def graphicsscene_help_event(scene: QGraphicsScene,
event: QGraphicsSceneHelpEvent) -> None:
"""
Send the help event to every graphics item that is under the `event`
scene position.
"""
widget = event.widget()
if widget is not None and isinstance(widget.parentWidget(), QGraphicsView):
view = widget.parentWidget()
deviceTransform = view.viewportTransform()
else:
deviceTransform = QTransform()
items = scene.items(
event.scenePos(),
Qt.IntersectsItemShape,
Qt.DescendingOrder,
deviceTransform,
)
text = ""
event.setAccepted(False)
for item in items:
scene.sendEvent(item, event)
if event.isAccepted():
return
elif item.toolTip():
text = item.toolTip()
break
QToolTip.showText(event.screenPos(), text, event.widget())
event.setAccepted(bool(text))
class OWBoxPlot(widget.OWWidget):
name = "Box Plot"
description = "Visualize the distribution of feature values in a box plot."
icon = "icons/BoxPlot.svg"
priority = 100
keywords = ["whisker"]
class Inputs:
data = Input("Data", Orange.data.Table)
class Outputs:
selected_data = Output("Selected Data",
Orange.data.Table,
default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Orange.data.Table)
class Warning(widget.OWWidget.Warning):
no_vars = widget.Msg(
"Data contains no categorical or numeric variables")
buttons_area_orientation = None
#: Comparison types for continuous variables
CompareNone, CompareMedians, CompareMeans = 0, 1, 2
settingsHandler = DomainContextHandler()
# If this was a list, context handler would try to match its elements to
# variable names!
selection = ContextSetting((), schema_only=True)
attribute = ContextSetting(None)
order_by_importance = Setting(False)
order_grouping_by_importance = Setting(False)
group_var = ContextSetting(None)
show_annotations = Setting(True)
compare = Setting(CompareMeans)
stattest = Setting(0)
sig_threshold = Setting(0.05)
stretched = Setting(True)
show_labels = Setting(True)
sort_freqs = Setting(False)
_sorting_criteria_attrs = {
CompareNone: "",
CompareMedians: "median",
CompareMeans: "mean"
}
_pen_axis_tick = QPen(Qt.white, 5)
_pen_axis = QPen(Qt.darkGray, 3)
_pen_median = QPen(QBrush(QColor(0xff, 0xff, 0x00)), 2)
_pen_paramet = QPen(QBrush(QColor(0x33, 0x00, 0xff)), 2)
_pen_dotted = QPen(QBrush(QColor(0x33, 0x00, 0xff)), 1)
_pen_dotted.setStyle(Qt.DotLine)
_post_line_pen = QPen(Qt.lightGray, 2)
_post_grp_pen = QPen(Qt.lightGray, 4)
for pen in (_pen_paramet, _pen_median, _pen_dotted, _pen_axis,
_pen_axis_tick, _post_line_pen, _post_grp_pen):
pen.setCosmetic(True)
pen.setCapStyle(Qt.RoundCap)
pen.setJoinStyle(Qt.RoundJoin)
_pen_axis_tick.setCapStyle(Qt.FlatCap)
_box_brush = QBrush(QColor(0x33, 0x88, 0xff, 0xc0))
_attr_brush = QBrush(QColor(0x33, 0x00, 0xff))
graph_name = "box_scene"
def __init__(self):
super().__init__()
self._axis_font = QFont()
self._axis_font.setPixelSize(12)
self._label_font = QFont()
self._label_font.setPixelSize(11)
self.dataset = None
self.stats = []
self.dist = self.conts = None
self.posthoc_lines = []
self.label_txts = self.mean_labels = self.boxes = self.labels = \
self.label_txts_all = self.attr_labels = self.order = []
self.scale_x = 1
self.scene_min_x = self.scene_max_x = self.scene_width = 0
self.label_width = 0
self.attrs = VariableListModel()
sorted_model = SortProxyModel(sortRole=Qt.UserRole)
sorted_model.setSourceModel(self.attrs)
sorted_model.sort(0)
box = gui.vBox(self.controlArea, "Variable")
view = self.attr_list = ListViewSearch()
view.setModel(sorted_model)
view.setSelectionMode(view.SingleSelection)
view.selectionModel().selectionChanged.connect(self.attr_changed)
view.setMinimumSize(QSize(30, 30))
# Any other policy than Ignored will let the QListBox's scrollbar
# set the minimal height (see the penultimate paragraph of
# http://doc.qt.io/qt-4.8/qabstractscrollarea.html#addScrollBarWidget)
view.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Ignored)
box.layout().addWidget(view)
gui.checkBox(box,
self,
"order_by_importance",
"Order by relevance to subgroups",
tooltip="Order by 𝜒² or ANOVA over the subgroups",
callback=self.apply_attr_sorting)
self.group_vars = VariableListModel(placeholder="None")
sorted_model = SortProxyModel(sortRole=Qt.UserRole)
sorted_model.setSourceModel(self.group_vars)
sorted_model.sort(0)
box = gui.vBox(self.controlArea, "Subgroups")
view = self.group_list = ListViewSearch()
view.setModel(sorted_model)
view.selectionModel().selectionChanged.connect(self.grouping_changed)
view.setMinimumSize(QSize(30, 30))
# See the comment above
view.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Ignored)
box.layout().addWidget(view)
gui.checkBox(box,
self,
"order_grouping_by_importance",
"Order by relevance to variable",
tooltip="Order by 𝜒² or ANOVA over the variable values",
callback=self.apply_group_sorting)
# TODO: move Compare median/mean to grouping box
# The vertical size policy is needed to let only the list views expand
self.display_box = gui.vBox(self.controlArea,
"Display",
sizePolicy=(QSizePolicy.Minimum,
QSizePolicy.Maximum))
gui.checkBox(self.display_box,
self,
"show_annotations",
"Annotate",
callback=self.update_graph)
self.compare_rb = gui.radioButtonsInBox(
self.display_box,
self,
'compare',
btnLabels=["No comparison", "Compare medians", "Compare means"],
callback=self.update_graph)
# The vertical size policy is needed to let only the list views expand
self.stretching_box = box = gui.vBox(self.controlArea,
box="Display",
sizePolicy=(QSizePolicy.Minimum,
QSizePolicy.Fixed))
self.stretching_box.sizeHint = self.display_box.sizeHint
gui.checkBox(box,
self,
'stretched',
"Stretch bars",
callback=self.update_graph,
stateWhenDisabled=False)
gui.checkBox(box,
self,
'show_labels',
"Show box labels",
callback=self.update_graph)
self.sort_cb = gui.checkBox(box,
self,
'sort_freqs',
"Sort by subgroup frequencies",
callback=self.update_graph,
stateWhenDisabled=False)
gui.vBox(self.mainArea)
self.box_scene = QGraphicsScene(self)
self.box_scene.selectionChanged.connect(self.on_selection_changed)
self.box_view = QGraphicsView(self.box_scene)
self.box_view.setRenderHints(QPainter.Antialiasing
| QPainter.TextAntialiasing
| QPainter.SmoothPixmapTransform)
self.box_view.viewport().installEventFilter(self)
self.mainArea.layout().addWidget(self.box_view)
self.stat_test = ""
self.mainArea.setMinimumWidth(300)
self.update_box_visibilities()
def sizeHint(self):
return QSize(900, 500)
def eventFilter(self, obj, event):
if obj is self.box_view.viewport() and \
event.type() == QEvent.Resize:
self.update_graph()
return super().eventFilter(obj, event)
@property
def show_stretched(self):
return self.stretched and self.group_var is not self.attribute
def reset_attrs(self):
domain = self.dataset.domain
self.attrs[:] = [
var for var in chain(domain.class_vars, domain.metas,
domain.attributes)
if var.is_primitive() and not var.attributes.get("hidden", False)
]
def reset_groups(self):
domain = self.dataset.domain
self.group_vars[:] = [None] + [
var for var in chain(domain.class_vars, domain.metas,
domain.attributes)
if var.is_discrete and not var.attributes.get("hidden", False)
]
@Inputs.data
def set_data(self, dataset):
self.closeContext()
self._reset_all_data()
if dataset and not (len(dataset.domain.variables)
or any(var.is_primitive()
for var in dataset.domain.metas)):
self.Warning.no_vars()
dataset = None
self.dataset = dataset
if dataset:
self.reset_attrs()
self.reset_groups()
self._select_default_variables()
self.openContext(self.dataset)
self._set_list_view_selections()
self.compute_box_data()
self.apply_attr_sorting()
self.apply_group_sorting()
self.update_graph()
self.select_box_items()
self.update_box_visibilities()
self.commit()
def _reset_all_data(self):
self.clear_scene()
self.Warning.no_vars.clear()
self.stats = []
self.dist = self.conts = None
self.group_var = None
self.attribute = None
self.stat_test = ""
self.attrs[:] = []
self.group_vars[:] = [None]
self.selection = ()
def _select_default_variables(self):
# visualize first non-class variable, group by class (if present)
domain = self.dataset.domain
if len(self.attrs) > len(domain.class_vars):
self.attribute = self.attrs[len(domain.class_vars)]
elif self.attrs:
self.attribute = self.attrs[0]
if domain.class_var and domain.class_var.is_discrete:
self.group_var = domain.class_var
def _set_list_view_selections(self):
for view, var, callback in ((self.attr_list, self.attribute,
self.attr_changed),
(self.group_list, self.group_var,
self.grouping_changed)):
src_model = view.model().sourceModel()
if var not in src_model:
continue
sel_model = view.selectionModel()
sel_model.selectionChanged.disconnect(callback)
row = src_model.indexOf(var)
index = view.model().index(row, 0)
sel_model.select(index, sel_model.ClearAndSelect)
self._ensure_selection_visible(view)
sel_model.selectionChanged.connect(callback)
def apply_attr_sorting(self):
def compute_score(attr):
# This function and the one in apply_group_sorting are similar, but
# different in too many details, so they are kept as separate
# functions.
# If you discover a bug in this function, check the other one, too.
if attr is group_var:
return 3
if attr.is_continuous:
# One-way ANOVA
col = data.get_column_view(attr)[0].astype(float)
groups = (col[group_col == i] for i in range(n_groups))
groups = (col[~np.isnan(col)] for col in groups)
groups = [group for group in groups if len(group)]
p = f_oneway(*groups)[1] if len(groups) > 1 else 2
else:
p = self._chi_square(group_var, attr)[1]
if math.isnan(p):
return 2
return p
data = self.dataset
if data is None:
return
domain = data.domain
group_var = self.group_var
if self.order_by_importance and group_var is not None:
n_groups = len(group_var.values)
group_col = data.get_column_view(group_var)[0] if \
domain.has_continuous_attributes(
include_class=True, include_metas=True) else None
self._sort_list(self.attrs, self.attr_list, compute_score)
else:
self._sort_list(self.attrs, self.attr_list, None)
def apply_group_sorting(self):
def compute_stat(group):
# This function and the one in apply_attr_sorting are similar, but
# different in too many details, so they are kept as separate
# functions.
# If you discover a bug in this function, check the other one, too.
if group is attr:
return 3
if group is None:
return -1
if attr.is_continuous:
group_col = data.get_column_view(group)[0].astype(float)
groups = (attr_col[group_col == i]
for i in range(len(group.values)))
groups = (col[~np.isnan(col)] for col in groups)
groups = [group for group in groups if len(group)]
p = f_oneway(*groups)[1] if len(groups) > 1 else 2
else:
p = self._chi_square(group, attr)[1]
if math.isnan(p):
return 2
return p
data = self.dataset
if data is None:
return
attr = self.attribute
if self.order_grouping_by_importance:
if attr.is_continuous:
attr_col = data.get_column_view(attr)[0].astype(float)
self._sort_list(self.group_vars, self.group_list, compute_stat)
else:
self._sort_list(self.group_vars, self.group_list, None)
def _sort_list(self, source_model, view, key=None):
if key is None:
c = count()
def key(_): # pylint: disable=function-redefined
return next(c)
for i, attr in enumerate(source_model):
source_model.setData(source_model.index(i), key(attr), Qt.UserRole)
self._ensure_selection_visible(view)
@staticmethod
def _ensure_selection_visible(view):
selection = view.selectedIndexes()
if len(selection) == 1:
view.scrollTo(selection[0])
def _chi_square(self, group_var, attr):
# Chi-square with the given distribution into groups
if not attr.values or not group_var.values:
return 0, 2, 0
observed = np.array(
contingency.get_contingency(self.dataset, group_var, attr))
observed = observed[observed.sum(axis=1) != 0, :]
observed = observed[:, observed.sum(axis=0) != 0]
if min(observed.shape) < 2:
return 0, 2, 0
return chi2_contingency(observed)[:3]
def grouping_changed(self, selected):
if not selected:
return # should never come here
self.group_var = selected.indexes()[0].data(gui.TableVariable)
self._variables_changed(self.apply_attr_sorting)
def attr_changed(self, selected):
if not selected:
return # should never come here
self.attribute = selected.indexes()[0].data(gui.TableVariable)
self._variables_changed(self.apply_group_sorting)
def _variables_changed(self, sorting):
self.selection = ()
self.compute_box_data()
sorting()
self.update_graph()
self.update_box_visibilities()
self.commit()
def update_graph(self):
pending_selection = self.selection
self.box_scene.selectionChanged.disconnect(self.on_selection_changed)
try: # not for exceptions, just to reconnect after all possible paths
self.clear_scene()
if self.dataset is None or self.attribute is None:
return
if self.attribute.is_continuous:
self._display_changed_cont()
else:
self._display_changed_disc()
self.selection = pending_selection
self.draw_stat()
self.select_box_items()
if self.attribute.is_continuous:
heights = 90 if self.show_annotations else 60
self.box_view.centerOn(
self.scene_min_x + self.scene_width / 2,
-30 - len(self.stats) * heights / 2 + 45)
else:
self.box_view.centerOn(self.scene_width / 2,
-30 - len(self.boxes) * 40 / 2 + 45)
finally:
self.box_scene.selectionChanged.connect(self.on_selection_changed)
def select_box_items(self):
selection = set(self.selection)
for box in self.box_scene.items():
if isinstance(box, FilterGraphicsRectItem):
box.setSelected(box.data_range in selection)
def compute_box_data(self):
attr = self.attribute
if not attr:
return
dataset = self.dataset
if dataset is None \
or not attr.is_continuous and not attr.values \
or self.group_var and not self.group_var.values:
self.stats = []
self.dist = self.conts = None
return
if self.group_var:
self.dist = None
self.conts = contingency.get_contingency(dataset, attr,
self.group_var)
missing_val_str = f"missing '{self.group_var.name}'"
group_var_labels = self.group_var.values + ("", )
if self.attribute.is_continuous:
stats, label_texts = [], []
for cont, value in zip(self.conts.array_with_unknowns,
group_var_labels):
if np.sum(cont[1]):
stats.append(BoxData(cont, value))
label_texts.append(value or missing_val_str)
self.stats = stats
self.label_txts_all = label_texts
else:
self.label_txts_all = [
v or missing_val_str for v, c in zip(
group_var_labels, self.conts.array_with_unknowns)
if np.sum(c) > 0
]
else:
self.dist = distribution.get_distribution(dataset, attr)
self.conts = None
if self.attribute.is_continuous:
self.stats = [BoxData(self.dist, None)]
self.label_txts_all = [""]
self.label_txts = [
txts for stat, txts in zip(self.stats, self.label_txts_all)
if stat.n > 0
]
self.stats = [stat for stat in self.stats if stat.n > 0]
def update_box_visibilities(self):
self.controls.stretched.setDisabled(self.group_var is self.attribute)
if not self.attribute:
self.stretching_box.hide()
self.display_box.hide()
elif self.attribute.is_continuous:
self.stretching_box.hide()
self.display_box.show()
self.compare_rb.setEnabled(self.group_var is not None)
else:
self.stretching_box.show()
self.display_box.hide()
self.sort_cb.setEnabled(self.group_var is not None)
def clear_scene(self):
self.box_scene.clear()
self.box_view.viewport().update()
self.attr_labels = []
self.labels = []
self.boxes = []
self.mean_labels = []
self.posthoc_lines = []
def _display_changed_cont(self):
self.mean_labels = [
self.mean_label(stat, self.attribute, lab)
for stat, lab in zip(self.stats, self.label_txts)
]
self.draw_axis()
self.boxes = [self.box_group(stat) for stat in self.stats]
self.labels = [
self.label_group(stat, self.attribute, mean_lab)
for stat, mean_lab in zip(self.stats, self.mean_labels)
]
self.attr_labels = [
QGraphicsSimpleTextItem(lab) for lab in self.label_txts
]
for it in chain(self.labels, self.attr_labels):
self.box_scene.addItem(it)
self.order = list(range(len(self.stats)))
criterion = self._sorting_criteria_attrs[self.compare]
if criterion:
vals = [getattr(stat, criterion) for stat in self.stats]
overmax = max((val for val in vals if val is not None), default=0) \
+ 1
vals = [val if val is not None else overmax for val in vals]
self.order = sorted(self.order, key=vals.__getitem__)
heights = 90 if self.show_annotations else 60
for row, box_index in enumerate(self.order):
y = (-len(self.stats) + row) * heights + 10
for item in self.boxes[box_index]:
self.box_scene.addItem(item)
item.setY(y)
labels = self.labels[box_index]
if self.show_annotations:
labels.show()
labels.setY(y)
else:
labels.hide()
label = self.attr_labels[box_index]
label.setY(y - 15 - label.boundingRect().height())
if self.show_annotations:
label.hide()
else:
stat = self.stats[box_index]
if self.compare == OWBoxPlot.CompareMedians and \
stat.median is not None:
pos = stat.median + 5 / self.scale_x
elif self.compare == OWBoxPlot.CompareMeans or stat.q25 is None:
pos = stat.mean + 5 / self.scale_x
else:
pos = stat.q25
label.setX(pos * self.scale_x)
label.show()
r = QRectF(self.scene_min_x, -30 - len(self.stats) * heights,
self.scene_width,
len(self.stats) * heights + 90)
self.box_scene.setSceneRect(r)
self._compute_tests_cont()
self._show_posthoc()
def _display_changed_disc(self):
self.clear_scene()
self.attr_labels = [
QGraphicsSimpleTextItem(lab) for lab in self.label_txts_all
]
if not self.show_stretched:
if self.group_var:
self.labels = [
QGraphicsTextItem("{}".format(int(sum(cont))))
for cont in self.conts.array_with_unknowns
if np.sum(cont) > 0
]
else:
self.labels = [QGraphicsTextItem(str(int(sum(self.dist))))]
self.order = list(range(len(self.attr_labels)))
self.draw_axis_disc()
if self.group_var:
conts = self.conts.array_with_unknowns
self.boxes = [
self.strudel(cont, val)
for cont, val in zip(conts, self.group_var.values + ("", ))
if np.sum(cont) > 0
]
sums_ = np.sum(conts, axis=1)
sums_ = sums_[sums_ > 0] # only bars with sum > 0 are shown
if self.sort_freqs:
# pylint: disable=invalid-unary-operand-type
self.order = sorted(self.order, key=(-sums_).__getitem__)
else:
conts = self.dist.array_with_unknowns
self.boxes = [self.strudel(conts)]
sums_ = [np.sum(conts)]
for row, box_index in enumerate(self.order):
y = (-len(self.boxes) + row) * 40 + 10
box = self.boxes[box_index]
bars, labels = box[::2], box[1::2]
self.__draw_group_labels(y, box_index)
if not self.show_stretched:
self.__draw_row_counts(y, self.labels[box_index],
sums_[box_index])
if self.show_labels and self.attribute is not self.group_var:
self.__draw_bar_labels(y, bars, labels)
self.__draw_bars(y, bars)
self.box_scene.setSceneRect(-self.label_width - 5,
-30 - len(self.boxes) * 40,
self.scene_width,
len(self.boxes * 40) + 90)
self._compute_tests_disc()
def __draw_group_labels(self, y, row):
"""Draw group labels
Parameters
----------
y: int
vertical offset of bars
row: int
row index
"""
label = self.attr_labels[row]
b = label.boundingRect()
label.setPos(-b.width() - 10, y - b.height() / 2)
self.box_scene.addItem(label)
def __draw_row_counts(self, y, label, row_sum_):
"""Draw row counts
Parameters
----------
y: int
vertical offset of bars
label: QGraphicsSimpleTextItem
Label for group
row_sum_: int
Sum for the group
"""
assert not self.attribute.is_continuous
b = label.boundingRect()
right = self.scale_x * row_sum_
label.setPos(right + 10, y - b.height() / 2)
self.box_scene.addItem(label)
def __draw_bar_labels(self, y, bars, labels):
"""Draw bar labels
Parameters
----------
y: int
vertical offset of bars
bars: List[FilterGraphicsRectItem]
list of bars being drawn
labels: List[QGraphicsTextItem]
list of labels for corresponding bars
"""
for text_item, bar_part in zip(labels, bars):
label = self.Label(text_item.toPlainText())
label.setPos(bar_part.boundingRect().x(),
y - label.boundingRect().height() - 8)
label.setMaxWidth(bar_part.boundingRect().width())
self.box_scene.addItem(label)
def __draw_bars(self, y, bars):
"""Draw bars
Parameters
----------
y: int
vertical offset of bars
bars: List[FilterGraphicsRectItem]
list of bars to draw
"""
for item in bars:
item.setPos(0, y)
self.box_scene.addItem(item)
# noinspection PyPep8Naming
def _compute_tests_cont(self):
# The t-test and ANOVA are implemented here since they efficiently use
# the widget-specific data in self.stats.
# The non-parametric tests can't do this, so we use statistics.tests
# pylint: disable=comparison-with-itself
def stat_ttest():
d1, d2 = self.stats
if d1.n < 2 or d2.n < 2:
return np.nan, np.nan
pooled_var = d1.var / d1.n + d2.var / d2.n
# pylint: disable=comparison-with-itself
if pooled_var == 0 or np.isnan(pooled_var):
return np.nan, np.nan
df = pooled_var ** 2 / \
((d1.var / d1.n) ** 2 / (d1.n - 1) +
(d2.var / d2.n) ** 2 / (d2.n - 1))
t = abs(d1.mean - d2.mean) / math.sqrt(pooled_var)
p = 2 * (1 - scipy.special.stdtr(df, t))
return t, p
# TODO: Check this function
# noinspection PyPep8Naming
def stat_ANOVA():
if any(stat.n == 0 for stat in self.stats):
return np.nan, np.nan
n = sum(stat.n for stat in self.stats)
grand_avg = sum(stat.n * stat.mean for stat in self.stats) / n
var_between = sum(stat.n * (stat.mean - grand_avg)**2
for stat in self.stats)
df_between = len(self.stats) - 1
var_within = sum(stat.n * stat.var for stat in self.stats)
df_within = n - len(self.stats)
if var_within == 0 or df_within == 0 or df_between == 0:
return np.nan, np.nan
F = (var_between / df_between) / (var_within / df_within)
p = 1 - scipy.special.fdtr(df_between, df_within, F)
return F, p
n = len(self.dataset)
if self.compare == OWBoxPlot.CompareNone or len(self.stats) < 2:
t = ""
elif any(s.n <= 1 for s in self.stats):
t = "At least one group has just one instance, " \
"cannot compute significance"
elif len(self.stats) == 2:
if self.compare == OWBoxPlot.CompareMedians:
t = ""
# z, p = tests.wilcoxon_rank_sum(
# self.stats[0].dist, self.stats[1].dist)
# t = "Mann-Whitney's z: %.1f (p=%.3f)" % (z, p)
else:
t, p = stat_ttest()
t = "" if np.isnan(
t) else f"Student's t: {t:.3f} (p={p:.3f}, N={n})"
else:
if self.compare == OWBoxPlot.CompareMedians:
t = ""
# U, p = -1, -1
# t = "Kruskal Wallis's U: %.1f (p=%.3f)" % (U, p)
else:
F, p = stat_ANOVA()
t = "" if np.isnan(F) else f"ANOVA: {F:.3f} (p={p:.3f}, N={n})"
self.stat_test = t
def _compute_tests_disc(self):
if self.group_var is None or self.attribute is None:
self.stat_test = ""
else:
chi, p, dof = self._chi_square(self.group_var, self.attribute)
if np.isnan(p):
self.stat_test = ""
else:
self.stat_test = f"χ²: {chi:.2f} (p={p:.3f}, dof={dof})"
def mean_label(self, stat, attr, val_name):
label = QGraphicsItemGroup()
t = QGraphicsSimpleTextItem(attr.str_val(stat.mean), label)
t.setFont(self._label_font)
bbox = t.boundingRect()
w2, h = bbox.width() / 2, bbox.height()
t.setPos(-w2, -h)
tpm = QGraphicsSimpleTextItem(
" \u00b1 " + "%.*f" % (attr.number_of_decimals + 1, stat.dev),
label)
tpm.setFont(self._label_font)
tpm.setPos(w2, -h)
if val_name:
vnm = QGraphicsSimpleTextItem(val_name + ": ", label)
vnm.setFont(self._label_font)
vnm.setBrush(self._attr_brush)
vb = vnm.boundingRect()
label.min_x = -w2 - vb.width()
vnm.setPos(label.min_x, -h)
else:
label.min_x = -w2
return label
def draw_axis(self):
"""Draw the horizontal axis and sets self.scale_x"""
misssing_stats = not self.stats
stats = self.stats or [BoxData(np.array([[0.], [1.]]), self.attribute)]
mean_labels = self.mean_labels or [
self.mean_label(stats[0], self.attribute, "")
]
bottom = min(stat.a_min for stat in stats)
top = max(stat.a_max for stat in stats)
first_val, step = compute_scale(bottom, top)
while bottom <= first_val:
first_val -= step
bottom = first_val
no_ticks = math.ceil((top - first_val) / step) + 1
top = max(top, first_val + no_ticks * step)
gbottom = min(bottom, min(stat.mean - stat.dev for stat in stats))
gtop = max(top, max(stat.mean + stat.dev for stat in stats))
bv = self.box_view
viewrect = bv.viewport().rect().adjusted(15, 15, -15, -30)
self.scale_x = scale_x = viewrect.width() / (gtop - gbottom)
# In principle we should repeat this until convergence since the new
# scaling is too conservative. (No chance am I doing this.)
mlb = min(stat.mean + mean_lab.min_x / scale_x
for stat, mean_lab in zip(stats, mean_labels))
if mlb < gbottom:
gbottom = mlb
self.scale_x = scale_x = viewrect.width() / (gtop - gbottom)
self.scene_min_x = gbottom * scale_x
self.scene_max_x = gtop * scale_x
self.scene_width = self.scene_max_x - self.scene_min_x
val = first_val
last_text = self.scene_min_x
while True:
l = self.box_scene.addLine(val * scale_x, -1, val * scale_x, 1,
self._pen_axis_tick)
l.setZValue(100)
t = QGraphicsSimpleTextItem(
self.attribute.str_val(val) if not misssing_stats else "?")
t.setFont(self._axis_font)
t.setFlag(QGraphicsItem.ItemIgnoresTransformations)
r = t.boundingRect()
x_start = val * scale_x - r.width() / 2
x_finish = x_start + r.width()
if x_start > last_text + 10 and x_finish < self.scene_max_x:
t.setPos(x_start, 8)
self.box_scene.addItem(t)
last_text = x_finish
if val >= top:
break
val += step
self.box_scene.addLine(bottom * scale_x - 4, 0, top * scale_x + 4, 0,
self._pen_axis)
def draw_stat(self):
if self.stat_test:
label = QGraphicsSimpleTextItem(self.stat_test)
brect = self.box_scene.sceneRect()
label.setPos(brect.center().x() - label.boundingRect().width() / 2,
8 + self._axis_font.pixelSize() * 2)
label.setFlag(QGraphicsItem.ItemIgnoresTransformations)
self.box_scene.addItem(label)
def draw_axis_disc(self):
"""
Draw the horizontal axis and sets self.scale_x for discrete attributes
"""
assert not self.attribute.is_continuous
if self.show_stretched:
if not self.attr_labels:
return
step = steps = 10
else:
if self.group_var:
max_box = max(
float(np.sum(dist))
for dist in self.conts.array_with_unknowns)
else:
max_box = float(np.sum(self.dist.array_with_unknowns))
if max_box == 0:
self.scale_x = 1
return
_, step = compute_scale(0, max_box)
step = int(step) if step > 1 else 1
steps = int(math.ceil(max_box / step))
max_box = step * steps
bv = self.box_view
viewrect = bv.viewport().rect().adjusted(15, 15, -15, -30)
self.scene_width = viewrect.width()
lab_width = max(lab.boundingRect().width() for lab in self.attr_labels)
lab_width = max(lab_width, 40)
lab_width = min(lab_width, self.scene_width / 3)
self.label_width = lab_width
right_offset = 0 # offset for the right label
if not self.show_stretched and self.labels:
if self.group_var:
rows = list(zip(self.conts.array_with_unknowns, self.labels))
else:
rows = [(self.dist, self.labels[0])]
# available space left of the 'group labels'
available = self.scene_width - lab_width - 10
scale_x = (available - right_offset) / max_box
max_right = max(
sum(dist) * scale_x + 10 + lbl.boundingRect().width()
for dist, lbl in rows)
right_offset = max(0, max_right - max_box * scale_x)
self.scale_x = scale_x = \
(self.scene_width - lab_width - 10 - right_offset) / max_box
self.box_scene.addLine(0, 0, max_box * scale_x, 0, self._pen_axis)
for val in range(0, step * steps + 1, step):
l = self.box_scene.addLine(val * scale_x, -1, val * scale_x, 1,
self._pen_axis_tick)
l.setZValue(100)
t = self.box_scene.addSimpleText(str(val), self._axis_font)
t.setPos(val * scale_x - t.boundingRect().width() / 2, 8)
if self.show_stretched:
self.scale_x *= 100
def label_group(self, stat, attr, mean_lab):
def centered_text(val, pos):
t = QGraphicsSimpleTextItem(attr.str_val(val), labels)
t.setFont(self._label_font)
bbox = t.boundingRect()
t.setPos(pos - bbox.width() / 2, 22)
return t
def line(x, down=1):
QGraphicsLineItem(x, 12 * down, x, 20 * down, labels)
def move_label(label, frm, to):
label.setX(to)
to += t_box.width() / 2
path = QPainterPath()
path.lineTo(0, 4)
path.lineTo(to - frm, 4)
path.lineTo(to - frm, 8)
p = QGraphicsPathItem(path)
p.setPos(frm, 12)
labels.addToGroup(p)
labels = QGraphicsItemGroup()
labels.addToGroup(mean_lab)
m = stat.mean * self.scale_x
mean_lab.setPos(m, -22)
line(m, -1)
if stat.median is not None:
msc = stat.median * self.scale_x
med_t = centered_text(stat.median, msc)
med_box_width2 = med_t.boundingRect().width() / 2
line(msc)
if stat.q25 is not None:
x = stat.q25 * self.scale_x
t = centered_text(stat.q25, x)
t_box = t.boundingRect()
med_left = msc - med_box_width2
if x + t_box.width() / 2 >= med_left - 5:
move_label(t, x, med_left - t_box.width() - 5)
else:
line(x)
if stat.q75 is not None:
x = stat.q75 * self.scale_x
t = centered_text(stat.q75, x)
t_box = t.boundingRect()
med_right = msc + med_box_width2
if x - t_box.width() / 2 <= med_right + 5:
move_label(t, x, med_right + 5)
else:
line(x)
return labels
def box_group(self, stat, height=20):
def line(x0, y0, x1, y1, *args):
return QGraphicsLineItem(x0 * scale_x, y0, x1 * scale_x, y1, *args)
scale_x = self.scale_x
box = []
whisker1 = line(stat.a_min, -1.5, stat.a_min, 1.5)
whisker2 = line(stat.a_max, -1.5, stat.a_max, 1.5)
vert_line = line(stat.a_min, 0, stat.a_max, 0)
mean_line = line(stat.mean, -height / 3, stat.mean, height / 3)
for it in (whisker1, whisker2, mean_line):
it.setPen(self._pen_paramet)
vert_line.setPen(self._pen_dotted)
var_line = line(stat.mean - stat.dev, 0, stat.mean + stat.dev, 0)
var_line.setPen(self._pen_paramet)
box.extend([whisker1, whisker2, vert_line, mean_line, var_line])
if stat.q25 is not None or stat.q75 is not None:
# if any of them is None it means that its value is equal to median
box_from = stat.q25 or stat.median
box_to = stat.q75 or stat.median
mbox = FilterGraphicsRectItem(stat.data_range, box_from * scale_x,
-height / 2,
(box_to - box_from) * scale_x,
height)
mbox.setBrush(self._box_brush)
mbox.setPen(QPen(Qt.NoPen))
mbox.setZValue(-200)
box.append(mbox)
if stat.median is not None:
median_line = line(stat.median, -height / 2, stat.median,
height / 2)
median_line.setPen(self._pen_median)
median_line.setZValue(-150)
box.append(median_line)
return box
def strudel(self, dist, group_val=None):
attr = self.attribute
ss = np.sum(dist)
box = []
if ss < 1e-6:
cond = DiscDataRange(None, group_val)
box.append(FilterGraphicsRectItem(cond, 0, -10, 1, 10))
cum = 0
missing_val_str = f"missing '{attr.name}'"
values = attr.values + ("", )
colors = attr.palette.qcolors_w_nan
total = sum(dist)
for freq, value, color in zip(dist, values, colors):
if freq < 1e-6:
continue
v = freq
if self.show_stretched:
v /= ss
v *= self.scale_x
cond = DiscDataRange(value, group_val)
rect = FilterGraphicsRectItem(cond, cum + 1, -6, v - 2, 12)
rect.setBrush(QBrush(color))
rect.setPen(QPen(Qt.NoPen))
value = value or missing_val_str
if self.show_stretched:
tooltip = f"{value}: {100 * freq / total:.2f}%"
else:
tooltip = f"{value}: ({int(freq)})"
rect.setToolTip(tooltip)
text = QGraphicsTextItem(value)
box.append(rect)
box.append(text)
cum += v
return box
def on_selection_changed(self):
self.selection = tuple(item.data_range
for item in self.box_scene.selectedItems()
if item.data_range)
self.commit()
def commit(self):
conditions = self._gather_conditions()
if conditions:
selected = Values(conditions, conjunction=False)(self.dataset)
selection = np.in1d(self.dataset.ids,
selected.ids,
assume_unique=True).nonzero()[0]
else:
selected, selection = None, []
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(
create_annotated_table(self.dataset, selection))
def _gather_conditions(self):
conditions = []
attr = self.attribute
group_attr = self.group_var
for data_range in self.selection:
if attr.is_discrete:
# If some value was removed from the data (in case settings are
# loaded from a scheme), do not include the corresponding
# filter; this is appropriate since data with such value does
# not exist anyway
if not data_range.value:
condition = IsDefined([attr], negate=True)
elif data_range.value not in attr.values:
continue
else:
condition = FilterDiscrete(attr, [data_range.value])
else:
condition = FilterContinuous(attr, FilterContinuous.Between,
data_range.low, data_range.high)
if data_range.group_value:
if not data_range.group_value:
grp_filter = IsDefined([group_attr], negate=True)
elif data_range.group_value not in group_attr.values:
continue
else:
grp_filter = FilterDiscrete(group_attr,
[data_range.group_value])
condition = Values([condition, grp_filter], conjunction=True)
conditions.append(condition)
return conditions
def _show_posthoc(self):
def line(y0, y1):
it = self.box_scene.addLine(x, y0, x, y1, self._post_line_pen)
it.setZValue(-100)
self.posthoc_lines.append(it)
while self.posthoc_lines:
self.box_scene.removeItem(self.posthoc_lines.pop())
if self.compare == OWBoxPlot.CompareNone or len(self.stats) < 2:
return
if self.compare == OWBoxPlot.CompareMedians:
crit_line = "median"
else:
crit_line = "mean"
xs = []
height = 90 if self.show_annotations else 60
y_up = -len(self.stats) * height + 10
for pos, box_index in enumerate(self.order):
stat = self.stats[box_index]
x = getattr(stat, crit_line)
if x is None:
continue
x *= self.scale_x
xs.append(x * self.scale_x)
by = y_up + pos * height
line(by + 12, 0)
used_to = []
last_to = to = 0
for frm, frm_x in enumerate(xs[:-1]):
for to in range(frm + 1, len(xs)):
if xs[to] - frm_x > 1.5:
to -= 1
break
if to in (last_to, frm):
continue
for rowi, used in enumerate(used_to):
if used < frm:
used_to[rowi] = to
break
else:
rowi = len(used_to)
used_to.append(to)
y = -6 - rowi * 6
it = self.box_scene.addLine(frm_x - 2, y, xs[to] + 2, y,
self._post_grp_pen)
self.posthoc_lines.append(it)
last_to = to
def get_widget_name_extension(self):
return self.attribute.name if self.attribute else None
def send_report(self):
self.report_plot()
text = ""
if self.attribute:
text += "Box plot for attribute '{}' ".format(self.attribute.name)
if self.group_var:
text += "grouped by '{}'".format(self.group_var.name)
if text:
self.report_caption(text)
class Label(QGraphicsSimpleTextItem):
"""Boxplot Label with settable maxWidth"""
# Minimum width to display label text
MIN_LABEL_WIDTH = 25
# padding bellow the text
PADDING = 3
__max_width = None
def maxWidth(self):
return self.__max_width
def setMaxWidth(self, max_width):
self.__max_width = max_width
def paint(self, painter, option, widget):
"""Overrides QGraphicsSimpleTextItem.paint
If label text is too long, it is elided
to fit into the allowed region
"""
if self.__max_width is None:
width = option.rect.width()
else:
width = self.__max_width
if width < self.MIN_LABEL_WIDTH:
# if space is too narrow, no label
return
fm = painter.fontMetrics()
text = fm.elidedText(self.text(), Qt.ElideRight, width)
painter.drawText(
option.rect.x(),
option.rect.y() + self.boundingRect().height() - self.PADDING,
text)
def items(self, *args, **kwargs):
items = QGraphicsScene.items(self, *args, **kwargs)
return list(map(toGraphicsObjectIfPossible, items))
class OWBoxPlot(widget.OWWidget):
"""
Here's how the widget's functions call each other:
- `set_data` is a signal handler fills the list boxes and calls
`grouping_changed`.
- `grouping_changed` handles changes of grouping attribute: it enables or
disables the box for ordering, orders attributes and calls `attr_changed`.
- `attr_changed` handles changes of attribute. It recomputes box data by
calling `compute_box_data`, shows the appropriate display box
(discrete/continuous) and then calls`layout_changed`
- `layout_changed` constructs all the elements for the scene (as lists of
QGraphicsItemGroup) and calls `display_changed`. It is called when the
attribute or grouping is changed (by attr_changed) and on resize event.
- `display_changed` puts the elements corresponding to the current display
settings on the scene. It is called when the elements are reconstructed
(layout is changed due to selection of attributes or resize event), or
when the user changes display settings or colors.
For discrete attributes, the flow is a bit simpler: the elements are not
constructed in advance (by layout_changed). Instead, layout_changed and
display_changed call display_changed_disc that draws everything.
"""
name = "箱形图"
description = "在方框图中可视化特征值的分布。"
icon = "icons/BoxPlot.svg"
priority = 100
keywords = ["whisker"]
class Inputs:
data = Input("数据", Orange.data.Table)
class Outputs:
selected_data = Output("所选数据", Orange.data.Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Orange.data.Table)
#: Comparison types for continuous variables
CompareNone, CompareMedians, CompareMeans = 0, 1, 2
settingsHandler = DomainContextHandler()
conditions = ContextSetting([])
attribute = ContextSetting(None)
order_by_importance = Setting(False)
group_var = ContextSetting(None)
show_annotations = Setting(True)
compare = Setting(CompareMeans)
stattest = Setting(0)
sig_threshold = Setting(0.05)
stretched = Setting(True)
show_labels = Setting(True)
sort_freqs = Setting(False)
auto_commit = Setting(True)
_sorting_criteria_attrs = {
CompareNone: "",
CompareMedians: "median",
CompareMeans: "mean"
}
_pen_axis_tick = QPen(Qt.white, 5)
_pen_axis = QPen(Qt.darkGray, 3)
_pen_median = QPen(QBrush(QColor(0xff, 0xff, 0x00)), 2)
_pen_paramet = QPen(QBrush(QColor(0x33, 0x00, 0xff)), 2)
_pen_dotted = QPen(QBrush(QColor(0x33, 0x00, 0xff)), 1)
_pen_dotted.setStyle(Qt.DotLine)
_post_line_pen = QPen(Qt.lightGray, 2)
_post_grp_pen = QPen(Qt.lightGray, 4)
for pen in (_pen_paramet, _pen_median, _pen_dotted, _pen_axis,
_pen_axis_tick, _post_line_pen, _post_grp_pen):
pen.setCosmetic(True)
pen.setCapStyle(Qt.RoundCap)
pen.setJoinStyle(Qt.RoundJoin)
_pen_axis_tick.setCapStyle(Qt.FlatCap)
_box_brush = QBrush(QColor(0x33, 0x88, 0xff, 0xc0))
_axis_font = QFont()
_axis_font.setPixelSize(12)
_label_font = QFont()
_label_font.setPixelSize(11)
_attr_brush = QBrush(QColor(0x33, 0x00, 0xff))
graph_name = "盒式布景"
def __init__(self):
super().__init__()
self.stats = []
self.dataset = None
self.posthoc_lines = []
self.label_txts = self.mean_labels = self.boxes = self.labels = \
self.label_txts_all = self.attr_labels = self.order = []
self.p = -1.0
self.scale_x = self.scene_min_x = self.scene_width = 0
self.label_width = 0
self.attrs = VariableListModel()
view = gui.listView(self.controlArea,
self,
"attribute",
box="变量",
model=self.attrs,
callback=self.attr_changed)
view.setMinimumSize(QSize(30, 30))
# Any other policy than Ignored will let the QListBox's scrollbar
# set the minimal height (see the penultimate paragraph of
# http://doc.qt.io/qt-4.8/qabstractscrollarea.html#addScrollBarWidget)
view.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Ignored)
gui.separator(view.box, 6, 6)
self.cb_order = gui.checkBox(view.box,
self,
"order_by_importance",
"按相关性排序",
tooltip="由𝜒²或方差对子群排序",
callback=self.apply_sorting)
self.group_vars = DomainModel(placeholder="无",
separators=False,
valid_types=Orange.data.DiscreteVariable)
self.group_view = view = gui.listView(self.controlArea,
self,
"group_var",
box="子群",
model=self.group_vars,
callback=self.grouping_changed)
view.setEnabled(False)
view.setMinimumSize(QSize(30, 30))
# See the comment above
view.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Ignored)
# TODO: move Compare median/mean to grouping box
# The vertical size policy is needed to let only the list views expand
self.display_box = gui.vBox(self.controlArea,
"Display",
sizePolicy=(QSizePolicy.Minimum,
QSizePolicy.Maximum),
addSpace=False)
gui.checkBox(self.display_box,
self,
"show_annotations",
"Annotate",
callback=self.display_changed)
self.compare_rb = gui.radioButtonsInBox(
self.display_box,
self,
'compare',
btnLabels=["无比较", "中位数比较", "均值比较"],
callback=self.layout_changed)
# The vertical size policy is needed to let only the list views expand
self.stretching_box = box = gui.vBox(self.controlArea,
box="显示",
sizePolicy=(QSizePolicy.Minimum,
QSizePolicy.Fixed))
self.stretching_box.sizeHint = self.display_box.sizeHint
gui.checkBox(box,
self,
'stretched',
"拉杆",
callback=self.display_changed)
gui.checkBox(box,
self,
'show_labels',
"显示框标签",
callback=self.display_changed)
self.sort_cb = gui.checkBox(box,
self,
'sort_freqs',
"按子组频率排序",
callback=self.display_changed)
gui.rubber(box)
gui.auto_commit(self.controlArea, self, "auto_commit", "选中发送", "自动发送")
gui.vBox(self.mainArea, addSpace=True)
self.box_scene = QGraphicsScene()
self.box_scene.selectionChanged.connect(self.commit)
self.box_view = QGraphicsView(self.box_scene)
self.box_view.setRenderHints(QPainter.Antialiasing
| QPainter.TextAntialiasing
| QPainter.SmoothPixmapTransform)
self.box_view.viewport().installEventFilter(self)
self.mainArea.layout().addWidget(self.box_view)
e = gui.hBox(self.mainArea, addSpace=False)
self.infot1 = gui.widgetLabel(e, "<center>没有测试结果。</center>")
self.mainArea.setMinimumWidth(600)
self.stats = self.dist = self.conts = []
self.is_continuous = False
self.update_display_box()
def sizeHint(self):
return QSize(100, 500) # Vertical size is regulated by mainArea
def eventFilter(self, obj, event):
if obj is self.box_view.viewport() and \
event.type() == QEvent.Resize:
self.layout_changed()
return super().eventFilter(obj, event)
def reset_attrs(self, domain):
self.attrs[:] = [
var for var in chain(domain.class_vars, domain.metas,
domain.attributes) if var.is_primitive()
]
# noinspection PyTypeChecker
@Inputs.data
def set_data(self, dataset):
if dataset is not None and (not bool(dataset)
or not len(dataset.domain) and
not any(var.is_primitive()
for var in dataset.domain.metas)):
dataset = None
self.closeContext()
self.dataset = dataset
self.dist = self.stats = self.conts = []
self.group_var = None
self.attribute = None
if dataset:
domain = dataset.domain
self.group_vars.set_domain(domain)
self.group_view.setEnabled(len(self.group_vars) > 1)
self.reset_attrs(domain)
self.select_default_variables(domain)
self.openContext(self.dataset)
self.grouping_changed()
else:
self.reset_all_data()
self.commit()
def select_default_variables(self, domain):
# visualize first non-class variable, group by class (if present)
if len(self.attrs) > len(domain.class_vars):
self.attribute = self.attrs[len(domain.class_vars)]
elif self.attrs:
self.attribute = self.attrs[0]
if domain.class_var and domain.class_var.is_discrete:
self.group_var = domain.class_var
else:
self.group_var = None # Reset to trigger selection via callback
def apply_sorting(self):
def compute_score(attr):
if attr is group_var:
return 3
if attr.is_continuous:
# One-way ANOVA
col = data.get_column_view(attr)[0].astype(float)
groups = (col[group_col == i] for i in range(n_groups))
groups = (col[~np.isnan(col)] for col in groups)
groups = [group for group in groups if len(group)]
p = f_oneway(*groups)[1] if len(groups) > 1 else 2
else:
# Chi-square with the given distribution into groups
# (see degrees of freedom in computation of the p-value)
if not attr.values or not group_var.values:
return 2
observed = np.array(
contingency.get_contingency(data, group_var, attr))
observed = observed[observed.sum(axis=1) != 0, :]
observed = observed[:, observed.sum(axis=0) != 0]
if min(observed.shape) < 2:
return 2
expected = \
np.outer(observed.sum(axis=1), observed.sum(axis=0)) / \
np.sum(observed)
p = chisquare(observed.ravel(),
f_exp=expected.ravel(),
ddof=n_groups - 1)[1]
if math.isnan(p):
return 2
return p
data = self.dataset
if data is None:
return
domain = data.domain
attribute = self.attribute
group_var = self.group_var
if self.order_by_importance and group_var is not None:
n_groups = len(group_var.values)
group_col = data.get_column_view(group_var)[0] if \
domain.has_continuous_attributes(
include_class=True, include_metas=True) else None
self.attrs.sort(key=compute_score)
else:
self.reset_attrs(domain)
self.attribute = attribute
def reset_all_data(self):
self.clear_scene()
self.infot1.setText("")
self.attrs.clear()
self.group_vars.set_domain(None)
self.group_view.setEnabled(False)
self.is_continuous = False
self.update_display_box()
def grouping_changed(self):
self.cb_order.setEnabled(self.group_var is not None)
self.apply_sorting()
self.attr_changed()
def select_box_items(self):
temp_cond = self.conditions.copy()
for box in self.box_scene.items():
if isinstance(box, FilterGraphicsRectItem):
box.setSelected(
box.filter.conditions in [c.conditions for c in temp_cond])
def attr_changed(self):
self.compute_box_data()
self.update_display_box()
self.layout_changed()
if self.is_continuous:
heights = 90 if self.show_annotations else 60
self.box_view.centerOn(self.scene_min_x + self.scene_width / 2,
-30 - len(self.stats) * heights / 2 + 45)
else:
self.box_view.centerOn(self.scene_width / 2,
-30 - len(self.boxes) * 40 / 2 + 45)
def compute_box_data(self):
attr = self.attribute
if not attr:
return
dataset = self.dataset
self.is_continuous = attr.is_continuous
if dataset is None or not self.is_continuous and not attr.values or \
self.group_var and not self.group_var.values:
self.stats = self.dist = self.conts = []
return
if self.group_var:
self.dist = []
self.conts = contingency.get_contingency(dataset, attr,
self.group_var)
if self.is_continuous:
stats, label_texts = [], []
for i, cont in enumerate(self.conts):
if np.sum(cont[1]):
stats.append(BoxData(cont, attr, i, self.group_var))
label_texts.append(self.group_var.values[i])
self.stats = stats
self.label_txts_all = label_texts
else:
self.label_txts_all = \
[v for v, c in zip(self.group_var.values, self.conts)
if np.sum(c) > 0]
else:
self.dist = distribution.get_distribution(dataset, attr)
self.conts = []
if self.is_continuous:
self.stats = [BoxData(self.dist, attr, None)]
self.label_txts_all = [""]
self.label_txts = [
txts for stat, txts in zip(self.stats, self.label_txts_all)
if stat.n > 0
]
self.stats = [stat for stat in self.stats if stat.n > 0]
def update_display_box(self):
if self.is_continuous:
self.stretching_box.hide()
self.display_box.show()
self.compare_rb.setEnabled(self.group_var is not None)
else:
self.stretching_box.show()
self.display_box.hide()
self.sort_cb.setEnabled(self.group_var is not None)
def clear_scene(self):
self.closeContext()
self.box_scene.clearSelection()
self.box_scene.clear()
self.box_view.viewport().update()
self.attr_labels = []
self.labels = []
self.boxes = []
self.mean_labels = []
self.posthoc_lines = []
self.openContext(self.dataset)
def layout_changed(self):
attr = self.attribute
if not attr:
return
self.clear_scene()
if self.dataset is None or len(self.conts) == len(self.dist) == 0:
return
if not self.is_continuous:
self.display_changed_disc()
return
self.mean_labels = [
self.mean_label(stat, attr, lab)
for stat, lab in zip(self.stats, self.label_txts)
]
self.draw_axis()
self.boxes = [self.box_group(stat) for stat in self.stats]
self.labels = [
self.label_group(stat, attr, mean_lab)
for stat, mean_lab in zip(self.stats, self.mean_labels)
]
self.attr_labels = [
QGraphicsSimpleTextItem(lab) for lab in self.label_txts
]
for it in chain(self.labels, self.attr_labels):
self.box_scene.addItem(it)
self.display_changed()
def display_changed(self):
if self.dataset is None:
return
if not self.is_continuous:
self.display_changed_disc()
return
self.order = list(range(len(self.stats)))
criterion = self._sorting_criteria_attrs[self.compare]
if criterion:
vals = [getattr(stat, criterion) for stat in self.stats]
overmax = max((val for val in vals if val is not None), default=0) \
+ 1
vals = [val if val is not None else overmax for val in vals]
self.order = sorted(self.order, key=vals.__getitem__)
heights = 90 if self.show_annotations else 60
for row, box_index in enumerate(self.order):
y = (-len(self.stats) + row) * heights + 10
for item in self.boxes[box_index]:
self.box_scene.addItem(item)
item.setY(y)
labels = self.labels[box_index]
if self.show_annotations:
labels.show()
labels.setY(y)
else:
labels.hide()
label = self.attr_labels[box_index]
label.setY(y - 15 - label.boundingRect().height())
if self.show_annotations:
label.hide()
else:
stat = self.stats[box_index]
if self.compare == OWBoxPlot.CompareMedians and \
stat.median is not None:
pos = stat.median + 5 / self.scale_x
elif self.compare == OWBoxPlot.CompareMeans or stat.q25 is None:
pos = stat.mean + 5 / self.scale_x
else:
pos = stat.q25
label.setX(pos * self.scale_x)
label.show()
r = QRectF(self.scene_min_x, -30 - len(self.stats) * heights,
self.scene_width,
len(self.stats) * heights + 90)
self.box_scene.setSceneRect(r)
self.compute_tests()
self.show_posthoc()
self.select_box_items()
def display_changed_disc(self):
assert not self.is_continuous
self.clear_scene()
self.attr_labels = [
QGraphicsSimpleTextItem(lab) for lab in self.label_txts_all
]
if not self.stretched:
if self.group_var:
self.labels = [
QGraphicsTextItem("{}".format(int(sum(cont))))
for cont in self.conts if np.sum(cont) > 0
]
else:
self.labels = [QGraphicsTextItem(str(int(sum(self.dist))))]
self.order = list(range(len(self.attr_labels)))
self.draw_axis_disc()
if self.group_var:
self.boxes = \
[self.strudel(cont, i) for i, cont in enumerate(self.conts)
if np.sum(cont) > 0]
self.conts = self.conts[np.sum(np.array(self.conts), axis=1) > 0]
if self.sort_freqs:
# pylint: disable=invalid-unary-operand-type
self.order = sorted(
self.order, key=(-np.sum(self.conts, axis=1)).__getitem__)
else:
self.boxes = [self.strudel(self.dist)]
for row, box_index in enumerate(self.order):
y = (-len(self.boxes) + row) * 40 + 10
box = self.boxes[box_index]
bars, labels = box[::2], box[1::2]
self.__draw_group_labels(y, box_index)
if not self.stretched:
self.__draw_row_counts(y, box_index)
if self.show_labels and self.attribute is not self.group_var:
self.__draw_bar_labels(y, bars, labels)
self.__draw_bars(y, bars)
self.box_scene.setSceneRect(-self.label_width - 5,
-30 - len(self.boxes) * 40,
self.scene_width,
len(self.boxes * 40) + 90)
self.infot1.setText("")
self.select_box_items()
def __draw_group_labels(self, y, row):
"""Draw group labels
Parameters
----------
y: int
vertical offset of bars
row: int
row index
"""
label = self.attr_labels[row]
b = label.boundingRect()
label.setPos(-b.width() - 10, y - b.height() / 2)
self.box_scene.addItem(label)
def __draw_row_counts(self, y, row):
"""Draw row counts
Parameters
----------
y: int
vertical offset of bars
row: int
row index
"""
assert not self.is_continuous
label = self.labels[row]
b = label.boundingRect()
if self.group_var:
right = self.scale_x * sum(self.conts[row])
else:
right = self.scale_x * sum(self.dist)
label.setPos(right + 10, y - b.height() / 2)
self.box_scene.addItem(label)
def __draw_bar_labels(self, y, bars, labels):
"""Draw bar labels
Parameters
----------
y: int
vertical offset of bars
bars: List[FilterGraphicsRectItem]
list of bars being drawn
labels: List[QGraphicsTextItem]
list of labels for corresponding bars
"""
label = bar_part = None
for text_item, bar_part in zip(labels, bars):
label = self.Label(text_item.toPlainText())
label.setPos(bar_part.boundingRect().x(),
y - label.boundingRect().height() - 8)
label.setMaxWidth(bar_part.boundingRect().width())
self.box_scene.addItem(label)
def __draw_bars(self, y, bars):
"""Draw bars
Parameters
----------
y: int
vertical offset of bars
bars: List[FilterGraphicsRectItem]
list of bars to draw
"""
for item in bars:
item.setPos(0, y)
self.box_scene.addItem(item)
# noinspection PyPep8Naming
def compute_tests(self):
# The t-test and ANOVA are implemented here since they efficiently use
# the widget-specific data in self.stats.
# The non-parametric tests can't do this, so we use statistics.tests
def stat_ttest():
d1, d2 = self.stats
if d1.n == 0 or d2.n == 0:
return np.nan, np.nan
pooled_var = d1.var / d1.n + d2.var / d2.n
df = pooled_var ** 2 / \
((d1.var / d1.n) ** 2 / (d1.n - 1) +
(d2.var / d2.n) ** 2 / (d2.n - 1))
if pooled_var == 0:
return np.nan, np.nan
t = abs(d1.mean - d2.mean) / math.sqrt(pooled_var)
p = 2 * (1 - scipy.special.stdtr(df, t))
return t, p
# TODO: Check this function
# noinspection PyPep8Naming
def stat_ANOVA():
if any(stat.n == 0 for stat in self.stats):
return np.nan, np.nan
n = sum(stat.n for stat in self.stats)
grand_avg = sum(stat.n * stat.mean for stat in self.stats) / n
var_between = sum(stat.n * (stat.mean - grand_avg)**2
for stat in self.stats)
df_between = len(self.stats) - 1
var_within = sum(stat.n * stat.var for stat in self.stats)
df_within = n - len(self.stats)
F = (var_between / df_between) / (var_within / df_within)
p = 1 - scipy.special.fdtr(df_between, df_within, F)
return F, p
if self.compare == OWBoxPlot.CompareNone or len(self.stats) < 2:
t = ""
elif any(s.n <= 1 for s in self.stats):
t = "At least one group has just one instance," \
"cannot compute significance"
elif len(self.stats) == 2:
if self.compare == OWBoxPlot.CompareMedians:
t = ""
# z, self.p = tests.wilcoxon_rank_sum(
# self.stats[0].dist, self.stats[1].dist)
# t = "Mann-Whitney's z: %.1f (p=%.3f)" % (z, self.p)
else:
t, self.p = stat_ttest()
t = "Student's t: %.3f (p=%.3f)" % (t, self.p)
else:
if self.compare == OWBoxPlot.CompareMedians:
t = ""
# U, self.p = -1, -1
# t = "Kruskal Wallis's U: %.1f (p=%.3f)" % (U, self.p)
else:
F, self.p = stat_ANOVA()
t = "ANOVA: %.3f (p=%.3f)" % (F, self.p)
self.infot1.setText("<center>%s</center>" % t)
def mean_label(self, stat, attr, val_name):
label = QGraphicsItemGroup()
t = QGraphicsSimpleTextItem(
"%.*f" % (attr.number_of_decimals + 1, stat.mean), label)
t.setFont(self._label_font)
bbox = t.boundingRect()
w2, h = bbox.width() / 2, bbox.height()
t.setPos(-w2, -h)
tpm = QGraphicsSimpleTextItem(
" \u00b1 " + "%.*f" % (attr.number_of_decimals + 1, stat.dev),
label)
tpm.setFont(self._label_font)
tpm.setPos(w2, -h)
if val_name:
vnm = QGraphicsSimpleTextItem(val_name + ": ", label)
vnm.setFont(self._label_font)
vnm.setBrush(self._attr_brush)
vb = vnm.boundingRect()
label.min_x = -w2 - vb.width()
vnm.setPos(label.min_x, -h)
else:
label.min_x = -w2
return label
def draw_axis(self):
"""Draw the horizontal axis and sets self.scale_x"""
misssing_stats = not self.stats
stats = self.stats or [BoxData(np.array([[0.], [1.]]), self.attribute)]
mean_labels = self.mean_labels or [
self.mean_label(stats[0], self.attribute, "")
]
bottom = min(stat.a_min for stat in stats)
top = max(stat.a_max for stat in stats)
first_val, step = compute_scale(bottom, top)
while bottom <= first_val:
first_val -= step
bottom = first_val
no_ticks = math.ceil((top - first_val) / step) + 1
top = max(top, first_val + no_ticks * step)
gbottom = min(bottom, min(stat.mean - stat.dev for stat in stats))
gtop = max(top, max(stat.mean + stat.dev for stat in stats))
bv = self.box_view
viewrect = bv.viewport().rect().adjusted(15, 15, -15, -30)
self.scale_x = scale_x = viewrect.width() / (gtop - gbottom)
# In principle we should repeat this until convergence since the new
# scaling is too conservative. (No chance am I doing this.)
mlb = min(stat.mean + mean_lab.min_x / scale_x
for stat, mean_lab in zip(stats, mean_labels))
if mlb < gbottom:
gbottom = mlb
self.scale_x = scale_x = viewrect.width() / (gtop - gbottom)
self.scene_min_x = gbottom * scale_x
self.scene_width = (gtop - gbottom) * scale_x
val = first_val
decimals = max(3, 4 - int(math.log10(step)))
while True:
l = self.box_scene.addLine(val * scale_x, -1, val * scale_x, 1,
self._pen_axis_tick)
l.setZValue(100)
t = self.box_scene.addSimpleText(
repr(round(val, decimals)) if not misssing_stats else "?",
self._axis_font)
t.setFlags(t.flags() | QGraphicsItem.ItemIgnoresTransformations)
r = t.boundingRect()
t.setPos(val * scale_x - r.width() / 2, 8)
if val >= top:
break
val += step
self.box_scene.addLine(bottom * scale_x - 4, 0, top * scale_x + 4, 0,
self._pen_axis)
def draw_axis_disc(self):
"""
Draw the horizontal axis and sets self.scale_x for discrete attributes
"""
assert not self.is_continuous
if self.stretched:
if not self.attr_labels:
return
step = steps = 10
else:
if self.group_var:
max_box = max(float(np.sum(dist)) for dist in self.conts)
else:
max_box = float(np.sum(self.dist))
if max_box == 0:
self.scale_x = 1
return
_, step = compute_scale(0, max_box)
step = int(step) if step > 1 else 1
steps = int(math.ceil(max_box / step))
max_box = step * steps
bv = self.box_view
viewrect = bv.viewport().rect().adjusted(15, 15, -15, -30)
self.scene_width = viewrect.width()
lab_width = max(lab.boundingRect().width() for lab in self.attr_labels)
lab_width = max(lab_width, 40)
lab_width = min(lab_width, self.scene_width / 3)
self.label_width = lab_width
right_offset = 0 # offset for the right label
if not self.stretched and self.labels:
if self.group_var:
rows = list(zip(self.conts, self.labels))
else:
rows = [(self.dist, self.labels[0])]
# available space left of the 'group labels'
available = self.scene_width - lab_width - 10
scale_x = (available - right_offset) / max_box
max_right = max(
sum(dist) * scale_x + 10 + lbl.boundingRect().width()
for dist, lbl in rows)
right_offset = max(0, max_right - max_box * scale_x)
self.scale_x = scale_x = \
(self.scene_width - lab_width - 10 - right_offset) / max_box
self.box_scene.addLine(0, 0, max_box * scale_x, 0, self._pen_axis)
for val in range(0, step * steps + 1, step):
l = self.box_scene.addLine(val * scale_x, -1, val * scale_x, 1,
self._pen_axis_tick)
l.setZValue(100)
t = self.box_scene.addSimpleText(str(val), self._axis_font)
t.setPos(val * scale_x - t.boundingRect().width() / 2, 8)
if self.stretched:
self.scale_x *= 100
def label_group(self, stat, attr, mean_lab):
def centered_text(val, pos):
t = QGraphicsSimpleTextItem(
"%.*f" % (attr.number_of_decimals + 1, val), labels)
t.setFont(self._label_font)
bbox = t.boundingRect()
t.setPos(pos - bbox.width() / 2, 22)
return t
def line(x, down=1):
QGraphicsLineItem(x, 12 * down, x, 20 * down, labels)
def move_label(label, frm, to):
label.setX(to)
to += t_box.width() / 2
path = QPainterPath()
path.lineTo(0, 4)
path.lineTo(to - frm, 4)
path.lineTo(to - frm, 8)
p = QGraphicsPathItem(path)
p.setPos(frm, 12)
labels.addToGroup(p)
labels = QGraphicsItemGroup()
labels.addToGroup(mean_lab)
m = stat.mean * self.scale_x
mean_lab.setPos(m, -22)
line(m, -1)
if stat.median is not None:
msc = stat.median * self.scale_x
med_t = centered_text(stat.median, msc)
med_box_width2 = med_t.boundingRect().width() / 2
line(msc)
if stat.q25 is not None:
x = stat.q25 * self.scale_x
t = centered_text(stat.q25, x)
t_box = t.boundingRect()
med_left = msc - med_box_width2
if x + t_box.width() / 2 >= med_left - 5:
move_label(t, x, med_left - t_box.width() - 5)
else:
line(x)
if stat.q75 is not None:
x = stat.q75 * self.scale_x
t = centered_text(stat.q75, x)
t_box = t.boundingRect()
med_right = msc + med_box_width2
if x - t_box.width() / 2 <= med_right + 5:
move_label(t, x, med_right + 5)
else:
line(x)
return labels
def box_group(self, stat, height=20):
def line(x0, y0, x1, y1, *args):
return QGraphicsLineItem(x0 * scale_x, y0, x1 * scale_x, y1, *args)
scale_x = self.scale_x
box = []
whisker1 = line(stat.a_min, -1.5, stat.a_min, 1.5)
whisker2 = line(stat.a_max, -1.5, stat.a_max, 1.5)
vert_line = line(stat.a_min, 0, stat.a_max, 0)
mean_line = line(stat.mean, -height / 3, stat.mean, height / 3)
for it in (whisker1, whisker2, mean_line):
it.setPen(self._pen_paramet)
vert_line.setPen(self._pen_dotted)
var_line = line(stat.mean - stat.dev, 0, stat.mean + stat.dev, 0)
var_line.setPen(self._pen_paramet)
box.extend([whisker1, whisker2, vert_line, mean_line, var_line])
if stat.q25 is not None and stat.q75 is not None:
mbox = FilterGraphicsRectItem(stat.conditions, stat.q25 * scale_x,
-height / 2,
(stat.q75 - stat.q25) * scale_x,
height)
mbox.setBrush(self._box_brush)
mbox.setPen(QPen(Qt.NoPen))
mbox.setZValue(-200)
box.append(mbox)
if stat.median is not None:
median_line = line(stat.median, -height / 2, stat.median,
height / 2)
median_line.setPen(self._pen_median)
median_line.setZValue(-150)
box.append(median_line)
return box
def strudel(self, dist, group_val_index=None):
attr = self.attribute
ss = np.sum(dist)
box = []
if ss < 1e-6:
cond = [FilterDiscrete(attr, None)]
if group_val_index is not None:
cond.append(FilterDiscrete(self.group_var, [group_val_index]))
box.append(FilterGraphicsRectItem(cond, 0, -10, 1, 10))
cum = 0
for i, v in enumerate(dist):
if v < 1e-6:
continue
if self.stretched:
v /= ss
v *= self.scale_x
cond = [FilterDiscrete(attr, [i])]
if group_val_index is not None:
cond.append(FilterDiscrete(self.group_var, [group_val_index]))
rect = FilterGraphicsRectItem(cond, cum + 1, -6, v - 2, 12)
rect.setBrush(QBrush(QColor(*attr.colors[i])))
rect.setPen(QPen(Qt.NoPen))
if self.stretched:
tooltip = "{}: {:.2f}%".format(attr.values[i],
100 * dist[i] / sum(dist))
else:
tooltip = "{}: {}".format(attr.values[i], int(dist[i]))
rect.setToolTip(tooltip)
text = QGraphicsTextItem(attr.values[i])
box.append(rect)
box.append(text)
cum += v
return box
def commit(self):
self.conditions = [
item.filter for item in self.box_scene.selectedItems()
if item.filter
]
selected, selection = None, []
if self.conditions:
selected = Values(self.conditions, conjunction=False)(self.dataset)
selection = np.in1d(self.dataset.ids,
selected.ids,
assume_unique=True).nonzero()[0]
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(
create_annotated_table(self.dataset, selection))
def show_posthoc(self):
def line(y0, y1):
it = self.box_scene.addLine(x, y0, x, y1, self._post_line_pen)
it.setZValue(-100)
self.posthoc_lines.append(it)
while self.posthoc_lines:
self.box_scene.removeItem(self.posthoc_lines.pop())
if self.compare == OWBoxPlot.CompareNone or len(self.stats) < 2:
return
if self.compare == OWBoxPlot.CompareMedians:
crit_line = "median"
else:
crit_line = "mean"
xs = []
height = 90 if self.show_annotations else 60
y_up = -len(self.stats) * height + 10
for pos, box_index in enumerate(self.order):
stat = self.stats[box_index]
x = getattr(stat, crit_line)
if x is None:
continue
x *= self.scale_x
xs.append(x * self.scale_x)
by = y_up + pos * height
line(by + 12, 3)
line(by - 12, by - 25)
used_to = []
last_to = to = 0
for frm, frm_x in enumerate(xs[:-1]):
for to in range(frm + 1, len(xs)):
if xs[to] - frm_x > 1.5:
to -= 1
break
if to in (last_to, frm):
continue
for rowi, used in enumerate(used_to):
if used < frm:
used_to[rowi] = to
break
else:
rowi = len(used_to)
used_to.append(to)
y = -6 - rowi * 6
it = self.box_scene.addLine(frm_x - 2, y, xs[to] + 2, y,
self._post_grp_pen)
self.posthoc_lines.append(it)
last_to = to
def get_widget_name_extension(self):
return self.attribute.name if self.attribute else None
def send_report(self):
self.report_plot()
text = ""
if self.attribute:
text += "Box plot for attribute '{}' ".format(self.attribute.name)
if self.group_var:
text += "grouped by '{}'".format(self.group_var.name)
if text:
self.report_caption(text)
class Label(QGraphicsSimpleTextItem):
"""Boxplot Label with settable maxWidth"""
# Minimum width to display label text
MIN_LABEL_WIDTH = 25
# padding bellow the text
PADDING = 3
__max_width = None
def maxWidth(self):
return self.__max_width
def setMaxWidth(self, max_width):
self.__max_width = max_width
def paint(self, painter, option, widget):
"""Overrides QGraphicsSimpleTextItem.paint
If label text is too long, it is elided
to fit into the allowed region
"""
if self.__max_width is None:
width = option.rect.width()
else:
width = self.__max_width
if width < self.MIN_LABEL_WIDTH:
# if space is too narrow, no label
return
fm = painter.fontMetrics()
text = fm.elidedText(self.text(), Qt.ElideRight, width)
painter.drawText(
option.rect.x(),
option.rect.y() + self.boundingRect().height() - self.PADDING,
text)
class OWSieveDiagram(OWWidget):
name = "Sieve Diagram"
description = "Visualize the observed and expected frequencies " \
"for a combination of values."
icon = "icons/SieveDiagram.svg"
priority = 200
keywords = []
class Inputs:
data = Input("Data", Table, default=True)
features = Input("Features", AttributeList)
class Outputs:
selected_data = Output("Selected Data", Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Table)
graph_name = "canvas"
want_control_area = False
settings_version = 1
settingsHandler = DomainContextHandler()
attr_x = ContextSetting(None)
attr_y = ContextSetting(None)
selection = ContextSetting(set())
xy_changed_manually = Signal(Variable, Variable)
def __init__(self):
# pylint: disable=missing-docstring
super().__init__()
self.data = self.discrete_data = None
self.attrs = []
self.input_features = None
self.areas = []
self.selection = set()
self.attr_box = gui.hBox(self.mainArea)
self.domain_model = DomainModel(valid_types=DomainModel.PRIMITIVE)
combo_args = dict(widget=self.attr_box,
master=self,
contentsLength=12,
callback=self.attr_changed,
sendSelectedValue=True,
valueType=str,
model=self.domain_model)
fixed_size = (QSizePolicy.Fixed, QSizePolicy.Fixed)
gui.comboBox(value="attr_x", **combo_args)
gui.widgetLabel(self.attr_box, "\u2715", sizePolicy=fixed_size)
gui.comboBox(value="attr_y", **combo_args)
self.vizrank, self.vizrank_button = SieveRank.add_vizrank(
self.attr_box, self, "Score Combinations", self.set_attr)
self.vizrank_button.setSizePolicy(*fixed_size)
self.canvas = QGraphicsScene()
self.canvasView = ViewWithPress(self.canvas,
self.mainArea,
handler=self.reset_selection)
self.mainArea.layout().addWidget(self.canvasView)
self.canvasView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.canvasView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
def sizeHint(self):
return QSize(450, 550)
def resizeEvent(self, event):
super().resizeEvent(event)
self.update_graph()
def showEvent(self, event):
super().showEvent(event)
self.update_graph()
@classmethod
def migrate_context(cls, context, version):
if not version:
settings.rename_setting(context, "attrX", "attr_x")
settings.rename_setting(context, "attrY", "attr_y")
settings.migrate_str_to_variable(context)
@Inputs.data
def set_data(self, data):
"""
Discretize continuous attributes, and put all attributes and discrete
metas into self.attrs.
Select the first two attributes unless context overrides this.
Method `resolve_shown_attributes` is called to use the attributes from
the input, if it exists and matches the attributes in the data.
Remove selection; again let the context override this.
Initialize the vizrank dialog, but don't show it.
Args:
data (Table): input data
"""
if isinstance(data, SqlTable) and data.approx_len() > LARGE_TABLE:
data = data.sample_time(DEFAULT_SAMPLE_TIME)
self.closeContext()
self.data = data
self.areas = []
self.selection = set()
if self.data is None:
self.attrs[:] = []
self.domain_model.set_domain(None)
self.discrete_data = None
else:
self.domain_model.set_domain(data.domain)
self.attrs = [x for x in self.domain_model if isinstance(x, Variable)]
if self.attrs:
self.attr_x = self.attrs[0]
self.attr_y = self.attrs[len(self.attrs) > 1]
else:
self.attr_x = self.attr_y = None
self.areas = []
self.selection = set()
self.openContext(self.data)
if self.data:
self.discrete_data = self.sparse_to_dense(data, True)
self.resolve_shown_attributes()
self.update_graph()
self.update_selection()
self.vizrank.initialize()
self.vizrank_button.setEnabled(self.data is not None
and len(self.data) > 1
and len(self.data.domain.attributes) > 1
and not self.data.is_sparse())
def set_attr(self, attr_x, attr_y):
self.attr_x, self.attr_y = attr_x, attr_y
self.update_attr()
def attr_changed(self):
self.update_attr()
self.xy_changed_manually.emit(self.attr_x, self.attr_y)
def update_attr(self):
"""Update the graph and selection."""
self.selection = set()
self.discrete_data = self.sparse_to_dense(self.data)
self.update_graph()
self.update_selection()
def sparse_to_dense(self, data, init=False):
"""
Extracts two selected columns from sparse matrix.
GH-2260
"""
def discretizer(data):
if any(attr.is_continuous for attr in chain(
data.domain.variables, data.domain.metas)):
discretize = Discretize(method=EqualFreq(n=4),
remove_const=False,
discretize_classes=True,
discretize_metas=True)
return discretize(data).to_dense()
return data
if not data.is_sparse() and not init:
return self.discrete_data
if data.is_sparse():
attrs = {self.attr_x, self.attr_y}
new_domain = data.domain.select_columns(attrs)
data = Table.from_table(new_domain, data)
return discretizer(data)
@Inputs.features
def set_input_features(self, attr_list):
"""
Handler for the Features signal.
The method stores the attributes and calls `resolve_shown_attributes`
Args:
attr_list (AttributeList): data from the signal
"""
self.input_features = attr_list
self.resolve_shown_attributes()
self.update_selection()
def resolve_shown_attributes(self):
"""
Use the attributes from the input signal if the signal is present
and at least two attributes appear in the domain. If there are
multiple, use the first two. Combos are disabled if inputs are used.
"""
self.warning()
self.attr_box.setEnabled(True)
self.vizrank.setEnabled(True)
if not self.input_features: # None or empty
return
features = [f for f in self.input_features if f in self.domain_model]
if not features:
self.warning(
"Features from the input signal are not present in the data")
return
old_attrs = self.attr_x, self.attr_y
self.attr_x, self.attr_y = [f for f in (features * 2)[:2]]
self.attr_box.setEnabled(False)
self.vizrank.setEnabled(False)
if (self.attr_x, self.attr_y) != old_attrs:
self.selection = set()
self.update_graph()
def reset_selection(self):
self.selection = set()
self.update_selection()
def select_area(self, area, event):
"""
Add or remove the clicked area from the selection
Args:
area (QRect): the area that is clicked
event (QEvent): event description
"""
if event.button() != Qt.LeftButton:
return
index = self.areas.index(area)
if event.modifiers() & Qt.ControlModifier:
self.selection ^= {index}
else:
self.selection = {index}
self.update_selection()
def update_selection(self):
"""
Update the graph (pen width) to show the current selection.
Filter and output the data.
"""
if self.areas is None or not self.selection:
self.Outputs.selected_data.send(None)
self.Outputs.annotated_data.send(
create_annotated_table(self.data, []))
return
filts = []
for i, area in enumerate(self.areas):
if i in self.selection:
width = 4
val_x, val_y = area.value_pair
filts.append(
filter.Values([
filter.FilterDiscrete(self.attr_x.name, [val_x]),
filter.FilterDiscrete(self.attr_y.name, [val_y])
]))
else:
width = 1
pen = area.pen()
pen.setWidth(width)
area.setPen(pen)
if len(filts) == 1:
filts = filts[0]
else:
filts = filter.Values(filts, conjunction=False)
selection = filts(self.discrete_data)
idset = set(selection.ids)
sel_idx = [i for i, id in enumerate(self.data.ids) if id in idset]
if self.discrete_data is not self.data:
selection = self.data[sel_idx]
self.Outputs.selected_data.send(selection)
self.Outputs.annotated_data.send(
create_annotated_table(self.data, sel_idx))
def update_graph(self):
# Function uses weird names like r, g, b, but it does it with utmost
# caution, hence
# pylint: disable=invalid-name
"""Update the graph."""
def text(txt, *args, **kwargs):
text = html_text = None
if "max_width" in kwargs:
text = txt
else:
html_text = to_html(txt)
return CanvasText(self.canvas,
text,
html_text=html_text,
*args,
**kwargs)
def width(txt):
return text(txt, 0, 0, show=False).boundingRect().width()
def height(txt):
return text(txt, 0, 0, show=False).boundingRect().height()
def fmt(val):
return str(int(val)) if val % 1 == 0 else "{:.2f}".format(val)
def show_pearson(rect, pearson, pen_width):
"""
Color the given rectangle according to its corresponding
standardized Pearson residual.
Args:
rect (QRect): the rectangle being drawn
pearson (float): signed standardized pearson residual
pen_width (int): pen width (bolder pen is used for selection)
"""
r = rect.rect()
x, y, w, h = r.x(), r.y(), r.width(), r.height()
if w == 0 or h == 0:
return
r = b = 255
if pearson > 0:
r = g = max(255 - 20 * pearson, 55)
elif pearson < 0:
b = g = max(255 + 20 * pearson, 55)
else:
r = g = b = 224
rect.setBrush(QBrush(QColor(r, g, b)))
pen_color = QColor(255 * (r == 255), 255 * (g == 255),
255 * (b == 255))
pen = QPen(pen_color, pen_width)
rect.setPen(pen)
if pearson > 0:
pearson = min(pearson, 10)
dist = 20 - 1.6 * pearson
else:
pearson = max(pearson, -10)
dist = 20 - 8 * pearson
pen.setWidth(1)
def _offseted_line(ax, ay):
r = QGraphicsLineItem(x + ax, y + ay, x + (ax or w),
y + (ay or h))
self.canvas.addItem(r)
r.setPen(pen)
ax = dist
while ax < w:
_offseted_line(ax, 0)
ax += dist
ay = dist
while ay < h:
_offseted_line(0, ay)
ay += dist
def make_tooltip():
"""Create the tooltip. The function uses local variables from
the enclosing scope."""
# pylint: disable=undefined-loop-variable
def _oper(attr, txt):
if self.data.domain[attr.name] == ddomain[attr.name]:
return " = "
return " " if txt[0] in "<≥" else " in "
xt, yt = [
"<b>{attr}{eq}{val_name}</b>: {obs}/{n} ({p:.0f} %)".format(
attr=to_html(attr.name),
eq=_oper(attr, val_name),
val_name=to_html(val_name),
obs=fmt(prob * n),
n=int(n),
p=100 * prob) for attr, val_name, prob in [(
attr_x, xval_name,
chi.probs_x[x]), (attr_y, yval_name, chi.probs_y[y])]
]
ct = """<b>combination of values: </b><br/>
expected {exp} ({p_exp:.0f} %)<br/>
observed {obs} ({p_obs:.0f} %)""".format(
exp=fmt(chi.expected[y, x]),
p_exp=100 * chi.expected[y, x] / n,
obs=fmt(chi.observed[y, x]),
p_obs=100 * chi.observed[y, x] / n)
return f"{xt}<br/>{yt}<hr/>{ct}"
for item in self.canvas.items():
self.canvas.removeItem(item)
if self.data is None or len(self.data) == 0 or \
self.attr_x is None or self.attr_y is None:
return
ddomain = self.discrete_data.domain
attr_x, attr_y = self.attr_x, self.attr_y
disc_x, disc_y = ddomain[attr_x.name], ddomain[attr_y.name]
view = self.canvasView
chi = ChiSqStats(self.discrete_data, disc_x, disc_y)
max_ylabel_w = max((width(val) for val in disc_y.values), default=0)
max_ylabel_w = min(max_ylabel_w, 200)
x_off = height(attr_y.name) + max_ylabel_w
y_off = 15
square_size = min(view.width() - x_off - 35,
view.height() - y_off - 80)
square_size = max(square_size, 10)
self.canvasView.setSceneRect(0, 0, view.width(), view.height())
if not disc_x.values or not disc_y.values:
text_ = "Features {} and {} have no values".format(disc_x, disc_y) \
if not disc_x.values and \
not disc_y.values and \
disc_x != disc_y \
else \
"Feature {} has no values".format(
disc_x if not disc_x.values else disc_y)
text(text_,
view.width() / 2 + 70,
view.height() / 2, Qt.AlignRight | Qt.AlignVCenter)
return
n = chi.n
curr_x = x_off
max_xlabel_h = 0
self.areas = []
for x, (px, xval_name) in enumerate(zip(chi.probs_x, disc_x.values)):
if px == 0:
continue
width = square_size * px
curr_y = y_off
for y in range(len(chi.probs_y) - 1, -1, -1): # bottom-up order
py = chi.probs_y[y]
yval_name = disc_y.values[y]
if py == 0:
continue
height = square_size * py
selected = len(self.areas) in self.selection
rect = CanvasRectangle(self.canvas,
curr_x + 2,
curr_y + 2,
width - 4,
height - 4,
z=-10,
onclick=self.select_area)
rect.value_pair = x, y
self.areas.append(rect)
show_pearson(rect, chi.residuals[y, x], 3 * selected)
rect.setToolTip(make_tooltip())
if x == 0:
text(yval_name, x_off, curr_y + height / 2,
Qt.AlignRight | Qt.AlignVCenter)
curr_y += height
xl = text(xval_name,
curr_x + width / 2,
y_off + square_size,
Qt.AlignHCenter | Qt.AlignTop,
max_width=width)
max_xlabel_h = max(int(xl.boundingRect().height()), max_xlabel_h)
curr_x += width
bottom = y_off + square_size + max_xlabel_h
text(attr_y.name,
0,
y_off + square_size / 2,
Qt.AlignLeft | Qt.AlignVCenter,
bold=True,
vertical=True)
text(attr_x.name,
x_off + square_size / 2,
bottom,
Qt.AlignHCenter | Qt.AlignTop,
bold=True)
bottom += 30
xl = text("χ²={:.2f}, p={:.3f}".format(chi.chisq, chi.p), 0, bottom)
# Assume similar height for both lines
text("N = " + fmt(chi.n), 0, bottom - xl.boundingRect().height())
def get_widget_name_extension(self):
if self.data is not None:
return "{} vs {}".format(self.attr_x.name, self.attr_y.name)
return None
def send_report(self):
self.report_plot()
class OWSieveDiagram(OWWidget):
name = "Sieve Diagram"
description = "Visualize the observed and expected frequencies " \
"for a combination of values."
icon = "icons/SieveDiagram.svg"
priority = 200
class Inputs:
data = Input("Data", Table, default=True)
features = Input("Features", AttributeList)
class Outputs:
selected_data = Output("Selected Data", Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Table)
graph_name = "canvas"
want_control_area = False
settings_version = 1
settingsHandler = DomainContextHandler()
attr_x = ContextSetting(None)
attr_y = ContextSetting(None)
selection = ContextSetting(set())
def __init__(self):
# pylint: disable=missing-docstring
super().__init__()
self.data = self.discrete_data = None
self.attrs = []
self.input_features = None
self.areas = []
self.selection = set()
self.attr_box = gui.hBox(self.mainArea)
self.domain_model = DomainModel(valid_types=DomainModel.PRIMITIVE)
combo_args = dict(
widget=self.attr_box, master=self, contentsLength=12,
callback=self.update_attr, sendSelectedValue=True, valueType=str,
model=self.domain_model)
fixed_size = (QSizePolicy.Fixed, QSizePolicy.Fixed)
gui.comboBox(value="attr_x", **combo_args)
gui.widgetLabel(self.attr_box, "\u2715", sizePolicy=fixed_size)
gui.comboBox(value="attr_y", **combo_args)
self.vizrank, self.vizrank_button = SieveRank.add_vizrank(
self.attr_box, self, "Score Combinations", self.set_attr)
self.vizrank_button.setSizePolicy(*fixed_size)
self.canvas = QGraphicsScene()
self.canvasView = ViewWithPress(
self.canvas, self.mainArea, handler=self.reset_selection)
self.mainArea.layout().addWidget(self.canvasView)
self.canvasView.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.canvasView.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
def sizeHint(self):
return QSize(450, 550)
def resizeEvent(self, event):
super().resizeEvent(event)
self.update_graph()
def showEvent(self, event):
super().showEvent(event)
self.update_graph()
@classmethod
def migrate_context(cls, context, version):
if not version:
settings.rename_setting(context, "attrX", "attr_x")
settings.rename_setting(context, "attrY", "attr_y")
settings.migrate_str_to_variable(context)
@Inputs.data
def set_data(self, data):
"""
Discretize continuous attributes, and put all attributes and discrete
metas into self.attrs.
Select the first two attributes unless context overrides this.
Method `resolve_shown_attributes` is called to use the attributes from
the input, if it exists and matches the attributes in the data.
Remove selection; again let the context override this.
Initialize the vizrank dialog, but don't show it.
Args:
data (Table): input data
"""
if isinstance(data, SqlTable) and data.approx_len() > LARGE_TABLE:
data = data.sample_time(DEFAULT_SAMPLE_TIME)
self.closeContext()
self.data = data
self.areas = []
self.selection = set()
if self.data is None:
self.attrs[:] = []
self.domain_model.set_domain(None)
self.discrete_data = None
else:
self.domain_model.set_domain(data.domain)
self.attrs = [x for x in self.domain_model if isinstance(x, Variable)]
if self.attrs:
self.attr_x = self.attrs[0]
self.attr_y = self.attrs[len(self.attrs) > 1]
else:
self.attr_x = self.attr_y = None
self.areas = []
self.selection = set()
self.openContext(self.data)
if self.data:
self.discrete_data = self.sparse_to_dense(data, True)
self.resolve_shown_attributes()
self.update_graph()
self.update_selection()
self.vizrank.initialize()
self.vizrank_button.setEnabled(
self.data is not None and len(self.data) > 1 and
len(self.data.domain.attributes) > 1 and not self.data.is_sparse())
def set_attr(self, attr_x, attr_y):
self.attr_x, self.attr_y = attr_x, attr_y
self.update_attr()
def update_attr(self):
"""Update the graph and selection."""
self.selection = set()
self.discrete_data = self.sparse_to_dense(self.data)
self.update_graph()
self.update_selection()
def sparse_to_dense(self, data, init=False):
"""
Extracts two selected columns from sparse matrix.
GH-2260
"""
def discretizer(data):
if any(attr.is_continuous for attr in chain(data.domain.variables, data.domain.metas)):
discretize = Discretize(
method=EqualFreq(n=4), remove_const=False,
discretize_classes=True, discretize_metas=True)
return discretize(data).to_dense()
return data
if not data.is_sparse() and not init:
return self.discrete_data
if data.is_sparse():
attrs = {self.attr_x,
self.attr_y}
new_domain = data.domain.select_columns(attrs)
data = Table.from_table(new_domain, data)
return discretizer(data)
@Inputs.features
def set_input_features(self, attr_list):
"""
Handler for the Features signal.
The method stores the attributes and calls `resolve_shown_attributes`
Args:
attr_list (AttributeList): data from the signal
"""
self.input_features = attr_list
self.resolve_shown_attributes()
self.update_selection()
def resolve_shown_attributes(self):
"""
Use the attributes from the input signal if the signal is present
and at least two attributes appear in the domain. If there are
multiple, use the first two. Combos are disabled if inputs are used.
"""
self.warning()
self.attr_box.setEnabled(True)
if not self.input_features: # None or empty
return
features = [f for f in self.input_features if f in self.domain_model]
if not features:
self.warning(
"Features from the input signal are not present in the data")
return
old_attrs = self.attr_x, self.attr_y
self.attr_x, self.attr_y = [f for f in (features * 2)[:2]]
self.attr_box.setEnabled(False)
if (self.attr_x, self.attr_y) != old_attrs:
self.selection = set()
self.update_graph()
def reset_selection(self):
self.selection = set()
self.update_selection()
def select_area(self, area, event):
"""
Add or remove the clicked area from the selection
Args:
area (QRect): the area that is clicked
event (QEvent): event description
"""
if event.button() != Qt.LeftButton:
return
index = self.areas.index(area)
if event.modifiers() & Qt.ControlModifier:
self.selection ^= {index}
else:
self.selection = {index}
self.update_selection()
def update_selection(self):
"""
Update the graph (pen width) to show the current selection.
Filter and output the data.
"""
if self.areas is None or not self.selection:
self.Outputs.selected_data.send(None)
self.Outputs.annotated_data.send(create_annotated_table(self.data, []))
return
filts = []
for i, area in enumerate(self.areas):
if i in self.selection:
width = 4
val_x, val_y = area.value_pair
filts.append(
filter.Values([
filter.FilterDiscrete(self.attr_x.name, [val_x]),
filter.FilterDiscrete(self.attr_y.name, [val_y])
]))
else:
width = 1
pen = area.pen()
pen.setWidth(width)
area.setPen(pen)
if len(filts) == 1:
filts = filts[0]
else:
filts = filter.Values(filts, conjunction=False)
selection = filts(self.discrete_data)
idset = set(selection.ids)
sel_idx = [i for i, id in enumerate(self.data.ids) if id in idset]
if self.discrete_data is not self.data:
selection = self.data[sel_idx]
self.Outputs.selected_data.send(selection)
self.Outputs.annotated_data.send(create_annotated_table(self.data, sel_idx))
def update_graph(self):
# Function uses weird names like r, g, b, but it does it with utmost
# caution, hence
# pylint: disable=invalid-name
"""Update the graph."""
def text(txt, *args, **kwargs):
return CanvasText(self.canvas, "", html_text=to_html(txt),
*args, **kwargs)
def width(txt):
return text(txt, 0, 0, show=False).boundingRect().width()
def fmt(val):
return str(int(val)) if val % 1 == 0 else "{:.2f}".format(val)
def show_pearson(rect, pearson, pen_width):
"""
Color the given rectangle according to its corresponding
standardized Pearson residual.
Args:
rect (QRect): the rectangle being drawn
pearson (float): signed standardized pearson residual
pen_width (int): pen width (bolder pen is used for selection)
"""
r = rect.rect()
x, y, w, h = r.x(), r.y(), r.width(), r.height()
if w == 0 or h == 0:
return
r = b = 255
if pearson > 0:
r = g = max(255 - 20 * pearson, 55)
elif pearson < 0:
b = g = max(255 + 20 * pearson, 55)
else:
r = g = b = 224
rect.setBrush(QBrush(QColor(r, g, b)))
pen_color = QColor(255 * (r == 255), 255 * (g == 255),
255 * (b == 255))
pen = QPen(pen_color, pen_width)
rect.setPen(pen)
if pearson > 0:
pearson = min(pearson, 10)
dist = 20 - 1.6 * pearson
else:
pearson = max(pearson, -10)
dist = 20 - 8 * pearson
pen.setWidth(1)
def _offseted_line(ax, ay):
r = QGraphicsLineItem(x + ax, y + ay, x + (ax or w),
y + (ay or h))
self.canvas.addItem(r)
r.setPen(pen)
ax = dist
while ax < w:
_offseted_line(ax, 0)
ax += dist
ay = dist
while ay < h:
_offseted_line(0, ay)
ay += dist
def make_tooltip():
"""Create the tooltip. The function uses local variables from
the enclosing scope."""
# pylint: disable=undefined-loop-variable
def _oper(attr, txt):
if self.data.domain[attr.name] is ddomain[attr.name]:
return "="
return " " if txt[0] in "<≥" else " in "
return (
"<b>{attr_x}{xeq}{xval_name}</b>: {obs_x}/{n} ({p_x:.0f} %)".
format(attr_x=to_html(attr_x.name),
xeq=_oper(attr_x, xval_name),
xval_name=to_html(xval_name),
obs_x=fmt(chi.probs_x[x] * n),
n=int(n),
p_x=100 * chi.probs_x[x]) +
"<br/>" +
"<b>{attr_y}{yeq}{yval_name}</b>: {obs_y}/{n} ({p_y:.0f} %)".
format(attr_y=to_html(attr_y.name),
yeq=_oper(attr_y, yval_name),
yval_name=to_html(yval_name),
obs_y=fmt(chi.probs_y[y] * n),
n=int(n),
p_y=100 * chi.probs_y[y]) +
"<hr/>" +
"""<b>combination of values: </b><br/>
expected {exp} ({p_exp:.0f} %)<br/>
observed {obs} ({p_obs:.0f} %)""".
format(exp=fmt(chi.expected[y, x]),
p_exp=100 * chi.expected[y, x] / n,
obs=fmt(chi.observed[y, x]),
p_obs=100 * chi.observed[y, x] / n))
for item in self.canvas.items():
self.canvas.removeItem(item)
if self.data is None or len(self.data) == 0 or \
self.attr_x is None or self.attr_y is None:
return
ddomain = self.discrete_data.domain
attr_x, attr_y = self.attr_x, self.attr_y
disc_x, disc_y = ddomain[attr_x.name], ddomain[attr_y.name]
view = self.canvasView
chi = ChiSqStats(self.discrete_data, disc_x, disc_y)
max_ylabel_w = max((width(val) for val in disc_y.values), default=0)
max_ylabel_w = min(max_ylabel_w, 200)
x_off = width(attr_x.name) + max_ylabel_w
y_off = 15
square_size = min(view.width() - x_off - 35, view.height() - y_off - 80)
square_size = max(square_size, 10)
self.canvasView.setSceneRect(0, 0, view.width(), view.height())
if not disc_x.values or not disc_y.values:
text_ = "Features {} and {} have no values".format(disc_x, disc_y) \
if not disc_x.values and \
not disc_y.values and \
disc_x != disc_y \
else \
"Feature {} has no values".format(
disc_x if not disc_x.values else disc_y)
text(text_, view.width() / 2 + 70, view.height() / 2,
Qt.AlignRight | Qt.AlignVCenter)
return
n = chi.n
curr_x = x_off
max_xlabel_h = 0
self.areas = []
for x, (px, xval_name) in enumerate(zip(chi.probs_x, disc_x.values)):
if px == 0:
continue
width = square_size * px
curr_y = y_off
for y in range(len(chi.probs_y) - 1, -1, -1): # bottom-up order
py = chi.probs_y[y]
yval_name = disc_y.values[y]
if py == 0:
continue
height = square_size * py
selected = len(self.areas) in self.selection
rect = CanvasRectangle(
self.canvas, curr_x + 2, curr_y + 2, width - 4, height - 4,
z=-10, onclick=self.select_area)
rect.value_pair = x, y
self.areas.append(rect)
show_pearson(rect, chi.residuals[y, x], 3 * selected)
rect.setToolTip(make_tooltip())
if x == 0:
text(yval_name, x_off, curr_y + height / 2,
Qt.AlignRight | Qt.AlignVCenter)
curr_y += height
xl = text(xval_name, curr_x + width / 2, y_off + square_size,
Qt.AlignHCenter | Qt.AlignTop)
max_xlabel_h = max(int(xl.boundingRect().height()), max_xlabel_h)
curr_x += width
bottom = y_off + square_size + max_xlabel_h
text(attr_y.name, 0, y_off + square_size / 2,
Qt.AlignLeft | Qt.AlignVCenter, bold=True, vertical=True)
text(attr_x.name, x_off + square_size / 2, bottom,
Qt.AlignHCenter | Qt.AlignTop, bold=True)
bottom += 30
xl = text("χ²={:.2f}, p={:.3f}".format(chi.chisq, chi.p),
0, bottom)
# Assume similar height for both lines
text("N = " + fmt(chi.n), 0, bottom - xl.boundingRect().height())
def get_widget_name_extension(self):
if self.data is not None:
return "{} vs {}".format(self.attr_x.name, self.attr_y.name)
def send_report(self):
self.report_plot()
class MainForm(QDialog):
def __init__(self, parent=None, filename=None):
super(MainForm, self).__init__(parent)
self.view = GraphicsView()
background = QPixmap(filename)
# assume screnshots were taken on the same system stamper is being used on
# DPI check might be more robust, can be added if needed
background.setDevicePixelRatio(self.devicePixelRatioF())
self.filename = os.path.splitext(filename)[0]
if ("-%s" % ORG) in self.filename:
self.filename = self.filename[:-len(ORG)-5] + ".png"
# self.view.setBackgroundBrush(QBrush(background))
# self.view.setCacheMode(QGraphicsView.CacheBackground)
# self.view.setDragMode(QGraphicsView.ScrollHandDrag)
self.scene = QGraphicsScene(self)
self.scene.addPixmap(background)
global scene
scene = self.scene
self.view.dialog = self
self.view.setScene(self.scene)
self.prevPoint = QPoint()
self.lastStamp = -1
buttonLayout = QVBoxLayout()
for text, slot in (
("&Tag", self.addTag),
("Align &bottom", self.alignBottom),
("Align &left", self.alignLeft),
("&Save", self.save),
("&Quit", self.accept)):
button = QPushButton(text)
button.clicked.connect(slot)
if not MAC:
button.setFocusPolicy(Qt.NoFocus)
self.lineedit.returnPressed.connect(self.updateUi)
if text == "&Save":
buttonLayout.addStretch(5)
if text == "&Quit":
buttonLayout.addStretch(1)
buttonLayout.addWidget(button)
buttonLayout.addStretch()
size = background.size() / background.devicePixelRatioF()
self.view.resize(size.width(), size.height())
self.scene.setSceneRect(0, 0, size.width(), size.height())
self.view.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)
layout = QHBoxLayout()
layout.addWidget(self.view, 1)
layout.addLayout(buttonLayout)
self.setLayout(layout)
self.setWindowTitle(AppName)
info_name = self.filename + "-" + TAG + ".txt"
if os.path.exists(info_name):
for tag, x, y in [line.strip().split("\t") for line in open(info_name, "rt").readlines()]:
self.addTag(int(tag), QPointF(int(x), int(y)), adjust_position=False)
global Dirty; Dirty=False
self.show()
self.raise_()
def reject(self):
self.accept()
def accept(self):
self.offerSave()
QDialog.accept(self)
def offerSave(self):
if (Dirty and QMessageBox.question(self, "%s - Unsaved Changes" % AppName, "Save unsaved changes?",
QMessageBox.Yes|QMessageBox.No) == QMessageBox.Yes):
self.save()
def position(self):
return QPointF(self.view.mapFromGlobal(QCursor.pos()))
def addTag(self, stamp=None, position=None, adjust_position=True):
if not position: position = self.position()
if stamp is not None:
self.lastStamp = stamp
else:
self.lastStamp += 1
item = StampItem(self.lastStamp, position, self.scene, adjust_position=adjust_position)
item.update()
global Dirty; Dirty = True
def save(self):
vals = sorted([(item.stamp, item.pos().x(), item.pos().y()) for item in self.scene.items() if isinstance(item, StampItem)])
info_name = "%s-%s.txt" %(self.filename, TAG)
f = open(info_name, "wt")
for tag, x, y in vals:
f.write("%s\t%d\t%d\n" % (tag, x, y))
f.close()
save_png(self.filename, [(x-5, y-5) for _, x, y in vals], self.devicePixelRatioF())
global Dirty; Dirty = False
def renumberTags(self):
"""Number the tags by skipping the missing values"""
vals = sorted([(item.stamp, item) for item in self.scene.items() if isinstance(item, StampItem)])
for i, (_, v) in enumerate(vals):
v.setStamp(i)
if not vals: i = -1
self.lastStamp = i
global Dirty; Dirty = True
def alignBottom(self):
"""Align tags horizontally, use bottom margin as a reference"""
items = [item for item in self.scene.selectedItems() if isinstance(item, StampItem)]
if items:
ref_y = max(item.pos().y() for item in items)
for item in items:
item.setY(ref_y)
global Dirty; Dirty = True
def alignLeft(self):
"""Align tags vertically, use left margin as a reference"""
items = [item for item in self.scene.selectedItems() if isinstance(item, StampItem)]
if items:
ref_x = min(item.pos().x() for item in items)
for item in items:
item.setX(ref_x)
global Dirty; Dirty = True
def items(self, *args, **kwargs):
items = QGraphicsScene.items(self, *args, **kwargs)
return list(map(toGraphicsObjectIfPossible, items))
class OWBoxPlot(widget.OWWidget):
"""
Here's how the widget's functions call each other:
- `set_data` is a signal handler fills the list boxes and calls
`grouping_changed`.
- `grouping_changed` handles changes of grouping attribute: it enables or
disables the box for ordering, orders attributes and calls `attr_changed`.
- `attr_changed` handles changes of attribute. It recomputes box data by
calling `compute_box_data`, shows the appropriate display box
(discrete/continuous) and then calls`layout_changed`
- `layout_changed` constructs all the elements for the scene (as lists of
QGraphicsItemGroup) and calls `display_changed`. It is called when the
attribute or grouping is changed (by attr_changed) and on resize event.
- `display_changed` puts the elements corresponding to the current display
settings on the scene. It is called when the elements are reconstructed
(layout is changed due to selection of attributes or resize event), or
when the user changes display settings or colors.
For discrete attributes, the flow is a bit simpler: the elements are not
constructed in advance (by layout_changed). Instead, layout_changed and
display_changed call display_changed_disc that draws everything.
"""
name = "Box Plot"
description = "Visualize the distribution of feature values in a box plot."
icon = "icons/BoxPlot.svg"
priority = 100
keywords = ["whisker"]
class Inputs:
data = Input("Data", Orange.data.Table)
class Outputs:
selected_data = Output("Selected Data", Orange.data.Table, default=True)
annotated_data = Output(ANNOTATED_DATA_SIGNAL_NAME, Orange.data.Table)
#: Comparison types for continuous variables
CompareNone, CompareMedians, CompareMeans = 0, 1, 2
settingsHandler = DomainContextHandler()
conditions = ContextSetting([])
attribute = ContextSetting(None)
order_by_importance = Setting(False)
group_var = ContextSetting(None)
show_annotations = Setting(True)
compare = Setting(CompareMeans)
stattest = Setting(0)
sig_threshold = Setting(0.05)
stretched = Setting(True)
show_labels = Setting(True)
sort_freqs = Setting(False)
auto_commit = Setting(True)
_sorting_criteria_attrs = {
CompareNone: "", CompareMedians: "median", CompareMeans: "mean"
}
_pen_axis_tick = QPen(Qt.white, 5)
_pen_axis = QPen(Qt.darkGray, 3)
_pen_median = QPen(QBrush(QColor(0xff, 0xff, 0x00)), 2)
_pen_paramet = QPen(QBrush(QColor(0x33, 0x00, 0xff)), 2)
_pen_dotted = QPen(QBrush(QColor(0x33, 0x00, 0xff)), 1)
_pen_dotted.setStyle(Qt.DotLine)
_post_line_pen = QPen(Qt.lightGray, 2)
_post_grp_pen = QPen(Qt.lightGray, 4)
for pen in (_pen_paramet, _pen_median, _pen_dotted,
_pen_axis, _pen_axis_tick, _post_line_pen, _post_grp_pen):
pen.setCosmetic(True)
pen.setCapStyle(Qt.RoundCap)
pen.setJoinStyle(Qt.RoundJoin)
_pen_axis_tick.setCapStyle(Qt.FlatCap)
_box_brush = QBrush(QColor(0x33, 0x88, 0xff, 0xc0))
_axis_font = QFont()
_axis_font.setPixelSize(12)
_label_font = QFont()
_label_font.setPixelSize(11)
_attr_brush = QBrush(QColor(0x33, 0x00, 0xff))
graph_name = "box_scene"
def __init__(self):
super().__init__()
self.stats = []
self.dataset = None
self.posthoc_lines = []
self.label_txts = self.mean_labels = self.boxes = self.labels = \
self.label_txts_all = self.attr_labels = self.order = []
self.scale_x = self.scene_min_x = self.scene_width = 0
self.label_width = 0
self.attrs = VariableListModel()
view = gui.listView(
self.controlArea, self, "attribute", box="Variable",
model=self.attrs, callback=self.attr_changed)
view.setMinimumSize(QSize(30, 30))
# Any other policy than Ignored will let the QListBox's scrollbar
# set the minimal height (see the penultimate paragraph of
# http://doc.qt.io/qt-4.8/qabstractscrollarea.html#addScrollBarWidget)
view.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Ignored)
gui.separator(view.box, 6, 6)
self.cb_order = gui.checkBox(
view.box, self, "order_by_importance",
"Order by relevance",
tooltip="Order by 𝜒² or ANOVA over the subgroups",
callback=self.apply_sorting)
self.group_vars = DomainModel(
placeholder="None", separators=False,
valid_types=Orange.data.DiscreteVariable)
self.group_view = view = gui.listView(
self.controlArea, self, "group_var", box="Subgroups",
model=self.group_vars, callback=self.grouping_changed)
view.setEnabled(False)
view.setMinimumSize(QSize(30, 30))
# See the comment above
view.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Ignored)
# TODO: move Compare median/mean to grouping box
# The vertical size policy is needed to let only the list views expand
self.display_box = gui.vBox(
self.controlArea, "Display",
sizePolicy=(QSizePolicy.Minimum, QSizePolicy.Maximum),
addSpace=False)
gui.checkBox(self.display_box, self, "show_annotations", "Annotate",
callback=self.display_changed)
self.compare_rb = gui.radioButtonsInBox(
self.display_box, self, 'compare',
btnLabels=["No comparison", "Compare medians", "Compare means"],
callback=self.layout_changed)
# The vertical size policy is needed to let only the list views expand
self.stretching_box = box = gui.vBox(
self.controlArea, box="Display",
sizePolicy=(QSizePolicy.Minimum, QSizePolicy.Fixed))
self.stretching_box.sizeHint = self.display_box.sizeHint
gui.checkBox(
box, self, 'stretched', "Stretch bars",
callback=self.display_changed)
gui.checkBox(
box, self, 'show_labels', "Show box labels",
callback=self.display_changed)
self.sort_cb = gui.checkBox(
box, self, 'sort_freqs', "Sort by subgroup frequencies",
callback=self.display_changed)
gui.rubber(box)
gui.auto_commit(self.controlArea, self, "auto_commit",
"Send Selection", "Send Automatically")
gui.vBox(self.mainArea, addSpace=True)
self.box_scene = QGraphicsScene()
self.box_scene.selectionChanged.connect(self.commit)
self.box_view = QGraphicsView(self.box_scene)
self.box_view.setRenderHints(QPainter.Antialiasing |
QPainter.TextAntialiasing |
QPainter.SmoothPixmapTransform)
self.box_view.viewport().installEventFilter(self)
self.mainArea.layout().addWidget(self.box_view)
e = gui.hBox(self.mainArea, addSpace=False)
self.infot1 = gui.widgetLabel(e, "<center>No test results.</center>")
self.mainArea.setMinimumWidth(300)
self.stats = self.dist = self.conts = []
self.is_continuous = False
self.update_display_box()
def sizeHint(self):
return QSize(900, 500)
def eventFilter(self, obj, event):
if obj is self.box_view.viewport() and \
event.type() == QEvent.Resize:
self.layout_changed()
return super().eventFilter(obj, event)
def reset_attrs(self, domain):
self.attrs[:] = [
var for var in chain(
domain.class_vars, domain.metas, domain.attributes)
if var.is_primitive()]
# noinspection PyTypeChecker
@Inputs.data
def set_data(self, dataset):
if dataset is not None and (
not bool(dataset) or not len(dataset.domain) and not
any(var.is_primitive() for var in dataset.domain.metas)):
dataset = None
self.closeContext()
self.dataset = dataset
self.dist = self.stats = self.conts = []
self.group_var = None
self.attribute = None
if dataset:
domain = dataset.domain
self.group_vars.set_domain(domain)
self.group_view.setEnabled(len(self.group_vars) > 1)
self.reset_attrs(domain)
self.select_default_variables(domain)
self.openContext(self.dataset)
self.grouping_changed()
else:
self.reset_all_data()
self.commit()
def select_default_variables(self, domain):
# visualize first non-class variable, group by class (if present)
if len(self.attrs) > len(domain.class_vars):
self.attribute = self.attrs[len(domain.class_vars)]
elif self.attrs:
self.attribute = self.attrs[0]
if domain.class_var and domain.class_var.is_discrete:
self.group_var = domain.class_var
else:
self.group_var = None # Reset to trigger selection via callback
def apply_sorting(self):
def compute_score(attr):
if attr is group_var:
return 3
if attr.is_continuous:
# One-way ANOVA
col = data.get_column_view(attr)[0].astype(float)
groups = (col[group_col == i] for i in range(n_groups))
groups = (col[~np.isnan(col)] for col in groups)
groups = [group for group in groups if len(group)]
p = f_oneway(*groups)[1] if len(groups) > 1 else 2
else:
# Chi-square with the given distribution into groups
# (see degrees of freedom in computation of the p-value)
if not attr.values or not group_var.values:
return 2
observed = np.array(
contingency.get_contingency(data, group_var, attr))
observed = observed[observed.sum(axis=1) != 0, :]
observed = observed[:, observed.sum(axis=0) != 0]
if min(observed.shape) < 2:
return 2
expected = \
np.outer(observed.sum(axis=1), observed.sum(axis=0)) / \
np.sum(observed)
p = chisquare(observed.ravel(), f_exp=expected.ravel(),
ddof=n_groups - 1)[1]
if math.isnan(p):
return 2
return p
data = self.dataset
if data is None:
return
domain = data.domain
attribute = self.attribute
group_var = self.group_var
if self.order_by_importance and group_var is not None:
n_groups = len(group_var.values)
group_col = data.get_column_view(group_var)[0] if \
domain.has_continuous_attributes(
include_class=True, include_metas=True) else None
self.attrs.sort(key=compute_score)
else:
self.reset_attrs(domain)
self.attribute = attribute
def reset_all_data(self):
self.clear_scene()
self.infot1.setText("")
self.attrs.clear()
self.group_vars.set_domain(None)
self.group_view.setEnabled(False)
self.is_continuous = False
self.update_display_box()
def grouping_changed(self):
self.cb_order.setEnabled(self.group_var is not None)
self.apply_sorting()
self.attr_changed()
def select_box_items(self):
temp_cond = self.conditions.copy()
for box in self.box_scene.items():
if isinstance(box, FilterGraphicsRectItem):
box.setSelected(box.filter.conditions in
[c.conditions for c in temp_cond])
def attr_changed(self):
self.compute_box_data()
self.update_display_box()
self.layout_changed()
if self.is_continuous:
heights = 90 if self.show_annotations else 60
self.box_view.centerOn(self.scene_min_x + self.scene_width / 2,
-30 - len(self.stats) * heights / 2 + 45)
else:
self.box_view.centerOn(self.scene_width / 2,
-30 - len(self.boxes) * 40 / 2 + 45)
def compute_box_data(self):
attr = self.attribute
if not attr:
return
dataset = self.dataset
self.is_continuous = attr.is_continuous
if dataset is None or not self.is_continuous and not attr.values or \
self.group_var and not self.group_var.values:
self.stats = self.dist = self.conts = []
return
if self.group_var:
self.dist = []
self.conts = contingency.get_contingency(
dataset, attr, self.group_var)
if self.is_continuous:
stats, label_texts = [], []
for i, cont in enumerate(self.conts):
if np.sum(cont[1]):
stats.append(BoxData(cont, attr, i, self.group_var))
label_texts.append(self.group_var.values[i])
self.stats = stats
self.label_txts_all = label_texts
else:
self.label_txts_all = \
[v for v, c in zip(self.group_var.values, self.conts)
if np.sum(c) > 0]
else:
self.dist = distribution.get_distribution(dataset, attr)
self.conts = []
if self.is_continuous:
self.stats = [BoxData(self.dist, attr, None)]
self.label_txts_all = [""]
self.label_txts = [txts for stat, txts in zip(self.stats,
self.label_txts_all)
if stat.n > 0]
self.stats = [stat for stat in self.stats if stat.n > 0]
def update_display_box(self):
if self.is_continuous:
self.stretching_box.hide()
self.display_box.show()
self.compare_rb.setEnabled(self.group_var is not None)
else:
self.stretching_box.show()
self.display_box.hide()
self.sort_cb.setEnabled(self.group_var is not None)
def clear_scene(self):
self.closeContext()
self.box_scene.clearSelection()
self.box_scene.clear()
self.box_view.viewport().update()
self.attr_labels = []
self.labels = []
self.boxes = []
self.mean_labels = []
self.posthoc_lines = []
self.openContext(self.dataset)
def layout_changed(self):
attr = self.attribute
if not attr:
return
self.clear_scene()
if self.dataset is None or len(self.conts) == len(self.dist) == 0:
return
if not self.is_continuous:
self.display_changed_disc()
return
self.mean_labels = [self.mean_label(stat, attr, lab)
for stat, lab in zip(self.stats, self.label_txts)]
self.draw_axis()
self.boxes = [self.box_group(stat) for stat in self.stats]
self.labels = [self.label_group(stat, attr, mean_lab)
for stat, mean_lab in zip(self.stats, self.mean_labels)]
self.attr_labels = [QGraphicsSimpleTextItem(lab)
for lab in self.label_txts]
for it in chain(self.labels, self.attr_labels):
self.box_scene.addItem(it)
self.display_changed()
def display_changed(self):
if self.dataset is None:
return
if not self.is_continuous:
self.display_changed_disc()
return
self.order = list(range(len(self.stats)))
criterion = self._sorting_criteria_attrs[self.compare]
if criterion:
vals = [getattr(stat, criterion) for stat in self.stats]
overmax = max((val for val in vals if val is not None), default=0) \
+ 1
vals = [val if val is not None else overmax for val in vals]
self.order = sorted(self.order, key=vals.__getitem__)
heights = 90 if self.show_annotations else 60
for row, box_index in enumerate(self.order):
y = (-len(self.stats) + row) * heights + 10
for item in self.boxes[box_index]:
self.box_scene.addItem(item)
item.setY(y)
labels = self.labels[box_index]
if self.show_annotations:
labels.show()
labels.setY(y)
else:
labels.hide()
label = self.attr_labels[box_index]
label.setY(y - 15 - label.boundingRect().height())
if self.show_annotations:
label.hide()
else:
stat = self.stats[box_index]
if self.compare == OWBoxPlot.CompareMedians and \
stat.median is not None:
pos = stat.median + 5 / self.scale_x
elif self.compare == OWBoxPlot.CompareMeans or stat.q25 is None:
pos = stat.mean + 5 / self.scale_x
else:
pos = stat.q25
label.setX(pos * self.scale_x)
label.show()
r = QRectF(self.scene_min_x, -30 - len(self.stats) * heights,
self.scene_width, len(self.stats) * heights + 90)
self.box_scene.setSceneRect(r)
self.compute_tests()
self.show_posthoc()
self.select_box_items()
def display_changed_disc(self):
assert not self.is_continuous
self.clear_scene()
self.attr_labels = [QGraphicsSimpleTextItem(lab)
for lab in self.label_txts_all]
if not self.stretched:
if self.group_var:
self.labels = [
QGraphicsTextItem("{}".format(int(sum(cont))))
for cont in self.conts if np.sum(cont) > 0]
else:
self.labels = [
QGraphicsTextItem(str(int(sum(self.dist))))]
self.order = list(range(len(self.attr_labels)))
self.draw_axis_disc()
if self.group_var:
self.boxes = \
[self.strudel(cont, i) for i, cont in enumerate(self.conts)
if np.sum(cont) > 0]
self.conts = self.conts[np.sum(np.array(self.conts), axis=1) > 0]
if self.sort_freqs:
# pylint: disable=invalid-unary-operand-type
self.order = sorted(self.order, key=(-np.sum(self.conts, axis=1)).__getitem__)
else:
self.boxes = [self.strudel(self.dist)]
for row, box_index in enumerate(self.order):
y = (-len(self.boxes) + row) * 40 + 10
box = self.boxes[box_index]
bars, labels = box[::2], box[1::2]
self.__draw_group_labels(y, box_index)
if not self.stretched:
self.__draw_row_counts(y, box_index)
if self.show_labels and self.attribute is not self.group_var:
self.__draw_bar_labels(y, bars, labels)
self.__draw_bars(y, bars)
self.box_scene.setSceneRect(-self.label_width - 5,
-30 - len(self.boxes) * 40,
self.scene_width, len(self.boxes * 40) + 90)
self.infot1.setText("")
self.select_box_items()
def __draw_group_labels(self, y, row):
"""Draw group labels
Parameters
----------
y: int
vertical offset of bars
row: int
row index
"""
label = self.attr_labels[row]
b = label.boundingRect()
label.setPos(-b.width() - 10, y - b.height() / 2)
self.box_scene.addItem(label)
def __draw_row_counts(self, y, row):
"""Draw row counts
Parameters
----------
y: int
vertical offset of bars
row: int
row index
"""
assert not self.is_continuous
label = self.labels[row]
b = label.boundingRect()
if self.group_var:
right = self.scale_x * sum(self.conts[row])
else:
right = self.scale_x * sum(self.dist)
label.setPos(right + 10, y - b.height() / 2)
self.box_scene.addItem(label)
def __draw_bar_labels(self, y, bars, labels):
"""Draw bar labels
Parameters
----------
y: int
vertical offset of bars
bars: List[FilterGraphicsRectItem]
list of bars being drawn
labels: List[QGraphicsTextItem]
list of labels for corresponding bars
"""
label = bar_part = None
for text_item, bar_part in zip(labels, bars):
label = self.Label(
text_item.toPlainText())
label.setPos(bar_part.boundingRect().x(),
y - label.boundingRect().height() - 8)
label.setMaxWidth(bar_part.boundingRect().width())
self.box_scene.addItem(label)
def __draw_bars(self, y, bars):
"""Draw bars
Parameters
----------
y: int
vertical offset of bars
bars: List[FilterGraphicsRectItem]
list of bars to draw
"""
for item in bars:
item.setPos(0, y)
self.box_scene.addItem(item)
# noinspection PyPep8Naming
def compute_tests(self):
# The t-test and ANOVA are implemented here since they efficiently use
# the widget-specific data in self.stats.
# The non-parametric tests can't do this, so we use statistics.tests
# pylint: disable=comparison-with-itself
def stat_ttest():
d1, d2 = self.stats
if d1.n < 2 or d2.n < 2:
return np.nan, np.nan
pooled_var = d1.var / d1.n + d2.var / d2.n
# pylint: disable=comparison-with-itself
if pooled_var == 0 or np.isnan(pooled_var):
return np.nan, np.nan
df = pooled_var ** 2 / \
((d1.var / d1.n) ** 2 / (d1.n - 1) +
(d2.var / d2.n) ** 2 / (d2.n - 1))
t = abs(d1.mean - d2.mean) / math.sqrt(pooled_var)
p = 2 * (1 - scipy.special.stdtr(df, t))
return t, p
# TODO: Check this function
# noinspection PyPep8Naming
def stat_ANOVA():
if any(stat.n == 0 for stat in self.stats):
return np.nan, np.nan
n = sum(stat.n for stat in self.stats)
grand_avg = sum(stat.n * stat.mean for stat in self.stats) / n
var_between = sum(stat.n * (stat.mean - grand_avg) ** 2
for stat in self.stats)
df_between = len(self.stats) - 1
var_within = sum(stat.n * stat.var for stat in self.stats)
df_within = n - len(self.stats)
if var_within == 0 or df_within == 0 or df_between == 0:
return np.nan, np.nan
F = (var_between / df_between) / (var_within / df_within)
p = 1 - scipy.special.fdtr(df_between, df_within, F)
return F, p
if self.compare == OWBoxPlot.CompareNone or len(self.stats) < 2:
t = ""
elif any(s.n <= 1 for s in self.stats):
t = "At least one group has just one instance, " \
"cannot compute significance"
elif len(self.stats) == 2:
if self.compare == OWBoxPlot.CompareMedians:
t = ""
# z, p = tests.wilcoxon_rank_sum(
# self.stats[0].dist, self.stats[1].dist)
# t = "Mann-Whitney's z: %.1f (p=%.3f)" % (z, p)
else:
t, p = stat_ttest()
t = "" if np.isnan(t) else f"Student's t: {t:.3f} (p={p:.3f})"
else:
if self.compare == OWBoxPlot.CompareMedians:
t = ""
# U, p = -1, -1
# t = "Kruskal Wallis's U: %.1f (p=%.3f)" % (U, p)
else:
F, p = stat_ANOVA()
t = "" if np.isnan(F) else f"ANOVA: {F:.3f} (p={p:.3f})"
self.infot1.setText("<center>%s</center>" % t)
def mean_label(self, stat, attr, val_name):
label = QGraphicsItemGroup()
t = QGraphicsSimpleTextItem(
"%.*f" % (attr.number_of_decimals + 1, stat.mean), label)
t.setFont(self._label_font)
bbox = t.boundingRect()
w2, h = bbox.width() / 2, bbox.height()
t.setPos(-w2, -h)
tpm = QGraphicsSimpleTextItem(
" \u00b1 " + "%.*f" % (attr.number_of_decimals + 1, stat.dev),
label)
tpm.setFont(self._label_font)
tpm.setPos(w2, -h)
if val_name:
vnm = QGraphicsSimpleTextItem(val_name + ": ", label)
vnm.setFont(self._label_font)
vnm.setBrush(self._attr_brush)
vb = vnm.boundingRect()
label.min_x = -w2 - vb.width()
vnm.setPos(label.min_x, -h)
else:
label.min_x = -w2
return label
def draw_axis(self):
"""Draw the horizontal axis and sets self.scale_x"""
misssing_stats = not self.stats
stats = self.stats or [BoxData(np.array([[0.], [1.]]), self.attribute)]
mean_labels = self.mean_labels or [self.mean_label(stats[0], self.attribute, "")]
bottom = min(stat.a_min for stat in stats)
top = max(stat.a_max for stat in stats)
first_val, step = compute_scale(bottom, top)
while bottom <= first_val:
first_val -= step
bottom = first_val
no_ticks = math.ceil((top - first_val) / step) + 1
top = max(top, first_val + no_ticks * step)
gbottom = min(bottom, min(stat.mean - stat.dev for stat in stats))
gtop = max(top, max(stat.mean + stat.dev for stat in stats))
bv = self.box_view
viewrect = bv.viewport().rect().adjusted(15, 15, -15, -30)
self.scale_x = scale_x = viewrect.width() / (gtop - gbottom)
# In principle we should repeat this until convergence since the new
# scaling is too conservative. (No chance am I doing this.)
mlb = min(stat.mean + mean_lab.min_x / scale_x
for stat, mean_lab in zip(stats, mean_labels))
if mlb < gbottom:
gbottom = mlb
self.scale_x = scale_x = viewrect.width() / (gtop - gbottom)
self.scene_min_x = gbottom * scale_x
self.scene_width = (gtop - gbottom) * scale_x
val = first_val
decimals = max(3, 4 - int(math.log10(step)))
while True:
l = self.box_scene.addLine(val * scale_x, -1, val * scale_x, 1,
self._pen_axis_tick)
l.setZValue(100)
t = self.box_scene.addSimpleText(
repr(round(val, decimals)) if not misssing_stats else "?",
self._axis_font)
t.setFlags(
t.flags() | QGraphicsItem.ItemIgnoresTransformations)
r = t.boundingRect()
t.setPos(val * scale_x - r.width() / 2, 8)
if val >= top:
break
val += step
self.box_scene.addLine(
bottom * scale_x - 4, 0, top * scale_x + 4, 0, self._pen_axis)
def draw_axis_disc(self):
"""
Draw the horizontal axis and sets self.scale_x for discrete attributes
"""
assert not self.is_continuous
if self.stretched:
if not self.attr_labels:
return
step = steps = 10
else:
if self.group_var:
max_box = max(float(np.sum(dist)) for dist in self.conts)
else:
max_box = float(np.sum(self.dist))
if max_box == 0:
self.scale_x = 1
return
_, step = compute_scale(0, max_box)
step = int(step) if step > 1 else 1
steps = int(math.ceil(max_box / step))
max_box = step * steps
bv = self.box_view
viewrect = bv.viewport().rect().adjusted(15, 15, -15, -30)
self.scene_width = viewrect.width()
lab_width = max(lab.boundingRect().width() for lab in self.attr_labels)
lab_width = max(lab_width, 40)
lab_width = min(lab_width, self.scene_width / 3)
self.label_width = lab_width
right_offset = 0 # offset for the right label
if not self.stretched and self.labels:
if self.group_var:
rows = list(zip(self.conts, self.labels))
else:
rows = [(self.dist, self.labels[0])]
# available space left of the 'group labels'
available = self.scene_width - lab_width - 10
scale_x = (available - right_offset) / max_box
max_right = max(sum(dist) * scale_x + 10 +
lbl.boundingRect().width()
for dist, lbl in rows)
right_offset = max(0, max_right - max_box * scale_x)
self.scale_x = scale_x = \
(self.scene_width - lab_width - 10 - right_offset) / max_box
self.box_scene.addLine(0, 0, max_box * scale_x, 0, self._pen_axis)
for val in range(0, step * steps + 1, step):
l = self.box_scene.addLine(val * scale_x, -1, val * scale_x, 1,
self._pen_axis_tick)
l.setZValue(100)
t = self.box_scene.addSimpleText(str(val), self._axis_font)
t.setPos(val * scale_x - t.boundingRect().width() / 2, 8)
if self.stretched:
self.scale_x *= 100
def label_group(self, stat, attr, mean_lab):
def centered_text(val, pos):
t = QGraphicsSimpleTextItem(
"%.*f" % (attr.number_of_decimals + 1, val), labels)
t.setFont(self._label_font)
bbox = t.boundingRect()
t.setPos(pos - bbox.width() / 2, 22)
return t
def line(x, down=1):
QGraphicsLineItem(x, 12 * down, x, 20 * down, labels)
def move_label(label, frm, to):
label.setX(to)
to += t_box.width() / 2
path = QPainterPath()
path.lineTo(0, 4)
path.lineTo(to - frm, 4)
path.lineTo(to - frm, 8)
p = QGraphicsPathItem(path)
p.setPos(frm, 12)
labels.addToGroup(p)
labels = QGraphicsItemGroup()
labels.addToGroup(mean_lab)
m = stat.mean * self.scale_x
mean_lab.setPos(m, -22)
line(m, -1)
if stat.median is not None:
msc = stat.median * self.scale_x
med_t = centered_text(stat.median, msc)
med_box_width2 = med_t.boundingRect().width() / 2
line(msc)
if stat.q25 is not None:
x = stat.q25 * self.scale_x
t = centered_text(stat.q25, x)
t_box = t.boundingRect()
med_left = msc - med_box_width2
if x + t_box.width() / 2 >= med_left - 5:
move_label(t, x, med_left - t_box.width() - 5)
else:
line(x)
if stat.q75 is not None:
x = stat.q75 * self.scale_x
t = centered_text(stat.q75, x)
t_box = t.boundingRect()
med_right = msc + med_box_width2
if x - t_box.width() / 2 <= med_right + 5:
move_label(t, x, med_right + 5)
else:
line(x)
return labels
def box_group(self, stat, height=20):
def line(x0, y0, x1, y1, *args):
return QGraphicsLineItem(x0 * scale_x, y0, x1 * scale_x, y1, *args)
scale_x = self.scale_x
box = []
whisker1 = line(stat.a_min, -1.5, stat.a_min, 1.5)
whisker2 = line(stat.a_max, -1.5, stat.a_max, 1.5)
vert_line = line(stat.a_min, 0, stat.a_max, 0)
mean_line = line(stat.mean, -height / 3, stat.mean, height / 3)
for it in (whisker1, whisker2, mean_line):
it.setPen(self._pen_paramet)
vert_line.setPen(self._pen_dotted)
var_line = line(stat.mean - stat.dev, 0, stat.mean + stat.dev, 0)
var_line.setPen(self._pen_paramet)
box.extend([whisker1, whisker2, vert_line, mean_line, var_line])
if stat.q25 is not None and stat.q75 is not None:
mbox = FilterGraphicsRectItem(
stat.conditions, stat.q25 * scale_x, -height / 2,
(stat.q75 - stat.q25) * scale_x, height)
mbox.setBrush(self._box_brush)
mbox.setPen(QPen(Qt.NoPen))
mbox.setZValue(-200)
box.append(mbox)
if stat.median is not None:
median_line = line(stat.median, -height / 2,
stat.median, height / 2)
median_line.setPen(self._pen_median)
median_line.setZValue(-150)
box.append(median_line)
return box
def strudel(self, dist, group_val_index=None):
attr = self.attribute
ss = np.sum(dist)
box = []
if ss < 1e-6:
cond = [FilterDiscrete(attr, None)]
if group_val_index is not None:
cond.append(FilterDiscrete(self.group_var, [group_val_index]))
box.append(FilterGraphicsRectItem(cond, 0, -10, 1, 10))
cum = 0
for i, v in enumerate(dist):
if v < 1e-6:
continue
if self.stretched:
v /= ss
v *= self.scale_x
cond = [FilterDiscrete(attr, [i])]
if group_val_index is not None:
cond.append(FilterDiscrete(self.group_var, [group_val_index]))
rect = FilterGraphicsRectItem(cond, cum + 1, -6, v - 2, 12)
rect.setBrush(QBrush(QColor(*attr.colors[i])))
rect.setPen(QPen(Qt.NoPen))
if self.stretched:
tooltip = "{}: {:.2f}%".format(attr.values[i],
100 * dist[i] / sum(dist))
else:
tooltip = "{}: {}".format(attr.values[i], int(dist[i]))
rect.setToolTip(tooltip)
text = QGraphicsTextItem(attr.values[i])
box.append(rect)
box.append(text)
cum += v
return box
def commit(self):
self.conditions = [item.filter for item in
self.box_scene.selectedItems() if item.filter]
selected, selection = None, []
if self.conditions:
selected = Values(self.conditions, conjunction=False)(self.dataset)
selection = np.in1d(
self.dataset.ids, selected.ids, assume_unique=True).nonzero()[0]
self.Outputs.selected_data.send(selected)
self.Outputs.annotated_data.send(
create_annotated_table(self.dataset, selection))
def show_posthoc(self):
def line(y0, y1):
it = self.box_scene.addLine(x, y0, x, y1, self._post_line_pen)
it.setZValue(-100)
self.posthoc_lines.append(it)
while self.posthoc_lines:
self.box_scene.removeItem(self.posthoc_lines.pop())
if self.compare == OWBoxPlot.CompareNone or len(self.stats) < 2:
return
if self.compare == OWBoxPlot.CompareMedians:
crit_line = "median"
else:
crit_line = "mean"
xs = []
height = 90 if self.show_annotations else 60
y_up = -len(self.stats) * height + 10
for pos, box_index in enumerate(self.order):
stat = self.stats[box_index]
x = getattr(stat, crit_line)
if x is None:
continue
x *= self.scale_x
xs.append(x * self.scale_x)
by = y_up + pos * height
line(by + 12, 3)
line(by - 12, by - 25)
used_to = []
last_to = to = 0
for frm, frm_x in enumerate(xs[:-1]):
for to in range(frm + 1, len(xs)):
if xs[to] - frm_x > 1.5:
to -= 1
break
if to in (last_to, frm):
continue
for rowi, used in enumerate(used_to):
if used < frm:
used_to[rowi] = to
break
else:
rowi = len(used_to)
used_to.append(to)
y = - 6 - rowi * 6
it = self.box_scene.addLine(frm_x - 2, y, xs[to] + 2, y,
self._post_grp_pen)
self.posthoc_lines.append(it)
last_to = to
def get_widget_name_extension(self):
return self.attribute.name if self.attribute else None
def send_report(self):
self.report_plot()
text = ""
if self.attribute:
text += "Box plot for attribute '{}' ".format(self.attribute.name)
if self.group_var:
text += "grouped by '{}'".format(self.group_var.name)
if text:
self.report_caption(text)
class Label(QGraphicsSimpleTextItem):
"""Boxplot Label with settable maxWidth"""
# Minimum width to display label text
MIN_LABEL_WIDTH = 25
# padding bellow the text
PADDING = 3
__max_width = None
def maxWidth(self):
return self.__max_width
def setMaxWidth(self, max_width):
self.__max_width = max_width
def paint(self, painter, option, widget):
"""Overrides QGraphicsSimpleTextItem.paint
If label text is too long, it is elided
to fit into the allowed region
"""
if self.__max_width is None:
width = option.rect.width()
else:
width = self.__max_width
if width < self.MIN_LABEL_WIDTH:
# if space is too narrow, no label
return
fm = painter.fontMetrics()
text = fm.elidedText(self.text(), Qt.ElideRight, width)
painter.drawText(
option.rect.x(),
option.rect.y() + self.boundingRect().height() - self.PADDING,
text)
