File: ownomogram.py

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import time
import warnings
from itertools import chain
from typing import List
from functools import singledispatch
from enum import IntEnum
from collections import OrderedDict

import numpy as np

from AnyQt.QtWidgets import (
    QGraphicsView, QGraphicsScene, QGraphicsItem, QGraphicsSimpleTextItem,
    QGraphicsTextItem, QGraphicsLineItem, QGraphicsWidget, QGraphicsRectItem,
    QGraphicsEllipseItem, QGraphicsLinearLayout, QGridLayout, QLabel, QFrame,
    QSizePolicy, QFormLayout
)
from AnyQt.QtGui import QColor, QPainter, QFont, QPen, QBrush, QFontMetrics
from AnyQt.QtCore import Qt, QRectF, QSize, QPropertyAnimation, QObject, \
    pyqtProperty

from orangewidget.io import ClipboardFormat
from orangewidget.utils import saveplot

from Orange.data import Table, Domain, DiscreteVariable, ContinuousVariable, \
    Variable
from Orange.statistics.util import nanmin, nanmax, nanmean, unique
from Orange.classification import Model
from Orange.classification.naive_bayes import NaiveBayesModel
from Orange.classification.logistic_regression import \
    LogisticRegressionClassifier
from Orange.widgets.settings import Setting, ContextSetting, \
    ClassValuesContextHandler
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import OWWidget, Msg, Input, Output, AttributeList
from Orange.widgets import gui


def collides(item, items):
    return any(item.collidesWithItem(i) for i in items)


class SortBy(IntEnum):
    NO_SORTING, NAME, ABSOLUTE, POSITIVE, NEGATIVE = 0, 1, 2, 3, 4

    @staticmethod
    def items():
        return ["Original order", "Alphabetically", "Absolute importance",
                "Positive influence", "Negative influence"]


class MovableToolTip(QLabel):
    def __init__(self):
        super().__init__()
        self.setFrameShape(QFrame.StyledPanel)
        self.setWindowFlags(Qt.ToolTip)
        self.hide()

    def show(self, pos, text, change_y=True):
        self.setText(text)
        self.adjustSize()

        x, y = pos.x(), (pos.y() + 15 if change_y else self.y())
        avail = self.screen().availableGeometry()
        if x + self.width() > avail.right():
            x -= self.width()
        if y + self.height() > avail.bottom():
            y = (pos.y() - 10 - self.height() if change_y else self.y() - self.height())
        self.move(x, y)
        super().show()


DOT_COLOR = QColor(170, 220, 255, 255)


class DotItem(QGraphicsEllipseItem):
    TOOLTIP_STYLE = """ul {margin-top: 1px; margin-bottom: 1px;}"""
    TOOLTIP_TEMPLATE = """<html><head><style type="text/css">{}</style>
    </head><body><b>{}</b><hr/>{}</body></html>
    """

    def __init__(self, radius, scale, offset, min_x, max_x):
        super().__init__(0, 0, radius, radius)
        self._min_x = min_x * scale - radius / 2 + offset
        self._max_x = max_x * scale - radius / 2 + offset
        self._scale = scale
        self._offset = offset
        self.setPos(0, - radius / 2)
        self.setFlag(QGraphicsItem.ItemIsMovable)
        self.setBrush(DOT_COLOR)
        self.setPen(QPen(QBrush(QColor(20, 130, 250, 255)), 2))
        self.setZValue(100)
        self.tool_tip = MovableToolTip()
        self.setAcceptHoverEvents(True)

    @property
    def value(self):
        return (self.x() + self.rect().width() / 2 - self._offset) / self._scale

    def move(self, x):
        self.setX(x)

    def move_to_val(self, val):
        x = np.clip(self._scale * val - self.rect().width() / 2 + self._offset,
                    self._min_x, self._max_x)
        self.move(x)

    def hoverEnterEvent(self, event):
        self.tool_tip.show(event.screenPos(), self.get_tooltip_text())

    def hoverLeaveEvent(self, event):
        self.tool_tip.hide()

    def mouseMoveEvent(self, _):
        # Prevent click-moving of these items
        return


class ProbabilitiesDotItem(DotItem):
    def __init__(self, radius, scale, offset, min_x, max_x, title,
                 get_probabilities):
        self.title = title
        self.get_probabilities = get_probabilities
        self.movable_dot_items = []
        self._invisible_sum = 0
        super().__init__(radius, scale, offset, min_x, max_x)
        self.setBrush(QColor(150, 150, 150, 255))
        self.setPen(QPen(QBrush(QColor(75, 75, 75, 255)), 2))

    def move_to_sum(self, invisible_sum: float = None):
        total = sum(item.value for item in self.movable_dot_items)

        if invisible_sum is not None:
            self._invisible_sum = invisible_sum
        total += self._invisible_sum

        self.move_to_val(total)
        self.parentItem().rescale()

    def get_tooltip_text(self):
        text = "Total: {} <br/>Probability: {:.0%}".format(
            np.round(self.value, 2),
            np.round(self.get_probabilities(self.value), 2))
        return self.TOOLTIP_TEMPLATE.format(
            self.TOOLTIP_STYLE, self.title, text)


class MovableDotItem(DotItem):
    def __init__(self, radius, scale, offset, min_x, max_x):
        self.tooltip_labels = []
        self.tooltip_values = []
        super().__init__(radius, scale, offset, min_x, max_x)
        self._x = min_x * scale - radius / 2 + offset
        self._point_dot = None
        self._total_dot = None
        self._probs_dot = None
        self._vertical_line = None
        self._mousePressFunc = None

    @property
    def vertical_line(self):
        return self._vertical_line

    @vertical_line.setter
    def vertical_line(self, line):
        line.setVisible(False)
        self._vertical_line = line

    @property
    def point_dot(self):
        return self._point_dot

    @point_dot.setter
    def point_dot(self, dot):
        dot.setVisible(False)
        self._point_dot = dot

    @property
    def total_dot(self):
        return self._total_dot

    @total_dot.setter
    def total_dot(self, dot):
        self._total_dot = dot
        self._total_dot.movable_dot_items.append(self)

    @property
    def probs_dot(self):
        return self._probs_dot

    @probs_dot.setter
    def probs_dot(self, dot):
        self._probs_dot = dot
        self._probs_dot.movable_dot_items.append(self)

    def hookOnMousePress(self, func):
        self._mousePressFunc = func

    def unhookOnMousePress(self):
        self._mousePressFunc = None

    def mousePressEvent(self, event):
        if self._mousePressFunc:
            self._mousePressFunc()
            self._mousePressFunc = None
        self.tool_tip.show(event.screenPos(), self.get_tooltip_text(), False)
        self._x = event.pos().x()
        self.setBrush(QColor(50, 180, 250, 255))
        self._show_vertical_line_and_point_dot()
        return super().mousePressEvent(event)

    def mouseMoveEvent(self, event):
        self.tool_tip.show(event.screenPos(), self.get_tooltip_text(), False)
        delta_x = event.pos().x() - self._x
        if self._min_x <= self.x() + delta_x <= self._max_x:
            self.move(self.x() + delta_x)
            mod_tooltip_values = [0] + list(self.tooltip_values)
            if np.round(self.value, 1) in np.round(mod_tooltip_values, 1):
                index = np.where(np.round(mod_tooltip_values, 1) ==
                                 np.round(self.value, 1))
                time.sleep(0.05)
                self.move_to_val(mod_tooltip_values[index[0][0]])
        elif self.x() + delta_x < self._min_x:
            self.move(self._min_x)
        elif self.x() + delta_x > self._max_x:
            self.move(self._max_x)
        self._show_vertical_line_and_point_dot()
        self.probs_dot.move_to_sum()

    def mouseReleaseEvent(self, event):
        self.tool_tip.hide()
        self.setBrush(QColor(170, 220, 255, 255))
        self.point_dot.setVisible(False)
        self.vertical_line.setVisible(False)
        return super().mousePressEvent(event)

    def _show_vertical_line_and_point_dot(self):
        self.vertical_line.setX(self.x() + self.rect().width() / 2 - self._offset)
        self.vertical_line.setVisible(True)
        self.point_dot.move_to_val(self.value)
        self.point_dot.setVisible(True)


class DiscreteMovableDotItem(MovableDotItem):
    def get_tooltip_text(self):
        labels = self._get_tooltip_labels_with_percentages()
        return self.TOOLTIP_TEMPLATE.format(
            self.TOOLTIP_STYLE, "Points: {}".format(np.round(self.value, 2)),
            "".join("{}: {:.0%}<br/>".format(l, v) for l, v in labels)[:-5])

    def _get_tooltip_labels_with_percentages(self):
        if not len(self.tooltip_labels):
            return []
        for i, val in enumerate(self.tooltip_values):
            if val > self.value:
                break
        # pylint: disable=undefined-loop-variable
        diff = self.tooltip_values[i] - self.tooltip_values[i - 1]
        p1 = 0 if diff < 1e-6 else (-self.value + self.tooltip_values[i]) / diff
        return [(self.tooltip_labels[i - 1].replace("<", "&lt;"), abs(p1)),
                (self.tooltip_labels[i].replace("<", "&lt;"), abs(1 - p1))]


class GraphicsColorAnimator(QObject):
    @pyqtProperty(QColor)
    def brushColor(self):
        return self.__brushColor

    @brushColor.setter
    def brushColor(self, value):
        self.__brushColor = value
        for item in self.__items:
            item.setBrush(value)

    def __init__(self, parent, duration, keyValues):
        super().__init__(parent)
        self.__items = []
        self.__defaultColor = defaultColor = keyValues[0][1]
        self.__brushColor = defaultColor

        self.__animation = QPropertyAnimation(self, b'brushColor', self)
        self.__animation.setStartValue(defaultColor)
        self.__animation.setEndValue(defaultColor)
        self.__animation.setDuration(duration)
        self.__animation.setKeyValues(keyValues)
        self.__animation.setLoopCount(-1)

    def setGraphicsItems(self, items):
        if self.__animation.state() == QPropertyAnimation.Running:
            self.__animation.stop()
        self.__items = items
        for item in items:
            item.hookOnMousePress(self.stop)

    def start(self):
        self.__animation.start()

    def stop(self):
        if self.__animation.state() != QPropertyAnimation.Running:
            return
        self.__animation.stop()
        for item in self.__items:
            item.setBrush(self.__defaultColor)

    def clear(self):
        for item in self.__items:
            item.unhookOnMousePress()
        self.__items = []


class ContinuousItemMixin:
    def get_tooltip_text(self):
        return self.TOOLTIP_TEMPLATE.format(
            self.TOOLTIP_STYLE, "Points: {}".format(np.round(self.value, 2)),
            "Value: {}".format(np.round(self._get_tooltip_label_value(), 1)))

    def _get_tooltip_label_value(self):
        if not len(self.tooltip_labels):
            return self.value
        start = float(self.tooltip_labels[0])
        stop = float(self.tooltip_labels[-1])
        delta = (self.tooltip_values[-1] - self.tooltip_values[0])
        if not delta:
            return np.nan
        return start + self.value * (stop - start) / delta


class ContinuousMovableDotItem(MovableDotItem, ContinuousItemMixin):
    pass


class Continuous2DMovableDotItem(MovableDotItem, ContinuousItemMixin):
    def __init__(self, radius, scale, offset, min_x, max_x, min_y, max_y):
        super().__init__(radius, scale, offset, min_x, max_x)
        self._min_y = min_y
        self._max_y = max_y
        self._horizontal_line = None

    @property
    def horizontal_line(self):
        return self._horizontal_line

    @horizontal_line.setter
    def horizontal_line(self, line):
        line.setVisible(False)
        self._horizontal_line = line

    def move(self, x):
        super().move(x)
        diff_ = np.nan_to_num(self._max_x - self._min_x)
        k = (x - self._min_x) / diff_ if diff_ else 0
        self.setY(self._min_y - self.rect().width() / 2 + (self._max_y - self._min_y) * k)

    def mousePressEvent(self, event):
        self._show_horizontal_line()
        return super().mousePressEvent(event)

    def mouseMoveEvent(self, event):
        super().mouseMoveEvent(event)
        self._show_horizontal_line()

    def mouseReleaseEvent(self, event):
        self.horizontal_line.setVisible(False)
        return super().mouseReleaseEvent(event)

    def _show_horizontal_line(self):
        self.horizontal_line.setY(self.y() + self.rect().width() / 2 -
                                  abs(self._max_y - self._min_y) / 2)
        self.horizontal_line.setVisible(True)


class RulerItem(QGraphicsWidget):
    tick_height = 6
    tick_width = 0
    DOT_RADIUS = 12
    half_tick_height = 3
    bold_label = True
    DOT_ITEM_CLS = DotItem

    def __init__(self, name, values, scale, name_offset, offset, labels=None):
        super().__init__()

        # leading label
        font = name.document().defaultFont()
        if self.bold_label:
            font.setWeight(QFont.Bold)
        name.setFont(font)
        name.setPos(name_offset, -10)
        name.setParentItem(self)

        # prediction marker
        self.dot = self.DOT_ITEM_CLS(self.DOT_RADIUS, scale, offset, values[0],
                                     values[-1])
        self.dot.setParentItem(self)

        # pylint: disable=unused-variable
        # line
        line = QGraphicsLineItem(min(values) * scale + offset, 0,
                                 max(values) * scale + offset, 0,
                                 self)

        if labels is None:
            labels = [str(abs(v) if v == -0 else v) for v in values]

        old_x_tick = None
        shown_items = []
        w = QGraphicsSimpleTextItem(labels[0]).boundingRect().width()
        text_finish = values[0] * scale - w + offset - 10
        for i, (label, value) in enumerate(zip(labels, values)):
            text = QGraphicsSimpleTextItem(label)
            x_text = value * scale - text.boundingRect().width() / 2 + offset
            if text_finish > x_text - 10:
                y_text, y_tick = self.DOT_RADIUS * 0.7, 0
                text_finish = values[0] * scale + offset
            else:
                y_text = - text.boundingRect().height() - self.DOT_RADIUS * 0.7
                y_tick = - self.tick_height
                text_finish = x_text + text.boundingRect().width()
            text.setPos(x_text, y_text)
            if not collides(text, shown_items):
                text.setParentItem(self)
                shown_items.append(text)

            x_tick = value * scale - self.tick_width / 2 + offset
            tick = QGraphicsRectItem(
                x_tick, y_tick, self.tick_width, self.tick_height,
                self)
            tick.setBrush(QColor(Qt.black))

            if self.half_tick_height and i:
                x = x_tick - (x_tick - old_x_tick) / 2
                half_tick = QGraphicsLineItem(x, - self.half_tick_height, x, 0,
                                              self)
            old_x_tick = x_tick


class ProbabilitiesRulerItem(QGraphicsWidget):
    tick_height = 6
    DOT_RADIUS = 14
    y_diff = 4

    def __init__(self, name, values, scale, name_offset, offset, get_points,
                 title, get_probabilities):
        super().__init__()
        self.scale = scale
        self.offset = offset
        self.get_points = get_points
        self.min_val = min(values)
        self.max_val = max(values)

        # leading labels
        font = name.document().defaultFont()
        font.setWeight(QFont.Bold)
        name_total = QGraphicsTextItem("Total", self)
        name_total.setFont(font)
        name_total.setPos(name_offset, -25)
        name.setFont(font)
        name.setPos(name_offset, 10)
        name.setParentItem(self)

        # prediction marker
        self.dot = ProbabilitiesDotItem(
            self.DOT_RADIUS, scale, offset, values[0], values[-1],
            title, get_probabilities)
        self.dot.setPos(0, (- self.DOT_RADIUS + self.y_diff) / 2)
        self.dot.setParentItem(self)

        # pylint: disable=unused-variable
        # two lines
        t_line = QGraphicsLineItem(self.min_val * scale + offset, 0,
                                   self.max_val * scale + offset, 0,
                                   self)
        p_line = QGraphicsLineItem(self.min_val * scale + offset, self.y_diff,
                                   self.max_val * scale + offset, self.y_diff,
                                   self)

        # ticks and labels
        old_x_tick = values[0] * scale + offset
        for i, value in enumerate(values[1:]):
            x_tick = value * scale + offset
            x = x_tick - (x_tick - old_x_tick) / 2
            half_tick = QGraphicsLineItem(x, - self.tick_height / 2, x, 0,
                                          self)
            old_x_tick = x_tick
            if i == len(values) - 2:
                break
            text = QGraphicsTextItem(str(abs(value) if value == -0 else value),
                                     self)
            x_text = value * scale - text.boundingRect().width() / 2 + offset
            y_text = - text.boundingRect().height() - self.DOT_RADIUS * 0.7
            text.setPos(x_text, y_text)
            tick = QGraphicsLineItem(x_tick, -self.tick_height, x_tick, 0,
                                     self)

        self.prob_items = [
            (i / 10, QGraphicsTextItem(" " + str(i * 10) + " "),
             QGraphicsLineItem(0, 0, 0, 0)) for i in range(1, 10)]

    def rescale(self):
        shown_items = []
        for prob, text, tick in self.prob_items:
            pts = self.get_points(prob)
            x = pts * self.scale - text.boundingRect().width() / 2 + self.offset
            text.setPos(x, 10 + self.y_diff)
            x = pts * self.scale + self.offset
            tick.setLine(x, 0 + self.y_diff, x, self.tick_height + self.y_diff)
            text.setParentItem(None)
            tick.setParentItem(None)
            text.setVisible(False)
            tick.setVisible(False)
            if self.min_val < pts < self.max_val:
                tick.setParentItem(self)
                tick.setVisible(True)
                text.setParentItem(self)
                if not collides(text, shown_items):
                    text.setVisible(True)
                    shown_items.append(text)


class DiscreteFeatureItem(RulerItem):
    tick_height = 6
    tick_width = 2
    half_tick_height = 0
    bold_label = False
    DOT_ITEM_CLS = DiscreteMovableDotItem

    def __init__(self, name, variable, values, scale, name_offset, offset):
        labels = _get_labels(variable)
        indices = np.argsort(values)
        labels, values = labels[indices], values[indices]
        super().__init__(name, values, scale, name_offset, offset, labels)
        self.dot.tooltip_labels = labels
        self.dot.tooltip_values = values


class ContinuousFeatureItem(RulerItem):
    tick_height = 6
    tick_width = 2
    half_tick_height = 0
    bold_label = False
    DOT_ITEM_CLS = ContinuousMovableDotItem

    def __init__(self, name, variable, data_extremes, values, scale,
                 name_offset, offset):
        labels = _get_labels(variable, data_extremes, values)
        super().__init__(name, values, scale, name_offset, offset, labels)
        self.dot.tooltip_labels = labels
        self.dot.tooltip_values = values


@singledispatch
def _get_labels(*_):
    return []


@_get_labels.register(DiscreteVariable)
def _(var: DiscreteVariable, *_):
    return np.array(var.values)


@_get_labels.register(ContinuousVariable)
def _(_: ContinuousVariable, data_extremes: List, values: np.ndarray, *__):
    diff_ = np.nan_to_num(values[-1] - values[0])
    k = (data_extremes[1] - data_extremes[0]) / diff_ if diff_ else 0
    return [str(np.round(v * k + data_extremes[0], 1)) for v in values]


class ContinuousFeature2DItem(QGraphicsWidget):
    tick_height = 6
    tick_width = 2
    DOT_RADIUS = 12
    y_diff = 80
    n_tck = 4

    def __init__(self, name, _, data_extremes, values, scale, name_offset,
                 offset):
        super().__init__()
        data_start, data_stop = data_extremes[0], data_extremes[1]
        labels = [str(np.round(data_start + (data_stop - data_start) * i /
                               (self.n_tck - 1), 1)) for i in range(self.n_tck)]

        # leading label
        font = name.document().defaultFont()
        name.setFont(font)
        name.setPos(name_offset, -10)
        name.setParentItem(self)

        # labels
        ascending = data_start < data_stop
        y_start, y_stop = (self.y_diff, 0) if ascending else (0, self.y_diff)
        for i in range(self.n_tck):
            text = QGraphicsSimpleTextItem(labels[i], self)
            w = text.boundingRect().width()
            y = y_start + (y_stop - y_start) / (self.n_tck - 1) * i
            text.setPos(-5 - w, y - 8)
            tick = QGraphicsLineItem(-2, y, 2, y, self)

        # prediction marker
        self.dot = Continuous2DMovableDotItem(
            self.DOT_RADIUS, scale, offset, values[0], values[-1], y_start, y_stop)
        self.dot.tooltip_labels = labels
        self.dot.tooltip_values = values
        self.dot.setParentItem(self)
        h_line = QGraphicsLineItem(values[0] * scale + offset, self.y_diff / 2,
                                   values[-1] * scale + offset, self.y_diff / 2,
                                   self)
        pen = QPen(Qt.DashLine)
        pen.setBrush(QColor(Qt.red))
        h_line.setPen(pen)
        self.dot.horizontal_line = h_line

        # pylint: disable=unused-variable
        # line
        line = QGraphicsLineItem(values[0] * scale + offset, y_start,
                                 values[-1] * scale + offset, y_stop,
                                 self)

        # ticks
        for value in values:
            diff_ = np.nan_to_num(values[-1] - values[0])
            k = (value - values[0]) / diff_ if diff_ else 0
            y_tick = (y_stop - y_start) * k + y_start - self.tick_height / 2
            x_tick = value * scale - self.tick_width / 2 + offset
            tick = QGraphicsRectItem(
                x_tick, y_tick, self.tick_width, self.tick_height,
                self)
            tick.setBrush(QColor(Qt.black))

        # rect
        rect = QGraphicsRectItem(
            values[0] * scale + offset, -self.y_diff * 0.125,
            values[-1] * scale + offset, self.y_diff * 1.25,
            self)
        pen = QPen(Qt.DotLine)
        pen.setBrush(QColor(50, 150, 200, 255))
        rect.setPen(pen)
        self.setPreferredSize(self.preferredWidth(), self.y_diff * 1.5)


class NomogramItem(QGraphicsWidget):
    def __init__(self):
        super().__init__()
        self._items = []
        self.setLayout(QGraphicsLinearLayout(Qt.Vertical))

    def add_items(self, items):
        self._items = items
        for item in items:
            self.layout().addItem(item)


class OWNomogram(OWWidget):
    name = "Nomogram"
    description = " Nomograms for Visualization of Naive Bayesian" \
                  " and Logistic Regression Classifiers."
    icon = "icons/Nomogram.svg"
    priority = 2000
    keywords = "nomogram"

    class Inputs:
        classifier = Input("Classifier", Model)
        data = Input("Data", Table)

    class Outputs:
        features = Output("Features", AttributeList)

    MAX_N_ATTRS = 1000
    POINT_SCALE = 0
    ALIGN_LEFT = 0
    ALIGN_ZERO = 1
    ACCEPTABLE = (NaiveBayesModel, LogisticRegressionClassifier)
    settingsHandler = ClassValuesContextHandler()
    target_class_index = ContextSetting(0)
    normalize_probabilities = Setting(False)
    scale = Setting(1)
    display_index = Setting(1)
    n_attributes = Setting(10)
    sort_index = Setting(SortBy.ABSOLUTE)
    cont_feature_dim_index = Setting(0)

    # This is defined so that base widget shows the button for saving graph
    # and connects the shortcut for copying to clipboard. The value itself
    # is not used because send_report, save_graph and copy_to_clipboard are
    # overridden.
    graph_name = "scene"  # QGraphicsScene

    class Error(OWWidget.Error):
        invalid_classifier = Msg("Nomogram accepts only Naive Bayes and "
                                 "Logistic Regression classifiers.")

    def __init__(self):
        super().__init__()
        self.instances = None
        self.domain = None
        self.data = None
        self.classifier = None
        self.align = OWNomogram.ALIGN_ZERO
        self.log_odds_ratios = []
        self.log_reg_coeffs = []
        self.log_reg_coeffs_orig = []
        self.log_reg_cont_data_extremes = []
        self.p = None
        self.b0 = None
        self.points = []
        self.feature_items = {}
        self.feature_marker_values = []
        self.scale_marker_values = lambda x: x
        self.nomogram_main = None
        self.vertical_line = None
        self.hidden_vertical_line = None
        self.old_target_class_index = self.target_class_index
        self.repaint = False

        # GUI
        lab_align = QFormLayout().labelAlignment()

        grid = QGridLayout()
        grid.setColumnStretch(1, 1)
        gui.widgetBox(self.controlArea, True, orientation=grid)
        self.class_combo = gui.comboBox(
            None, self, "target_class_index",
            callback=self._class_combo_changed,
            sizePolicy=(QSizePolicy.MinimumExpanding, QSizePolicy.Fixed),
            searchable=True)
        grid.addWidget(QLabel("Target class: "), 0, 0, lab_align)
        grid.addWidget(self.class_combo, 0, 1)

        self.norm_check = gui.checkBox(
            None, self, "normalize_probabilities", "Normalize probabilities",
            hidden=True, callback=self.update_scene,
            tooltip="For multiclass data 1 vs. all probabilities do not"
                    " sum to 1 and therefore could be normalized.")
        self.norm_check.setStyleSheet("margin-bottom: 12px")
        grid.addWidget(self.norm_check, 1, 1)

        group = gui.radioButtons(
            None, self, "scale", callback=self.update_scene)
        grid.addWidget(QLabel("Scale: "), 2, 0, lab_align)
        grid.addWidget(gui.appendRadioButton(
            group, "Point scale", addToLayout=False), 2, 1)
        grid.addWidget(gui.appendRadioButton(
            group, "Log odds ratios", addToLayout=False), 3, 1)

        grid = QGridLayout()
        gui.widgetBox(self.controlArea, "Displayed features", orientation=grid)

        self.sort_combo = gui.comboBox(
            None, self, "sort_index", items=SortBy.items(),
            callback=self.update_scene,
            sizePolicy=(QSizePolicy.MinimumExpanding, QSizePolicy.Fixed)
        )
        grid.addWidget(QLabel("Order: "), 0, 0, lab_align)
        grid.addWidget(self.sort_combo, 0, 1, 1, 2)

        radio_group = gui.radioButtons(
            None, self, "display_index", callback=self.update_scene)
        radio_all = gui.appendRadioButton(
            radio_group, "All features", addToLayout=False)
        radio_best = gui.appendRadioButton(
            radio_group, "Best ranked:", addToLayout=False)
        self.n_spin = gui.spin(
            None, self, "n_attributes", 1, self.MAX_N_ATTRS,
            callback=self._n_spin_changed, alignment=Qt.AlignRight,
            sizePolicy=(QSizePolicy.MinimumExpanding, QSizePolicy.Fixed)
        )
        grid.addWidget(QLabel("Show: "), 1, 0, lab_align)
        grid.addWidget(radio_all, 1, 1, 1, 2)
        grid.addWidget(radio_best, 2, 1)
        grid.addWidget(self.n_spin, 2, 2, Qt.AlignLeft)

        self.cont_feature_dim_combo = gui.comboBox(
            None, self, "cont_feature_dim_index", label="Numeric features:",
            items=["1D projection", "2D curve"], orientation=Qt.Horizontal,
            callback=self.update_scene,
            sizePolicy=(QSizePolicy.MinimumExpanding, QSizePolicy.Fixed))
        grid.setRowMinimumHeight(3, 12)
        grid.addWidget(self.cont_feature_dim_combo.box, 4, 0, 1, 3)


        gui.rubber(self.controlArea)

        class _GraphicsView(QGraphicsView):
            def __init__(self, scene, parent, **kwargs):
                for k, v in dict(verticalScrollBarPolicy=Qt.ScrollBarAlwaysOff,
                                 horizontalScrollBarPolicy=Qt.ScrollBarAlwaysOff,
                                 viewportUpdateMode=QGraphicsView.BoundingRectViewportUpdate,
                                 renderHints=(QPainter.Antialiasing |
                                              QPainter.TextAntialiasing |
                                              QPainter.SmoothPixmapTransform),
                                 alignment=(Qt.AlignTop |
                                            Qt.AlignLeft),
                                 sizePolicy=QSizePolicy(QSizePolicy.MinimumExpanding,
                                                        QSizePolicy.MinimumExpanding)).items():
                    kwargs.setdefault(k, v)

                super().__init__(scene, parent, **kwargs)

        class GraphicsView(_GraphicsView):
            def __init__(self, scene, parent):
                super().__init__(scene, parent,
                                 verticalScrollBarPolicy=Qt.ScrollBarAlwaysOn,
                                 styleSheet='QGraphicsView {background: white}')
                self.viewport().setMinimumWidth(300)  # XXX: This prevents some tests failing
                self._is_resizing = False

            w = self

            def resizeEvent(self, resizeEvent):
                # Recompute main scene on window width change
                if resizeEvent.size().width() != resizeEvent.oldSize().width():
                    self._is_resizing = True
                    self.w.update_scene()
                    self._is_resizing = False
                return super().resizeEvent(resizeEvent)

            def is_resizing(self):
                return self._is_resizing

            def sizeHint(self):
                return QSize(500, 200)

        class FixedSizeGraphicsView(_GraphicsView):
            def __init__(self, scene, parent):
                super().__init__(scene, parent,
                                 sizePolicy=QSizePolicy(QSizePolicy.MinimumExpanding,
                                                        QSizePolicy.Minimum))

            def sizeHint(self):
                return QSize(400, 85)

        scene = self.scene = QGraphicsScene(self)

        top_view = self.top_view = FixedSizeGraphicsView(scene, self)
        mid_view = self.view = GraphicsView(scene, self)
        bottom_view = self.bottom_view = FixedSizeGraphicsView(scene, self)

        for view in (top_view, mid_view, bottom_view):
            self.mainArea.layout().addWidget(view)

        self.dot_animator = GraphicsColorAnimator(
            self, 3000,
            [
                (0.9, DOT_COLOR),
                (0.925, DOT_COLOR.lighter(115)),
                (0.95, DOT_COLOR),
                (0.975, DOT_COLOR.lighter(115)),
                (1.0, DOT_COLOR)
            ]
        )

    def _class_combo_changed(self):
        with np.errstate(invalid='ignore'):
            coeffs = [np.nan_to_num(p[self.target_class_index] /
                                    p[self.old_target_class_index])
                      for p in self.points]
        points = [p[self.old_target_class_index] for p in self.points]
        self.feature_marker_values = [
            self.get_points_from_coeffs(v, c, p) for (v, c, p) in
            zip(self.feature_marker_values, coeffs, points)]
        self.feature_marker_values = np.asarray(self.feature_marker_values)
        self.update_scene()
        self.old_target_class_index = self.target_class_index

    def _n_spin_changed(self):
        self.display_index = 1
        self.update_scene()

    def update_controls(self):
        self.class_combo.clear()
        self.norm_check.setHidden(True)
        self.cont_feature_dim_combo.setEnabled(True)
        if self.domain is not None:
            values = self.domain.class_vars[0].values
            if values:
                self.class_combo.addItems(values)
                self.target_class_index = 0
            if len(self.domain.attributes) > self.MAX_N_ATTRS:
                self.display_index = 1
            if len(self.domain.class_vars[0].values) > 2:
                self.norm_check.setHidden(False)
            if not self.domain.has_continuous_attributes():
                self.cont_feature_dim_combo.setEnabled(False)
                self.cont_feature_dim_index = 0
        model = self.sort_combo.model()
        item = model.item(SortBy.POSITIVE)
        item.setFlags(item.flags() | Qt.ItemIsEnabled)
        item = model.item(SortBy.NEGATIVE)
        item.setFlags(item.flags() | Qt.ItemIsEnabled)
        self.align = OWNomogram.ALIGN_ZERO
        if self.classifier and isinstance(self.classifier,
                                          LogisticRegressionClassifier):
            self.align = OWNomogram.ALIGN_LEFT

    @Inputs.data
    def set_data(self, data):
        self.instances = data
        self.feature_marker_values = []
        self.set_feature_marker_values()
        self.update_scene()

    @Inputs.classifier
    def set_classifier(self, classifier):
        self.closeContext()
        self.classifier = classifier
        self.Error.clear()
        if self.classifier and not isinstance(self.classifier, self.ACCEPTABLE):
            self.Error.invalid_classifier()
            self.classifier = None
        self.domain = self.classifier.domain if self.classifier else None
        self.data = None
        self.calculate_log_odds_ratios()
        self.calculate_log_reg_coefficients()
        self.update_controls()
        self.openContext(self.domain.class_var if self.domain is not None
                         else None)
        self.points = self.log_odds_ratios or self.log_reg_coeffs
        self.feature_marker_values = []
        self.old_target_class_index = self.target_class_index
        self.update_scene()

    def calculate_log_odds_ratios(self):
        self.log_odds_ratios = []
        self.p = None
        if self.classifier is None or self.domain is None:
            return
        if not isinstance(self.classifier, NaiveBayesModel):
            return

        log_cont_prob = self.classifier.log_cont_prob
        class_prob = self.classifier.class_prob
        for i in range(len(self.domain.attributes)):
            ca = np.exp(log_cont_prob[i]) * class_prob[:, None]
            _or = (ca / (1 - ca)) / (class_prob / (1 - class_prob))[:, None]
            self.log_odds_ratios.append(np.log(_or))
        self.p = class_prob

    def calculate_log_reg_coefficients(self):
        self.log_reg_coeffs = []
        self.log_reg_cont_data_extremes = []
        self.b0 = None
        if self.classifier is None or self.domain is None:
            return
        if not isinstance(self.classifier, LogisticRegressionClassifier):
            return

        self.domain = self.reconstruct_domain(self.classifier, self.domain)
        self.data = self.classifier.original_data.transform(self.domain)
        attrs, ranges, start = self.domain.attributes, [], 0
        for attr in attrs:
            stop = start + len(attr.values) if attr.is_discrete else start + 1
            ranges.append(slice(start, stop))
            start = stop

        self.b0 = self.classifier.intercept
        coeffs = self.classifier.coefficients
        if len(self.domain.class_var.values) == 2:
            self.b0 = np.hstack((self.b0 * (-1), self.b0))
            coeffs = np.vstack((coeffs * (-1), coeffs))
        self.log_reg_coeffs = [coeffs[:, r] for r in ranges]
        self.log_reg_coeffs_orig = self.log_reg_coeffs.copy()

        min_values = nanmin(self.data.X, axis=0)
        max_values = nanmax(self.data.X, axis=0)

        for i, min_t, max_t in zip(range(len(self.log_reg_coeffs)),
                                   min_values, max_values):
            if self.log_reg_coeffs[i].shape[1] == 1:
                coef = self.log_reg_coeffs[i]
                self.log_reg_coeffs[i] = np.hstack((coef * min_t, coef * max_t))
                self.log_reg_cont_data_extremes.append(
                    [sorted([min_t, max_t], reverse=bool(c < 0)) for c in coef.flat])
            else:
                self.log_reg_cont_data_extremes.append([None])

    def update_scene(self):
        self.clear_scene()
        if self.domain is None or not len(self.points[0]):
            self.Outputs.features.send(None)
            return

        n_attrs = self.n_attributes if self.display_index else int(1e10)
        attr_inds, attributes = zip(*self.get_ordered_attributes()[:n_attrs])
        self.Outputs.features.send(AttributeList(attributes))

        point_text = QGraphicsTextItem("Points")
        metric = QFontMetrics(point_text.font())

        def text_item(text):
            elided_text = metric.elidedText(text, Qt.ElideRight, 200)
            item = QGraphicsTextItem(elided_text)
            item.setToolTip(text)
            return item

        name_items = [text_item(attr.name) for attr in attributes]

        probs_text = QGraphicsTextItem("Probabilities (%)")
        all_items = name_items + [point_text, probs_text]
        name_offset = -max(t.boundingRect().width() for t in all_items) - 30
        w = self.view.viewport().rect().width()
        max_width = w + name_offset - 30

        points = [self.points[i][self.target_class_index]
                  for i in attr_inds]
        if self.align == OWNomogram.ALIGN_LEFT:
            points = [p - p.min() for p in points]
        max_ = np.nan_to_num(max(max(abs(p)) for p in points))
        d = 100 / max_ if max_ else 1
        minimums = [p[self.target_class_index].min() for p in self.points]
        if self.scale == OWNomogram.POINT_SCALE:
            points = [p * d for p in points]

            if self.align == OWNomogram.ALIGN_LEFT:
                self.scale_marker_values = lambda x: (x - minimums) * d
            else:
                self.scale_marker_values = lambda x: x * d
        else:
            if self.align == OWNomogram.ALIGN_LEFT:
                self.scale_marker_values = lambda x: x - minimums
            else:
                self.scale_marker_values = lambda x: x

        point_item, nomogram_head = self.create_main_nomogram(
            attributes, attr_inds,
            name_items, points, max_width, point_text, name_offset)
        probs_item, nomogram_foot = self.create_footer_nomogram(
            probs_text, d, minimums, max_width, name_offset)
        for item in self.feature_items.values():
            item.dot.point_dot = point_item.dot
            item.dot.probs_dot = probs_item.dot
            item.dot.vertical_line = self.hidden_vertical_line

        self.dot_animator.setGraphicsItems(
            [item.dot for item in self.feature_items.values()]
        )
        self.dot_animator.start()

        self.nomogram = nomogram = NomogramItem()
        nomogram.add_items([nomogram_head, self.nomogram_main, nomogram_foot])
        self.scene.addItem(nomogram)

        self.set_feature_marker_values()

        rect = QRectF(self.scene.itemsBoundingRect().x(),
                      self.scene.itemsBoundingRect().y(),
                      self.scene.itemsBoundingRect().width(),
                      self.nomogram.preferredSize().height()).adjusted(10, 0, 20, 0)
        self.scene.setSceneRect(rect)

        # Clip top and bottom (60 and 150) parts from the main view
        self.view.setSceneRect(rect.x(), rect.y() + 80, rect.width() - 10, rect.height() - 160)
        self.view.viewport().setMaximumHeight(int(rect.height() - 160))
        # Clip main part from top/bottom views
        # below point values are imprecise (less/more than required) but this
        # is not a problem due to clipped scene content still being drawn
        self.top_view.setSceneRect(rect.x(), rect.y() + 3, rect.width() - 10, 20)
        self.bottom_view.setSceneRect(rect.x(), rect.height() - 110, rect.width() - 10, 30)

    @staticmethod
    def _adjust_scale(attributes, points, max_width, diff, attr_inds,
                      log_reg_cont_data_extremes, cls_index):
        if not diff:
            return max_width

        def offset(name, point):
            text_ = QGraphicsTextItem(name).boundingRect()
            return scale * point + text_.width() / 2

        lr = log_reg_cont_data_extremes
        scale = max_width / diff
        names = list(chain.from_iterable(
            [_get_labels(a, lr and lr[i] and lr[i][0] and lr[i][cls_index],
                         OWNomogram.get_ruler_values(p.min(), p.max(),
                                                     scale * np.ptp(p), False))
             for i, a, p in zip(attr_inds, attributes, points)]))
        points = list(chain.from_iterable(points))

        old_scale = scale + 1
        while old_scale > scale:
            old_scale = scale
            offsets = [offset(n, p) for n, p in zip(names, points)]
            most_right_name = names[np.argmax(offsets)]
            text = QGraphicsTextItem(most_right_name).boundingRect()
            scale = (max_width - text.width() / 2) / diff
        return scale

    def create_main_nomogram(self, attributes, attr_inds, name_items, points,
                             max_width, point_text, name_offset):
        cls_index = self.target_class_index
        min_p = min(p.min() for p in points)
        max_p = max(p.max() for p in points)
        values = self.get_ruler_values(min_p, max_p, max_width)
        min_p, max_p = min(values), max(values)
        diff_ = np.nan_to_num(max_p - min_p)
        scale_x = self._adjust_scale(
            attributes, points, max_width, diff_, attr_inds,
            self.log_reg_cont_data_extremes, cls_index
        )

        nomogram_header = NomogramItem()
        point_item = RulerItem(point_text, values, scale_x, name_offset,
                               - scale_x * min_p)
        point_item.setPreferredSize(point_item.preferredWidth(), 35)
        nomogram_header.add_items([point_item])

        self.nomogram_main = NomogramItem()
        cont_feature_item_class = ContinuousFeature2DItem if \
            self.cont_feature_dim_index else ContinuousFeatureItem

        feature_items = [
            DiscreteFeatureItem(
                name_item, attr, point,
                scale_x, name_offset, - scale_x * min_p)
            if attr.is_discrete else
            cont_feature_item_class(
                name_item, attr, self.log_reg_cont_data_extremes[i][cls_index],
                self.get_ruler_values(
                    point.min(), point.max(),
                    scale_x * np.ptp(point), False),
                scale_x, name_offset, - scale_x * min_p)
            for i, attr, name_item, point in
            zip(attr_inds, attributes, name_items, points)]

        self.nomogram_main.add_items(feature_items)
        self.feature_items = OrderedDict(sorted(zip(attr_inds, feature_items)))

        x = - scale_x * min_p
        y = self.nomogram_main.layout().preferredHeight() + 10
        self.vertical_line = QGraphicsLineItem(x, -6, x, y)
        self.vertical_line.setPen(QPen(Qt.DotLine))
        self.vertical_line.setParentItem(point_item)
        self.hidden_vertical_line = QGraphicsLineItem(x, -6, x, y)
        pen = QPen(Qt.DashLine)
        pen.setBrush(QColor(Qt.red))
        self.hidden_vertical_line.setPen(pen)
        self.hidden_vertical_line.setParentItem(point_item)

        return point_item, nomogram_header

    def get_ordered_attributes(self):
        """Return (in_domain_index, attr) pairs, ordered by method in SortBy combo"""
        if self.domain is None or not self.domain.attributes:
            return []

        attrs = self.domain.attributes
        sort_by = self.sort_index
        class_value = self.target_class_index

        if sort_by == SortBy.NO_SORTING:
            return list(enumerate(attrs))

        elif sort_by == SortBy.NAME:

            def key(x):
                _, attr = x
                return attr.name.lower()

        elif sort_by == SortBy.ABSOLUTE:

            def key(x):
                i, attr = x
                if attr.is_discrete:
                    ptp = np.ptp(self.points[i][class_value])
                else:
                    coef = np.abs(self.log_reg_coeffs_orig[i][class_value]).mean()
                    ptp = coef * np.ptp(self.log_reg_cont_data_extremes[i][class_value])
                return -ptp

        elif sort_by == SortBy.POSITIVE:

            def key(x):
                i, attr = x
                max_value = (self.points[i][class_value].max()
                             if attr.is_discrete else
                             np.mean(self.log_reg_cont_data_extremes[i][class_value]))
                return -max_value

        elif sort_by == SortBy.NEGATIVE:

            def key(x):
                i, attr = x
                min_value = (self.points[i][class_value].min()
                             if attr.is_discrete else
                             np.mean(self.log_reg_cont_data_extremes[i][class_value]))
                return min_value

        return sorted(enumerate(attrs), key=key)


    def create_footer_nomogram(self, probs_text, d, minimums,
                               max_width, name_offset):
        # pylint: disable=invalid-unary-operand-type
        eps, d_ = 0.05, 1
        k = - np.log(self.p / (1 - self.p)) if self.p is not None else - self.b0
        min_sum = k[self.target_class_index] - np.log((1 - eps) / eps)
        max_sum = k[self.target_class_index] - np.log(eps / (1 - eps))
        if self.align == OWNomogram.ALIGN_LEFT:
            max_sum = max_sum - sum(minimums)
            min_sum = min_sum - sum(minimums)
            for i in range(len(k)):  # pylint: disable=consider-using-enumerate
                k[i] = k[i] - sum([min(q) for q in [p[i] for p in self.points]])
        if self.scale == OWNomogram.POINT_SCALE:
            min_sum *= d
            max_sum *= d
            d_ = d

        values = self.get_ruler_values(min_sum, max_sum, max_width)
        min_sum, max_sum = min(values), max(values)
        diff_ = np.nan_to_num(max_sum - min_sum)
        scale_x = max_width / diff_ if diff_ else max_width
        cls_var, cls_index = self.domain.class_var, self.target_class_index
        nomogram_footer = NomogramItem()

        def get_normalized_probabilities(val):
            if not self.normalize_probabilities:
                return 1 / (1 + np.exp(k[cls_index] - val / d_))
            totals = self.__get_totals_for_class_values(minimums)
            p_sum = np.sum(1 / (1 + np.exp(k - totals / d_)))
            return 1 / (1 + np.exp(k[cls_index] - val / d_)) / p_sum

        def get_points(prob):
            if not self.normalize_probabilities:
                return (k[cls_index] - np.log(1 / prob - 1)) * d_
            totals = self.__get_totals_for_class_values(minimums)
            p_sum = np.sum(1 / (1 + np.exp(k - totals / d_)))
            return (k[cls_index] - np.log(1 / (prob * p_sum) - 1)) * d_

        probs_item = ProbabilitiesRulerItem(
            probs_text, values, scale_x, name_offset, - scale_x * min_sum,
            get_points=get_points,
            title="{}='{}'".format(cls_var.name, cls_var.values[cls_index]),
            get_probabilities=get_normalized_probabilities)
        nomogram_footer.add_items([probs_item])
        return probs_item, nomogram_footer

    def __get_totals_for_class_values(self, minimums):
        cls_index = self.target_class_index
        marker_values = self.scale_marker_values(self.feature_marker_values)
        totals = np.full(len(self.domain.class_var.values), np.nan)
        totals[cls_index] = marker_values.sum()
        for i in range(len(self.domain.class_var.values)):
            if i == cls_index:
                continue
            coeffs = [np.nan_to_num(p[i] / p[cls_index]) for p in self.points]
            points = [p[cls_index] for p in self.points]
            total = sum([self.get_points_from_coeffs(v, c, p) for (v, c, p)
                         in zip(self.feature_marker_values, coeffs, points)])
            if self.align == OWNomogram.ALIGN_LEFT:
                points = [p - m for m, p in zip(minimums, points)]
                total -= sum([min(p) for p in [p[i] for p in self.points]])
            d = 100 / max(max(abs(p)) for p in points)
            if self.scale == OWNomogram.POINT_SCALE:
                total *= d
            totals[i] = total
        assert not np.any(np.isnan(totals))
        return totals

    def set_feature_marker_values(self):
        if not (len(self.points) and len(self.feature_items)):
            return
        if not len(self.feature_marker_values):
            self._init_feature_marker_values()
        marker_values = self.scale_marker_values(self.feature_marker_values)

        invisible_sum = 0
        for i, marker in enumerate(marker_values):
            try:
                item = self.feature_items[i]
            except KeyError:
                invisible_sum += marker
            else:
                item.dot.move_to_val(marker)

        item.dot.probs_dot.move_to_sum(invisible_sum)

    def _init_feature_marker_values(self):
        self.feature_marker_values = []
        cls_index = self.target_class_index
        instances = self.instances.transform(self.domain) \
            if self.instances else None
        values = []
        for i, attr in enumerate(self.domain.attributes):
            value, feature_val = 0, None
            if len(self.log_reg_coeffs):
                if attr.is_discrete:
                    ind, n = unique(self.data.X[:, i], return_counts=True)
                    feature_val = np.nan_to_num(ind[np.argmax(n)])
                else:
                    feature_val = nanmean(self.data.X[:, i])

            # If data is provided on a separate signal, use the first data
            # instance to position the points instead of the mean
            inst_in_dom = instances and attr in instances.domain
            if inst_in_dom and not np.isnan(instances[0][attr]):
                feature_val = instances[0][attr]

            if feature_val is not None:
                value = (self.points[i][cls_index][int(feature_val)]
                         if attr.is_discrete else
                         self.log_reg_coeffs_orig[i][cls_index][0] * feature_val)
            values.append(value)
        self.feature_marker_values = np.asarray(values)

    def clear_scene(self):
        self.feature_items = {}
        self.scale_marker_values = lambda x: x
        self.nomogram = None
        self.nomogram_main = None
        self.vertical_line = None
        self.hidden_vertical_line = None
        self.dot_animator.clear()
        self.scene.clear()

    def get_nomogram_view(self):
        view = QGraphicsView(self.scene, self)
        scene_rect = self.scene.itemsBoundingRect()
        view.setSceneRect(scene_rect)
        view.resize(scene_rect.size().toSize())
        return view

    def copy_to_clipboard(self):
        ClipboardFormat.write_image(None, self.get_nomogram_view())

    def save_graph(self):
        saveplot.save_plot(self.get_nomogram_view(), self.graph_writers)

    def send_report(self):
        # self.report_plot(name="", plot=self.get_nomogram_view())
        # would work, but the resulting nomogram is too small
        # The drawback of the below is that the space between top_view and view
        self.report_plot(name="", plot=self.top_view)
        self.report_plot(name="", plot=self.view)
        self.report_plot(name="", plot=self.bottom_view)

    @staticmethod
    def reconstruct_domain(classifier: Model, preprocessed: Domain) -> Domain:
        # abuse dict to make "in" comparisons faster
        original = classifier.original_domain
        attrs = OrderedDict()
        for attr in preprocessed.attributes:
            cv = attr._compute_value.variable._compute_value
            if cv and isinstance(getattr(cv, "variable", None), Variable):
                var = cv.variable
            else:
                var = original[attr.name]
            var = original[var.name] if var.name in original else attr
            if var in attrs:    # the reason for OrderedDict
                continue
            attrs[var] = None   # we only need keys
        attrs = list(attrs.keys())

        orig_clv = original.class_var
        orig_data = classifier.original_data
        values = (orig_clv.values[int(i)]
                  for i in np.unique(orig_data.get_column(orig_clv)))
        class_var = DiscreteVariable(original.class_var.name, values)
        return Domain(attrs, class_var, original.metas)

    @staticmethod
    def get_ruler_values(start, stop, max_width, round_to_nearest=True):
        if max_width == 0:
            return [0]
        diff = np.nan_to_num((stop - start) / max_width)
        if diff <= 0:
            return [0]
        decimals = int(np.floor(np.log10(diff)))
        if diff > 4 * pow(10, decimals):
            step = 5 * pow(10, decimals + 2)
        elif diff > 2 * pow(10, decimals):
            step = 2 * pow(10, decimals + 2)
        elif diff > 1 * pow(10, decimals):
            step = 1 * pow(10, decimals + 2)
        else:
            step = 5 * pow(10, decimals + 1)
        round_by = int(- np.floor(np.log10(step)))
        r = start % step
        if not round_to_nearest:
            _range = np.arange(start + step, stop + r, step) - r
            start, stop = np.floor(start * 100) / 100, np.ceil(stop * 100) / 100
            return np.round(np.hstack((start, _range, stop)), 2)
        return np.round(np.arange(start, stop + r + step, step) - r, round_by)

    @staticmethod
    def get_points_from_coeffs(current_value, coefficients, possible_values):
        if np.isnan(possible_values).any():
            return 0
        # pylint: disable=undefined-loop-variable
        indices = np.argsort(possible_values)
        sorted_values = possible_values[indices]
        sorted_coefficients = coefficients[indices]
        for i, val in enumerate(sorted_values):
            if current_value < val:
                break
        diff = sorted_values[i] - sorted_values[i - 1]
        k = 0 if diff < 1e-6 else (sorted_values[i] - current_value) / \
                                  (sorted_values[i] - sorted_values[i - 1])
        return sorted_coefficients[i - 1] * sorted_values[i - 1] * k + \
               sorted_coefficients[i] * sorted_values[i] * (1 - k)

    def reset_settings(self):
        with warnings.catch_warnings():
            # setting target_class_index will trigger this innocent warning
            warnings.filterwarnings(
                "ignore", "combo box 'target_class_index' is empty")
            self._reset_settings()
        self.update_scene()


if __name__ == "__main__":  # pragma: no cover
    from Orange.classification import NaiveBayesLearner  #, LogisticRegressionLearner
    data = Table("heart_disease")
    clf = NaiveBayesLearner()(data)
    # clf = LogisticRegressionLearner()(data)
    WidgetPreview(OWNomogram).run(set_classifier=clf, set_data=data)