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Current coverage is 88.96% (diff: 100%)@@ master #1787 diff @@
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pavlin-policar
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lanzagar
requested changes
Dec 1, 2016
| self.base_estimator = self.DEFAULT_BASE_ESTIMATOR | ||
| else: | ||
| self.base_estimator = learner or self.DEFAULT_BASE_ESTIMATOR | ||
| self.base_label.setText( |
Contributor
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This should be outside of the else block (remove one indent).
It sets the base_estimator correctly now, but not the label.
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Thanks, I missed that.
| from Orange.classification import SklTreeLearner, KNNLearner | ||
| from Orange.classification import KNNLearner | ||
| from Orange.classification import RandomForestLearner | ||
| from Orange.classification import SklTreeLearner |
Contributor
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Grouping is preferred for imports like these (same as 2 lines below).
| @@ -1,5 +1,6 @@ | |||
| # Test methods with long descriptive names can omit docstrings | |||
| # pylint: disable=missing-docstring | |||
| from Orange.regression import RandomForestRegressionLearner | |||
lanzagar
requested changes
Dec 1, 2016
| self.assertTrue(self.widget.Error.no_weight_support.is_shown()) | ||
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| def test_error_message_cleared_when_valid_learner_on_input(self): | ||
| # Disconneting an invalid learner should use the default one and hide |
| self.assertTrue(self.widget.Error.no_weight_support.is_shown()) | ||
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| def test_error_message_cleared_when_valid_learner_on_input(self): | ||
| # Disconneting an invalid learner should use the default one and hide |
lanzagar
reviewed
Dec 1, 2016
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| DEFAULT_BASE_ESTIMATOR = SklTreeLearner() | ||
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| class Error(OWBaseLearner.Error): |
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We decided it is best to have an Error message and require the user removes the invalid input before continuing.
The widget simply crashed if an invalid learner was set as its base estimator. Now, we use the widget Error messages and fail gracefully.
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lanzagar
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Dec 2, 2016
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Issue
AdaBoost simply crashed when a learner without support for
sample_weightswas put onto the input.Description of changes
This does not happen any more, but it uses the Orange widget Error that tells us that the learner doesn't support sample weights. I've also changed the control widths to match most of the other learner widgets.
Includes