def __init__(self): super().__init__() # sets self.curvePoints, self.steps equidistant points from # 1/self.steps to 1 self.updateCurvePoints() # [start-snippet-2] self.scoring = [ ("Classification Accuracy", Orange.evaluation.scoring.CA), ("AUC", Orange.evaluation.scoring.AUC), ("Precision", Orange.evaluation.scoring.Precision), ("Recall", Orange.evaluation.scoring.Recall) ] # [end-snippet-2] #: input data on which to construct the learning curve self.data = None #: A {input_id: Learner} mapping of current learners from input channel self.learners = OrderedDict() #: A {input_id: List[Results]} mapping of input id to evaluation #: results list, one for each curve point self.results = OrderedDict() #: A {input_id: List[float]} mapping of input id to learning curve #: point scores self.curves = OrderedDict() # GUI box = gui.widgetBox(self.controlArea, "Info") self.infoa = gui.widgetLabel(box, 'No data on input.') self.infob = gui.widgetLabel(box, 'No learners.') gui.separator(self.controlArea) box = gui.widgetBox(self.controlArea, "Evaluation Scores") gui.comboBox(box, self, "scoringF", items=[x[0] for x in self.scoring], callback=self._invalidate_curves) gui.separator(self.controlArea) box = gui.widgetBox(self.controlArea, "Options") gui.spin(box, self, 'folds', 2, 100, step=1, label='Cross validation folds: ', keyboardTracking=False, callback=lambda: self._invalidate_results() if self.commitOnChange else None) gui.spin(box, self, 'steps', 2, 100, step=1, label='Learning curve points: ', keyboardTracking=False, callback=[self.updateCurvePoints, lambda: self._invalidate_results() if self.commitOnChange else None]) gui.checkBox(box, self, 'commitOnChange', 'Apply setting on any change') self.commitBtn = gui.button(box, self, "Apply Setting", callback=self._invalidate_results, disabled=True) gui.rubber(self.controlArea) # table widget self.table = gui.table(self.mainArea, selectionMode=QTableWidget.NoSelection)
def __init__(self): super().__init__() # sets self.curvePoints, self.steps equidistant points from # 1/self.steps to 1 self.updateCurvePoints() self.scoring = [("Classification Accuracy", Orange.evaluation.scoring.CA), ("AUC", Orange.evaluation.scoring.AUC), ("Precision", Orange.evaluation.scoring.Precision), ("Recall", Orange.evaluation.scoring.Recall)] #: Input data on which to construct the learning curve self.data = None #: Optional test data self.testdata = None #: LearnerData for each learner input self.learners: List[LearnerData] = [] # [start-snippet-3] #: The current evaluating task (if any) self._task = None # type: Optional[Task] #: An executor we use to submit learner evaluations into a thread pool self._executor = concurrent.futures.ThreadPoolExecutor() # [end-snippet-3] # GUI box = gui.widgetBox(self.controlArea, "Info") self.infoa = gui.widgetLabel(box, 'No data on input.') self.infob = gui.widgetLabel(box, 'No learners.') gui.separator(self.controlArea) box = gui.widgetBox(self.controlArea, "Evaluation Scores") gui.comboBox(box, self, "scoringF", items=[x[0] for x in self.scoring], callback=self._invalidate_curves) gui.separator(self.controlArea) box = gui.widgetBox(self.controlArea, "Options") gui.spin(box, self, 'folds', 2, 100, step=1, label='Cross validation folds: ', keyboardTracking=False, callback=lambda: self._invalidate_results() if self.commitOnChange else None) gui.spin(box, self, 'steps', 2, 100, step=1, label='Learning curve points: ', keyboardTracking=False, callback=[ self.updateCurvePoints, lambda: self._invalidate_results() if self.commitOnChange else None ]) gui.checkBox(box, self, 'commitOnChange', 'Apply setting on any change') self.commitBtn = gui.button(box, self, "Apply Setting", callback=self._invalidate_results, disabled=True) gui.rubber(self.controlArea) # table widget self.table = gui.table(self.mainArea, selectionMode=QTableWidget.NoSelection)