{
    "file_diffs": [
        {
            "old_file_content": "from Orange.classification import CalibratedLearner, ThresholdLearner, \\\n    NaiveBayesLearner\nfrom Orange.data import Table\nfrom Orange.modelling import Learner\nfrom Orange.widgets import gui\nfrom Orange.widgets.widget import Input\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\n\n\nclass OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWCalibratedLearner).run(\n        Table(\"heart_disease\"),\n        set_learner=NaiveBayesLearner())\n",
            "new_file_content": "import copy\n\nfrom Orange.classification import CalibratedLearner, ThresholdLearner, \\\n    NaiveBayesLearner\nfrom Orange.data import Table\nfrom Orange.modelling import Learner\nfrom Orange.widgets import gui\nfrom Orange.widgets.widget import Input\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\n\n\nclass OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if learner is self.base_learner:\n            learner = copy.deepcopy(learner)\n        if self.preprocessors:\n            learner.preprocessors = (self.preprocessors, )\n        assert learner is not self.base_learner\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWCalibratedLearner).run(\n        Table(\"heart_disease\"),\n        set_learner=NaiveBayesLearner())\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/model/owcalibratedlearner.py"
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            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/model/owcalibratedlearner.py"
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                        "section": ""
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "import copy",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.classification import CalibratedLearner, ThresholdLearner, \\",
                                "type": "context"
                            },
                            {
                                "content": "    NaiveBayesLearner",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data import Table",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
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                        {
                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
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                        "section": "class OWCalibratedLearner(OWBaseLearner):"
                    },
                    "line_group": {
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                            {
                                "content": "        self.learner = self.model = None",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def _set_default_name(self):",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "deleted"
                            },
                            {
                                "content": "        if self.base_learner is None:",
                                "type": "context"
                            },
                            {
                                "content": "            self.set_default_learner_name(\"\")",
                                "type": "context"
                            },
                            {
                                "content": "        else:",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                            "type": "class",
                            "name": "OWCalibratedLearner",
                            "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 12,
                            "end_lineno": 106,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                            "type": "method",
                            "name": "OWCalibratedLearner._set_default_name",
                            "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 67,
                            "end_lineno": 76,
                            "parent": {
                                "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                                "type": "class",
                                "name": "OWCalibratedLearner",
                                "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 12,
                                "end_lineno": 106,
                                "parent": null
                            }
                        }
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                        "section": "class OWCalibratedLearner(OWBaseLearner):"
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                                "content": "        self.apply()",
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                            {
                                "content": "",
                                "type": "context"
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                            {
                                "content": "    def create_learner(self):",
                                "type": "context"
                            },
                            {
                                "content": "        class IdentityWrapper(Learner):",
                                "type": "deleted"
                            },
                            {
                                "content": "            def fit_storage(self, data):",
                                "type": "deleted"
                            },
                            {
                                "content": "                return self.base_learner.fit_storage(data)",
                                "type": "deleted"
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                            {
                                "content": "",
                                "type": "deleted"
                            },
                            {
                                "content": "        if self.base_learner is None:",
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                            },
                            {
                                "content": "            return None",
                                "type": "context"
                            },
                            {
                                "content": "        learner = self.base_learner",
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                    },
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                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                            "type": "class",
                            "name": "OWCalibratedLearner",
                            "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 12,
                            "end_lineno": 106,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                            "type": "method",
                            "name": "OWCalibratedLearner.create_learner",
                            "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 82,
                            "end_lineno": 100,
                            "parent": {
                                "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                                "type": "class",
                                "name": "OWCalibratedLearner",
                                "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 12,
                                "end_lineno": 106,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 93,
                            "length": 10
                        },
                        "new_range": {
                            "start": 90,
                            "length": 11
                        },
                        "section": "class OWCalibratedLearner(OWBaseLearner):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        if self.threshold != self.NoThresholdOptimization:",
                                "type": "context"
                            },
                            {
                                "content": "            learner = ThresholdLearner(learner,",
                                "type": "context"
                            },
                            {
                                "content": "                                       self.ThresholdMap[self.threshold])",
                                "type": "context"
                            },
                            {
                                "content": "        if learner is self.base_learner:",
                                "type": "added"
                            },
                            {
                                "content": "            learner = copy.deepcopy(learner)",
                                "type": "added"
                            },
                            {
                                "content": "        if self.preprocessors:",
                                "type": "context"
                            },
                            {
                                "content": "            if learner is self.base_learner:",
                                "type": "deleted"
                            },
                            {
                                "content": "                learner = IdentityWrapper()",
                                "type": "deleted"
                            },
                            {
                                "content": "            learner.preprocessors = (self.preprocessors, )",
                                "type": "context"
                            },
                            {
                                "content": "        assert learner is not self.base_learner",
                                "type": "added"
                            },
                            {
                                "content": "        return learner",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def get_learner_parameters(self):",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                            "type": "class",
                            "name": "OWCalibratedLearner",
                            "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 12,
                            "end_lineno": 106,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                            "type": "method",
                            "name": "OWCalibratedLearner.create_learner",
                            "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 82,
                            "end_lineno": 100,
                            "parent": {
                                "file_name": "Orange/widgets/model/owcalibratedlearner.py",
                                "type": "class",
                                "name": "OWCalibratedLearner",
                                "content": "class OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 12,
                                "end_lineno": 106,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "from unittest.mock import Mock\n\nfrom Orange.classification import ThresholdLearner, CalibratedLearner, \\\n    NaiveBayesLearner, ThresholdClassifier, CalibratedClassifier\nfrom Orange.classification.base_classification import ModelClassification, \\\n    LearnerClassification\nfrom Orange.classification.naive_bayes import NaiveBayesModel\nfrom Orange.data import Table\nfrom Orange.widgets.model.owcalibratedlearner import OWCalibratedLearner\nfrom Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin, \\\n    datasets\nfrom Orange.widgets.tests.utils import qbuttongroup_emit_clicked\n\n\nclass TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWCalibratedLearner, stored_settings={\"auto_apply\": False})\n        self.send_signal(self.widget.Inputs.base_learner, NaiveBayesLearner())\n\n        self.data = Table(\"heart_disease\")\n        self.valid_datasets = (self.data,)\n        self.inadequate_dataset = (Table(datasets.path(\"testing_dataset_reg\")),)\n        self.learner_class = LearnerClassification\n        self.model_class = ModelClassification\n        self.model_name = 'Calibrated classifier'\n        self.parameters = []\n\n    def test_output_learner(self):\n        \"\"\"Check if learner is on output after apply\"\"\"\n        # Overridden to change the output type in the last test\n        initial = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNotNone(initial, \"Does not initialize the learner output\")\n        self.click_apply()\n        newlearner = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNot(initial, newlearner,\n                         \"Does not send a new learner instance on `Apply`.\")\n        self.assertIsNotNone(newlearner)\n        self.assertIsInstance(\n            newlearner,\n            (CalibratedLearner, ThresholdLearner, NaiveBayesLearner))\n\n    def test_output_model(self):\n        \"\"\"Check if model is on output after sending data and apply\"\"\"\n        # Overridden to change the output type in the last two test\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.click_apply()\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.send_signal(self.widget.Inputs.data, self.data)\n        self.click_apply()\n        self.wait_until_stop_blocking()\n        model = self.get_output(self.widget.Outputs.model)\n        self.assertIsNotNone(model)\n        self.assertIsInstance(\n            model, (CalibratedClassifier, ThresholdClassifier, NaiveBayesModel))\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIs(learner, self.widget.base_learner)\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.base_learner = None\n        learner = self.widget.create_learner()\n        self.assertIsNone(learner)\n\n    def test_preprocessors(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n        self.widget.base_learner.preprocessors = ()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.preprocessors = Mock()\n        learner = self.widget.create_learner()\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n        self.assertEqual(learner.base_learner.preprocessors, ())\n        self.assertEqual(learner.base_learner.base_learner.preprocessors, ())\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n        self.assertFalse(\n            isinstance(learner, (CalibratedLearner, ThresholdLearner)))\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n\n    def test_set_learner_calls_unconditional_apply(self):\n        widget = self.widget\n        self.assertIsNotNone(self.get_output(widget.Outputs.learner))\n\n        widget.auto_apply = False\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertIsNone(self.get_output(widget.Outputs.learner))\n\n    def test_name_changes(self):\n        widget = self.widget\n        widget.auto_apply = True\n        learner = NaiveBayesLearner()\n        learner.name = \"foo\"\n        self.send_signal(widget.Inputs.base_learner, learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.IsotonicCalibration)\n\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + Isotonic + CA\")\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.NoCalibration)\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + CA\")\n\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertEqual(widget.controls.learner_name.placeholderText(),\n                         \"Calibrated Learner\")\n",
            "new_file_content": "from unittest.mock import Mock\n\nfrom Orange.classification import ThresholdLearner, CalibratedLearner, \\\n    NaiveBayesLearner, ThresholdClassifier, CalibratedClassifier\nfrom Orange.classification.base_classification import ModelClassification, \\\n    LearnerClassification\nfrom Orange.classification.naive_bayes import NaiveBayesModel\nfrom Orange.data import Table\nfrom Orange.widgets.model.owcalibratedlearner import OWCalibratedLearner\nfrom Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin, \\\n    datasets\nfrom Orange.widgets.tests.utils import qbuttongroup_emit_clicked\n\n\nclass TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWCalibratedLearner, stored_settings={\"auto_apply\": False})\n        self.send_signal(self.widget.Inputs.base_learner, NaiveBayesLearner())\n\n        self.data = Table(\"heart_disease\")\n        self.valid_datasets = (self.data,)\n        self.inadequate_dataset = (Table(datasets.path(\"testing_dataset_reg\")),)\n        self.learner_class = LearnerClassification\n        self.model_class = ModelClassification\n        self.model_name = 'Calibrated classifier'\n        self.parameters = []\n\n    def test_output_learner(self):\n        \"\"\"Check if learner is on output after apply\"\"\"\n        # Overridden to change the output type in the last test\n        initial = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNotNone(initial, \"Does not initialize the learner output\")\n        self.click_apply()\n        newlearner = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNot(initial, newlearner,\n                         \"Does not send a new learner instance on `Apply`.\")\n        self.assertIsNotNone(newlearner)\n        self.assertIsInstance(\n            newlearner,\n            (CalibratedLearner, ThresholdLearner, NaiveBayesLearner))\n\n    def test_output_model(self):\n        \"\"\"Check if model is on output after sending data and apply\"\"\"\n        # Overridden to change the output type in the last two test\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.click_apply()\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.send_signal(self.widget.Inputs.data, self.data)\n        self.click_apply()\n        self.wait_until_stop_blocking()\n        model = self.get_output(self.widget.Outputs.model)\n        self.assertIsNotNone(model)\n        self.assertIsInstance(\n            model, (CalibratedClassifier, ThresholdClassifier, NaiveBayesModel))\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.base_learner = None\n        learner = self.widget.create_learner()\n        self.assertIsNone(learner)\n\n    def test_preprocessors(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n        self.widget.base_learner.preprocessors = ()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.preprocessors = Mock()\n        learner = self.widget.create_learner()\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n        self.assertEqual(learner.base_learner.preprocessors, ())\n        self.assertEqual(learner.base_learner.base_learner.preprocessors, ())\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n        self.assertFalse(\n            isinstance(learner, (CalibratedLearner, ThresholdLearner)))\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n\n    def test_set_learner_calls_unconditional_apply(self):\n        widget = self.widget\n        self.assertIsNotNone(self.get_output(widget.Outputs.learner))\n\n        widget.auto_apply = False\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertIsNone(self.get_output(widget.Outputs.learner))\n\n    def test_name_changes(self):\n        widget = self.widget\n        widget.auto_apply = True\n        learner = NaiveBayesLearner()\n        learner.name = \"foo\"\n        self.send_signal(widget.Inputs.base_learner, learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.IsotonicCalibration)\n\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + Isotonic + CA\")\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.NoCalibration)\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + CA\")\n\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertEqual(widget.controls.learner_name.placeholderText(),\n                         \"Calibrated Learner\")\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/model/tests/test_owcalibratedlearner.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "27dcf378b",
                "new_commit_hash": "2058d73db",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/model/tests/test_owcalibratedlearner.py"
            },
            "plus_file": {
                "path": "b/Orange/widgets/model/tests/test_owcalibratedlearner.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 95,
                            "length": 7
                        },
                        "new_range": {
                            "start": 95,
                            "length": 7
                        },
                        "section": "class TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        widget.calibration = widget.NoCalibration",
                                "type": "context"
                            },
                            {
                                "content": "        widget.threshold = widget.NoThresholdOptimization",
                                "type": "context"
                            },
                            {
                                "content": "        learner = self.widget.create_learner()",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertIs(learner, self.widget.base_learner)",
                                "type": "deleted"
                            },
                            {
                                "content": "        self.assertIsNot(learner, self.widget.base_learner)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        widget.calibration = widget.SigmoidCalibration",
                                "type": "context"
                            },
                            {
                                "content": "        widget.threshold = widget.OptimizeF1",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/model/tests/test_owcalibratedlearner.py",
                            "type": "method",
                            "name": "TestOWCalibratedLearner.test_create_learner",
                            "content": "class TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWCalibratedLearner, stored_settings={\"auto_apply\": False})\n        self.send_signal(self.widget.Inputs.base_learner, NaiveBayesLearner())\n\n        self.data = Table(\"heart_disease\")\n        self.valid_datasets = (self.data,)\n        self.inadequate_dataset = (Table(datasets.path(\"testing_dataset_reg\")),)\n        self.learner_class = LearnerClassification\n        self.model_class = ModelClassification\n        self.model_name = 'Calibrated classifier'\n        self.parameters = []\n\n    def test_output_learner(self):\n        \"\"\"Check if learner is on output after apply\"\"\"\n        # Overridden to change the output type in the last test\n        initial = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNotNone(initial, \"Does not initialize the learner output\")\n        self.click_apply()\n        newlearner = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNot(initial, newlearner,\n                         \"Does not send a new learner instance on `Apply`.\")\n        self.assertIsNotNone(newlearner)\n        self.assertIsInstance(\n            newlearner,\n            (CalibratedLearner, ThresholdLearner, NaiveBayesLearner))\n\n    def test_output_model(self):\n        \"\"\"Check if model is on output after sending data and apply\"\"\"\n        # Overridden to change the output type in the last two test\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.click_apply()\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.send_signal(self.widget.Inputs.data, self.data)\n        self.click_apply()\n        self.wait_until_stop_blocking()\n        model = self.get_output(self.widget.Outputs.model)\n        self.assertIsNotNone(model)\n        self.assertIsInstance(\n            model, (CalibratedClassifier, ThresholdClassifier, NaiveBayesModel))\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIs(learner, self.widget.base_learner)\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.base_learner = None\n        learner = self.widget.create_learner()\n        self.assertIsNone(learner)\n\n    def test_preprocessors(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n        self.widget.base_learner.preprocessors = ()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.preprocessors = Mock()\n        learner = self.widget.create_learner()\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n        self.assertEqual(learner.base_learner.preprocessors, ())\n        self.assertEqual(learner.base_learner.base_learner.preprocessors, ())\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n        self.assertFalse(\n            isinstance(learner, (CalibratedLearner, ThresholdLearner)))\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n\n    def test_set_learner_calls_unconditional_apply(self):\n        widget = self.widget\n        self.assertIsNotNone(self.get_output(widget.Outputs.learner))\n\n        widget.auto_apply = False\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertIsNone(self.get_output(widget.Outputs.learner))\n\n    def test_name_changes(self):\n        widget = self.widget\n        widget.auto_apply = True\n        learner = NaiveBayesLearner()\n        learner.name = \"foo\"\n        self.send_signal(widget.Inputs.base_learner, learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.IsotonicCalibration)\n\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + Isotonic + CA\")\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.NoCalibration)\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + CA\")\n\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertEqual(widget.controls.learner_name.placeholderText(),\n                         \"Calibrated Learner\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 57,
                            "end_lineno": 104,
                            "parent": {
                                "file_name": "Orange/widgets/model/tests/test_owcalibratedlearner.py",
                                "type": "class",
                                "name": "TestOWCalibratedLearner",
                                "content": "class TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWCalibratedLearner, stored_settings={\"auto_apply\": False})\n        self.send_signal(self.widget.Inputs.base_learner, NaiveBayesLearner())\n\n        self.data = Table(\"heart_disease\")\n        self.valid_datasets = (self.data,)\n        self.inadequate_dataset = (Table(datasets.path(\"testing_dataset_reg\")),)\n        self.learner_class = LearnerClassification\n        self.model_class = ModelClassification\n        self.model_name = 'Calibrated classifier'\n        self.parameters = []\n\n    def test_output_learner(self):\n        \"\"\"Check if learner is on output after apply\"\"\"\n        # Overridden to change the output type in the last test\n        initial = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNotNone(initial, \"Does not initialize the learner output\")\n        self.click_apply()\n        newlearner = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNot(initial, newlearner,\n                         \"Does not send a new learner instance on `Apply`.\")\n        self.assertIsNotNone(newlearner)\n        self.assertIsInstance(\n            newlearner,\n            (CalibratedLearner, ThresholdLearner, NaiveBayesLearner))\n\n    def test_output_model(self):\n        \"\"\"Check if model is on output after sending data and apply\"\"\"\n        # Overridden to change the output type in the last two test\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.click_apply()\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.send_signal(self.widget.Inputs.data, self.data)\n        self.click_apply()\n        self.wait_until_stop_blocking()\n        model = self.get_output(self.widget.Outputs.model)\n        self.assertIsNotNone(model)\n        self.assertIsInstance(\n            model, (CalibratedClassifier, ThresholdClassifier, NaiveBayesModel))\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIs(learner, self.widget.base_learner)\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.base_learner = None\n        learner = self.widget.create_learner()\n        self.assertIsNone(learner)\n\n    def test_preprocessors(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n        self.widget.base_learner.preprocessors = ()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.preprocessors = Mock()\n        learner = self.widget.create_learner()\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n        self.assertEqual(learner.base_learner.preprocessors, ())\n        self.assertEqual(learner.base_learner.base_learner.preprocessors, ())\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n        self.assertFalse(\n            isinstance(learner, (CalibratedLearner, ThresholdLearner)))\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n\n    def test_set_learner_calls_unconditional_apply(self):\n        widget = self.widget\n        self.assertIsNotNone(self.get_output(widget.Outputs.learner))\n\n        widget.auto_apply = False\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertIsNone(self.get_output(widget.Outputs.learner))\n\n    def test_name_changes(self):\n        widget = self.widget\n        widget.auto_apply = True\n        learner = NaiveBayesLearner()\n        learner.name = \"foo\"\n        self.send_signal(widget.Inputs.base_learner, learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.IsotonicCalibration)\n\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + Isotonic + CA\")\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.NoCalibration)\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + CA\")\n\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertEqual(widget.controls.learner_name.placeholderText(),\n                         \"Calibrated Learner\")",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 15,
                                "end_lineno": 159,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/model/tests/test_owcalibratedlearner.py",
                            "type": "class",
                            "name": "TestOWCalibratedLearner",
                            "content": "class TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWCalibratedLearner, stored_settings={\"auto_apply\": False})\n        self.send_signal(self.widget.Inputs.base_learner, NaiveBayesLearner())\n\n        self.data = Table(\"heart_disease\")\n        self.valid_datasets = (self.data,)\n        self.inadequate_dataset = (Table(datasets.path(\"testing_dataset_reg\")),)\n        self.learner_class = LearnerClassification\n        self.model_class = ModelClassification\n        self.model_name = 'Calibrated classifier'\n        self.parameters = []\n\n    def test_output_learner(self):\n        \"\"\"Check if learner is on output after apply\"\"\"\n        # Overridden to change the output type in the last test\n        initial = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNotNone(initial, \"Does not initialize the learner output\")\n        self.click_apply()\n        newlearner = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNot(initial, newlearner,\n                         \"Does not send a new learner instance on `Apply`.\")\n        self.assertIsNotNone(newlearner)\n        self.assertIsInstance(\n            newlearner,\n            (CalibratedLearner, ThresholdLearner, NaiveBayesLearner))\n\n    def test_output_model(self):\n        \"\"\"Check if model is on output after sending data and apply\"\"\"\n        # Overridden to change the output type in the last two test\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.click_apply()\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.send_signal(self.widget.Inputs.data, self.data)\n        self.click_apply()\n        self.wait_until_stop_blocking()\n        model = self.get_output(self.widget.Outputs.model)\n        self.assertIsNotNone(model)\n        self.assertIsInstance(\n            model, (CalibratedClassifier, ThresholdClassifier, NaiveBayesModel))\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIs(learner, self.widget.base_learner)\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.base_learner = None\n        learner = self.widget.create_learner()\n        self.assertIsNone(learner)\n\n    def test_preprocessors(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n        self.widget.base_learner.preprocessors = ()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.preprocessors = Mock()\n        learner = self.widget.create_learner()\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n        self.assertEqual(learner.base_learner.preprocessors, ())\n        self.assertEqual(learner.base_learner.base_learner.preprocessors, ())\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n        self.assertFalse(\n            isinstance(learner, (CalibratedLearner, ThresholdLearner)))\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n\n    def test_set_learner_calls_unconditional_apply(self):\n        widget = self.widget\n        self.assertIsNotNone(self.get_output(widget.Outputs.learner))\n\n        widget.auto_apply = False\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertIsNone(self.get_output(widget.Outputs.learner))\n\n    def test_name_changes(self):\n        widget = self.widget\n        widget.auto_apply = True\n        learner = NaiveBayesLearner()\n        learner.name = \"foo\"\n        self.send_signal(widget.Inputs.base_learner, learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.IsotonicCalibration)\n\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + Isotonic + CA\")\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.NoCalibration)\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + CA\")\n\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertEqual(widget.controls.learner_name.placeholderText(),\n                         \"Calibrated Learner\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 15,
                            "end_lineno": 159,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "c27a803027d75e302c6a71902ebe4be5b9052706^",
    "new_commit_hash": "c27a803027d75e302c6a71902ebe4be5b9052706",
    "commit_message": "Merge pull request #6917 from ales-erjavec/fixes/owcalibratedlearner-base-learner-modify\n\n[FIX] Calibrated Learner: Prevent in place modification of base learner",
    "commit_date": "2024-10-25T11:34:04+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "c27a803027d75e302c6a71902ebe4be5b9052706",
    "test_file_codes": [
        "from unittest.mock import Mock\n\nfrom Orange.classification import ThresholdLearner, CalibratedLearner, \\\n    NaiveBayesLearner, ThresholdClassifier, CalibratedClassifier\nfrom Orange.classification.base_classification import ModelClassification, \\\n    LearnerClassification\nfrom Orange.classification.naive_bayes import NaiveBayesModel\nfrom Orange.data import Table\nfrom Orange.widgets.model.owcalibratedlearner import OWCalibratedLearner\nfrom Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin, \\\n    datasets\nfrom Orange.widgets.tests.utils import qbuttongroup_emit_clicked\n\n\nclass TestOWCalibratedLearner(WidgetTest, WidgetLearnerTestMixin):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWCalibratedLearner, stored_settings={\"auto_apply\": False})\n        self.send_signal(self.widget.Inputs.base_learner, NaiveBayesLearner())\n\n        self.data = Table(\"heart_disease\")\n        self.valid_datasets = (self.data,)\n        self.inadequate_dataset = (Table(datasets.path(\"testing_dataset_reg\")),)\n        self.learner_class = LearnerClassification\n        self.model_class = ModelClassification\n        self.model_name = 'Calibrated classifier'\n        self.parameters = []\n\n    def test_output_learner(self):\n        \"\"\"Check if learner is on output after apply\"\"\"\n        # Overridden to change the output type in the last test\n        initial = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNotNone(initial, \"Does not initialize the learner output\")\n        self.click_apply()\n        newlearner = self.get_output(self.widget.Outputs.learner)\n        self.assertIsNot(initial, newlearner,\n                         \"Does not send a new learner instance on `Apply`.\")\n        self.assertIsNotNone(newlearner)\n        self.assertIsInstance(\n            newlearner,\n            (CalibratedLearner, ThresholdLearner, NaiveBayesLearner))\n\n    def test_output_model(self):\n        \"\"\"Check if model is on output after sending data and apply\"\"\"\n        # Overridden to change the output type in the last two test\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.click_apply()\n        self.assertIsNone(self.get_output(self.widget.Outputs.model))\n        self.send_signal(self.widget.Inputs.data, self.data)\n        self.click_apply()\n        self.wait_until_stop_blocking()\n        model = self.get_output(self.widget.Outputs.model)\n        self.assertIsNotNone(model)\n        self.assertIsInstance(\n            model, (CalibratedClassifier, ThresholdClassifier, NaiveBayesModel))\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.base_learner = None\n        learner = self.widget.create_learner()\n        self.assertIsNone(learner)\n\n    def test_preprocessors(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n        self.widget.base_learner.preprocessors = ()\n\n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        widget.preprocessors = Mock()\n        learner = self.widget.create_learner()\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n        self.assertEqual(learner.base_learner.preprocessors, ())\n        self.assertEqual(learner.base_learner.base_learner.preprocessors, ())\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsNot(learner, self.widget.base_learner)\n        self.assertFalse(\n            isinstance(learner, (CalibratedLearner, ThresholdLearner)))\n        self.assertEqual(learner.preprocessors, (widget.preprocessors, ))\n\n    def test_set_learner_calls_unconditional_apply(self):\n        widget = self.widget\n        self.assertIsNotNone(self.get_output(widget.Outputs.learner))\n\n        widget.auto_apply = False\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertIsNone(self.get_output(widget.Outputs.learner))\n\n    def test_name_changes(self):\n        widget = self.widget\n        widget.auto_apply = True\n        learner = NaiveBayesLearner()\n        learner.name = \"foo\"\n        self.send_signal(widget.Inputs.base_learner, learner)\n\n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.IsotonicCalibration)\n\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + Isotonic + CA\")\n\n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        qbuttongroup_emit_clicked(widget.controls.calibration.group,\n                                  widget.NoCalibration)\n        learner = self.get_output(widget.Outputs.learner)\n        self.assertEqual(learner.name, \"Foo + CA\")\n\n        self.send_signal(widget.Inputs.base_learner, None)\n        self.assertEqual(widget.controls.learner_name.placeholderText(),\n                         \"Calibrated Learner\")\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7,3.8 installation failed, trying Python 3.8...\nAttempting installation with Python 3.10...\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-310/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/classification/_simple_tree.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_contingency.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_io.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_valuecount.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-310/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/data/_variable.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-310/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/distance/_distance.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-310/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/preprocess/_discretize.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-310/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-310/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/preprocess/_relieff.cpython-310-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-310/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-310/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-310/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-310/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-310/Orange/projection/_som.cpython-310-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_simple_tree.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.cpython-310-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_contingency.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_io.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_valuecount.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_variable.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/distance/_distance.cpython-310-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_discretize.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying 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the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_simple_tree.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/widgets/utils/_grid_density.cpython-310-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-310/Orange/classification/_tree_scorers.cpython-310-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_contingency.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_io.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_valuecount.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/data/_variable.cpython-310-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-310/Orange/distance/_distance.cpython-310-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_discretize.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-310/Orange/preprocess/_relieff.cpython-310-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-310/Orange/projection/_som.cpython-310-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.38.0.dev0+c27a803 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706\nProcessing dependencies for Orange3==3.38.0.dev0+c27a803\nSearching for XlsxWriter==3.2.0\nBest match: XlsxWriter 3.2.0\nAdding XlsxWriter 3.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for xgboost==2.0.3\nBest match: xgboost 2.0.3\nAdding xgboost 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for scipy==1.15.0\nBest match: scipy 1.15.0\nAdding scipy 1.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for scikit-learn==1.6.0\nBest match: scikit-learn 1.6.0\nAdding scikit-learn 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for requests==2.32.3\nBest match: requests 2.32.3\nAdding requests 2.32.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for qtconsole==5.6.1\nBest match: qtconsole 5.6.1\nAdding qtconsole 5.6.1 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for PyYAML==6.0.2\nBest match: PyYAML 6.0.2\nAdding PyYAML 6.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pyqtgraph==0.13.7\nBest match: pyqtgraph 0.13.7\nAdding pyqtgraph 0.13.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pygments==2.19.1\nBest match: pygments 2.19.1\nAdding pygments 2.19.1 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pip==24.3.1\nBest match: pip 24.3.1\nAdding pip 24.3.1 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pandas==2.2.3\nBest match: pandas 2.2.3\nAdding pandas 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for packaging==24.2\nBest match: packaging 24.2\nAdding packaging 24.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/_vendor\nSearching for orange-widget-base==4.25.1\nBest match: orange-widget-base 4.25.1\nAdding orange-widget-base 4.25.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for orange-canvas-core==0.2.5\nBest match: orange-canvas-core 0.2.5\nAdding orange-canvas-core 0.2.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for openpyxl==3.1.5\nBest match: openpyxl 3.1.5\nAdding openpyxl 3.1.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for openTSNE==1.0.2\nBest match: openTSNE 1.0.2\nAdding openTSNE 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for numpy==1.26.4\nBest match: numpy 1.26.4\nAdding numpy 1.26.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for networkx==3.4.2\nBest match: networkx 3.4.2\nAdding networkx 3.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for matplotlib==3.10.0\nBest match: matplotlib 3.10.0\nAdding matplotlib 3.10.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for keyrings.alt==5.0.2\nBest match: keyrings.alt 5.0.2\nAdding keyrings.alt 5.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for keyring==25.6.0\nBest match: keyring 25.6.0\nAdding keyring 25.6.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for joblib==1.4.2\nBest match: joblib 1.4.2\nAdding joblib 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for httpx==0.28.1\nBest match: httpx 0.28.1\nAdding httpx 0.28.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for catboost==1.2.7\nBest match: catboost 1.2.7\nAdding catboost 1.2.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for Bottleneck==1.4.2\nBest match: Bottleneck 1.4.2\nAdding Bottleneck 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for threadpoolctl==3.5.0\nBest match: threadpoolctl 3.5.0\nAdding threadpoolctl 3.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for urllib3==2.3.0\nBest match: urllib3 2.3.0\nAdding urllib3 2.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for ipykernel==6.29.5\nBest match: ipykernel 6.29.5\nAdding ipykernel 6.29.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jupyter-client==8.6.3\nBest match: jupyter-client 8.6.3\nAdding jupyter-client 8.6.3 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jupyter-core==5.7.2\nBest match: jupyter-core 5.7.2\nAdding jupyter-core 5.7.2 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for traitlets==5.14.3\nBest match: traitlets 5.14.3\nAdding traitlets 5.14.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for tzdata==2024.2\nBest match: tzdata 2024.2\nAdding tzdata 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\ntyping-extensions 4.12.2 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for requests-cache==1.2.1\nBest match: requests-cache 1.2.1\nAdding requests-cache 1.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for docutils==0.21.2\nBest match: docutils 0.21.2\nAdding docutils 0.21.2 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2html script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2html4 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2html5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2latex script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2man script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2odt script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2pseudoxml script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2s5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2xetex script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling rst2xml script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for et-xmlfile==2.0.0\nBest match: et-xmlfile 2.0.0\nAdding et-xmlfile 2.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pyparsing==3.2.1\nBest match: pyparsing 3.2.1\nAdding pyparsing 3.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pillow==11.1.0\nBest match: pillow 11.1.0\nAdding pillow 11.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for kiwisolver==1.4.8\nBest match: kiwisolver 1.4.8\nAdding kiwisolver 1.4.8 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for fonttools==4.55.3\nBest match: fonttools 4.55.3\nAdding fonttools 4.55.3 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for cycler==0.12.1\nBest match: cycler 0.12.1\nAdding cycler 0.12.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for contourpy==1.3.1\nBest match: contourpy 1.3.1\nAdding contourpy 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jaraco.context==6.0.1\nBest match: jaraco.context 6.0.1\nAdding jaraco.context 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jaraco.classes==3.4.0\nBest match: jaraco.classes 3.4.0\nAdding jaraco.classes 3.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jaraco.functools==4.1.0\nBest match: jaraco.functools 4.1.0\nAdding jaraco.functools 4.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for httpcore==1.0.7\nBest match: httpcore 1.0.7\nAdding httpcore 1.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for anyio==4.8.0\nBest match: anyio 4.8.0\nAdding anyio 4.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for plotly==5.24.1\nBest match: plotly 5.24.1\nAdding plotly 5.24.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for graphviz==0.20.3\nBest match: graphviz 0.20.3\nAdding graphviz 0.20.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for tornado==6.4.2\nBest match: tornado 6.4.2\nAdding tornado 6.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for matplotlib-inline==0.1.7\nBest match: matplotlib-inline 0.1.7\nAdding matplotlib-inline 0.1.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for ipython==8.31.0\nBest match: ipython 8.31.0\nAdding ipython 8.31.0 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for debugpy==1.8.11\nBest match: debugpy 1.8.11\nAdding debugpy 1.8.11 to easy-install.pth file\nInstalling debugpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for comm==0.2.2\nBest match: comm 0.2.2\nAdding comm 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for platformdirs==4.3.6\nBest match: platformdirs 4.3.6\nAdding platformdirs 4.3.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for cattrs==24.1.2\nBest match: cattrs 24.1.2\nAdding cattrs 24.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for attrs==24.3.0\nBest match: attrs 24.3.0\nAdding attrs 24.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for backports.tarfile==1.2.0\nBest match: backports.tarfile 1.2.0\nbackports.tarfile 1.2.0 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/_vendor\nSearching for more-itertools==10.5.0\nBest match: more-itertools 10.5.0\nAdding more-itertools 10.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for zipp==3.21.0\nBest match: zipp 3.21.0\nAdding zipp 3.21.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for tenacity==9.0.0\nBest match: tenacity 9.0.0\nAdding tenacity 9.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for stack-data==0.6.3\nBest match: stack-data 0.6.3\nAdding stack-data 0.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for cffi==1.17.1\nBest match: cffi 1.17.1\nAdding cffi 1.17.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pure-eval==0.2.3\nBest match: pure-eval 0.2.3\nAdding pure-eval 0.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for asttokens==3.0.0\nBest match: asttokens 3.0.0\nAdding asttokens 3.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for executing==2.1.0\nBest match: executing 2.1.0\nAdding executing 2.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nSearching for pycparser==2.22\nBest match: pycparser 2.22\nAdding pycparser 2.22 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages\nFinished processing dependencies for Orange3==3.38.0.dev0+c27a803\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.10\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 132ms\nPrepared 18 packages in 25ms\nInstalled 18 packages in 166ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and all of:\n          pandas>=1.4.0,<=1.4.4\n          pandas>=1.5.1,<=1.5.3\n          pandas>=2.0.1,<=2.0.3\n      depend on Python>=3.8, we can conclude that all of:\n          pandas>=1.4.0,<=1.4.4\n          pandas>=1.5.1,<=1.5.3\n          pandas>=2.0.1,<=2.0.3\n       cannot be used.\n      And because only the following versions of pandas are available:\n          pandas<=1.4.0\n          pandas==1.4.1\n          pandas==1.4.2\n          pandas==1.4.3\n          pandas==1.4.4\n          pandas==1.5.0\n          pandas==1.5.1\n          pandas==1.5.2\n          pandas==1.5.3\n          pandas==2.0.0\n          pandas==2.0.1\n          pandas==2.0.2\n          pandas==2.0.3\n          pandas==2.1.0\n          pandas==2.1.1\n          pandas==2.1.2\n          pandas==2.1.3\n          pandas==2.1.4\n          pandas==2.2.0\n          pandas==2.2.1\n          pandas==2.2.2\n          pandas==2.2.3\n      we can conclude that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0,<2.1.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pandas>=2.1.0 depends on Python>=3.9, we can\n      conclude that pandas>=2.1.0 cannot be used.\n      And because we know from (1) that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0,<2.1.0\n       cannot be used, we can conclude that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0\n       cannot be used.\n      And because you require one of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0\n      we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `pandas` in the requested range (e.g., 2.2.0rc0), but pre-releases weren't enabled\n      (try: `--prerelease=allow`)\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and orange-canvas-core==0.2.2 depends on\n      Python>=3.8, we can conclude that orange-canvas-core==0.2.2 cannot be used.\n      And because only the following versions of orange-canvas-core are available:\n          orange-canvas-core<=0.2.2\n          orange-canvas-core==0.2.3\n          orange-canvas-core==0.2.4\n          orange-canvas-core==0.2.5\n      and orange-canvas-core==0.2.3 was yanked (reason: Source package is missing some files), we can conclude that\n      orange-canvas-core>=0.2.2,<0.2.4 cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and orange-canvas-core>=0.2.4 depends on\n      Python>=3.9, we can conclude that orange-canvas-core>=0.2.4 cannot be used.\n      And because we know from (1) that orange-canvas-core>=0.2.2,<0.2.4 cannot be used, we can conclude that\n      orange-canvas-core>=0.2.2 cannot be used.\n      And because you require orange-canvas-core>=0.2.2, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 60ms\nInstalled 2 packages in 17ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 30ms\nInstalled 4 packages in 55ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + threadpoolctl==3.1.0\nTraceback (most recent call last):\n  File \"setup.py\", line 14, in <module>\n    from trubar import translate\nModuleNotFoundError: No module named 'trubar'\nTraceback (most recent call last):\n  File \"setup.py\", line 14, in <module>\n    from trubar import translate\nModuleNotFoundError: No module named 'trubar'\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 221ms\nPrepared 15 packages in 2ms\nInstalled 15 packages in 109ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==1.24.4\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.15.0\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.3.0\n + tomli==2.2.1\n + wheel==0.45.1\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.8.20) does not satisfy Python>=3.9 and scikit-learn>=1.5.1 depends on Python>=3.9,\n      we can conclude that scikit-learn>=1.5.1 cannot be used.\n      And because only the following versions of scikit-learn are available:\n          scikit-learn<=1.5.1\n          scikit-learn==1.5.2\n          scikit-learn==1.6.0\n      and you require scikit-learn>=1.5.1, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scikit-learn` in the requested range (e.g., 1.6.0rc1), but pre-releases weren't\n      enabled (try: `--prerelease=allow`)\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.8.20) does not satisfy Python>=3.9 and orange-widget-base>=4.25.0 depends on\n      Python>=3.9, we can conclude that orange-widget-base>=4.25.0 cannot be used.\n      And because only the following versions of orange-widget-base are available:\n          orange-widget-base<=4.25.0\n          orange-widget-base==4.25.1\n      and you require orange-widget-base>=4.25.0, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 2ms\nInstalled 2 packages in 5ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nTraceback (most recent call last):\n  File \"setup.py\", line 14, in <module>\n    from trubar import translate\nModuleNotFoundError: No module named 'trubar'\nTraceback (most recent call last):\n  File \"setup.py\", line 14, in <module>\n    from trubar import translate\nModuleNotFoundError: No module named 'trubar'\nUsing CPython 3.10.16\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 194ms\nPrepared 15 packages in 2ms\nInstalled 15 packages in 95ms\n + cython==3.0.11\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==2.2.1\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.16.1\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.8.0\n + tomli==2.2.1\n + wheel==0.45.1\nResolved 62 packages in 78ms\nUninstalled 1 package in 21ms\nInstalled 60 packages in 1.05s\n + anyio==4.8.0\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.2\n + catboost==1.2.7\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.3.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\n + graphviz==0.20.3\n + h11==0.14.0\n + httpcore==1.0.7\n + httpx==0.28.1\n + idna==3.10\n + importlib-metadata==8.5.0\n + jaraco-classes==3.4.0\n + jaraco-context==6.0.1\n + jaraco-functools==4.1.0\n + jeepney==0.8.0\n + joblib==1.4.2\n + keyring==25.6.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.8\n + matplotlib==3.10.0\n + more-itertools==10.5.0\n + networkx==3.4.2\n - numpy==2.2.1\n + numpy==1.26.4\n + openpyxl==3.1.5\n + opentsne==1.0.2\n + pandas==2.2.3\n + pillow==11.1.0\n + pip==24.3.1\n + plotly==5.24.1\n + pycparser==2.22\n + pyparsing==3.2.1\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.2\n + requests==2.32.3\n + scikit-learn==1.6.0\n + scipy==1.15.0\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + tenacity==9.0.0\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.3.0\n + xgboost==2.0.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.21.0\nResolved 58 packages in 92ms\nInstalled 39 packages in 180ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.21.2\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.31.0\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.2.5\n + orange-widget-base==4.25.1\n + parso==0.8.4\n + pexpect==4.9.0\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.7\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 39 packages in 85ms\nInstalled 25 packages in 172ms\n + alabaster==1.0.0\n + astroid==3.3.8\n + babel==2.16.0\n + colorama==0.4.6\n + dill==0.3.9\n + imagesize==1.4.1\n + isort==5.13.2\n + jinja2==3.1.5\n + libcst==1.5.1\n + mando==0.7.1\n + markupsafe==3.0.2\n + mccabe==0.7.0\n + pylint==3.3.3\n + radon==6.0.1\n + recommonmark==0.7.1\n + snowballstemmer==2.2.0\n + sphinx==8.1.3\n + sphinxcontrib-applehelp==2.0.0\n + sphinxcontrib-devhelp==2.0.0\n + sphinxcontrib-htmlhelp==2.1.0\n + sphinxcontrib-jsmath==1.0.1\n + sphinxcontrib-qthelp==2.0.0\n + sphinxcontrib-serializinghtml==2.0.0\n + tomlkit==0.13.2\n + trubar==0.3.4\nResolved 2 packages in 4ms\nInstalled 2 packages in 8ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nperformance hint: Orange/preprocess/_relieff.pyx:63:5: Exception check on 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:119:5: Exception check on 'k_nearest_reg' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_reg' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_reg' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:183:5: Exception check on 'k_nearest_per_class' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_per_class' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_per_class' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:161:23: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:177:23: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:201:23: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:217:27: Exception check after calling 'calc_difference' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'calc_difference' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'calc_difference' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:250:25: Exception check after calling 'k_nearest_reg' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_reg' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_reg' to allow an error code to be returned.\nperformance hint: Orange/preprocess/_relieff.pyx:287:31: Exception check after calling 'k_nearest_per_class' will always require the GIL to be acquired.\nPossible solutions:\n\t1. Declare 'k_nearest_per_class' as 'noexcept' if you control the definition and you're sure you don't want the function to raise exceptions.\n\t2. Use an 'int' return type on 'k_nearest_per_class' to allow an error code to be returned.\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/_distutils/dist.py:270: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\nIn file included from /home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10/Python.h:8,\n                 from Orange/classification/_simple_tree.c:781:\n/home/gcpuser/.local/share/uv/python/cpython-3.10.16-linux-x86_64-gnu/include/python3.10/pyconfig.h:1621: warning: \"_GNU_SOURCE\" redefined\n 1621 | #define _GNU_SOURCE 1\n      | \nOrange/classification/_simple_tree.c:2: note: this is the location of the previous definition\n    2 | #define _GNU_SOURCE\n      | \nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:20278:26: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n20278 |   __pyx_t_7 = (__pyx_v_N <= __pyx_v_min_leaf);\n      |                          ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:21847:26: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n21847 |   __pyx_t_1 = (__pyx_v_N <= __pyx_v_min_leaf);\n      |                          ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:22657:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n22657 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:22778:38: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n22778 |           __pyx_t_20 = (__pyx_v_left >= __pyx_v_min_leaf);\n      |                                      ^~\nOrange/classification/_tree_scorers.c:22784:52: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n22784 |           __pyx_t_20 = ((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf);\n      |                                                    ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:24434:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n24434 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:25156:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n25156 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:25216:149: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n25216 |               __pyx_t_16 = ((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i);\n      |                                                                                                                                                     ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:5091:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5091 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:5261:23: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5261 |           __pyx_t_5 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                       ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:1148:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 1148 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:2863:7: note: ‘__pyx_v_attr_index’ was declared here\n 2863 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:4691:30: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4691 |           (__pyx_v_atomp[0]) = 0;\n      |           ~~~~~~~~~~~~~~~~~~~^~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:1252:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:1265:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarraytypes.h:1929,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:1253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:20732:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n20732 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:20731:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n20731 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:22819:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n22819 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:22818:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n22818 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:21996:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21996 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:21995:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21995 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:19911:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n19911 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:19558:7: note: ‘__pyx_v_win_y’ was declared here\n19558 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:19900:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n19900 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:19557:7: note: ‘__pyx_v_win_x’ was declared here\n19557 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:20161:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n20161 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:21717:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21717 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:21234:7: note: ‘__pyx_v_win_y’ was declared here\n21234 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:21706:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n21706 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:21233:7: note: ‘__pyx_v_win_x’ was declared here\n21233 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/_distutils/dist.py:270: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706/.venv/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:79: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\nResolved 102 packages in 3.25s\nPrepared 1 package in 50.71s\nUninstalled 1 package in 0.25ms\nInstalled 1 package in 1ms\n - orange3==3.38.0.dev0+c27a803\n + orange3==3.38.0.dev0+c27a803 (from file:///home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706)\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706\nconfigfile: pyproject.toml\nplugins: anyio-4.8.0, typeguard-4.3.0\ncollected 26 items\n\nr2e_tests/test_1.py sss.......................                           [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_create_learner\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_has_unconditional_apply\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_image_export\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_data\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_data_disconnect\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_data_learner_adequacy\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_preprocessor\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_preprocessor_disconnect\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_preprocessors\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_name_changes\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_learner\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_learner_name\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_model\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_model_name\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_model_picklable\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_parameters\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_parameters_default\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_params_trigger_settings_changed\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_preprocessors\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_send_report\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_set_learner_calls_unconditional_apply\nSKIPPED [1] .venv/lib/python3.10/site-packages/orangewidget/tests/base.py:248: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.10/site-packages/orangewidget/tests/base.py:242: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.10/site-packages/orangewidget/tests/base.py:254: msg base class not tested as .widget was not set\n======================== 23 passed, 3 skipped in 6.01s =========================\nWidgets left: 0    Max widgets: 1216 \n\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c27a803027d75e302c6a71902ebe4be5b9052706\nconfigfile: pyproject.toml\nplugins: anyio-4.8.0, typeguard-4.3.0\ncollected 26 items\n\nr2e_tests/test_1.py sssF......................                           [100%]\n\n=================================== FAILURES ===================================\n_________________ TestOWCalibratedLearner.test_create_learner __________________\n\nself = <r2e_tests.test_1.TestOWCalibratedLearner testMethod=test_create_learner>\n\n    def test_create_learner(self):\n        widget = self.widget  #: OWCalibratedLearner\n        self.widget.base_learner = Mock()\n    \n        widget.calibration = widget.SigmoidCalibration\n        widget.threshold = widget.OptimizeF1\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeF1)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Sigmoid)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n    \n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        cal_learner = learner.base_learner\n        self.assertIsInstance(cal_learner, CalibratedLearner)\n        self.assertEqual(cal_learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(cal_learner.base_learner, self.widget.base_learner)\n    \n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.OptimizeCA\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, ThresholdLearner)\n        self.assertEqual(learner.threshold_criterion, learner.OptimizeCA)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n    \n        widget.calibration = widget.IsotonicCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n        self.assertIsInstance(learner, CalibratedLearner)\n        self.assertEqual(learner.calibration_method, cal_learner.Isotonic)\n        self.assertIs(learner.base_learner, self.widget.base_learner)\n    \n        widget.calibration = widget.NoCalibration\n        widget.threshold = widget.NoThresholdOptimization\n        learner = self.widget.create_learner()\n>       self.assertIsNot(learner, self.widget.base_learner)\nE       AssertionError: unexpectedly identical: <Mock id='137348735969232'>\n\nr2e_tests/test_1.py:98: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_has_unconditional_apply\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_image_export\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_data\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_data_disconnect\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_data_learner_adequacy\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_preprocessor\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_preprocessor_disconnect\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_input_preprocessors\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_name_changes\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_learner\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_learner_name\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_model\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_model_name\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_output_model_picklable\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_parameters\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_parameters_default\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_params_trigger_settings_changed\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_preprocessors\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_send_report\nPASSED r2e_tests/test_1.py::TestOWCalibratedLearner::test_set_learner_calls_unconditional_apply\nSKIPPED [1] .venv/lib/python3.10/site-packages/orangewidget/tests/base.py:248: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.10/site-packages/orangewidget/tests/base.py:242: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.10/site-packages/orangewidget/tests/base.py:254: msg base class not tested as .widget was not set\nFAILED r2e_tests/test_1.py::TestOWCalibratedLearner::test_create_learner - As...\n=================== 1 failed, 22 passed, 3 skipped in 3.67s ====================\nWidgets left: 0    Max widgets: 1216 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/model/owcalibratedlearner.py Orange/widgets/model/tests/test_owcalibratedlearner.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** In-Place Modification of Base Learner Causes Unexpected Behavior in Calibrated Learner

**Description:**
When configuring the `OWCalibratedLearner` without applying calibration or threshold optimization, the created learner instance is identical to the base learner. This leads to unintended modifications of the base learner when changes are made to the calibrated learner.

**Buggy Code Example:**
```python
widget = OWCalibratedLearner()
widget.base_learner = NaiveBayesLearner()
widget.calibration = widget.NoCalibration
widget.threshold = widget.NoThresholdOptimization
learner = widget.create_learner()
# learner is the same instance as widget.base_learner
```

**Expected Behavior:**
Creating a learner under these settings should return a new instance separate from the base learner. This ensures that modifications to the calibrated learner do not affect the original base learner.

**Actual Behavior:**
The `create_learner` method returns the same instance as the base learner (`widget.base_learner`). As a result, any changes made to the learner inadvertently modify the base learner, leading to potential side effects and unexpected behavior in the application.
[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### Orange/widgets/model/owcalibratedlearner.py
from Orange.classification import CalibratedLearner, ThresholdLearner, \
    NaiveBayesLearner
from Orange.data import Table
from Orange.modelling import Learner
from Orange.widgets import gui
from Orange.widgets.widget import Input
from Orange.widgets.settings import Setting
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.utils.widgetpreview import WidgetPreview


class OWCalibratedLearner(OWBaseLearner):
    name = "Calibrated Learner"
    description = "Wraps another learner with probability calibration and " \
                  "decision threshold optimization"
    icon = "icons/CalibratedLearner.svg"
    priority = 20
    keywords = "calibrated learner, calibration, threshold"

    LEARNER = CalibratedLearner

    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)
    CalibrationOptions = ("Sigmoid calibration",
                          "Isotonic calibration",
                          "No calibration")
    CalibrationShort = ("Sigmoid", "Isotonic", "")
    CalibrationMap = {
        SigmoidCalibration: CalibratedLearner.Sigmoid,
        IsotonicCalibration: CalibratedLearner.Isotonic}

    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)
    ThresholdOptions = ("Optimize classification accuracy",
                        "Optimize F1 score",
                        "No threshold optimization")
    ThresholdShort = ("CA", "F1", "")
    ThresholdMap = {
        OptimizeCA: ThresholdLearner.OptimizeCA,
        OptimizeF1: ThresholdLearner.OptimizeF1}

    learner_name = Setting("", schema_only=True)
    calibration = Setting(SigmoidCalibration)
    threshold = Setting(OptimizeCA)

    class Inputs(OWBaseLearner.Inputs):
        base_learner = Input("Base Learner", Learner)

    def __init__(self):
        super().__init__()
        self.base_learner = None

    def add_main_layout(self):
        gui.radioButtons(
            self.controlArea, self, "calibration", self.CalibrationOptions,
            box="Probability calibration",
            callback=self.calibration_options_changed)
        gui.radioButtons(
            self.controlArea, self, "threshold", self.ThresholdOptions,
            box="Decision threshold optimization",
            callback=self.calibration_options_changed)

    @Inputs.base_learner
    def set_learner(self, learner):
        self.base_learner = learner
        self._set_default_name()
        self.learner = self.model = None

    def _set_default_name(self):

        if self.base_learner is None:
            self.set_default_learner_name("")
        else:
            name = " + ".join(part for part in (
                self.base_learner.name.title(),
                self.CalibrationShort[self.calibration],
                self.ThresholdShort[self.threshold]) if part)
            self.set_default_learner_name(name)

    def calibration_options_changed(self):
        self._set_default_name()
        self.apply()

    def create_learner(self):
        class IdentityWrapper(Learner):
            def fit_storage(self, data):
                return self.base_learner.fit_storage(data)

        if self.base_learner is None:
            return None
        learner = self.base_learner
        if self.calibration != self.NoCalibration:
            learner = CalibratedLearner(learner,
                                        self.CalibrationMap[self.calibration])
        if self.threshold != self.NoThresholdOptimization:
            learner = ThresholdLearner(learner,
                                       self.ThresholdMap[self.threshold])
        if self.preprocessors:
            if learner is self.base_learner:
                learner = IdentityWrapper()
            learner.preprocessors = (self.preprocessors, )
        return learner

    def get_learner_parameters(self):
        return (("Calibrate probabilities",
                 self.CalibrationOptions[self.calibration]),
                ("Threshold optimization",
                 self.ThresholdOptions[self.threshold]))


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWCalibratedLearner).run(
        Table("heart_disease"),
        set_learner=NaiveBayesLearner())

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
[ISSUE]
**Title:** In-Place Modification of Base Learner Causes Unexpected Behavior in Calibrated Learner

**Description:**
When configuring the `OWCalibratedLearner` without applying calibration or threshold optimization, the created learner instance is identical to the base learner. This leads to unintended modifications of the base learner when changes are made to the calibrated learner.

**Buggy Code Example:**
```python
widget = OWCalibratedLearner()
widget.base_learner = NaiveBayesLearner()
widget.calibration = widget.NoCalibration
widget.threshold = widget.NoThresholdOptimization
learner = widget.create_learner()
# learner is the same instance as widget.base_learner
```

**Expected Behavior:**
Creating a learner under these settings should return a new instance separate from the base learner. This ensures that modifications to the calibrated learner do not affect the original base learner.

**Actual Behavior:**
The `create_learner` method returns the same instance as the base learner (`widget.base_learner`). As a result, any changes made to the learner inadvertently modify the base learner, leading to potential side effects and unexpected behavior in the application.
[/ISSUE]
{
    "TestOWCalibratedLearner.test_create_learner": "PASSED",
    "TestOWCalibratedLearner.test_has_unconditional_apply": "PASSED",
    "TestOWCalibratedLearner.test_image_export": "PASSED",
    "TestOWCalibratedLearner.test_input_data": "PASSED",
    "TestOWCalibratedLearner.test_input_data_disconnect": "PASSED",
    "TestOWCalibratedLearner.test_input_data_learner_adequacy": "PASSED",
    "TestOWCalibratedLearner.test_input_preprocessor": "PASSED",
    "TestOWCalibratedLearner.test_input_preprocessor_disconnect": "PASSED",
    "TestOWCalibratedLearner.test_input_preprocessors": "PASSED",
    "TestOWCalibratedLearner.test_minimum_size": "PASSED",
    "TestOWCalibratedLearner.test_msg_base_class": "PASSED",
    "TestOWCalibratedLearner.test_name_changes": "PASSED",
    "TestOWCalibratedLearner.test_output_learner": "PASSED",
    "TestOWCalibratedLearner.test_output_learner_name": "PASSED",
    "TestOWCalibratedLearner.test_output_model": "PASSED",
    "TestOWCalibratedLearner.test_output_model_name": "PASSED",
    "TestOWCalibratedLearner.test_output_model_picklable": "PASSED",
    "TestOWCalibratedLearner.test_parameters": "PASSED",
    "TestOWCalibratedLearner.test_parameters_default": "PASSED",
    "TestOWCalibratedLearner.test_params_trigger_settings_changed": "PASSED",
    "TestOWCalibratedLearner.test_preprocessors": "PASSED",
    "TestOWCalibratedLearner.test_send_report": "PASSED",
    "TestOWCalibratedLearner.test_set_learner_calls_unconditional_apply": "PASSED"
}
diff --git a/Orange/widgets/model/owcalibratedlearner.py b/Orange/widgets/model/owcalibratedlearner.py
index 7b4009573..8723015ca 100644
--- a/Orange/widgets/model/owcalibratedlearner.py
+++ b/Orange/widgets/model/owcalibratedlearner.py
@@ -1,3 +1,5 @@
+import copy
+
 from Orange.classification import CalibratedLearner, ThresholdLearner, \
     NaiveBayesLearner
 from Orange.data import Table
@@ -65,7 +67,6 @@ class OWCalibratedLearner(OWBaseLearner):
         self.learner = self.model = None
 
     def _set_default_name(self):
-
         if self.base_learner is None:
             self.set_default_learner_name("")
         else:
@@ -80,10 +81,6 @@ class OWCalibratedLearner(OWBaseLearner):
         self.apply()
 
     def create_learner(self):
-        class IdentityWrapper(Learner):
-            def fit_storage(self, data):
-                return self.base_learner.fit_storage(data)
-
         if self.base_learner is None:
             return None
         learner = self.base_learner
@@ -93,10 +90,11 @@ class OWCalibratedLearner(OWBaseLearner):
         if self.threshold != self.NoThresholdOptimization:
             learner = ThresholdLearner(learner,
                                        self.ThresholdMap[self.threshold])
+        if learner is self.base_learner:
+            learner = copy.deepcopy(learner)
         if self.preprocessors:
-            if learner is self.base_learner:
-                learner = IdentityWrapper()
             learner.preprocessors = (self.preprocessors, )
+        assert learner is not self.base_learner
         return learner
 
     def get_learner_parameters(self):

{"Orange/widgets/model/owcalibratedlearner.py": "from Orange.classification import CalibratedLearner, ThresholdLearner, \\\n    NaiveBayesLearner\nfrom Orange.data import Table\nfrom Orange.modelling import Learner\nfrom Orange.widgets import gui\nfrom Orange.widgets.widget import Input\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\n\n\nclass OWCalibratedLearner(OWBaseLearner):\n    name = \"Calibrated Learner\"\n    description = \"Wraps another learner with probability calibration and \" \\\n                  \"decision threshold optimization\"\n    icon = \"icons/CalibratedLearner.svg\"\n    priority = 20\n    keywords = \"calibrated learner, calibration, threshold\"\n\n    LEARNER = CalibratedLearner\n\n    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)\n    CalibrationOptions = (\"Sigmoid calibration\",\n                          \"Isotonic calibration\",\n                          \"No calibration\")\n    CalibrationShort = (\"Sigmoid\", \"Isotonic\", \"\")\n    CalibrationMap = {\n        SigmoidCalibration: CalibratedLearner.Sigmoid,\n        IsotonicCalibration: CalibratedLearner.Isotonic}\n\n    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)\n    ThresholdOptions = (\"Optimize classification accuracy\",\n                        \"Optimize F1 score\",\n                        \"No threshold optimization\")\n    ThresholdShort = (\"CA\", \"F1\", \"\")\n    ThresholdMap = {\n        OptimizeCA: ThresholdLearner.OptimizeCA,\n        OptimizeF1: ThresholdLearner.OptimizeF1}\n\n    learner_name = Setting(\"\", schema_only=True)\n    calibration = Setting(SigmoidCalibration)\n    threshold = Setting(OptimizeCA)\n\n    class Inputs(OWBaseLearner.Inputs):\n        base_learner = Input(\"Base Learner\", Learner)\n\n    def __init__(self):\n        super().__init__()\n        self.base_learner = None\n\n    def add_main_layout(self):\n        gui.radioButtons(\n            self.controlArea, self, \"calibration\", self.CalibrationOptions,\n            box=\"Probability calibration\",\n            callback=self.calibration_options_changed)\n        gui.radioButtons(\n            self.controlArea, self, \"threshold\", self.ThresholdOptions,\n            box=\"Decision threshold optimization\",\n            callback=self.calibration_options_changed)\n\n    @Inputs.base_learner\n    def set_learner(self, learner):\n        self.base_learner = learner\n        self._set_default_name()\n        self.learner = self.model = None\n\n    def _set_default_name(self):\n\n        if self.base_learner is None:\n            self.set_default_learner_name(\"\")\n        else:\n            name = \" + \".join(part for part in (\n                self.base_learner.name.title(),\n                self.CalibrationShort[self.calibration],\n                self.ThresholdShort[self.threshold]) if part)\n            self.set_default_learner_name(name)\n\n    def calibration_options_changed(self):\n        self._set_default_name()\n        self.apply()\n\n    def create_learner(self):\n        class IdentityWrapper(Learner):\n            def fit_storage(self, data):\n                return self.base_learner.fit_storage(data)\n\n        if self.base_learner is None:\n            return None\n        learner = self.base_learner\n        if self.calibration != self.NoCalibration:\n            learner = CalibratedLearner(learner,\n                                        self.CalibrationMap[self.calibration])\n        if self.threshold != self.NoThresholdOptimization:\n            learner = ThresholdLearner(learner,\n                                       self.ThresholdMap[self.threshold])\n        if self.preprocessors:\n            if learner is self.base_learner:\n                learner = IdentityWrapper()\n            learner.preprocessors = (self.preprocessors, )\n        return learner\n\n    def get_learner_parameters(self):\n        return ((\"Calibrate probabilities\",\n                 self.CalibrationOptions[self.calibration]),\n                (\"Threshold optimization\",\n                 self.ThresholdOptions[self.threshold]))\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWCalibratedLearner).run(\n        Table(\"heart_disease\"),\n        set_learner=NaiveBayesLearner())\n"}
A user will ask you to solve a task. You should first draft your thinking process (inner monologue). Then, generate the solution.

Your response format must follow the template below:
<think>
Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct solution.
</think>
<solution>
Final solution presented to the user.
</solution>
system
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** In-Place Modification of Base Learner Causes Unexpected Behavior in Calibrated Learner

**Description:**
When configuring the `OWCalibratedLearner` without applying calibration or threshold optimization, the created learner instance is identical to the base learner. This leads to unintended modifications of the base learner when changes are made to the calibrated learner.

**Buggy Code Example:**
```python
widget = OWCalibratedLearner()
widget.base_learner = NaiveBayesLearner()
widget.calibration = widget.NoCalibration
widget.threshold = widget.NoThresholdOptimization
learner = widget.create_learner()
# learner is the same instance as widget.base_learner
```

**Expected Behavior:**
Creating a learner under these settings should return a new instance separate from the base learner. This ensures that modifications to the calibrated learner do not affect the original base learner.

**Actual Behavior:**
The `create_learner` method returns the same instance as the base learner (`widget.base_learner`). As a result, any changes made to the learner inadvertently modify the base learner, leading to potential side effects and unexpected behavior in the application.
[/ISSUE]
--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.

--- BEGIN FILE ---
```
### Orange/widgets/model/owcalibratedlearner.py
from Orange.classification import CalibratedLearner, ThresholdLearner, \
    NaiveBayesLearner
from Orange.data import Table
from Orange.modelling import Learner
from Orange.widgets import gui
from Orange.widgets.widget import Input
from Orange.widgets.settings import Setting
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.utils.widgetpreview import WidgetPreview


class OWCalibratedLearner(OWBaseLearner):
    name = "Calibrated Learner"
    description = "Wraps another learner with probability calibration and " \
                  "decision threshold optimization"
    icon = "icons/CalibratedLearner.svg"
    priority = 20
    keywords = "calibrated learner, calibration, threshold"

    LEARNER = CalibratedLearner

    SigmoidCalibration, IsotonicCalibration, NoCalibration = range(3)
    CalibrationOptions = ("Sigmoid calibration",
                          "Isotonic calibration",
                          "No calibration")
    CalibrationShort = ("Sigmoid", "Isotonic", "")
    CalibrationMap = {
        SigmoidCalibration: CalibratedLearner.Sigmoid,
        IsotonicCalibration: CalibratedLearner.Isotonic}

    OptimizeCA, OptimizeF1, NoThresholdOptimization = range(3)
    ThresholdOptions = ("Optimize classification accuracy",
                        "Optimize F1 score",
                        "No threshold optimization")
    ThresholdShort = ("CA", "F1", "")
    ThresholdMap = {
        OptimizeCA: ThresholdLearner.OptimizeCA,
        OptimizeF1: ThresholdLearner.OptimizeF1}

    learner_name = Setting("", schema_only=True)
    calibration = Setting(SigmoidCalibration)
    threshold = Setting(OptimizeCA)

    class Inputs(OWBaseLearner.Inputs):
        base_learner = Input("Base Learner", Learner)

    def __init__(self):
        super().__init__()
        self.base_learner = None

    def add_main_layout(self):
        gui.radioButtons(
            self.controlArea, self, "calibration", self.CalibrationOptions,
            box="Probability calibration",
            callback=self.calibration_options_changed)
        gui.radioButtons(
            self.controlArea, self, "threshold", self.ThresholdOptions,
            box="Decision threshold optimization",
            callback=self.calibration_options_changed)

    @Inputs.base_learner
    def set_learner(self, learner):
        self.base_learner = learner
        self._set_default_name()
        self.learner = self.model = None

    def _set_default_name(self):

        if self.base_learner is None:
            self.set_default_learner_name("")
        else:
            name = " + ".join(part for part in (
                self.base_learner.name.title(),
                self.CalibrationShort[self.calibration],
                self.ThresholdShort[self.threshold]) if part)
            self.set_default_learner_name(name)

    def calibration_options_changed(self):
        self._set_default_name()
        self.apply()

    def create_learner(self):
        class IdentityWrapper(Learner):
            def fit_storage(self, data):
                return self.base_learner.fit_storage(data)

        if self.base_learner is None:
            return None
        learner = self.base_learner
        if self.calibration != self.NoCalibration:
            learner = CalibratedLearner(learner,
                                        self.CalibrationMap[self.calibration])
        if self.threshold != self.NoThresholdOptimization:
            learner = ThresholdLearner(learner,
                                       self.ThresholdMap[self.threshold])
        if self.preprocessors:
            if learner is self.base_learner:
                learner = IdentityWrapper()
            learner.preprocessors = (self.preprocessors, )
        return learner

    def get_learner_parameters(self):
        return (("Calibrate probabilities",
                 self.CalibrationOptions[self.calibration]),
                ("Threshold optimization",
                 self.ThresholdOptions[self.threshold]))


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWCalibratedLearner).run(
        Table("heart_disease"),
        set_learner=NaiveBayesLearner())

```
--- END FILE ---

Please first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.

Every *SEARCH/REPLACE* edit must use this format:
1. The file path
2. The start of search block: <<<<<<< SEARCH
3. A contiguous chunk of lines to search for in the existing source code
4. The dividing line: =======
5. The lines to replace into the source code
6. The end of the replace block: >>>>>>> REPLACE

Here is an example:

```python
### mathweb/flask/app.py
<<<<<<< SEARCH
from flask import Flask
=======
import math
from flask import Flask
>>>>>>> REPLACE
```
</solution>

Please note that the *SEARCH/REPLACE* edit REQUIRES PROPER INDENTATION. If you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!
Wrap each *SEARCH/REPLACE* edit in a code block as shown in the example above. If you have multiple *SEARCH/REPLACE* edits, use a separate code block for each one.
user