if self.resampling == OWTestLearners.TestOnTest:
if self.test_data is None:
self.warning(2, "Missing separate test data input")
return
elif self.test_data.domain.class_var != class_var:
self.error(2, ("Inconsistent class variable between test " +
"and train data sets"))
return
// items in need of an update
items = [(key, slot) for key, slot in self.learners.items()
if slot.results is None]
learners = [slot.learner for _, slot in items]
if len(items) == 0:
return
if self.test_data is not None and \
self.resampling != OWTestLearners.TestOnTest:
self.warning(1, "Test data is present but unused. "
"Select "Test on test data" to use it.")
rstate = 42
def update_progress(finished):
self.progressBarSet(100 * finished)
common_args = dict(
store_data=True,
preprocessor=self.preprocessor,
callback=update_progress,
n_jobs=-1,
)
self.setStatusMessage("Running")
with self.progressBar():
try:
folds = self.NFolds[self.n_folds]
if self.resampling == OWTestLearners.KFold:
if len(self.data) < folds:
self.error(4, "Number of folds exceeds the data size")
return
warnings = []
results = Orange.evaluation.CrossValidation(
self.data, learners, k=folds,
After Change
Run/evaluate the learners.
self.Warning.test_data_unused.clear()
self.Warning.test_data_missing.clear()self.Warning.general.clear()self.Error.class_inconsistent.clear()
self.Error.too_many_folds.clear()
self.Error.general.clear()
if self.data is None:
return
class_var = self.data.domain.class_var
if self.resampling == OWTestLearners.TestOnTest:
if self.test_data is None:
self.Warning.test_data_missing()
return
elif self.test_data.domain.class_var != class_var:
self.Error.class_inconsistent()
return
// items in need of an update
items = [(key, slot) for key, slot in self.learners.items()
if slot.results is None]
learners = [slot.learner for _, slot in items]
if len(items) == 0:
return
if self.test_data is not None and \
self.resampling != OWTestLearners.TestOnTest:
self.Warning.test_data_unused()
rstate = 42
def update_progress(finished):
