Merge tag '3.33.0' into stable

.github/workflows/doc.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       fail-fast: False
       matrix:
-        python: [3.7]
+        python: [3.9]
         os: [ubuntu-18.04]
 
     steps:
@@ -31,4 +31,6 @@ jobs:
         run: pip install tox
 
       - name: Build documentation
-        run:  xvfb-run -a -s "-screen 0 1280x1024x24" tox -e build_doc
+        run: tox -e build_doc
+        env:
+          QT_QPA_PLATFORM: offscreen

.github/workflows/rebase.yml

@@ -14,6 +14,6 @@ jobs:
           token: ${{ secrets.BIOLAB_HELPER_PAT }}
           fetch-depth: 0 # otherwise, you will fail to push refs to dest repo
       - name: Automatic Rebase
-        uses: cirrus-actions/rebase@1.4
+        uses: cirrus-actions/rebase@1.6
         env:
           GITHUB_TOKEN: ${{ secrets.BIOLAB_HELPER_PAT }}

.github/workflows/test.yml

@@ -10,7 +10,7 @@ on:
 
 jobs:
   lint:
-    runs-on: ubuntu-18.04
+    runs-on: ubuntu-20.04
     steps:
       - uses: actions/checkout@v2
         with:
@@ -36,19 +36,24 @@ jobs:
     strategy:
       fail-fast: False
       matrix:
-        os: [ubuntu-18.04]
-        python-version: [3.7, 3.8, 3.9]
+        os: [ubuntu-20.04]
+        python-version: [3.8, 3.9, '3.10']
         tox_env: [orange-released]
         name: [Released]
         include:
-          - os: ubuntu-18.04
-            python-version: 3.8
+          - os: ubuntu-20.04
+            python-version: '3.10'
             tox_env: orange-latest
             name: Latest
           - os: ubuntu-18.04
-            python-version: 3.7
+            python-version: 3.8
             tox_env: orange-oldest
             name: Oldest dependencies
+          - os: ubuntu-20.04
+            python-version: 3.9
+            tox_env: pyqt6
+            name: PyQt6
+
 
     services:
       postgres:
@@ -77,17 +82,19 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install linux system dependencies
-        run: sudo apt-get install -y libxkbcommon-x11-0 
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 libegl1-mesa libxcb-shape0
 
       - name: Install Tox
         run: |
           python -m pip install --upgrade pip
           python -m pip install --upgrade tox tox-pip-version
 
       - name: Run Tox
-        run: xvfb-run -a -s "-screen 0 1280x1024x24" tox -e ${{ matrix.tox_env }}
+        run: catchsegv xvfb-run -a -s "$XVFBARGS" tox -e ${{ matrix.tox_env }}
         env:
-          # QT_QPA_PLATFORM: offscreen
+          XVFBARGS: "-screen 0 1280x1024x24"
           ORANGE_TEST_DB_URI: postgres://postgres_user:postgres_password@localhost:5432/postgres_db|mssql://SA:sqlServerPassw0rd@localhost:1433
 
       - name: Upload code coverage
@@ -105,19 +112,27 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [macos-10.15, windows-2019]
-        python-version: [3.7, 3.8, 3.9]
+        os: [macos-11, windows-2019]
+        python-version: [3.8, 3.9, '3.10']
         tox_env: [orange-released]
         name: [Released]
         include:
           - os: windows-latest
-            python-version: 3.8
+            python-version: '3.10'
             tox_env: orange-latest
             name: Latest
-          - os: macos-10.15
-            python-version: 3.8
+          - os: macos-11
+            python-version: '3.10'
             tox_env: orange-latest
             name: Latest
+          - os: windows-latest
+            python-version: '3.10'
+            tox_env: pyqt6
+            name: PyQt6
+          - os: macos-11
+            python-version: 3.9
+            tox_env: pyqt6
+            name: PyQt6
 
     steps:
       - uses: actions/checkout@v2
@@ -128,7 +143,7 @@ jobs:
 
       - name: Install system dependencies on MacOS
         run: brew install libomp
-        if: matrix.os == 'macos-10.15' || matrix.os == 'macos-11.0'
+        if: matrix.os == 'macos-10.15' || matrix.os == 'macos-11'
 
       - name: Install dependencies
         run: |

CHANGELOG.md

@@ -5,6 +5,36 @@ Change Log
 ------------
 
 
+[3.33.0] - 2022-09-30
+--------------------
+##### Enhancements
+* drophandlers: Preserve defaults history ([#6069](../../pull/6069))
+* owlogisticregression: Add option for no regularization ([#6093](../../pull/6093))
+* Predictions: Show errors ([#6012](../../pull/6012))
+* Add support for PyQt6 ([#5884](../../pull/5884))
+* Feature Constructor: Make concurrent ([#5992](../../pull/5992))
+* Feature constructor optimization ([#5975](../../pull/5975))
+* Discretize: Simplify interface, add nicer binning ([#5919](../../pull/5919))
+
+##### Bugfixes
+* owboxplot: Fix an error in widget cleanup during tests ([#6136](../../pull/6136))
+* Group By - fix std and sum for TimeVariable ([#6133](../../pull/6133))
+* Handle timezones in Edit Domain ([#6123](../../pull/6123))
+* owfeatureconstructor: Cast FeatureFunc result to array ([#6115](../../pull/6115))
+* Server embedder - send bytes as content ([#6051](../../pull/6051))
+* plotutils - replace MidButton with MiddleButton ([#6124](../../pull/6124))
+* Feature Constructor cast function is not picklable ([#6002](../../pull/6002))
+* test_owfile: Fix unreliable `test_warning_from_another_thread` ([#6085](../../pull/6085))
+* Embedders - fix proxies, default on http, tests ([#6028](../../pull/6028))
+* TableModel: Define foreground color when providing background ([#6011](../../pull/6011))
+* owdistancemap: Fix an implicit float->int conversion error ([#5998](../../pull/5998))
+* Neighbours - support Table derived output types ([#5986](../../pull/5986))
+* Predictions: allow predicting probabilities for classless data ([#5972](../../pull/5972))
+* owcsvimport: Reduce sample size ([#5960](../../pull/5960))
+* make usable_scorers backwards compatible ([#5943](../../pull/5943))
+* owpythonscript: Fix implicit float to int cast error ([#5944](../../pull/5944))
+
+
 [3.32.0] - 2022-04-01
 --------------------
 ##### Enhancements
@@ -1678,7 +1708,8 @@ Change Log
 * Initial version based on Python 1.5.2 and Qt 2.3
 
 
-[next]: https://github.com/biolab/orange3/compare/3.32.0...HEAD
+[next]: https://github.com/biolab/orange3/compare/3.33.0...HEAD
+[3.32.0]: https://github.com/biolab/orange3/compare/3.32.0...3.33.0
 [3.32.0]: https://github.com/biolab/orange3/compare/3.31.1...3.32.0
 [3.31.1]: https://github.com/biolab/orange3/compare/3.31.0...3.31.1
 [3.31.0]: https://github.com/biolab/orange3/compare/3.30.2...3.31.0

Orange/classification/_simple_tree.c

@@ -310,13 +310,13 @@ mse_c(struct Example *examples, int size, int attr, float cls_mse, struct Args *
 			var_lt.sum += ex->weight * cls_val;
 			var_lt.sum2 += ex->weight * cls_val * cls_val;
 
-			/* this calculation might be numarically unstable - fix */
+			/* this calculation might be numerically unstable - fix */
 			var_ge.n -= ex->weight;
 			var_ge.sum -= ex->weight * cls_val;
 			var_ge.sum2 -= ex->weight * cls_val * cls_val;
 		}
 
-		if (ex->x[attr] == ex_next->x[attr] || i + 1 < min_instances)
+		if (ex->x[attr] == ex_next->x[attr] || i + 1 < min_instances || var_lt.n == 0)
 			continue;
 
 		/* compute mse */
@@ -346,6 +346,10 @@ mse_d(struct Example *examples, int size, int attr, float cls_mse, struct Args *
 		float n, sum, sum2;
 	} *variances, *v, *v_end;
 
+	if (cls_mse <= 0) {
+	    return 0.0;
+	}
+
 	attr_vals = args->attr_vals[attr];
 
 	ASSERT(variances = (struct Variance *)calloc(attr_vals, sizeof *variances));
@@ -381,8 +385,8 @@ mse_d(struct Example *examples, int size, int attr, float cls_mse, struct Args *
 			score += v->sum2 - v->sum * v->sum / v->n;
 	score = (cls_mse - score / size_attr_cls_known) / cls_mse * (size_attr_known / size_weight);
 
-	if (size_attr_cls_known <= 0.0 || cls_mse <= 0.0 || size_weight <= 0.0)
-		score = 0.0;
+	if (size_attr_cls_known <= 0.0 || size_weight <= 0.0)
+		score = -INFINITY;
 
 finish:
 	free(attr_dist);
@@ -441,15 +445,6 @@ build_tree_(struct Example *examples, int size, int depth, struct SimpleTreeNode
 		float n, sum, sum2, cls_val;
 
 		assert(args->type == Regression);
-		if (size == 0) {
-			assert(parent);
-			node->type = PredictorNode;
-			node->children_size = 0;
-			node->n = parent->n;
-			node->sum = parent->sum;
-			return node;
-		}
-
 		n = sum = sum2 = 0.0;
 		for (ex = examples, ex_end = examples + size; ex < ex_end; ex++)
 			if (!isnan(ex->y)) {
@@ -461,6 +456,17 @@ build_tree_(struct Example *examples, int size, int depth, struct SimpleTreeNode
 
 		node->n = n;
 		node->sum = sum;
+
+		if (n == 0) {
+			node->type = PredictorNode;
+			node->children_size = 0;
+			if (parent) {
+				node->n = parent->n;
+				node->sum = parent->sum;
+			}
+			return node;
+		}
+
 		cls_mse = (sum2 - sum * sum / n) / n;
 
 		if (cls_mse < 1e-5) {
@@ -747,7 +753,7 @@ predict_regression(double *x, int size, struct SimpleTreeNode *node, int num_att
 	for (i = 0; i < size; i++) {
 		sum = n = 0;
 		predict_regression_(x + i * num_attrs, node, &sum, &n);
-		p[i] = sum / n;
+		p[i] = n > 0 ? sum / n : sum;
 	}
 }
 

Orange/classification/base_classification.py

@@ -16,7 +16,8 @@ def incompatibility_reason(self, domain):
 
 
 class ModelClassification(Model):
-    pass
+    def predict_proba(self, data):
+        return self(data, ret=Model.Probs)
 
 
 class SklModelClassification(SklModel, ModelClassification):

Orange/classification/catgb.py

@@ -4,8 +4,8 @@
 
 import catboost
 
-from Orange.base import CatGBBaseLearner
-from Orange.classification import Learner
+from Orange.base import CatGBBaseLearner, CatGBModel
+from Orange.classification import Learner, Model
 from Orange.data import Variable, DiscreteVariable, Table
 from Orange.preprocess.score import LearnerScorer
 
@@ -21,5 +21,10 @@ def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
         return model.cat_model.feature_importances_, model.domain.attributes
 
 
+class CatGBClsModel(CatGBModel, Model):
+    pass
+
+
 class CatGBClassifier(CatGBBaseLearner, Learner, _FeatureScorerMixin):
     __wraps__ = catboost.CatBoostClassifier
+    __returns__ = CatGBClsModel

Orange/classification/tests/test_simple_tree.py

@@ -0,0 +1,55 @@
+import unittest
+
+import numpy as np
+
+from Orange.classification import SimpleTreeLearner
+from Orange.data import ContinuousVariable, DiscreteVariable, Domain, \
+    Table
+
+
+class SimpleTreeTest(unittest.TestCase):
+    def test_nonan_classification(self):
+        x = ContinuousVariable("x")
+        y = DiscreteVariable("y", values=tuple("ab"))
+        d = Domain([x], y)
+        t = Table.from_numpy(d, [[0]], [np.nan])
+        m = SimpleTreeLearner()(t)
+        self.assertFalse(np.isnan(m(t)[0]))
+
+    def test_nonan_regression(self):
+        x = ContinuousVariable("x")
+        y = ContinuousVariable("y")
+        d = Domain([x], y)
+        t = Table.from_numpy(d, [[42]], [np.nan])
+        m = SimpleTreeLearner()(t)
+        # must not be nan ...
+        self.assertFalse(np.isnan(m(t)[0]))
+        # ... and currently, it's zero (although mathematicians disagree,
+        # we, engineers *know* that 0 is the mean of R)
+        self.assertEqual(m(t)[0], 0)
+
+        x2 = ContinuousVariable("x2")
+        d = Domain([x, x2], y)
+        t = Table.from_numpy(d,
+                             [[-1, np.nan], [1, -1], [1, 1]],
+                             [-20, 20, np.nan])
+        m = SimpleTreeLearner(min_instances=1)(t)
+        # must not be nan ...
+        self.assertFalse(np.isnan(m(t)[0]))
+        # ... and currently, it's zero (although mathematicians disagree,
+        # we, engineers *know* that 0 is the mean of R)
+        np.testing.assert_equal(m(t), [-20, 20, 20])
+
+    def test_stub(self):
+        x = ContinuousVariable("x")
+        y = ContinuousVariable("y")
+        d = Domain([x], y)
+        t = Table.from_numpy(d, [[-1], [1]], [-5, 0])
+        m = SimpleTreeLearner(min_instances=1)(t)
+        np.testing.assert_equal(m(t), [-5, 0])
+        m = SimpleTreeLearner()(t)
+        np.testing.assert_equal(m(t), [-2.5, -2.5])
+
+
+if __name__ == "__main__":
+    unittest.main()

Orange/classification/xgb.py

@@ -6,7 +6,7 @@
 import xgboost
 
 from Orange.base import XGBBase
-from Orange.classification import Learner
+from Orange.classification import Learner, SklModel
 from Orange.data import Variable, DiscreteVariable, Table
 from Orange.preprocess.score import LearnerScorer
 
@@ -24,6 +24,7 @@ def score(self, data: Table) -> Tuple[np.ndarray, Tuple[Variable]]:
 
 class XGBClassifier(XGBBase, Learner, _FeatureScorerMixin):
     __wraps__ = xgboost.XGBClassifier
+    __returns__ = SklModel
 
     def __init__(self,
                  max_depth=None,
@@ -87,6 +88,7 @@ def __init__(self,
 
 class XGBRFClassifier(XGBBase, Learner, _FeatureScorerMixin):
     __wraps__ = xgboost.XGBRFClassifier
+    __returns__ = SklModel
 
     def __init__(self,
                  max_depth=None,