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)