self._check_possible_opt()
self._change_placement()
self.commit()
def customEvent(self, event):
if event.type() == OWLinearProjection.ReplotRequest:
self.__replot_requested = False
self._setup_plot()
self.commit()
else:
super().customEvent(event)
def closeContext(self):
self.variable_state = VariablesSelection.encode_var_state(
[list(self.model_selected), list(self.model_other)]
)
super().closeContext()
def _initialize(self, data):
# Initialize the GUI controls from data's domain.
vars = [v for v in chain(data.domain.metas, data.domain.attributes) if v.is_continuous]
self.model_other[:] = vars[3:]
self.model_selected[:] = vars[:3]
def prepare_plot_data(self, variables):
def projection(variables):
if set(self.projection.domain.attributes).issuperset(variables):
axes = self.projection[:2, variables].X
elif set(f.name for f in
self.projection.domain.attributes).issuperset(f.name for f in variables):
axes = self.projection[:2, [f.name for f in variables]].X
else:
self.Error.proj_and_domain_match()
axes = None
return axes
def get_axes(variables):
self.Error.proj_and_domain_match.clear()
axes = None
if self.placement == self.Placement.Circular:
axes = LinProj.defaultaxes(len(variables))
elif self.placement == self.Placement.LDA:
axes = self._get_lda(self.data, variables)
elif self.placement == self.Placement.Projection and self.projection:
axes = projection(variables)
return axes
coords = [column_data(self.data, var, dtype=float) for var in variables]
coords = np.vstack(coords)
p, N = coords.shape
assert N == len(self.data), p == len(variables)
axes = get_axes(variables)
if axes is None:
return None, None, None
assert axes.shape == (2, p)
valid_mask = ~np.isnan(coords).any(axis=0)
coords = coords[:, valid_mask]
X, Y = np.dot(axes, coords)
if X.size and Y.size:
X = normalized(X)
Y = normalized(Y)
return valid_mask, np.stack((X, Y), axis=1), axes.T
def _setup_plot(self):
self._clear_plot()
if self.data is None:
return
self.__replot_requested = False
names = get_unique_names([v.name for v in chain(self.data.domain.variables,
self.data.domain.metas)],
["{}-x".format(self.Variable_name[self.placement]),
"{}-y".format(self.Variable_name[self.placement])])
self.variable_x = ContinuousVariable(names[0])
self.variable_y = ContinuousVariable(names[1])
if self.placement in [self.Placement.Circular, self.Placement.LDA]:
variables = list(self.model_selected)
elif self.placement == self.Placement.Projection:
variables = self.model_selected[:] + self.model_other[:]
elif self.placement == self.Placement.PCA:
variables = [var for var in self.data.domain.attributes if var.is_continuous]
if not variables:
self.graph.new_data(None)
return
if self.placement == self.Placement.PCA:
valid_mask, ec, axes = self._get_pca()
variables = self._pca.orig_domain.attributes
else:
valid_mask, ec, axes = self.prepare_plot_data(variables)
self.plotdata.variables = variables
self.plotdata.valid_mask = valid_mask
self.plotdata.embedding_coords = ec
self.plotdata.axes = axes
if any(e is None for e in (valid_mask, ec, axes)):
return
if not sum(valid_mask):
self.Error.no_valid_data()
self.graph.new_data(None, None)
return
self.Error.no_valid_data.clear()
self._anchor_circle(variables=variables)
self._plot()
def _plot(self):
domain = self.data.domain
new_metas = domain.metas + (self.variable_x, self.variable_y)
domain = Domain(attributes=domain.attributes, class_vars=domain.class_vars, metas=new_metas)
valid_mask = self.plotdata.valid_mask
array = np.zeros((len(self.data), 2), dtype=np.float)
array[valid_mask] = self.plotdata.embedding_coords
self.plotdata.data = data = self.data.transform(domain)
data[:, self.variable_x] = array[:, 0].reshape(-1, 1)
data[:, self.variable_y] = array[:, 1].reshape(-1, 1)
subset_data = data[self._subset_mask & valid_mask]\
if self._subset_mask is not None and len(self._subset_mask) else None
self.plotdata.data = data
self.graph.new_data(data[valid_mask], subset_data)
if self._selection is not None:
self.graph.selection = self._selection[valid_mask]
self.graph.update_data(self.variable_x, self.variable_y, False)
def _get_lda(self, data, variables):
domain = Domain(attributes=variables, class_vars=data.domain.class_vars)
data = data.transform(domain)
lda = LinearDiscriminantAnalysis(solver='eigen', n_components=2)
lda.fit(data.X, data.Y)
scalings = lda.scalings_[:, :2].T
if scalings.shape == (1, 1):
scalings = np.array([[1.], [0.]])
return scalings
def _get_pca(self):
data = self.data
MAX_COMPONENTS = 2
ncomponents = 2
DECOMPOSITIONS = [PCA] # TruncatedSVD
cls = DECOMPOSITIONS[0]
pca_projector = cls(n_components=MAX_COMPONENTS)
pca_projector.component = ncomponents
pca_projector.preprocessors = cls.preprocessors + [Normalize()]
pca = pca_projector(data)
variance_ratio = pca.explained_variance_ratio_
cumulative = np.cumsum(variance_ratio)
self._pca = pca
if not np.isfinite(cumulative[-1]):
self.Warning.trivial_components()
coords = pca(data).X
valid_mask = ~np.isnan(coords).any(axis=1)
# scale axes
max_radius = np.min([np.abs(np.min(coords, axis=0)), np.max(coords, axis=0)])
axes = pca.components_.T.copy()
axes *= max_radius / np.max(np.linalg.norm(axes, axis=1))
return valid_mask, coords, axes
def _update_graph(self, reset_view=False):
self.graph.zoomStack = []
if self.graph.data is None:
return
self.graph.update_data(self.variable_x, self.variable_y, reset_view)
def update_density(self):
self._update_graph(reset_view=False)
def selection_changed(self):
if self.graph.selection is not None:
self._selection = np.zeros(len(self.data), dtype=np.uint8)
self._selection[self.plotdata.valid_mask] = self.graph.selection
self.selection_indices = self._selection.tolist()
else:
self._selection = self.selection_indices = None
self.commit()
def prepare_data(self):
pass
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_report(self):
if self.data is None:
return
def name(var):
return var and var.name
def projection_name():
name = ("Circular Placement",
"Linear Discriminant Analysis",
"Principal Component Analysis",
"Input projection")
return name[self.placement]
caption = report.render_items_vert((
("Projection", projection_name()),
("Color", name(self.graph.attr_color)),
("Label", name(self.graph.attr_label)),
("Shape", name(self.graph.attr_shape)),
("Size", name(self.graph.attr_size)),
("Jittering", self.graph.jitter_size != 0 and "{} %".format(self.graph.jitter_size))))
self.report_plot()
if caption:
self.report_caption(caption)
@classmethod
def migrate_settings(cls, settings_, version):
if version < 2:
settings_["point_width"] = settings_["point_size"]
if version < 3:
settings_graph = {}
settings_graph["jitter_size"] = settings_["jitter_value"]
settings_graph["point_width"] = settings_["point_width"]
settings_graph["alpha_value"] = settings_["alpha_value"]
settings_graph["class_density"] = settings_["class_density"]
settings_["graph"] = settings_graph
@classmethod
def migrate_context(cls, context, version):
if version < 2:
domain = context.ordered_domain
c_domain = [t for t in context.ordered_domain if t[1] == 2]
d_domain = [t for t in context.ordered_domain if t[1] == 1]
for d, old_val, new_val in ((domain, "color_index", "attr_color"),
(d_domain, "shape_index", "attr_shape"),
(c_domain, "size_index", "attr_size")):
index = context.values[old_val][0] - 1
context.values[new_val] = (d[index][0], d[index][1] + 100) \
if 0 <= index < len(d) else None
if version < 3:
context.values["graph"] = {
"attr_color": context.values["attr_color"],
"attr_shape": context.values["attr_shape"],
"attr_size": context.values["attr_size"]
}
class OWLinearProjection(widget.OWWidget):
name = "Linear Projection"
description = "A multi-axis projection of data onto " \
"a two-dimensional plane."
icon = "icons/LinearProjection.svg"
priority = 240
keywords = []
selection_indices = settings.Setting(None, schema_only=True)
class Inputs:
data = Input("Data", Table, default=True)
data_subset = Input("Data Subset", Table)
projection = Input("Projection", Table)
class Outputs:
selected_data = Output("Selected Data", Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Table)
components = Output("Components", Table)
Placement = Enum("Placement",
dict(Circular=0, LDA=1, PCA=2, Projection=3),
type=int,
qualname="OWLinearProjection.Placement")
Component_name = {
Placement.Circular: "C",
Placement.LDA: "LD",
Placement.PCA: "PC"
}
Variable_name = {
Placement.Circular: "circular",
Placement.LDA: "lda",
Placement.PCA: "pca",
Placement.Projection: "projection"
}
jitter_sizes = [0, 0.1, 0.5, 1.0, 2.0]
settings_version = 3
settingsHandler = settings.DomainContextHandler()
variable_state = settings.ContextSetting({})
placement = settings.Setting(Placement.Circular)
radius = settings.Setting(0)
auto_commit = settings.Setting(True)
resolution = 256
graph = settings.SettingProvider(OWLinProjGraph)
ReplotRequest = QEvent.registerEventType()
vizrank = settings.SettingProvider(LinearProjectionVizRank)
graph_name = "graph.plot_widget.plotItem"
class Warning(widget.OWWidget.Warning):
no_cont_features = widget.Msg("Plotting requires numeric features")
not_enough_components = widget.Msg(
"Input projection has less than 2 components")
trivial_components = widget.Msg(
"All components of the PCA are trivial (explain 0 variance). "
"Input data is constant (or near constant).")
class Error(widget.OWWidget.Error):
proj_and_domain_match = widget.Msg(
"Projection and Data domains do not match")
no_valid_data = widget.Msg("No projection due to invalid data")
def __init__(self):
super().__init__()
self.data = None
self.projection = None
self.subset_data = None
self._subset_mask = None
self._selection = None
self.__replot_requested = False
self.n_cont_var = 0
#: Remember the saved state to restore
self.__pending_selection_restore = self.selection_indices
self.selection_indices = None
self.variable_x = None
self.variable_y = None
box = gui.vBox(self.mainArea, True, margin=0)
self.graph = OWLinProjGraph(self,
box,
"Plot",
view_box=LinProjInteractiveViewBox)
box.layout().addWidget(self.graph.plot_widget)
plot = self.graph.plot_widget
SIZE_POLICY = (QSizePolicy.Minimum, QSizePolicy.Maximum)
self.variables_selection = VariablesSelection()
self.model_selected = VariableListModel(enable_dnd=True)
self.model_other = VariableListModel(enable_dnd=True)
self.variables_selection(self, self.model_selected, self.model_other)
self.vizrank, self.btn_vizrank = LinearProjectionVizRank.add_vizrank(
self.controlArea, self, "Suggest Features", self._vizrank)
self.variables_selection.add_remove.layout().addWidget(
self.btn_vizrank)
box = gui.widgetBox(self.controlArea,
"Placement",
sizePolicy=SIZE_POLICY)
self.radio_placement = gui.radioButtonsInBox(
box,
self,
"placement",
btnLabels=[
"Circular Placement", "Linear Discriminant Analysis",
"Principal Component Analysis", "Use input projection"
],
callback=self._change_placement)
self.viewbox = plot.getViewBox()
self.replot = None
g = self.graph.gui
box = g.point_properties_box(self.controlArea)
self.models = g.points_models
g.add_widget(g.JitterSizeSlider, box)
box.setSizePolicy(*SIZE_POLICY)
box = gui.widgetBox(self.controlArea,
"Hide axes",
sizePolicy=SIZE_POLICY)
self.rslider = gui.hSlider(box,
self,
"radius",
minValue=0,
maxValue=100,
step=5,
label="Radius",
createLabel=False,
ticks=True,
callback=self.update_radius)
self.rslider.setTickInterval(0)
self.rslider.setPageStep(10)
box = gui.vBox(self.controlArea, "Plot Properties")
box.setSizePolicy(*SIZE_POLICY)
g.add_widgets([
g.ShowLegend, g.ToolTipShowsAll, g.ClassDensity,
g.LabelOnlySelected
], box)
box = self.graph.box_zoom_select(self.controlArea)
box.setSizePolicy(*SIZE_POLICY)
self.icons = gui.attributeIconDict
p = self.graph.plot_widget.palette()
self.graph.set_palette(p)
gui.auto_commit(self.controlArea,
self,
"auto_commit",
"Send Selection",
auto_label="Send Automatically")
self.graph.zoom_actions(self)
self._new_plotdata()
self._change_placement()
self.graph.jitter_continuous = True
def reset_graph_data(self):
if self.data is not None:
self.graph.rescale_data()
self._update_graph(reset_view=True)
def keyPressEvent(self, event):
super().keyPressEvent(event)
self.graph.update_tooltip(event.modifiers())
def keyReleaseEvent(self, event):
super().keyReleaseEvent(event)
self.graph.update_tooltip(event.modifiers())
def _vizrank(self, attrs):
self.variables_selection.display_none()
self.model_selected[:] = attrs[:]
self.model_other[:] = [
var for var in self.model_other if var not in attrs
]
def _change_placement(self):
placement = self.placement
p_Circular = self.Placement.Circular
p_LDA = self.Placement.LDA
self.variables_selection.set_enabled(placement in [p_Circular, p_LDA])
self._vizrank_color_change()
self.rslider.setEnabled(placement != p_Circular)
self._setup_plot()
self.commit()
def _get_min_radius(self):
return self.radius * np.max(np.linalg.norm(self.plotdata.axes,
axis=1)) / 100 + 1e-5
def update_radius(self):
# Update the anchor/axes visibility
pd = self.plotdata
assert pd is not None
if pd.hidecircle is None:
return
min_radius = self._get_min_radius()
for anchor, item in zip(pd.axes, pd.axisitems):
item.setVisible(np.linalg.norm(anchor) > min_radius)
pd.hidecircle.setRect(
QRectF(-min_radius, -min_radius, 2 * min_radius, 2 * min_radius))
def _new_plotdata(self):
self.plotdata = namespace(valid_mask=None,
embedding_coords=None,
axisitems=[],
axes=[],
variables=[],
data=None,
hidecircle=None)
def _anchor_circle(self, variables):
# minimum visible anchor radius (radius)
min_radius = self._get_min_radius()
axisitems = []
for anchor, var in zip(self.plotdata.axes, variables[:]):
axitem = AnchorItem(
line=QLineF(0, 0, *anchor),
text=var.name,
)
axitem.setVisible(np.linalg.norm(anchor) > min_radius)
axitem.setPen(pg.mkPen((100, 100, 100)))
axitem.setArrowVisible(True)
self.viewbox.addItem(axitem)
axisitems.append(axitem)
self.plotdata.axisitems = axisitems
if self.placement == self.Placement.Circular:
return
hidecircle = QGraphicsEllipseItem()
hidecircle.setRect(
QRectF(-min_radius, -min_radius, 2 * min_radius, 2 * min_radius))
_pen = QPen(Qt.lightGray, 1)
_pen.setCosmetic(True)
hidecircle.setPen(_pen)
self.viewbox.addItem(hidecircle)
self.plotdata.hidecircle = hidecircle
def update_colors(self):
self._vizrank_color_change()
def clear(self):
# Clear/reset the widget state
self.data = None
self.model_selected.clear()
self.model_other.clear()
self._clear_plot()
self.selection_indices = None
def _clear_plot(self):
self.Warning.trivial_components.clear()
for axisitem in self.plotdata.axisitems:
self.viewbox.removeItem(axisitem)
if self.plotdata.hidecircle:
self.viewbox.removeItem(self.plotdata.hidecircle)
self._new_plotdata()
self.graph.hide_axes()
def invalidate_plot(self):
"""
Schedule a delayed replot.
"""
if not self.__replot_requested:
self.__replot_requested = True
QApplication.postEvent(self, QEvent(self.ReplotRequest),
Qt.LowEventPriority - 10)
def init_attr_values(self):
self.graph.set_domain(self.data)
def _vizrank_color_change(self):
is_enabled = False
if self.data is None:
self.btn_vizrank.setToolTip("There is no data.")
return
vars = [
v
for v in chain(self.data.domain.variables, self.data.domain.metas)
if v.is_primitive and v is not self.graph.attr_color
]
self.n_cont_var = len(vars)
if self.placement not in [self.Placement.Circular, self.Placement.LDA]:
msg = "Suggest Features works only for Circular and " \
"Linear Discriminant Analysis Projection"
elif self.graph.attr_color is None:
msg = "Color variable has to be selected"
elif self.graph.attr_color.is_continuous and self.placement == self.Placement.LDA:
msg = "Suggest Features does not work for Linear Discriminant Analysis Projection " \
"when continuous color variable is selected."
elif len(vars) < 3:
msg = "Not enough available continuous variables"
else:
is_enabled = True
msg = ""
self.btn_vizrank.setToolTip(msg)
self.btn_vizrank.setEnabled(is_enabled)
self.vizrank.stop_and_reset(is_enabled)
@Inputs.projection
def set_projection(self, projection):
self.Warning.not_enough_components.clear()
if projection and len(projection) < 2:
self.Warning.not_enough_components()
projection = None
if projection is not None:
self.placement = self.Placement.Projection
self.projection = projection
@Inputs.data
def set_data(self, data):
"""
Set the input dataset.
Args:
data (Orange.data.table): data instances
"""
def sql(data):
if isinstance(data, SqlTable):
if data.approx_len() < 4000:
data = Table(data)
else:
self.information("Data has been sampled")
data_sample = data.sample_time(1, no_cache=True)
data_sample.download_data(2000, partial=True)
data = Table(data_sample)
return data
def settings(data):
# get the default encoded state, replacing the position with Inf
state = VariablesSelection.encode_var_state(
[list(self.model_selected),
list(self.model_other)])
state = {
key: (source_ind, np.inf)
for key, (source_ind, _) in state.items()
}
self.openContext(data.domain)
selected_keys = [
key for key, (sind, _) in self.variable_state.items()
if sind == 0
]
if set(selected_keys).issubset(set(state.keys())):
pass
if self.__pending_selection_restore is not None:
self._selection = np.array(self.__pending_selection_restore,
dtype=int)
self.__pending_selection_restore = None
# update the defaults state (the encoded state must contain
# all variables in the input domain)
state.update(self.variable_state)
# ... and restore it with saved positions taking precedence over
# the defaults
selected, other = VariablesSelection.decode_var_state(
state, [list(self.model_selected),
list(self.model_other)])
return selected, other
self.closeContext()
self.clear()
self.Warning.no_cont_features.clear()
self.information()
data = sql(data)
if data is not None:
domain = data.domain
vars = [
var for var in chain(domain.variables, domain.metas)
if var.is_continuous
]
if not len(vars):
self.Warning.no_cont_features()
data = None
self.data = data
self.init_attr_values()
if data is not None and len(data):
self._initialize(data)
self.model_selected[:], self.model_other[:] = settings(data)
self.vizrank.stop_and_reset()
self.vizrank.attrs = self.data.domain.attributes if self.data is not None else []
def _check_possible_opt(self):
def set_enabled(is_enabled):
for btn in self.radio_placement.buttons:
btn.setEnabled(is_enabled)
self.variables_selection.set_enabled(is_enabled)
p_Circular = self.Placement.Circular
p_LDA = self.Placement.LDA
p_Input = self.Placement.Projection
if self.data:
set_enabled(True)
domain = self.data.domain
if not domain.has_discrete_class or len(
domain.class_var.values) < 2:
self.radio_placement.buttons[p_LDA].setEnabled(False)
if self.placement == p_LDA:
self.placement = p_Circular
if not self.projection:
self.radio_placement.buttons[p_Input].setEnabled(False)
if self.placement == p_Input:
self.placement = p_Circular
self._setup_plot()
else:
self.graph.new_data(None)
self.rslider.setEnabled(False)
set_enabled(False)
self.commit()
@Inputs.data_subset
def set_subset_data(self, subset):
"""
Set the supplementary input subset dataset.
Args:
subset (Orange.data.table): subset of data instances
"""
self.subset_data = subset
self._subset_mask = None
self.controls.graph.alpha_value.setEnabled(subset is None)
def handleNewSignals(self):
if self.data is not None and self.subset_data is not None:
# Update the plot's highlight items
dataids = self.data.ids.ravel()
subsetids = np.unique(self.subset_data.ids)
self._subset_mask = np.in1d(dataids, subsetids, assume_unique=True)
self._check_possible_opt()
self._change_placement()
self.commit()
def customEvent(self, event):
if event.type() == OWLinearProjection.ReplotRequest:
self.__replot_requested = False
self._setup_plot()
self.commit()
else:
super().customEvent(event)
def closeContext(self):
self.variable_state = VariablesSelection.encode_var_state(
[list(self.model_selected),
list(self.model_other)])
super().closeContext()
def _initialize(self, data):
# Initialize the GUI controls from data's domain.
vars = [
v for v in chain(data.domain.metas, data.domain.attributes)
if v.is_continuous
]
self.model_other[:] = vars[3:]
self.model_selected[:] = vars[:3]
def prepare_plot_data(self, variables):
def projection(variables):
if set(self.projection.domain.attributes).issuperset(variables):
axes = self.projection[:2, variables].X
elif set(f.name
for f in self.projection.domain.attributes).issuperset(
f.name for f in variables):
axes = self.projection[:2, [f.name for f in variables]].X
else:
self.Error.proj_and_domain_match()
axes = None
return axes
def get_axes(variables):
self.Error.proj_and_domain_match.clear()
axes = None
if self.placement == self.Placement.Circular:
axes = LinProj.defaultaxes(len(variables))
elif self.placement == self.Placement.LDA:
axes = self._get_lda(self.data, variables)
elif self.placement == self.Placement.Projection and self.projection:
axes = projection(variables)
return axes
coords = [
column_data(self.data, var, dtype=float) for var in variables
]
coords = np.vstack(coords)
p, N = coords.shape
assert N == len(self.data), p == len(variables)
axes = get_axes(variables)
if axes is None:
return None, None, None
assert axes.shape == (2, p)
valid_mask = ~np.isnan(coords).any(axis=0)
coords = coords[:, valid_mask]
X, Y = np.dot(axes, coords)
if X.size and Y.size:
X = normalized(X)
Y = normalized(Y)
return valid_mask, np.stack((X, Y), axis=1), axes.T
def _setup_plot(self):
self._clear_plot()
if self.data is None:
return
self.__replot_requested = False
names = get_unique_names([
v.name
for v in chain(self.data.domain.variables, self.data.domain.metas)
], [
"{}-x".format(self.Variable_name[self.placement]), "{}-y".format(
self.Variable_name[self.placement])
])
self.variable_x = ContinuousVariable(names[0])
self.variable_y = ContinuousVariable(names[1])
if self.placement in [self.Placement.Circular, self.Placement.LDA]:
variables = list(self.model_selected)
elif self.placement == self.Placement.Projection:
variables = self.model_selected[:] + self.model_other[:]
elif self.placement == self.Placement.PCA:
variables = [
var for var in self.data.domain.attributes if var.is_continuous
]
if not variables:
self.graph.new_data(None)
return
if self.placement == self.Placement.PCA:
valid_mask, ec, axes = self._get_pca()
variables = self._pca.orig_domain.attributes
else:
valid_mask, ec, axes = self.prepare_plot_data(variables)
self.plotdata.variables = variables
self.plotdata.valid_mask = valid_mask
self.plotdata.embedding_coords = ec
self.plotdata.axes = axes
if any(e is None for e in (valid_mask, ec, axes)):
return
if not sum(valid_mask):
self.Error.no_valid_data()
self.graph.new_data(None, None)
return
self.Error.no_valid_data.clear()
self._anchor_circle(variables=variables)
self._plot()
def _plot(self):
domain = self.data.domain
new_metas = domain.metas + (self.variable_x, self.variable_y)
domain = Domain(attributes=domain.attributes,
class_vars=domain.class_vars,
metas=new_metas)
valid_mask = self.plotdata.valid_mask
array = np.zeros((len(self.data), 2), dtype=np.float)
array[valid_mask] = self.plotdata.embedding_coords
self.plotdata.data = data = self.data.transform(domain)
data[:, self.variable_x] = array[:, 0].reshape(-1, 1)
data[:, self.variable_y] = array[:, 1].reshape(-1, 1)
subset_data = data[self._subset_mask & valid_mask]\
if self._subset_mask is not None and len(self._subset_mask) else None
self.plotdata.data = data
self.graph.new_data(data[valid_mask], subset_data)
if self._selection is not None:
self.graph.selection = self._selection[valid_mask]
self.graph.update_data(self.variable_x, self.variable_y, False)
def _get_lda(self, data, variables):
domain = Domain(attributes=variables,
class_vars=data.domain.class_vars)
data = data.transform(domain)
lda = LinearDiscriminantAnalysis(solver='eigen', n_components=2)
lda.fit(data.X, data.Y)
scalings = lda.scalings_[:, :2].T
if scalings.shape == (1, 1):
scalings = np.array([[1.], [0.]])
return scalings
def _get_pca(self):
data = self.data
MAX_COMPONENTS = 2
ncomponents = 2
DECOMPOSITIONS = [PCA] # TruncatedSVD
cls = DECOMPOSITIONS[0]
pca_projector = cls(n_components=MAX_COMPONENTS)
pca_projector.component = ncomponents
pca_projector.preprocessors = cls.preprocessors + [Normalize()]
pca = pca_projector(data)
variance_ratio = pca.explained_variance_ratio_
cumulative = np.cumsum(variance_ratio)
self._pca = pca
if not np.isfinite(cumulative[-1]):
self.Warning.trivial_components()
coords = pca(data).X
valid_mask = ~np.isnan(coords).any(axis=1)
# scale axes
max_radius = np.min(
[np.abs(np.min(coords, axis=0)),
np.max(coords, axis=0)])
axes = pca.components_.T.copy()
axes *= max_radius / np.max(np.linalg.norm(axes, axis=1))
return valid_mask, coords, axes
def _update_graph(self, reset_view=False):
self.graph.zoomStack = []
if self.graph.data is None:
return
self.graph.update_data(self.variable_x, self.variable_y, reset_view)
def update_density(self):
self._update_graph(reset_view=False)
def selection_changed(self):
if self.graph.selection is not None:
self._selection = np.zeros(len(self.data), dtype=np.uint8)
self._selection[self.plotdata.valid_mask] = self.graph.selection
self.selection_indices = self._selection.tolist()
else:
self._selection = self.selection_indices = None
self.commit()
def prepare_data(self):
pass
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_report(self):
if self.data is None:
return
def name(var):
return var and var.name
def projection_name():
name = ("Circular Placement", "Linear Discriminant Analysis",
"Principal Component Analysis", "Input projection")
return name[self.placement]
caption = report.render_items_vert(
(("Projection", projection_name()), ("Color",
name(self.graph.attr_color)),
("Label", name(self.graph.attr_label)),
("Shape", name(self.graph.attr_shape)),
("Size", name(self.graph.attr_size)),
("Jittering", self.graph.jitter_size != 0
and "{} %".format(self.graph.jitter_size))))
self.report_plot()
if caption:
self.report_caption(caption)
@classmethod
def migrate_settings(cls, settings_, version):
if version < 2:
settings_["point_width"] = settings_["point_size"]
if version < 3:
settings_graph = {}
settings_graph["jitter_size"] = settings_["jitter_value"]
settings_graph["point_width"] = settings_["point_width"]
settings_graph["alpha_value"] = settings_["alpha_value"]
settings_graph["class_density"] = settings_["class_density"]
settings_["graph"] = settings_graph
@classmethod
def migrate_context(cls, context, version):
if version < 2:
domain = context.ordered_domain
c_domain = [t for t in context.ordered_domain if t[1] == 2]
d_domain = [t for t in context.ordered_domain if t[1] == 1]
for d, old_val, new_val in ((domain, "color_index", "attr_color"),
(d_domain, "shape_index",
"attr_shape"),
(c_domain, "size_index", "attr_size")):
index = context.values[old_val][0] - 1
context.values[new_val] = (d[index][0], d[index][1] + 100) \
if 0 <= index < len(d) else None
if version < 3:
context.values["graph"] = {
"attr_color": context.values["attr_color"],
"attr_shape": context.values["attr_shape"],
"attr_size": context.values["attr_size"]
}
class OWLinearProjection(OWAnchorProjectionWidget):
name = "Linear Projection"
description = "A multi-axis projection of data onto " \
"a two-dimensional plane."
icon = "icons/LinearProjection.svg"
priority = 240
keywords = []
class Inputs(OWAnchorProjectionWidget.Inputs):
projection_input = Input("Projection", Table)
Placement = Enum("Placement", dict(Circular=0, LDA=1, PCA=2, Projection=3),
type=int, qualname="OWLinearProjection.Placement")
Component_name = {Placement.Circular: "C", Placement.LDA: "LD",
Placement.PCA: "PC"}
Variable_name = {Placement.Circular: "circular",
Placement.LDA: "lda",
Placement.PCA: "pca",
Placement.Projection: "projection"}
Projection_name = {Placement.Circular: "Circular Placement",
Placement.LDA: "Linear Discriminant Analysis",
Placement.PCA: "Principal Component Analysis",
Placement.Projection: "Use input projection"}
settings_version = 4
placement = Setting(Placement.Circular)
selected_vars = ContextSetting([])
vizrank = SettingProvider(LinearProjectionVizRank)
GRAPH_CLASS = OWLinProjGraph
graph = SettingProvider(OWLinProjGraph)
class Warning(OWAnchorProjectionWidget.Warning):
not_enough_comp = Msg("Input projection has less than two components")
trivial_components = Msg(
"All components of the PCA are trivial (explain zero variance). "
"Input data is constant (or near constant).")
class Error(OWAnchorProjectionWidget.Error):
no_cont_features = Msg("Plotting requires numeric features")
proj_and_domain_match = Msg("Projection and Data domains do not match")
def __init__(self):
self.model_selected = VariableListModel(enable_dnd=True)
self.model_selected.rowsInserted.connect(self.__model_selected_changed)
self.model_selected.rowsRemoved.connect(self.__model_selected_changed)
self.model_other = VariableListModel(enable_dnd=True)
self.vizrank, self.btn_vizrank = LinearProjectionVizRank.add_vizrank(
None, self, "Suggest Features", self.__vizrank_set_attrs)
super().__init__()
self.projection_input = None
self.variables = None
def _add_controls(self):
self._add_controls_variables()
self._add_controls_placement()
super()._add_controls()
self.graph.gui.add_control(
self._effects_box, gui.hSlider, "Hide radius:", master=self.graph,
value="hide_radius", minValue=0, maxValue=100, step=10,
createLabel=False, callback=self.__radius_slider_changed
)
self.controlArea.layout().removeWidget(self.control_area_stretch)
self.control_area_stretch.setParent(None)
def _add_controls_variables(self):
self.variables_selection = VariablesSelection(
self, self.model_selected, self.model_other, self.controlArea
)
self.variables_selection.add_remove.layout().addWidget(
self.btn_vizrank
)
def _add_controls_placement(self):
box = gui.widgetBox(
self.controlArea, True,
sizePolicy=(QSizePolicy.Minimum, QSizePolicy.Maximum)
)
self.radio_placement = gui.radioButtonsInBox(
box, self, "placement",
btnLabels=[self.Projection_name[x] for x in self.Placement],
callback=self.__placement_radio_changed
)
@property
def continuous_variables(self):
if self.data is None or self.data.domain is None:
return []
dom = self.data.domain
return [v for v in chain(dom.variables, dom.metas) if v.is_continuous]
def __vizrank_set_attrs(self, attrs):
if not attrs:
return
self.model_selected[:] = attrs[:]
self.model_other[:] = [var for var in self.continuous_variables
if var not in attrs]
def __model_selected_changed(self):
self.selected_vars = [(var.name, vartype(var)) for var
in self.model_selected]
self.projection = None
self.variables = None
self._check_options()
self.setup_plot()
self.commit()
def __placement_radio_changed(self):
self.variables_selection.set_enabled(
self.placement in [self.Placement.Circular, self.Placement.LDA])
self.controls.graph.hide_radius.setEnabled(
self.placement != self.Placement.Circular)
self.projection = None
self.variables = None
self._init_vizrank()
self.setup_plot()
self.commit()
def __radius_slider_changed(self):
self.graph.update_radius()
def colors_changed(self):
super().colors_changed()
self._init_vizrank()
def set_data(self, data):
super().set_data(data)
if self.data is not None and len(self.selected_vars):
d, selected = self.data.domain, [v[0] for v in self.selected_vars]
self.model_selected[:] = [d[attr] for attr in selected]
self.model_other[:] = [d[attr.name] for attr in
self.continuous_variables
if attr.name not in selected]
elif self.data is not None:
self.model_selected[:] = self.continuous_variables[:3]
self.model_other[:] = self.continuous_variables[3:]
self._check_options()
self._init_vizrank()
def _check_options(self):
buttons = self.radio_placement.buttons
for btn in buttons:
btn.setEnabled(True)
if self.data is not None:
has_discrete_class = self.data.domain.has_discrete_class
if not has_discrete_class or len(np.unique(self.data.Y)) < 2:
buttons[self.Placement.LDA].setEnabled(False)
if self.placement == self.Placement.LDA:
self.placement = self.Placement.Circular
if not self.projection_input:
buttons[self.Placement.Projection].setEnabled(False)
if self.placement == self.Placement.Projection:
self.placement = self.Placement.Circular
self.variables_selection.set_enabled(
self.placement in [self.Placement.Circular, self.Placement.LDA])
self.controls.graph.hide_radius.setEnabled(
self.placement != self.Placement.Circular)
def _init_vizrank(self):
is_enabled, msg = False, ""
if self.data is None:
msg = "There is no data."
elif self.placement not in [self.Placement.Circular,
self.Placement.LDA]:
msg = "Suggest Features works only for Circular and " \
"Linear Discriminant Analysis Projection"
elif self.attr_color is None:
msg = "Color variable has to be selected"
elif self.attr_color.is_continuous and \
self.placement == self.Placement.LDA:
msg = "Suggest Features does not work for Linear " \
"Discriminant Analysis Projection when " \
"continuous color variable is selected."
elif len([v for v in self.continuous_variables
if v is not self.attr_color]) < 3:
msg = "Not enough available continuous variables"
elif len(self.data[self.valid_data]) < 2:
msg = "Not enough valid data instances"
else:
is_enabled = not np.isnan(self.data.get_column_view(
self.attr_color)[0].astype(float)).all()
self.btn_vizrank.setToolTip(msg)
self.btn_vizrank.setEnabled(is_enabled)
if is_enabled:
self.vizrank.initialize()
def check_data(self):
def error(err):
err()
self.data = None
super().check_data()
if self.data is not None:
if not len(self.continuous_variables):
error(self.Error.no_cont_features)
def init_attr_values(self):
super().init_attr_values()
self.selected_vars = []
@Inputs.projection_input
def set_projection(self, projection):
self.Warning.not_enough_comp.clear()
if projection and len(projection) < 2:
self.Warning.not_enough_comp()
projection = None
if projection is not None:
self.placement = self.Placement.Projection
self.projection_input = projection
self._check_options()
def get_embedding(self):
self.valid_data = None
if self.data is None or not self.variables:
return None
if self.placement == self.Placement.PCA:
self.valid_data, ec, self.projection = self._get_pca()
self.variables = self._pca.orig_domain.attributes
else:
self.valid_data, ec, self.projection = \
self.prepare_projection_data(self.variables)
self.Error.no_valid_data.clear()
if self.valid_data is None or not sum(self.valid_data) or \
self.projection is None or ec is None:
self.Error.no_valid_data()
return None
embedding = np.zeros((len(self.data), 2), dtype=np.float)
embedding[self.valid_data] = ec
return embedding
def prepare_projection_data(self, variables):
def projection(_vars):
attrs = self.projection_input.domain.attributes
if set(attrs).issuperset(_vars):
return self.projection_input[:2, _vars].X
elif set(f.name for f in attrs).issuperset(f.name for f in _vars):
return self.projection_input[:2, [f.name for f in _vars]].X
else:
self.Error.proj_and_domain_match()
return None
def get_axes(_vars):
self.Error.proj_and_domain_match.clear()
if self.placement == self.Placement.Circular:
return LinProj.defaultaxes(len(_vars))
elif self.placement == self.Placement.LDA:
return self._get_lda(self.data, _vars)
elif self.placement == self.Placement.Projection and \
self.projection_input is not None:
return projection(_vars)
else:
return None
coords = np.vstack(column_data(self.data, v, float) for v in variables)
axes = get_axes(variables)
if axes is None:
return None, None, None
valid_mask = ~np.isnan(coords).any(axis=0)
X, Y = np.dot(axes, coords[:, valid_mask])
if X.size and Y.size:
X = normalized(X)
Y = normalized(Y)
return valid_mask, np.stack((X, Y), axis=1), axes.T
def get_anchors(self):
if self.projection is None:
return None, None
return self.projection, [v.name for v in self.variables]
def setup_plot(self):
self.init_projection_variables()
super().setup_plot()
def init_projection_variables(self):
self.variables = None
if self.data is None:
return
if self.placement in [self.Placement.Circular, self.Placement.LDA]:
self.variables = self.model_selected[:]
elif self.placement == self.Placement.Projection:
self.variables = self.model_selected[:] + self.model_other[:]
elif self.placement == self.Placement.PCA:
self.variables = [var for var in self.data.domain.attributes
if var.is_continuous]
def _get_lda(self, data, variables):
data = data.transform(Domain(variables, data.domain.class_vars))
lda = LinearDiscriminantAnalysis(solver='eigen', n_components=2)
lda.fit(data.X, data.Y)
scalings = lda.scalings_[:, :2].T
if scalings.shape == (1, 1):
scalings = np.array([[1.], [0.]])
return scalings
def _get_pca(self):
pca_projector = PCA(n_components=2)
pca_projector.component = 2
pca_projector.preprocessors = PCA.preprocessors + [Normalize()]
pca = pca_projector(self.data)
variance_ratio = pca.explained_variance_ratio_
cumulative = np.cumsum(variance_ratio)
self._pca = pca
if not np.isfinite(cumulative[-1]):
self.Warning.trivial_components()
coords = pca(self.data).X
valid_mask = ~np.isnan(coords).any(axis=1)
# scale axes
max_radius = np.min([np.abs(np.min(coords, axis=0)),
np.max(coords, axis=0)])
axes = pca.components_.T.copy()
axes *= max_radius / np.max(np.linalg.norm(axes, axis=1))
return valid_mask, coords, axes
def send_components(self):
components = None
if self.data is not None and self.valid_data is not None and \
self.projection is not None:
if self.placement in [self.Placement.Circular, self.Placement.LDA]:
axes = self.projection
attrs = self.model_selected
elif self.placement == self.Placement.PCA:
axes = self._pca.components_.T
attrs = self._pca.orig_domain.attributes
if self.placement != self.Placement.Projection:
meta_attrs = [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(attrs, metas=meta_attrs),
axes.T, metas=metas)
components.name = self.data.name
else:
components = self.projection_input
self.Outputs.components.send(components)
def _get_projection_variables(self):
pn = self.Variable_name[self.placement]
self.embedding_variables_names = ("{}-x".format(pn), "{}-y".format(pn))
return super()._get_projection_variables()
def _get_send_report_caption(self):
def projection_name():
return self.Projection_name[self.placement]
return report.render_items_vert((
("Projection", projection_name()),
("Color", self._get_caption_var_name(self.attr_color)),
("Label", self._get_caption_var_name(self.attr_label)),
("Shape", self._get_caption_var_name(self.attr_shape)),
("Size", self._get_caption_var_name(self.attr_size)),
("Jittering", self.graph.jitter_size != 0 and
"{} %".format(self.graph.jitter_size))))
def clear(self):
self.variables = None
if self.model_selected:
self.model_selected.clear()
if self.model_other:
self.model_other.clear()
super().clear()
@classmethod
def migrate_settings(cls, settings_, version):
if version < 2:
settings_["point_width"] = settings_["point_size"]
if version < 3:
settings_graph = {}
settings_graph["jitter_size"] = settings_["jitter_value"]
settings_graph["point_width"] = settings_["point_width"]
settings_graph["alpha_value"] = settings_["alpha_value"]
settings_graph["class_density"] = settings_["class_density"]
settings_["graph"] = settings_graph
if version < 4:
if "radius" in settings_:
settings_["graph"]["hide_radius"] = settings_["radius"]
if "selection_indices" in settings_ and \
settings_["selection_indices"] is not None:
selection = settings_["selection_indices"]
settings_["selection"] = [(i, 1) for i, selected in
enumerate(selection) if selected]
@classmethod
def migrate_context(cls, context, version):
if version < 2:
domain = context.ordered_domain
c_domain = [t for t in context.ordered_domain if t[1] == 2]
d_domain = [t for t in context.ordered_domain if t[1] == 1]
for d, old_val, new_val in ((domain, "color_index", "attr_color"),
(d_domain, "shape_index", "attr_shape"),
(c_domain, "size_index", "attr_size")):
index = context.values[old_val][0] - 1
context.values[new_val] = (d[index][0], d[index][1] + 100) \
if 0 <= index < len(d) else None
if version < 3:
context.values["graph"] = {
"attr_color": context.values["attr_color"],
"attr_shape": context.values["attr_shape"],
"attr_size": context.values["attr_size"]
}
if version == 3:
values = context.values
values["attr_color"] = values["graph"]["attr_color"]
values["attr_size"] = values["graph"]["attr_size"]
values["attr_shape"] = values["graph"]["attr_shape"]
values["attr_label"] = values["graph"]["attr_label"]