class OWDiscretize(widget.OWWidget):
name = "Discretize"
description = "Discretize the numeric data features."
icon = "icons/Discretize.svg"
inputs = [
InputSignal("Data",
Orange.data.Table,
"set_data",
doc="Input data table")
]
outputs = [
OutputSignal("Data",
Orange.data.Table,
doc="Table with discretized features")
]
settingsHandler = settings.DomainContextHandler()
saved_var_states = settings.ContextSetting({})
default_method = settings.Setting(2)
default_k = settings.Setting(3)
autosend = settings.Setting(True)
#: Discretization methods
Default, Leave, MDL, EqualFreq, EqualWidth, Remove, Custom = range(7)
want_main_area = False
resizing_enabled = False
def __init__(self):
super().__init__()
#: input data
self.data = None
#: Current variable discretization state
self.var_state = {}
#: Saved variable discretization settings (context setting)
self.saved_var_states = {}
self.method = 0
self.k = 5
box = gui.vBox(self.controlArea, self.tr("Default Discretization"))
self.default_bbox = rbox = gui.radioButtons(
box, self, "default_method", callback=self._default_disc_changed)
rb = gui.hBox(rbox)
self.left = gui.vBox(rb)
right = gui.vBox(rb)
rb.layout().setStretch(0, 1)
rb.layout().setStretch(1, 1)
options = self.options = [
self.tr("Default"),
self.tr("Leave numeric"),
self.tr("Entropy-MDL discretization"),
self.tr("Equal-frequency discretization"),
self.tr("Equal-width discretization"),
self.tr("Remove numeric variables")
]
for opt in options[1:]:
t = gui.appendRadioButton(rbox, opt)
# This condition is ugly, but it keeps the same order of
# options for backward compatibility of saved schemata
[right, self.left][opt.startswith("Equal")].layout().addWidget(t)
gui.separator(right, 18, 18)
def _intbox(widget, attr, callback):
box = gui.indentedBox(widget)
s = gui.spin(box,
self,
attr,
minv=2,
maxv=10,
label="Num. of intervals:",
callback=callback)
s.setMaximumWidth(60)
s.setAlignment(Qt.AlignRight)
gui.rubber(s.box)
return box.box
self.k_general = _intbox(self.left, "default_k",
self._default_disc_changed)
self.k_general.layout().setContentsMargins(0, 0, 0, 0)
vlayout = QHBoxLayout()
box = gui.widgetBox(self.controlArea,
"Individual Attribute Settings",
orientation=vlayout,
spacing=8)
# List view with all attributes
self.varview = QListView(selectionMode=QListView.ExtendedSelection)
self.varview.setItemDelegate(DiscDelegate())
self.varmodel = itemmodels.VariableListModel()
self.varview.setModel(self.varmodel)
self.varview.selectionModel().selectionChanged.connect(
self._var_selection_changed)
vlayout.addWidget(self.varview)
# Controls for individual attr settings
self.bbox = controlbox = gui.radioButtons(
box, self, "method", callback=self._disc_method_changed)
vlayout.addWidget(controlbox)
for opt in options[:5]:
gui.appendRadioButton(controlbox, opt)
self.k_specific = _intbox(controlbox, "k", self._disc_method_changed)
gui.appendRadioButton(controlbox, "Remove attribute")
gui.rubber(controlbox)
controlbox.setEnabled(False)
self.controlbox = controlbox
box = gui.auto_commit(self.controlArea,
self,
"autosend",
"Apply",
orientation=Qt.Horizontal,
checkbox_label="Apply automatically")
box.layout().insertSpacing(0, 20)
box.layout().insertWidget(0, self.report_button)
self._update_spin_positions()
def set_data(self, data):
self.closeContext()
self.data = data
if self.data is not None:
self._initialize(data)
self.openContext(data)
# Restore the per variable discretization settings
self._restore(self.saved_var_states)
# Complete the induction of cut points
self._update_points()
else:
self._clear()
self.unconditional_commit()
def _initialize(self, data):
# Initialize the default variable states for new data.
self.class_var = data.domain.class_var
cvars = [var for var in data.domain if var.is_continuous]
self.varmodel[:] = cvars
class_var = data.domain.class_var
has_disc_class = data.domain.has_discrete_class
self.default_bbox.buttons[self.MDL - 1].setEnabled(has_disc_class)
self.bbox.buttons[self.MDL].setEnabled(has_disc_class)
# If the newly disabled MDL button is checked then change it
if not has_disc_class and self.default_method == self.MDL - 1:
self.default_method = 0
if not has_disc_class and self.method == self.MDL:
self.method = 0
# Reset (initialize) the variable discretization states.
self._reset()
def _restore(self, saved_state):
# Restore variable states from a saved_state dictionary.
def_method = self._current_default_method()
for i, var in enumerate(self.varmodel):
key = variable_key(var)
if key in saved_state:
state = saved_state[key]
if isinstance(state.method, Default):
state = DState(Default(def_method), None, None)
self._set_var_state(i, state)
def _reset(self):
# restore the individual variable settings back to defaults.
def_method = self._current_default_method()
self.var_state = {}
for i in range(len(self.varmodel)):
state = DState(Default(def_method), None, None)
self._set_var_state(i, state)
def _set_var_state(self, index, state):
# set the state of variable at `index` to `state`.
self.var_state[index] = state
self.varmodel.setData(self.varmodel.index(index), state, Qt.UserRole)
def _clear(self):
self.data = None
self.varmodel[:] = []
self.var_state = {}
self.saved_var_states = {}
self.default_bbox.buttons[self.MDL - 1].setEnabled(True)
self.bbox.buttons[self.MDL].setEnabled(True)
def _update_points(self):
"""
Update the induced cut points.
"""
if self.data is None or not len(self.data):
return
def induce_cuts(method, data, var):
dvar = _dispatch[type(method)](method, data, var)
if dvar is None:
# removed
return [], None
elif dvar is var:
# no transformation took place
return None, var
elif is_discretized(dvar):
return dvar.compute_value.points, dvar
else:
assert False
for i, var in enumerate(self.varmodel):
state = self.var_state[i]
if state.points is None and state.disc_var is None:
points, dvar = induce_cuts(state.method, self.data, var)
new_state = state._replace(points=points, disc_var=dvar)
self._set_var_state(i, new_state)
def _method_index(self, method):
return METHODS.index((type(method), ))
def _current_default_method(self):
method = self.default_method + 1
k = self.default_k
if method == OWDiscretize.Leave:
def_method = Leave()
elif method == OWDiscretize.MDL:
def_method = MDL()
elif method == OWDiscretize.EqualFreq:
def_method = EqualFreq(k)
elif method == OWDiscretize.EqualWidth:
def_method = EqualWidth(k)
elif method == OWDiscretize.Remove:
def_method = Remove()
else:
assert False
return def_method
def _current_method(self):
if self.method == OWDiscretize.Default:
method = Default(self._current_default_method())
elif self.method == OWDiscretize.Leave:
method = Leave()
elif self.method == OWDiscretize.MDL:
method = MDL()
elif self.method == OWDiscretize.EqualFreq:
method = EqualFreq(self.k)
elif self.method == OWDiscretize.EqualWidth:
method = EqualWidth(self.k)
elif self.method == OWDiscretize.Remove:
method = Remove()
elif self.method == OWDiscretize.Custom:
method = Custom(self.cutpoints)
else:
assert False
return method
def _update_spin_positions(self):
self.k_general.setDisabled(self.default_method not in [2, 3])
if self.default_method == 2:
self.left.layout().insertWidget(1, self.k_general)
elif self.default_method == 3:
self.left.layout().insertWidget(2, self.k_general)
self.k_specific.setDisabled(self.method not in [3, 4])
if self.method == 3:
self.bbox.layout().insertWidget(4, self.k_specific)
elif self.method == 4:
self.bbox.layout().insertWidget(5, self.k_specific)
def _default_disc_changed(self):
self._update_spin_positions()
method = self._current_default_method()
state = DState(Default(method), None, None)
for i, _ in enumerate(self.varmodel):
if isinstance(self.var_state[i].method, Default):
self._set_var_state(i, state)
self._update_points()
self.commit()
def _disc_method_changed(self):
self._update_spin_positions()
indices = self.selected_indices()
method = self._current_method()
state = DState(method, None, None)
for idx in indices:
self._set_var_state(idx, state)
self._update_points()
self.commit()
def _var_selection_changed(self, *args):
indices = self.selected_indices()
# set of all methods for the current selection
methods = [self.var_state[i].method for i in indices]
mset = set(methods)
self.controlbox.setEnabled(len(mset) > 0)
if len(mset) == 1:
method = mset.pop()
self.method = self._method_index(method)
if isinstance(method, (EqualFreq, EqualWidth)):
self.k = method.k
elif isinstance(method, Custom):
self.cutpoints = method.points
else:
# deselect the current button
self.method = -1
bg = self.controlbox.group
button_group_reset(bg)
self._update_spin_positions()
def selected_indices(self):
rows = self.varview.selectionModel().selectedRows()
return [index.row() for index in rows]
def discretized_var(self, source):
index = list(self.varmodel).index(source)
state = self.var_state[index]
if state.disc_var is None:
return None
elif state.disc_var is source:
return source
elif state.points == []:
return None
else:
return state.disc_var
def discretized_domain(self):
"""
Return the current effective discretized domain.
"""
if self.data is None:
return None
def disc_var(source):
if source and source.is_continuous:
return self.discretized_var(source)
else:
return source
attributes = [disc_var(v) for v in self.data.domain.attributes]
attributes = [v for v in attributes if v is not None]
class_var = disc_var(self.data.domain.class_var)
domain = Orange.data.Domain(attributes,
class_var,
metas=self.data.domain.metas)
return domain
def commit(self):
output = None
if self.data is not None and len(self.data):
domain = self.discretized_domain()
output = self.data.transform(domain)
self.send("Data", output)
def storeSpecificSettings(self):
super().storeSpecificSettings()
self.saved_var_states = {
variable_key(var): self.var_state[i]._replace(points=None,
disc_var=None)
for i, var in enumerate(self.varmodel)
}
def send_report(self):
self.report_items(
(("Default method", self.options[self.default_method + 1]), ))
if self.varmodel:
self.report_items(
"Thresholds",
[(var.name, DiscDelegate.cutsText(self.var_state[i])
or "leave numeric") for i, var in enumerate(self.varmodel)])
class OWDistributions(widget.OWWidget):
name = "Distributions"
description = "Display value distributions of a data feature in a graph."
icon = "icons/Distribution.svg"
priority = 100
inputs = [
InputSignal("Data",
Orange.data.Table,
"set_data",
doc="Set the input data set")
]
settingsHandler = settings.DomainContextHandler()
#: Selected variable index
variable_idx = settings.ContextSetting(-1)
#: Selected group variable
groupvar_idx = settings.ContextSetting(0)
Hist, ASH, Kernel = 0, 1, 2
#: Continuous variable density estimation method
cont_est_type = settings.Setting(ASH)
relative_freq = settings.Setting(False)
def __init__(self, parent=None):
super().__init__(parent)
self.data = None
self.distributions = None
self.contingencies = None
self.var = self.cvar = None
varbox = gui.widgetBox(self.controlArea, "Variable")
self.varmodel = itemmodels.VariableListModel()
self.groupvarmodel = itemmodels.VariableListModel()
self.varview = QtGui.QListView(
selectionMode=QtGui.QListView.SingleSelection)
self.varview.setSizePolicy(QtGui.QSizePolicy.Minimum,
QtGui.QSizePolicy.Expanding)
self.varview.setModel(self.varmodel)
self.varview.setSelectionModel(
itemmodels.ListSingleSelectionModel(self.varmodel))
self.varview.selectionModel().selectionChanged.connect(
self._on_variable_idx_changed)
varbox.layout().addWidget(self.varview)
gui.separator(varbox, 8, 8)
gui.comboBox(varbox,
self,
"cont_est_type",
label="Show continuous variables by",
valueType=int,
items=[
"Histograms", "Average shifted histograms",
"Kernel density estimators"
],
callback=self._on_cont_est_type_changed)
box = gui.widgetBox(self.controlArea, "Group by")
self.groupvarview = QtGui.QListView(
selectionMode=QtGui.QListView.SingleSelection)
self.groupvarview.setFixedHeight(100)
self.groupvarview.setSizePolicy(QtGui.QSizePolicy.Minimum,
QtGui.QSizePolicy.Preferred)
self.groupvarview.setModel(self.groupvarmodel)
self.groupvarview.selectionModel().selectionChanged.connect(
self._on_groupvar_idx_changed)
box.layout().addWidget(self.groupvarview)
self.cb_rel_freq = gui.checkBox(
box,
self,
"relative_freq",
"Show relative frequencies",
callback=self._on_relative_freq_changed)
plotview = pg.PlotWidget(background=None)
self.mainArea.layout().addWidget(plotview)
w = QtGui.QLabel()
w.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Fixed)
self.mainArea.layout().addWidget(w, Qt.AlignCenter)
self.plot = pg.PlotItem()
# self.plot.getViewBox().setMouseEnabled(False, False)
self.plot.getViewBox().setMenuEnabled(False)
plotview.setCentralItem(self.plot)
pen = QtGui.QPen(self.palette().color(QtGui.QPalette.Text))
for axis in ("left", "bottom"):
self.plot.getAxis(axis).setPen(pen)
def set_data(self, data):
self.closeContext()
self.clear()
self.data = data
if self.data is not None:
domain = self.data.domain
self.varmodel[:] = list(domain)
self.groupvarmodel[:] = \
["(None)"] + [var for var in domain if var.is_discrete]
if domain.has_discrete_class:
self.groupvar_idx = \
list(self.groupvarmodel).index(domain.class_var)
self.openContext(domain)
self.variable_idx = min(max(self.variable_idx, 0),
len(self.varmodel) - 1)
self.groupvar_idx = min(max(self.groupvar_idx, 0),
len(self.groupvarmodel) - 1)
itemmodels.select_row(self.groupvarview, self.groupvar_idx)
itemmodels.select_row(self.varview, self.variable_idx)
self._setup()
def clear(self):
self.plot.clear()
self.varmodel[:] = []
self.groupvarmodel[:] = []
self.variable_idx = -1
self.groupvar_idx = 0
def _setup(self):
self.plot.clear()
varidx = self.variable_idx
self.var = self.cvar = None
if varidx >= 0:
self.var = self.varmodel[varidx]
if self.groupvar_idx > 0:
self.cvar = self.groupvarmodel[self.groupvar_idx]
self.set_left_axis_name()
self.enable_disable_rel_freq()
if self.var is None:
return
if self.cvar:
self.contingencies = \
contingency.get_contingency(self.data, self.var, self.cvar)
self.display_contingency()
else:
self.distributions = \
distribution.get_distribution(self.data, self.var)
self.display_distribution()
def _density_estimator(self):
if self.cont_est_type == OWDistributions.Hist:
def hist(dist):
h, edges = numpy.histogram(dist[0, :],
bins=10,
weights=dist[1, :])
return edges, h
return hist
elif self.cont_est_type == OWDistributions.ASH:
return lambda dist: ash_curve(dist, m=5)
elif self.cont_est_type == OWDistributions.Kernel:
return rect_kernel_curve
def display_distribution(self):
dist = self.distributions
var = self.var
assert len(dist) > 0
self.plot.clear()
bottomaxis = self.plot.getAxis("bottom")
bottomaxis.setLabel(var.name)
self.set_left_axis_name()
if var and var.is_continuous:
bottomaxis.setTicks(None)
curve_est = self._density_estimator()
edges, curve = curve_est(dist)
item = pg.PlotCurveItem()
item.setData(edges,
curve,
antialias=True,
stepMode=True,
fillLevel=0,
brush=QtGui.QBrush(Qt.gray),
pen=QtGui.QColor(Qt.white))
self.plot.addItem(item)
else:
bottomaxis.setTicks([list(enumerate(var.values))])
for i, w in enumerate(dist):
geom = QtCore.QRectF(i - 0.33, 0, 0.66, w)
print(w, list(enumerate(var.values)))
item = DistributionBarItem(geom, [1.0],
[QtGui.QColor(128, 128, 128)])
self.plot.addItem(item)
def _on_relative_freq_changed(self):
self.set_left_axis_name()
if self.cvar and self.cvar.is_discrete:
self.display_contingency()
else:
self.display_distribution()
def display_contingency(self):
"""
Set the contingency to display.
"""
cont = self.contingencies
var, cvar = self.var, self.cvar
assert len(cont) > 0
self.plot.clear()
bottomaxis = self.plot.getAxis("bottom")
bottomaxis.setLabel(var.name)
palette = colorpalette.ColorPaletteGenerator(len(cvar.values))
colors = [palette[i] for i in range(len(cvar.values))]
if var and var.is_continuous:
bottomaxis.setTicks(None)
weights = numpy.array([numpy.sum(W) for _, W in cont])
weights /= numpy.sum(weights)
curve_est = self._density_estimator()
curves = [curve_est(dist) for dist in cont]
curves = [(X, Y * w) for (X, Y), w in zip(curves, weights)]
cum_curves = [curves[0]]
for X, Y in curves[1:]:
cum_curves.append(sum_rect_curve(X, Y, *cum_curves[-1]))
for (X, Y), color in reversed(list(zip(cum_curves, colors))):
item = pg.PlotCurveItem()
pen = QtGui.QPen(QtGui.QBrush(Qt.white), 0.5)
pen.setCosmetic(True)
item.setData(X,
Y,
antialias=True,
stepMode=True,
fillLevel=0,
brush=QtGui.QBrush(color.lighter()),
pen=pen)
self.plot.addItem(item)
# # XXX: sum the individual curves and not the distributions.
# # The conditional distributions might be 'smoother' than
# # the cumulative one
# cum_dist = [cont[0]]
# for dist in cont[1:]:
# cum_dist.append(dist_sum(dist, cum_dist[-1]))
#
# curves = [rect_kernel_curve(dist) for dist in cum_dist]
# colors = [Qt.blue, Qt.red, Qt.magenta]
# for (X, Y), color in reversed(list(zip(curves, colors))):
# item = pg.PlotCurveItem()
# item.setData(X, Y, antialias=True, stepMode=True,
# fillLevel=0, brush=QtGui.QBrush(color))
# item.setPen(QtGui.QPen(color))
# self.plot.addItem(item)
elif var and var.is_discrete:
bottomaxis.setTicks([list(enumerate(var.values))])
cont = numpy.array(cont)
for i, (value, dist) in enumerate(zip(var.values, cont.T)):
dsum = sum(dist)
geom = QtCore.QRectF(i - 0.333, 0, 0.666,
100 if self.relative_freq else dsum)
item = DistributionBarItem(geom, dist / dsum, colors)
self.plot.addItem(item)
def set_left_axis_name(self):
set_label = self.plot.getAxis("left").setLabel
if (self.var and self.var.is_continuous
and self.cont_est_type != OWDistributions.Hist):
set_label("Density")
else:
set_label(["Frequency",
"Relative frequency"][self.cvar is not None
and self.relative_freq])
def enable_disable_rel_freq(self):
self.cb_rel_freq.setDisabled(self.var is None or self.cvar is None
or self.var.is_continuous)
def _on_variable_idx_changed(self):
self.variable_idx = selected_index(self.varview)
self._setup()
def _on_groupvar_idx_changed(self):
self.groupvar_idx = selected_index(self.groupvarview)
self._setup()
def _on_cont_est_type_changed(self):
self.set_left_axis_name()
if self.data is not None:
self._setup()
def onDeleteWidget(self):
self.plot.clear()
super().onDeleteWidget()
class OWDistributions(widget.OWWidget):
name = "Distributions"
description = "Display value distributions of a data feature in a graph."
icon = "icons/Distribution.svg"
priority = 100
inputs = [
InputSignal("Data",
Orange.data.Table,
"set_data",
doc="Set the input data set")
]
settingsHandler = settings.DomainContextHandler(
match_values=settings.DomainContextHandler.MATCH_VALUES_ALL)
#: Selected variable index
variable_idx = settings.ContextSetting(-1)
#: Selected group variable
groupvar_idx = settings.ContextSetting(0)
relative_freq = settings.Setting(False)
disc_cont = settings.Setting(False)
smoothing_index = settings.Setting(5)
show_prob = settings.ContextSetting(0)
graph_name = "plot"
ASH_HIST = 50
bins = [2, 3, 4, 5, 8, 10, 12, 15, 20, 30, 50]
smoothing_facs = list(
reversed([0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 4, 6, 10]))
def __init__(self):
super().__init__()
self.data = None
self.distributions = None
self.contingencies = None
self.var = self.cvar = None
varbox = gui.widgetBox(self.controlArea, "Variable")
self.varmodel = itemmodels.VariableListModel()
self.groupvarmodel = []
self.varview = QtGui.QListView(
selectionMode=QtGui.QListView.SingleSelection)
self.varview.setSizePolicy(QtGui.QSizePolicy.Minimum,
QtGui.QSizePolicy.Expanding)
self.varview.setModel(self.varmodel)
self.varview.setSelectionModel(
itemmodels.ListSingleSelectionModel(self.varmodel))
self.varview.selectionModel().selectionChanged.connect(
self._on_variable_idx_changed)
varbox.layout().addWidget(self.varview)
box = gui.widgetBox(self.controlArea, "Precision")
gui.separator(self.controlArea, 4, 4)
box2 = gui.widgetBox(box, orientation="horizontal")
self.l_smoothing_l = gui.widgetLabel(box2, "Smooth")
gui.hSlider(box2,
self,
"smoothing_index",
minValue=0,
maxValue=len(self.smoothing_facs) - 1,
callback=self._on_set_smoothing,
createLabel=False)
self.l_smoothing_r = gui.widgetLabel(box2, "Precise")
self.cb_disc_cont = gui.checkBox(
gui.indentedBox(box, sep=4),
self,
"disc_cont",
"Bin continuous variables",
callback=self._on_groupvar_idx_changed,
tooltip="Show continuous variables as discrete.")
box = gui.widgetBox(self.controlArea, "Group by")
self.icons = gui.attributeIconDict
self.groupvarview = gui.comboBox(
box,
self,
"groupvar_idx",
callback=self._on_groupvar_idx_changed,
valueType=str,
contentsLength=12)
box2 = gui.indentedBox(box, sep=4)
self.cb_rel_freq = gui.checkBox(
box2,
self,
"relative_freq",
"Show relative frequencies",
callback=self._on_relative_freq_changed,
tooltip=
"Normalize probabilities so that probabilities for each group-by value sum to 1."
)
gui.separator(box2)
self.cb_prob = gui.comboBox(
box2,
self,
"show_prob",
label="Show probabilities",
orientation="horizontal",
callback=self._on_relative_freq_changed,
tooltip=
"Show probabilities for a chosen group-by value (at each point probabilities for all group-by values sum to 1)."
)
self.plotview = pg.PlotWidget(background=None)
self.plotview.setRenderHint(QtGui.QPainter.Antialiasing)
self.mainArea.layout().addWidget(self.plotview)
w = QtGui.QLabel()
w.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Fixed)
self.mainArea.layout().addWidget(w, Qt.AlignCenter)
self.ploti = pg.PlotItem()
self.plot = self.ploti.vb
self.ploti.hideButtons()
self.plotview.setCentralItem(self.ploti)
self.plot_prob = pg.ViewBox()
self.ploti.hideAxis('right')
self.ploti.scene().addItem(self.plot_prob)
self.ploti.getAxis("right").linkToView(self.plot_prob)
self.ploti.getAxis("right").setLabel("Probability")
self.plot_prob.setZValue(10)
self.plot_prob.setXLink(self.ploti)
self.update_views()
self.ploti.vb.sigResized.connect(self.update_views)
self.plot_prob.setRange(yRange=[0, 1])
self.inline_graph_report()
def disable_mouse(plot):
plot.setMouseEnabled(False, False)
plot.setMenuEnabled(False)
disable_mouse(self.plot)
disable_mouse(self.plot_prob)
self.tooltip_items = []
self.plot.scene().installEventFilter(
HelpEventDelegate(self.help_event, self))
pen = QtGui.QPen(self.palette().color(QtGui.QPalette.Text))
for axis in ("left", "bottom"):
self.ploti.getAxis(axis).setPen(pen)
self._legend = LegendItem()
self._legend.setParentItem(self.plot)
self._legend.hide()
self._legend.anchor((1, 0), (1, 0))
def update_views(self):
self.plot_prob.setGeometry(self.plot.sceneBoundingRect())
self.plot_prob.linkedViewChanged(self.plot, self.plot_prob.XAxis)
def set_data(self, data):
self.closeContext()
self.clear()
self.data = data
if self.data is not None:
domain = self.data.domain
self.varmodel[:] = list(domain)
self.groupvarview.clear()
self.groupvarmodel = \
["(None)"] + [var for var in domain if var.is_discrete]
self.groupvarview.addItem("(None)")
for var in self.groupvarmodel[1:]:
self.groupvarview.addItem(self.icons[var], var.name)
if domain.has_discrete_class:
self.groupvar_idx = \
self.groupvarmodel[1:].index(domain.class_var) + 1
self.openContext(domain)
self.variable_idx = min(max(self.variable_idx, 0),
len(self.varmodel) - 1)
self.groupvar_idx = min(max(self.groupvar_idx, 0),
len(self.groupvarmodel) - 1)
itemmodels.select_row(self.varview, self.variable_idx)
self._setup()
def clear(self):
self.plot.clear()
self.plot_prob.clear()
self.varmodel[:] = []
self.groupvarmodel = []
self.variable_idx = -1
self.groupvar_idx = 0
self._legend.clear()
self._legend.hide()
def _setup_smoothing(self):
if not self.disc_cont and self.var and self.var.is_continuous:
self.cb_disc_cont.setText("Bin continuous variables")
self.l_smoothing_l.setText("Smooth")
self.l_smoothing_r.setText("Precise")
else:
self.cb_disc_cont.setText(
"Bin continuous variables into {} bins".format(
self.bins[self.smoothing_index]))
self.l_smoothing_l.setText(" " + str(self.bins[0]))
self.l_smoothing_r.setText(" " + str(self.bins[-1]))
def _setup(self):
self.plot.clear()
self.plot_prob.clear()
self._legend.clear()
self._legend.hide()
varidx = self.variable_idx
self.var = self.cvar = None
if varidx >= 0:
self.var = self.varmodel[varidx]
if self.groupvar_idx > 0:
self.cvar = self.groupvarmodel[self.groupvar_idx]
self.cb_prob.clear()
self.cb_prob.addItem("(None)")
self.cb_prob.addItems(self.cvar.values)
self.cb_prob.addItem("(All)")
self.show_prob = min(max(self.show_prob, 0),
len(self.cvar.values) + 1)
data = self.data
self._setup_smoothing()
if self.var is None:
return
if self.disc_cont:
data = self.data[:,
(self.var, self.cvar) if self.cvar else self.var]
disc = Orange.preprocess.discretize.EqualWidth(
n=self.bins[self.smoothing_index])
data = Orange.preprocess.Discretize(data,
method=disc,
remove_const=False)
self.var = data.domain[0]
self.set_left_axis_name()
self.enable_disable_rel_freq()
if self.cvar:
self.contingencies = \
contingency.get_contingency(data, self.var, self.cvar)
self.display_contingency()
else:
self.distributions = \
distribution.get_distribution(data, self.var)
self.display_distribution()
self.plot.autoRange()
def help_event(self, ev):
in_graph_coor = self.plot.mapSceneToView(ev.scenePos())
ctooltip = []
for vb, item in self.tooltip_items:
if isinstance(item,
pg.PlotCurveItem) and item.mouseShape().contains(
vb.mapSceneToView(ev.scenePos())):
ctooltip.append(item.tooltip)
elif isinstance(
item,
DistributionBarItem) and item.boundingRect().contains(
vb.mapSceneToView(ev.scenePos())):
ctooltip.append(item.tooltip)
if ctooltip:
QToolTip.showText(ev.screenPos(),
"\n\n".join(ctooltip),
widget=self.plotview)
return True
return False
def display_distribution(self):
dist = self.distributions
var = self.var
assert len(dist) > 0
self.plot.clear()
self.plot_prob.clear()
self.ploti.hideAxis('right')
self.tooltip_items = []
bottomaxis = self.ploti.getAxis("bottom")
bottomaxis.setLabel(var.name)
bottomaxis.resizeEvent()
self.set_left_axis_name()
if var and var.is_continuous:
bottomaxis.setTicks(None)
if not len(dist[0]):
return
edges, curve = ash_curve(
dist,
None,
m=OWDistributions.ASH_HIST,
smoothing_factor=self.smoothing_facs[self.smoothing_index])
edges = edges + (edges[1] - edges[0]) / 2
edges = edges[:-1]
item = pg.PlotCurveItem()
pen = QtGui.QPen(QtGui.QBrush(Qt.white), 3)
pen.setCosmetic(True)
item.setData(edges,
curve,
antialias=True,
stepMode=False,
fillLevel=0,
brush=QtGui.QBrush(Qt.gray),
pen=pen)
self.plot.addItem(item)
item.tooltip = "Density"
self.tooltip_items.append((self.plot, item))
else:
bottomaxis.setTicks([list(enumerate(var.values))])
for i, w in enumerate(dist):
geom = QtCore.QRectF(i - 0.33, 0, 0.66, w)
item = DistributionBarItem(geom, [1.0],
[QtGui.QColor(128, 128, 128)])
self.plot.addItem(item)
item.tooltip = "Frequency for %s: %r" % (var.values[i], w)
self.tooltip_items.append((self.plot, item))
def _on_relative_freq_changed(self):
self.set_left_axis_name()
if self.cvar and self.cvar.is_discrete:
self.display_contingency()
else:
self.display_distribution()
self.plot.autoRange()
def display_contingency(self):
"""
Set the contingency to display.
"""
cont = self.contingencies
var, cvar = self.var, self.cvar
assert len(cont) > 0
self.plot.clear()
self.plot_prob.clear()
self._legend.clear()
self.tooltip_items = []
if self.show_prob:
self.ploti.showAxis('right')
else:
self.ploti.hideAxis('right')
bottomaxis = self.ploti.getAxis("bottom")
bottomaxis.setLabel(var.name)
bottomaxis.resizeEvent()
cvar_values = cvar.values
colors = [QtGui.QColor(*col) for col in cvar.colors]
if var and var.is_continuous:
bottomaxis.setTicks(None)
weights, cols, cvar_values, curves = [], [], [], []
for i, dist in enumerate(cont):
v, W = dist
if len(v):
weights.append(numpy.sum(W))
cols.append(colors[i])
cvar_values.append(cvar.values[i])
curves.append(
ash_curve(dist,
cont,
m=OWDistributions.ASH_HIST,
smoothing_factor=self.smoothing_facs[
self.smoothing_index]))
weights = numpy.array(weights)
sumw = numpy.sum(weights)
weights /= sumw
colors = cols
curves = [(X, Y * w) for (X, Y), w in zip(curves, weights)]
ncval = len(cvar_values)
curvesline = [] #from histograms to lines
for (X, Y) in curves:
X = X + (X[1] - X[0]) / 2
X = X[:-1]
X = numpy.array(X)
Y = numpy.array(Y)
curvesline.append((X, Y))
for t in ["fill", "line"]:
for (X, Y), color, w, cval in reversed(
list(zip(curvesline, colors, weights, cvar_values))):
item = pg.PlotCurveItem()
pen = QtGui.QPen(QtGui.QBrush(color), 3)
pen.setCosmetic(True)
color = QtGui.QColor(color)
color.setAlphaF(0.2)
item.setData(X,
Y / (w if self.relative_freq else 1),
antialias=True,
stepMode=False,
fillLevel=0 if t == "fill" else None,
brush=QtGui.QBrush(color),
pen=pen)
self.plot.addItem(item)
if t == "line":
item.tooltip = ("Normalized density " if self.relative_freq else "Density ") \
+ "\n"+ cvar.name + "=" + cval
self.tooltip_items.append((self.plot, item))
if self.show_prob:
M_EST = 5 #for M estimate
all_X = numpy.array(
numpy.unique(numpy.hstack([X for X, _ in curvesline])))
inter_X = numpy.array(
numpy.linspace(all_X[0], all_X[-1],
len(all_X) * 2))
curvesinterp = [
numpy.interp(inter_X, X, Y) for (X, Y) in curvesline
]
sumprob = numpy.sum(curvesinterp, axis=0)
# allcorrection = M_EST/sumw*numpy.sum(sumprob)/len(inter_X)
legal = sumprob > 0.05 * numpy.max(sumprob)
i = len(curvesinterp) + 1
show_all = self.show_prob == i
for Y, color, cval in reversed(
list(zip(curvesinterp, colors, cvar_values))):
i -= 1
if show_all or self.show_prob == i:
item = pg.PlotCurveItem()
pen = QtGui.QPen(QtGui.QBrush(color),
3,
style=QtCore.Qt.DotLine)
pen.setCosmetic(True)
#prob = (Y+allcorrection/ncval)/(sumprob+allcorrection)
prob = Y[legal] / sumprob[legal]
item.setData(inter_X[legal],
prob,
antialias=True,
stepMode=False,
fillLevel=None,
brush=None,
pen=pen)
self.plot_prob.addItem(item)
item.tooltip = "Probability that \n" + cvar.name + "=" + cval
self.tooltip_items.append((self.plot_prob, item))
elif var and var.is_discrete:
bottomaxis.setTicks([list(enumerate(var.values))])
cont = numpy.array(cont)
ncval = len(cvar_values)
maxh = 0 #maximal column height
maxrh = 0 #maximal relative column height
scvar = cont.sum(axis=1)
#a cvar with sum=0 with allways have distribution counts 0,
#therefore we can divide it by anything
scvar[scvar == 0] = 1
for i, (value, dist) in enumerate(zip(var.values, cont.T)):
maxh = max(maxh, max(dist))
maxrh = max(maxrh, max(dist / scvar))
for i, (value, dist) in enumerate(zip(var.values, cont.T)):
dsum = sum(dist)
geom = QtCore.QRectF(i - 0.333, 0, 0.666,
maxrh if self.relative_freq else maxh)
if self.show_prob:
prob = dist / dsum
ci = 1.96 * numpy.sqrt(prob * (1 - prob) / dsum)
else:
ci = None
item = DistributionBarItem(
geom, dist / scvar /
maxrh if self.relative_freq else dist / maxh, colors)
self.plot.addItem(item)
tooltip = "\n".join("%s: %.*f" %
(n, 3 if self.relative_freq else 1, v)
for n, v in zip(
cvar_values, dist /
scvar if self.relative_freq else dist))
item.tooltip = ("Normalized frequency " if self.relative_freq else "Frequency ") \
+ "(" + cvar.name + "=" + value + "):" \
+ "\n" + tooltip
self.tooltip_items.append((self.plot, item))
if self.show_prob:
item.tooltip += "\n\nProbabilities:"
for ic, a in enumerate(dist):
if self.show_prob - 1 != ic and \
self.show_prob - 1 != len(dist):
continue
position = -0.333 + ((ic + 0.5) * 0.666 / len(dist))
if dsum < 1e-6:
continue
prob = a / dsum
if not 1e-6 < prob < 1 - 1e-6:
continue
ci = 1.96 * sqrt(prob * (1 - prob) / dsum)
item.tooltip += "\n%s: %.3f ± %.3f" % (cvar_values[ic],
prob, ci)
mark = pg.ScatterPlotItem()
bar = pg.ErrorBarItem()
pen = QtGui.QPen(QtGui.QBrush(QtGui.QColor(0)), 1)
pen.setCosmetic(True)
bar.setData(x=[i + position],
y=[prob],
bottom=min(numpy.array([ci]), prob),
top=min(numpy.array([ci]), 1 - prob),
beam=numpy.array([0.05]),
brush=QtGui.QColor(1),
pen=pen)
mark.setData([i + position], [prob],
antialias=True,
symbol="o",
fillLevel=None,
pxMode=True,
size=10,
brush=QtGui.QColor(colors[ic]),
pen=pen)
self.plot_prob.addItem(bar)
self.plot_prob.addItem(mark)
for color, name in zip(colors, cvar_values):
self._legend.addItem(
ScatterPlotItem(pen=color, brush=color, size=10, shape="s"),
escape(name))
self._legend.show()
def set_left_axis_name(self):
leftaxis = self.ploti.getAxis("left")
set_label = leftaxis.setLabel
if self.var and self.var.is_continuous:
set_label(["Density", "Relative density"][self.cvar is not None
and self.relative_freq])
else:
set_label(["Frequency",
"Relative frequency"][self.cvar is not None
and self.relative_freq])
leftaxis.resizeEvent()
def enable_disable_rel_freq(self):
self.cb_prob.setDisabled(self.var is None or self.cvar is None)
self.cb_rel_freq.setDisabled(self.var is None or self.cvar is None)
def _on_variable_idx_changed(self):
self.variable_idx = selected_index(self.varview)
self._setup()
def _on_groupvar_idx_changed(self):
self._setup()
def _on_set_smoothing(self):
self._setup()
def onDeleteWidget(self):
self.plot.clear()
super().onDeleteWidget()
def get_widget_name_extension(self):
if self.variable_idx >= 0:
return self.varmodel[self.variable_idx]
def send_report(self):
if self.variable_idx < 0:
return
self.report_plot()
text = "Distribution of '{}'".format(self.varmodel[self.variable_idx])
if self.groupvar_idx:
group_var = self.groupvarmodel[self.groupvar_idx]
prob = self.cb_prob
indiv_probs = 0 < prob.currentIndex() < prob.count() - 1
if not indiv_probs or self.relative_freq:
text += " grouped by '{}'".format(group_var)
if self.relative_freq:
text += " (relative frequencies)"
if indiv_probs:
text += "; probabilites for '{}={}'".format(
group_var, prob.currentText())
self.report_caption(text)
class OWDiscretize(widget.OWWidget):
name = "Discretize"
description = "Discretize the continuous data features."
icon = "icons/Discretize.svg"
inputs = [InputSignal("Data", Orange.data.Table, "set_data",
doc="Input data table")]
outputs = [OutputSignal("Data", Orange.data.Table,
doc="Table with discretized features")]
settingsHandler = settings.DomainContextHandler()
saved_var_states = settings.ContextSetting({})
default_method = settings.Setting(2)
default_k = settings.Setting(3)
autosend = settings.Setting(True)
#: Discretization methods
Default, Leave, MDL, EqualFreq, EqualWidth, Remove, Custom = range(7)
want_main_area = False
def __init__(self):
super().__init__()
#: input data
self.data = None
#: Current variable discretization state
self.var_state = {}
#: Saved variable discretization settings (context setting)
self.saved_var_states = {}
self.method = 0
self.k = 5
box = gui.widgetBox(
self.controlArea, self.tr("Default Discretization"))
self.default_bbox = rbox = gui.radioButtons(
box, self, "default_method", callback=self._default_disc_changed)
options = [
self.tr("Default"),
self.tr("Leave continuous"),
self.tr("Entropy-MDL discretization"),
self.tr("Equal-frequency discretization"),
self.tr("Equal-width discretization"),
self.tr("Remove continuous attributes")
]
for opt in options[1:5]:
gui.appendRadioButton(rbox, opt)
s = gui.hSlider(gui.indentedBox(rbox),
self, "default_k", minValue=2, maxValue=10,
label="Num. of intervals:",
callback=self._default_disc_changed)
s.setTracking(False)
gui.appendRadioButton(rbox, options[-1])
vlayout = QHBoxLayout()
box = gui.widgetBox(
self.controlArea, "Individual Attribute Settings",
orientation=vlayout, spacing=8
)
# List view with all attributes
self.varview = QListView(selectionMode=QListView.ExtendedSelection)
self.varview.setItemDelegate(DiscDelegate())
self.varmodel = itemmodels.VariableListModel()
self.varview.setModel(self.varmodel)
self.varview.selectionModel().selectionChanged.connect(
self._var_selection_changed
)
vlayout.addWidget(self.varview)
# Controls for individual attr settings
self.bbox = controlbox = gui.radioButtons(
box, self, "method", callback=self._disc_method_changed
)
vlayout.addWidget(controlbox)
for opt in options[:5]:
gui.appendRadioButton(controlbox, opt)
s = gui.hSlider(gui.indentedBox(controlbox),
self, "k", minValue=2, maxValue=10,
label="Num. of intervals:",
callback=self._disc_method_changed)
s.setTracking(False)
gui.appendRadioButton(controlbox, "Remove attribute")
gui.rubber(controlbox)
controlbox.setEnabled(False)
self.controlbox = controlbox
gui.auto_commit(self.controlArea, self, "autosend", "Apply",
orientation="horizontal",
checkbox_label="Send data after every change")
def set_data(self, data):
self.closeContext()
self.data = data
if self.data is not None:
self._initialize(data)
self.openContext(data)
# Restore the per variable discretization settings
self._restore(self.saved_var_states)
# Complete the induction of cut points
self._update_points()
else:
self._clear()
self.unconditional_commit()
def _initialize(self, data):
# Initialize the default variable states for new data.
self.class_var = data.domain.class_var
cvars = [var for var in data.domain if var.is_continuous]
self.varmodel[:] = cvars
class_var = data.domain.class_var
has_disc_class = data.domain.has_discrete_class
self.default_bbox.buttons[self.MDL - 1].setEnabled(has_disc_class)
self.bbox.buttons[self.MDL].setEnabled(has_disc_class)
# If the newly disabled MDL button is checked then change it
if not has_disc_class and self.default_method == self.MDL - 1:
self.default_method = 0
if not has_disc_class and self.method == self.MDL:
self.method = 0
# Reset (initialize) the variable discretization states.
self._reset()
def _restore(self, saved_state):
# Restore variable states from a saved_state dictionary.
def_method = self._current_default_method()
for i, var in enumerate(self.varmodel):
key = variable_key(var)
if key in saved_state:
state = saved_state[key]
if isinstance(state.method, Default):
state = DState(Default(def_method), None, None)
self._set_var_state(i, state)
def _reset(self):
# restore the individual variable settings back to defaults.
def_method = self._current_default_method()
self.var_state = {}
for i in range(len(self.varmodel)):
state = DState(Default(def_method), None, None)
self._set_var_state(i, state)
def _set_var_state(self, index, state):
# set the state of variable at `index` to `state`.
self.var_state[index] = state
self.varmodel.setData(self.varmodel.index(index), state, Qt.UserRole)
def _clear(self):
self.data = None
self.varmodel[:] = []
self.var_state = {}
self.saved_var_states = {}
self.default_bbox.buttons[self.MDL - 1].setEnabled(True)
self.bbox.buttons[self.MDL].setEnabled(True)
def _update_points(self):
"""
Update the induced cut points.
"""
def induce_cuts(method, data, var):
dvar = _dispatch[type(method)](method, data, var)
if dvar is None:
# removed
return [], None
elif dvar is var:
# no transformation took place
return None, var
elif is_discretized(dvar):
return dvar.compute_value.points, dvar
else:
assert False
for i, var in enumerate(self.varmodel):
state = self.var_state[i]
if state.points is None and state.disc_var is None:
points, dvar = induce_cuts(state.method, self.data, var)
new_state = state._replace(points=points, disc_var=dvar)
self._set_var_state(i, new_state)
self.commit()
def _method_index(self, method):
return METHODS.index((type(method), ))
def _current_default_method(self):
method = self.default_method + 1
k = self.default_k
if method == OWDiscretize.Leave:
def_method = Leave()
elif method == OWDiscretize.MDL:
def_method = MDL()
elif method == OWDiscretize.EqualFreq:
def_method = EqualFreq(k)
elif method == OWDiscretize.EqualWidth:
def_method = EqualWidth(k)
elif method == OWDiscretize.Remove:
def_method = Remove()
else:
assert False
return def_method
def _current_method(self):
if self.method == OWDiscretize.Default:
method = Default(self._current_default_method())
elif self.method == OWDiscretize.Leave:
method = Leave()
elif self.method == OWDiscretize.MDL:
method = MDL()
elif self.method == OWDiscretize.EqualFreq:
method = EqualFreq(self.k)
elif self.method == OWDiscretize.EqualWidth:
method = EqualWidth(self.k)
elif self.method == OWDiscretize.Remove:
method = Remove()
elif self.method == OWDiscretize.Custom:
method = Custom(self.cutpoints)
else:
assert False
return method
def _default_disc_changed(self):
method = self._current_default_method()
state = DState(Default(method), None, None)
for i, _ in enumerate(self.varmodel):
if isinstance(self.var_state[i].method, Default):
self._set_var_state(i, state)
self._update_points()
def _disc_method_changed(self):
indices = self.selected_indices()
method = self._current_method()
state = DState(method, None, None)
for idx in indices:
self._set_var_state(idx, state)
self._update_points()
def _var_selection_changed(self, *args):
indices = self.selected_indices()
# set of all methods for the current selection
methods = [self.var_state[i].method for i in indices]
mset = set(methods)
self.controlbox.setEnabled(len(mset) > 0)
if len(mset) == 1:
method = mset.pop()
self.method = self._method_index(method)
if isinstance(method, (EqualFreq, EqualWidth)):
self.k = method.k
elif isinstance(method, Custom):
self.cutpoints = method.points
else:
# deselect the current button
self.method = -1
bg = self.controlbox.group
button_group_reset(bg)
def selected_indices(self):
rows = self.varview.selectionModel().selectedRows()
return [index.row() for index in rows]
def discretized_var(self, source):
index = list(self.varmodel).index(source)
state = self.var_state[index]
if state.disc_var is None:
return None
elif state.disc_var is source:
return source
elif state.points == []:
return None
else:
return state.disc_var
def discretized_domain(self):
"""
Return the current effective discretized domain.
"""
if self.data is None:
return None
def disc_var(source):
if source and source.is_continuous:
return self.discretized_var(source)
else:
return source
attributes = [disc_var(v) for v in self.data.domain.attributes]
attributes = [v for v in attributes if v is not None]
class_var = disc_var(self.data.domain.class_var)
domain = Orange.data.Domain(
attributes, class_var,
metas=self.data.domain.metas
)
return domain
def commit(self):
output = None
if self.data is not None:
domain = self.discretized_domain()
output = self.data.from_table(domain, self.data)
self.send("Data", output)
def storeSpecificSettings(self):
super().storeSpecificSettings()
self.saved_var_states = {
variable_key(var):
self.var_state[i]._replace(points=None, disc_var=None)
for i, var in enumerate(self.varmodel)
}