Merge tag '3.31.0' into stable

CHANGELOG.md

@@ -4,6 +4,26 @@ Change Log
 [next] - TBA
 ------------
 
+[3.31.0] - 2021-12-17
+--------------------
+##### Enhancements
+* oweditdomain: Indicate variables in error state ([#5732](../../pull/5732))
+* Scatterplot: Use opacity for contrast ([#5684](../../pull/5684))
+* Feature Constructor: Evaluate categorical variables to strings ([#5637](../../pull/5637))
+* New widget: Group By  ([#5541](../../pull/5541))
+* Table lock: tests run with tables that are read-only by default ([#5381](../../pull/5381))
+* config: sort example workflows ([#5600](../../pull/5600))
+
+##### Bugfixes
+* Paint Data: Fix ClearTool's issued commands ([#5718](../../pull/5718))
+* pandas_compat: fix table_from_frames for "normal" dataframe ([#5652](../../pull/5652))
+* pandas_compat: do not parse column of numbers (object dtype) to datetime ([#5681](../../pull/5681))
+* HeatMap: Color gradient center value edit ([#5647](../../pull/5647))
+* Distance Matrix: Fix crash on numeric meta vars as labels ([#5664](../../pull/5664))
+* Fix running OWS with widgets with WebView in Orange.canvas.run ([#5657](../../pull/5657))
+* main: Fix `--clear-widget-settings` parameter declaration ([#5619](../../pull/5619))
+
+
 [3.30.2] - 2021-10-27
 --------------------
 ##### Bugfixes
@@ -14,11 +34,13 @@ Change Log
 * Variable: fix timezone when parsing time variable ([#5617](../../pull/5617))
 * Require widget-base 4.15.1 and canvas-core 0.1.23 to fix some bugs/crashes
 
+
 [3.30.1] - 2021-09-24
 --------------------
 ##### Bugfixes
 * OWTable: fix select whole rows regression ([#5605](../../pull/5605))
 
+
 [3.30.0] - 2021-09-22
 --------------------
 ##### Enhancements
@@ -46,16 +68,19 @@ Change Log
 ##### Bugfixes
 * Create Class: fix incorrect value assignment
 
+
 [3.29.2] - 2021-06-08
 --------------------
 ##### Bugfixes
 * Bump orange-canvas-core minimum version requirement ([#5472](../../pull/5472))
 * owpca: fix component selection when dragging selection line ([#5469](../../pull/5469))
 * Save File when workflow basedir is an empty string ([#5459](../../pull/5459))
 
+
 [3.29.1] - 2021-05-31
 --------------------
 
+
 [3.29.0] - 2021-05-28
 --------------------
 ##### Enhancements
@@ -80,6 +105,7 @@ Change Log
 * Nomogram: Retain original compute_value ([#5382](../../pull/5382))
 * Radviz VizRank: Implement on_selection_changed ([#5338](../../pull/5338))
 
+
 [3.28.0] - 2021-03-05
 --------------------
 ##### Enhancements
@@ -1604,7 +1630,8 @@ Change Log
 * Initial version based on Python 1.5.2 and Qt 2.3
 
 
-[next]: https://github.com/biolab/orange3/compare/3.30.2...HEAD
+[next]: https://github.com/biolab/orange3/compare/3.31.0...HEAD
+[3.31.0]: https://github.com/biolab/orange3/compare/3.30.2...3.31.0
 [3.30.2]: https://github.com/biolab/orange3/compare/3.30.1...3.30.2
 [3.30.1]: https://github.com/biolab/orange3/compare/3.30.0...3.30.1
 [3.30.0]: https://github.com/biolab/orange3/compare/3.29.3...3.30.0

Orange/base.py

@@ -664,6 +664,13 @@ def __init__(self, cat_model, cat_features, domain):
         self.cat_model = cat_model
         self.cat_features = cat_features
 
+    def __call__(self, data, ret=Model.Value):
+        if isinstance(data, Table):
+            with data.force_unlocked(data.X):
+                return super().__call__(data, ret)
+        else:
+            return super().__call__(data, ret)
+
     def predict(self, X):
         if self.cat_features:
             X = X.astype(str)
@@ -824,17 +831,18 @@ def __call__(self, data, progress_callback=None):
         return m
 
     def fit_storage(self, data: Table):
-        domain, X, Y, W = data.domain, data.X, data.Y.reshape(-1), None
-        if self.supports_weights and data.has_weights():
-            W = data.W.reshape(-1)
-        # pylint: disable=not-callable
-        clf = self.__wraps__(**self.params)
-        cat_features = [i for i, attr in enumerate(domain.attributes)
-                        if attr.is_discrete]
-        if cat_features:
-            X = X.astype(str)
-        cat_model = clf.fit(X, Y, cat_features=cat_features, sample_weight=W)
-        return self.__returns__(cat_model, cat_features, domain)
+        with data.force_unlocked(data.X):
+            domain, X, Y, W = data.domain, data.X, data.Y.reshape(-1), None
+            if self.supports_weights and data.has_weights():
+                W = data.W.reshape(-1)
+            # pylint: disable=not-callable
+            clf = self.__wraps__(**self.params)
+            cat_features = [i for i, attr in enumerate(domain.attributes)
+                            if attr.is_discrete]
+            if cat_features:
+                X = X.astype(str)
+            cat_model = clf.fit(X, Y, cat_features=cat_features, sample_weight=W)
+            return self.__returns__(cat_model, cat_features, domain)
 
     def __getattr__(self, item):
         try: