def commit(self):
"""
Commit the results to output.
"""
self.Error.memory_error.clear()
valid = [
slot for slot in self.learners.values()
if slot.results is not None and slot.results.success
]
combined = None
predictions = None
if valid:
# Evaluation results
combined = results_merge([slot.results.value for slot in valid])
combined.learner_names = [
learner_name(slot.learner) for slot in valid
]
# Predictions & Probabilities
try:
predictions = combined.get_augmented_data(
combined.learner_names)
except MemoryError:
self.Error.memory_error()
self.Outputs.evaluations_results.send(combined)
self.Outputs.predictions.send(predictions)
def _update_scores(self):
model = self.score_table.model
model.clear()
scorers = usable_scorers(self.class_var) if self.class_var else []
self.score_table.update_header(scorers)
errors = []
for inputid, pred in self.predictors.items():
results = self.predictors[inputid].results
if not isinstance(results, Results) or results.predicted is None:
continue
row = [QStandardItem(learner_name(pred.predictor)),
QStandardItem("N/A"), QStandardItem("N/A")]
for scorer in scorers:
item = QStandardItem()
try:
score = scorer_caller(scorer, results)()[0]
item.setText(f"{score:.3f}")
except Exception as exc: # pylint: disable=broad-except
item.setToolTip(str(exc))
if scorer.name in self.score_table.shown_scores:
errors.append(str(exc))
row.append(item)
self.score_table.model.appendRow(row)
view = self.score_table.view
if model.rowCount():
view.setVisible(True)
view.ensurePolished()
view.setFixedHeight(
5 + view.horizontalHeader().height() +
view.verticalHeader().sectionSize(0) * model.rowCount())
else:
view.setVisible(False)
self.Error.scorer_failed("\n".join(errors), shown=bool(errors))
def _update_scores(self):
model = self.score_table.model
model.clear()
if self.data is None or self.data.domain.class_var is None:
scorers = []
else:
scorers = usable_scorers(self.data.domain.class_var)
self.score_table.update_header(scorers)
errors = []
for inputid, pred in self.predictors.items():
name = learner_name(pred.predictor)
head = QStandardItem(name)
# head.setData(key, Qt.UserRole)
row = [head]
results = self.predictors[inputid].results
if isinstance(results, str):
head.setToolTip(results)
head.setText("{} (error)".format(name))
head.setForeground(QBrush(Qt.red))
else:
for scorer in scorers:
item = QStandardItem()
try:
score = scorer_caller(scorer, results)()[0]
item.setText(f"{score:.3f}")
except Exception as exc: # pylint: disable=broad-except
item.setToolTip(str(exc))
if scorer.name in self.score_table.shown_scores:
errors.append(str(exc))
row.append(item)
self.score_table.model.appendRow(row)
self.Error.scorer_failed("\n".join(errors), shown=bool(errors))
def update_comparison_table(self):
self.comparison_table.clearContents()
slots = self._successful_slots()
if not (slots and self.scorers):
return
names = [learner_name(slot.learner) for slot in slots]
self._set_comparison_headers(names)
if self.resampling == OWTestLearners.KFold:
scores = self._scores_by_folds(slots)
self._fill_table(names, scores)
def update_stats_model(self):
# Update the results_model with up to date scores.
# Note: The target class specific scores (if requested) are
# computed as needed in this method.
model = self.score_table.model
# clear the table model, but preserving the header labels
for r in reversed(range(model.rowCount())):
model.takeRow(r)
target_index = None
if self.data is not None:
class_var = self.data.domain.class_var
if self.data.domain.has_discrete_class and \
self.class_selection != self.TARGET_AVERAGE:
target_index = class_var.values.index(self.class_selection)
else:
class_var = None
errors = []
has_missing_scores = False
names = []
for key, slot in self.learners.items():
name = learner_name(slot.learner)
names.append(name)
head = QStandardItem(name)
head.setData(key, Qt.UserRole)
results = slot.results
if results is not None and results.success:
train = QStandardItem("{:.3f}".format(
results.value.train_time))
train.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
train.setData(key, Qt.UserRole)
test = QStandardItem("{:.3f}".format(results.value.test_time))
test.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
test.setData(key, Qt.UserRole)
row = [head, train, test]
else:
row = [head]
if isinstance(results, Try.Fail):
head.setToolTip(str(results.exception))
head.setText("{} (error)".format(name))
head.setForeground(QtGui.QBrush(Qt.red))
if isinstance(results.exception, DomainTransformationError) \
and self.resampling == self.TestOnTest:
self.Error.test_data_incompatible()
self.Information.test_data_transformed.clear()
else:
errors.append("{name} failed with error:\n"
"{exc.__class__.__name__}: {exc!s}".format(
name=name, exc=slot.results.exception))
if class_var is not None and class_var.is_discrete and \
target_index is not None:
if slot.results is not None and slot.results.success:
ovr_results = results_one_vs_rest(slot.results.value,
target_index)
# Cell variable is used immediatelly, it's not stored
# pylint: disable=cell-var-from-loop
stats = [
Try(scorer_caller(scorer, ovr_results, target=1))
for scorer in self.scorers
]
else:
stats = None
else:
stats = slot.stats
if stats is not None:
for stat, scorer in zip(stats, self.scorers):
item = QStandardItem()
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
if stat.success:
item.setData(float(stat.value[0]), Qt.DisplayRole)
else:
item.setToolTip(str(stat.exception))
if scorer.name in self.score_table.shown_scores:
has_missing_scores = True
row.append(item)
model.appendRow(row)
# Resort rows based on current sorting
header = self.score_table.view.horizontalHeader()
model.sort(header.sortIndicatorSection(), header.sortIndicatorOrder())
self._set_comparison_headers(names)
self.error("\n".join(errors), shown=bool(errors))
self.Warning.scores_not_computed(shown=has_missing_scores)
