{
    "file_diffs": [
        {
            "old_file_content": "import numpy as np\nfrom AnyQt.QtCore import Qt\nimport scipy.sparse as sp\n\nfrom Orange.data import Table, Domain, ContinuousVariable, StringVariable, \\\n    DiscreteVariable\nfrom Orange.regression import PLSRegressionLearner\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.signals import Output\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Msg\n\n\nclass OWPLS(OWBaseLearner):\n    name = 'PLS'\n    description = \"Partial Least Squares Regression widget for multivariate data analysis\"\n    icon = \"icons/PLS.svg\"\n    priority = 85\n    keywords = [\"partial least squares\"]\n\n    LEARNER = PLSRegressionLearner\n\n    class Outputs(OWBaseLearner.Outputs):\n        coefsdata = Output(\"Coefficients and Loadings\", Table, explicit=True)\n        data = Output(\"Data with Scores\", Table)\n        components = Output(\"Components\", Table)\n\n    class Warning(OWBaseLearner.Warning):\n        sparse_data = Msg(\n            'Sparse input data: default preprocessing is to scale it.')\n\n    n_components = Setting(2)\n    max_iter = Setting(500)\n\n    def add_main_layout(self):\n        optimization_box = gui.vBox(\n            self.controlArea, \"Optimization Parameters\")\n        gui.spin(\n            optimization_box, self, \"n_components\", 1, 50, 1,\n            label=\"Components: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed)\n        gui.spin(\n            optimization_box, self, \"max_iter\", 5, 1000000, 50,\n            label=\"Iteration limit: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed,\n            checkCallback=self.settings_changed)\n\n    def update_model(self):\n        super().update_model()\n        coef_table = None\n        data = None\n        components = None\n        if self.model is not None:\n            coef_table = self._create_output_coeffs_loadings()\n            data = self._create_output_data()\n            components = self.model.components()\n        self.Outputs.coefsdata.send(coef_table)\n        self.Outputs.data.send(data)\n        self.Outputs.components.send(components)\n\n    def _create_output_coeffs_loadings(self) -> Table:\n        coefficients = self.model.coefficients.T\n        _, y_loadings = self.model.loadings\n        x_rotations, _ = self.model.rotations\n\n        n_features, n_targets = coefficients.shape\n        n_components = x_rotations.shape[1]\n\n        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]\n        names += [f\"Loading {i + 1}\" for i in range(n_components)]\n        domain = Domain(\n            [ContinuousVariable(n) for n in names],\n            metas=[StringVariable(\"Variable name\"),\n                   DiscreteVariable(\"Variable role\", (\"Feature\", \"Target\"))]\n        )\n\n        X = np.vstack((np.hstack((coefficients, x_rotations)),\n                       np.full((n_targets, n_targets + n_components), np.nan)))\n        X[-n_targets:, n_targets:] = y_loadings\n\n        M = np.array([[v.name for v in self.model.domain.variables],\n                      [0] * n_features + [1] * n_targets],\n                     dtype=object).T\n\n        table = Table.from_numpy(domain, X=X, metas=M)\n        table.name = \"Coefficients and Loadings\"\n        return table\n\n    def _create_output_data(self) -> Table:\n        projection = self.model.project(self.data)\n        normal_probs = self.model.residuals_normal_probability(self.data)\n        dmodx = self.model.dmodx(self.data)\n        data_domain = self.data.domain\n        proj_domain = projection.domain\n        nprobs_domain = normal_probs.domain\n        dmodx_domain = dmodx.domain\n        metas = proj_domain.metas + proj_domain.attributes + \\\n            nprobs_domain.attributes + dmodx_domain.attributes\n        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)\n        data: Table = self.data.transform(domain)\n        with data.unlocked(data.metas):\n            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \\\n                normal_probs.X\n            data.metas[:, -1] = dmodx.X[:, 0]\n        return data\n\n    @OWBaseLearner.Inputs.data\n    def set_data(self, data):\n        # reimplemented completely because the base learner does not\n        # allow multiclass\n\n        self.Warning.sparse_data.clear()\n\n        self.Error.data_error.clear()\n        self.data = data\n\n        if data is not None and data.domain.class_var is None and not data.domain.class_vars:\n            self.Error.data_error(\n                \"Data has no target variable.\\n\"\n                \"Select one with the Select Columns widget.\")\n            self.data = None\n\n        # invalidate the model so that handleNewSignals will update it\n        self.model = None\n\n        if self.data and sp.issparse(self.data.X):\n            self.Warning.sparse_data()\n\n    def create_learner(self):\n        common_args = {'preprocessors': self.preprocessors}\n        return PLSRegressionLearner(n_components=self.n_components,\n                                    max_iter=self.max_iter,\n                                    **common_args)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWPLS).run(Table(\"housing\"))\n",
            "new_file_content": "import numpy as np\nfrom AnyQt.QtCore import Qt\nimport scipy.sparse as sp\n\nfrom Orange.data import Table, Domain, ContinuousVariable, StringVariable, \\\n    DiscreteVariable\nfrom Orange.regression import PLSRegressionLearner\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.signals import Output\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Msg\n\n\nclass OWPLS(OWBaseLearner):\n    name = 'PLS'\n    description = \"Partial Least Squares Regression widget for multivariate data analysis\"\n    icon = \"icons/PLS.svg\"\n    priority = 85\n    keywords = [\"partial least squares\"]\n\n    LEARNER = PLSRegressionLearner\n\n    class Outputs(OWBaseLearner.Outputs):\n        coefsdata = Output(\"Coefficients and Loadings\", Table, explicit=True)\n        data = Output(\"Data with Scores\", Table)\n        components = Output(\"Components\", Table)\n\n    class Warning(OWBaseLearner.Warning):\n        sparse_data = Msg(\n            'Sparse input data: default preprocessing is to scale it.')\n\n    n_components = Setting(2)\n    max_iter = Setting(500)\n\n    def add_main_layout(self):\n        optimization_box = gui.vBox(\n            self.controlArea, \"Optimization Parameters\")\n        gui.spin(\n            optimization_box, self, \"n_components\", 1, 50, 1,\n            label=\"Components: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed)\n        gui.spin(\n            optimization_box, self, \"max_iter\", 5, 1000000, 50,\n            label=\"Iteration limit: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed,\n            checkCallback=self.settings_changed)\n\n    def update_model(self):\n        super().update_model()\n        coef_table = None\n        data = None\n        components = None\n        if self.model is not None:\n            coef_table = self._create_output_coeffs_loadings()\n            data = self._create_output_data()\n            components = self.model.components()\n        self.Outputs.coefsdata.send(coef_table)\n        self.Outputs.data.send(data)\n        self.Outputs.components.send(components)\n\n    def _create_output_coeffs_loadings(self) -> Table:\n        coefficients = self.model.coefficients.T\n        _, y_loadings = self.model.loadings\n        x_rotations, _ = self.model.rotations\n\n        n_features, n_targets = coefficients.shape\n        n_components = x_rotations.shape[1]\n\n        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]\n        names += [f\"w*c {i + 1}\" for i in range(n_components)]\n        domain = Domain(\n            [ContinuousVariable(n) for n in names],\n            metas=[StringVariable(\"Variable name\"),\n                   DiscreteVariable(\"Variable role\", (\"Feature\", \"Target\"))]\n        )\n\n        X = np.vstack((np.hstack((coefficients, x_rotations)),\n                       np.full((n_targets, n_targets + n_components), np.nan)))\n        X[-n_targets:, n_targets:] = y_loadings\n\n        M = np.array([[v.name for v in self.model.domain.variables],\n                      [0] * n_features + [1] * n_targets],\n                     dtype=object).T\n\n        table = Table.from_numpy(domain, X=X, metas=M)\n        table.name = \"Coefficients and Loadings\"\n        return table\n\n    def _create_output_data(self) -> Table:\n        projection = self.model.project(self.data)\n        normal_probs = self.model.residuals_normal_probability(self.data)\n        dmodx = self.model.dmodx(self.data)\n        data_domain = self.data.domain\n        proj_domain = projection.domain\n        nprobs_domain = normal_probs.domain\n        dmodx_domain = dmodx.domain\n        metas = proj_domain.metas + proj_domain.attributes + \\\n            nprobs_domain.attributes + dmodx_domain.attributes\n        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)\n        data: Table = self.data.transform(domain)\n        with data.unlocked(data.metas):\n            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \\\n                normal_probs.X\n            data.metas[:, -1] = dmodx.X[:, 0]\n        return data\n\n    @OWBaseLearner.Inputs.data\n    def set_data(self, data):\n        # reimplemented completely because the base learner does not\n        # allow multiclass\n\n        self.Warning.sparse_data.clear()\n\n        self.Error.data_error.clear()\n        self.data = data\n\n        if data is not None and data.domain.class_var is None and not data.domain.class_vars:\n            self.Error.data_error(\n                \"Data has no target variable.\\n\"\n                \"Select one with the Select Columns widget.\")\n            self.data = None\n\n        # invalidate the model so that handleNewSignals will update it\n        self.model = None\n\n        if self.data and sp.issparse(self.data.X):\n            self.Warning.sparse_data()\n\n    def create_learner(self):\n        common_args = {'preprocessors': self.preprocessors}\n        return PLSRegressionLearner(n_components=self.n_components,\n                                    max_iter=self.max_iter,\n                                    **common_args)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWPLS).run(Table(\"housing\"))\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/model/owpls.py"
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                "new_commit_hash": "cc1317c03",
                "mode": "100644"
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            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/model/owpls.py"
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                        "old_range": {
                            "start": 71,
                            "length": 7
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                            "start": 71,
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                        "section": "class OWPLS(OWBaseLearner):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        n_components = x_rotations.shape[1]",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]",
                                "type": "context"
                            },
                            {
                                "content": "        names += [f\"Loading {i + 1}\" for i in range(n_components)]",
                                "type": "deleted"
                            },
                            {
                                "content": "        names += [f\"w*c {i + 1}\" for i in range(n_components)]",
                                "type": "added"
                            },
                            {
                                "content": "        domain = Domain(",
                                "type": "context"
                            },
                            {
                                "content": "            [ContinuousVariable(n) for n in names],",
                                "type": "context"
                            },
                            {
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                        {
                            "file_name": "Orange/widgets/model/owpls.py",
                            "type": "method",
                            "name": "OWPLS._create_output_coeffs_loadings",
                            "content": "class OWPLS(OWBaseLearner):\n    name = 'PLS'\n    description = \"Partial Least Squares Regression widget for multivariate data analysis\"\n    icon = \"icons/PLS.svg\"\n    priority = 85\n    keywords = [\"partial least squares\"]\n\n    LEARNER = PLSRegressionLearner\n\n    class Outputs(OWBaseLearner.Outputs):\n        coefsdata = Output(\"Coefficients and Loadings\", Table, explicit=True)\n        data = Output(\"Data with Scores\", Table)\n        components = Output(\"Components\", Table)\n\n    class Warning(OWBaseLearner.Warning):\n        sparse_data = Msg(\n            'Sparse input data: default preprocessing is to scale it.')\n\n    n_components = Setting(2)\n    max_iter = Setting(500)\n\n    def add_main_layout(self):\n        optimization_box = gui.vBox(\n            self.controlArea, \"Optimization Parameters\")\n        gui.spin(\n            optimization_box, self, \"n_components\", 1, 50, 1,\n            label=\"Components: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed)\n        gui.spin(\n            optimization_box, self, \"max_iter\", 5, 1000000, 50,\n            label=\"Iteration limit: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed,\n            checkCallback=self.settings_changed)\n\n    def update_model(self):\n        super().update_model()\n        coef_table = None\n        data = None\n        components = None\n        if self.model is not None:\n            coef_table = self._create_output_coeffs_loadings()\n            data = self._create_output_data()\n            components = self.model.components()\n        self.Outputs.coefsdata.send(coef_table)\n        self.Outputs.data.send(data)\n        self.Outputs.components.send(components)\n\n    def _create_output_coeffs_loadings(self) -> Table:\n        coefficients = self.model.coefficients.T\n        _, y_loadings = self.model.loadings\n        x_rotations, _ = self.model.rotations\n\n        n_features, n_targets = coefficients.shape\n        n_components = x_rotations.shape[1]\n\n        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]\n        names += [f\"Loading {i + 1}\" for i in range(n_components)]\n        domain = Domain(\n            [ContinuousVariable(n) for n in names],\n            metas=[StringVariable(\"Variable name\"),\n                   DiscreteVariable(\"Variable role\", (\"Feature\", \"Target\"))]\n        )\n\n        X = np.vstack((np.hstack((coefficients, x_rotations)),\n                       np.full((n_targets, n_targets + n_components), np.nan)))\n        X[-n_targets:, n_targets:] = y_loadings\n\n        M = np.array([[v.name for v in self.model.domain.variables],\n                      [0] * n_features + [1] * n_targets],\n                     dtype=object).T\n\n        table = Table.from_numpy(domain, X=X, metas=M)\n        table.name = \"Coefficients and Loadings\"\n        return table\n\n    def _create_output_data(self) -> Table:\n        projection = self.model.project(self.data)\n        normal_probs = self.model.residuals_normal_probability(self.data)\n        dmodx = self.model.dmodx(self.data)\n        data_domain = self.data.domain\n        proj_domain = projection.domain\n        nprobs_domain = normal_probs.domain\n        dmodx_domain = dmodx.domain\n        metas = proj_domain.metas + proj_domain.attributes + \\\n            nprobs_domain.attributes + dmodx_domain.attributes\n        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)\n        data: Table = self.data.transform(domain)\n        with data.unlocked(data.metas):\n            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \\\n                normal_probs.X\n            data.metas[:, -1] = dmodx.X[:, 0]\n        return data\n\n    @OWBaseLearner.Inputs.data\n    def set_data(self, data):\n        # reimplemented completely because the base learner does not\n        # allow multiclass\n\n        self.Warning.sparse_data.clear()\n\n        self.Error.data_error.clear()\n        self.data = data\n\n        if data is not None and data.domain.class_var is None and not data.domain.class_vars:\n            self.Error.data_error(\n                \"Data has no target variable.\\n\"\n                \"Select one with the Select Columns widget.\")\n            self.data = None\n\n        # invalidate the model so that handleNewSignals will update it\n        self.model = None\n\n        if self.data and sp.issparse(self.data.X):\n            self.Warning.sparse_data()\n\n    def create_learner(self):\n        common_args = {'preprocessors': self.preprocessors}\n        return PLSRegressionLearner(n_components=self.n_components,\n                                    max_iter=self.max_iter,\n                                    **common_args)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 65,
                            "end_lineno": 91,
                            "parent": {
                                "file_name": "Orange/widgets/model/owpls.py",
                                "type": "class",
                                "name": "OWPLS",
                                "content": "class OWPLS(OWBaseLearner):\n    name = 'PLS'\n    description = \"Partial Least Squares Regression widget for multivariate data analysis\"\n    icon = \"icons/PLS.svg\"\n    priority = 85\n    keywords = [\"partial least squares\"]\n\n    LEARNER = PLSRegressionLearner\n\n    class Outputs(OWBaseLearner.Outputs):\n        coefsdata = Output(\"Coefficients and Loadings\", Table, explicit=True)\n        data = Output(\"Data with Scores\", Table)\n        components = Output(\"Components\", Table)\n\n    class Warning(OWBaseLearner.Warning):\n        sparse_data = Msg(\n            'Sparse input data: default preprocessing is to scale it.')\n\n    n_components = Setting(2)\n    max_iter = Setting(500)\n\n    def add_main_layout(self):\n        optimization_box = gui.vBox(\n            self.controlArea, \"Optimization Parameters\")\n        gui.spin(\n            optimization_box, self, \"n_components\", 1, 50, 1,\n            label=\"Components: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed)\n        gui.spin(\n            optimization_box, self, \"max_iter\", 5, 1000000, 50,\n            label=\"Iteration limit: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed,\n            checkCallback=self.settings_changed)\n\n    def update_model(self):\n        super().update_model()\n        coef_table = None\n        data = None\n        components = None\n        if self.model is not None:\n            coef_table = self._create_output_coeffs_loadings()\n            data = self._create_output_data()\n            components = self.model.components()\n        self.Outputs.coefsdata.send(coef_table)\n        self.Outputs.data.send(data)\n        self.Outputs.components.send(components)\n\n    def _create_output_coeffs_loadings(self) -> Table:\n        coefficients = self.model.coefficients.T\n        _, y_loadings = self.model.loadings\n        x_rotations, _ = self.model.rotations\n\n        n_features, n_targets = coefficients.shape\n        n_components = x_rotations.shape[1]\n\n        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]\n        names += [f\"Loading {i + 1}\" for i in range(n_components)]\n        domain = Domain(\n            [ContinuousVariable(n) for n in names],\n            metas=[StringVariable(\"Variable name\"),\n                   DiscreteVariable(\"Variable role\", (\"Feature\", \"Target\"))]\n        )\n\n        X = np.vstack((np.hstack((coefficients, x_rotations)),\n                       np.full((n_targets, n_targets + n_components), np.nan)))\n        X[-n_targets:, n_targets:] = y_loadings\n\n        M = np.array([[v.name for v in self.model.domain.variables],\n                      [0] * n_features + [1] * n_targets],\n                     dtype=object).T\n\n        table = Table.from_numpy(domain, X=X, metas=M)\n        table.name = \"Coefficients and Loadings\"\n        return table\n\n    def _create_output_data(self) -> Table:\n        projection = self.model.project(self.data)\n        normal_probs = self.model.residuals_normal_probability(self.data)\n        dmodx = self.model.dmodx(self.data)\n        data_domain = self.data.domain\n        proj_domain = projection.domain\n        nprobs_domain = normal_probs.domain\n        dmodx_domain = dmodx.domain\n        metas = proj_domain.metas + proj_domain.attributes + \\\n            nprobs_domain.attributes + dmodx_domain.attributes\n        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)\n        data: Table = self.data.transform(domain)\n        with data.unlocked(data.metas):\n            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \\\n                normal_probs.X\n            data.metas[:, -1] = dmodx.X[:, 0]\n        return data\n\n    @OWBaseLearner.Inputs.data\n    def set_data(self, data):\n        # reimplemented completely because the base learner does not\n        # allow multiclass\n\n        self.Warning.sparse_data.clear()\n\n        self.Error.data_error.clear()\n        self.data = data\n\n        if data is not None and data.domain.class_var is None and not data.domain.class_vars:\n            self.Error.data_error(\n                \"Data has no target variable.\\n\"\n                \"Select one with the Select Columns widget.\")\n            self.data = None\n\n        # invalidate the model so that handleNewSignals will update it\n        self.model = None\n\n        if self.data and sp.issparse(self.data.X):\n            self.Warning.sparse_data()\n\n    def create_learner(self):\n        common_args = {'preprocessors': self.preprocessors}\n        return PLSRegressionLearner(n_components=self.n_components,\n                                    max_iter=self.max_iter,\n                                    **common_args)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 16,
                                "end_lineno": 137,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/model/owpls.py",
                            "type": "class",
                            "name": "OWPLS",
                            "content": "class OWPLS(OWBaseLearner):\n    name = 'PLS'\n    description = \"Partial Least Squares Regression widget for multivariate data analysis\"\n    icon = \"icons/PLS.svg\"\n    priority = 85\n    keywords = [\"partial least squares\"]\n\n    LEARNER = PLSRegressionLearner\n\n    class Outputs(OWBaseLearner.Outputs):\n        coefsdata = Output(\"Coefficients and Loadings\", Table, explicit=True)\n        data = Output(\"Data with Scores\", Table)\n        components = Output(\"Components\", Table)\n\n    class Warning(OWBaseLearner.Warning):\n        sparse_data = Msg(\n            'Sparse input data: default preprocessing is to scale it.')\n\n    n_components = Setting(2)\n    max_iter = Setting(500)\n\n    def add_main_layout(self):\n        optimization_box = gui.vBox(\n            self.controlArea, \"Optimization Parameters\")\n        gui.spin(\n            optimization_box, self, \"n_components\", 1, 50, 1,\n            label=\"Components: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed)\n        gui.spin(\n            optimization_box, self, \"max_iter\", 5, 1000000, 50,\n            label=\"Iteration limit: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed,\n            checkCallback=self.settings_changed)\n\n    def update_model(self):\n        super().update_model()\n        coef_table = None\n        data = None\n        components = None\n        if self.model is not None:\n            coef_table = self._create_output_coeffs_loadings()\n            data = self._create_output_data()\n            components = self.model.components()\n        self.Outputs.coefsdata.send(coef_table)\n        self.Outputs.data.send(data)\n        self.Outputs.components.send(components)\n\n    def _create_output_coeffs_loadings(self) -> Table:\n        coefficients = self.model.coefficients.T\n        _, y_loadings = self.model.loadings\n        x_rotations, _ = self.model.rotations\n\n        n_features, n_targets = coefficients.shape\n        n_components = x_rotations.shape[1]\n\n        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]\n        names += [f\"Loading {i + 1}\" for i in range(n_components)]\n        domain = Domain(\n            [ContinuousVariable(n) for n in names],\n            metas=[StringVariable(\"Variable name\"),\n                   DiscreteVariable(\"Variable role\", (\"Feature\", \"Target\"))]\n        )\n\n        X = np.vstack((np.hstack((coefficients, x_rotations)),\n                       np.full((n_targets, n_targets + n_components), np.nan)))\n        X[-n_targets:, n_targets:] = y_loadings\n\n        M = np.array([[v.name for v in self.model.domain.variables],\n                      [0] * n_features + [1] * n_targets],\n                     dtype=object).T\n\n        table = Table.from_numpy(domain, X=X, metas=M)\n        table.name = \"Coefficients and Loadings\"\n        return table\n\n    def _create_output_data(self) -> Table:\n        projection = self.model.project(self.data)\n        normal_probs = self.model.residuals_normal_probability(self.data)\n        dmodx = self.model.dmodx(self.data)\n        data_domain = self.data.domain\n        proj_domain = projection.domain\n        nprobs_domain = normal_probs.domain\n        dmodx_domain = dmodx.domain\n        metas = proj_domain.metas + proj_domain.attributes + \\\n            nprobs_domain.attributes + dmodx_domain.attributes\n        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)\n        data: Table = self.data.transform(domain)\n        with data.unlocked(data.metas):\n            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \\\n                normal_probs.X\n            data.metas[:, -1] = dmodx.X[:, 0]\n        return data\n\n    @OWBaseLearner.Inputs.data\n    def set_data(self, data):\n        # reimplemented completely because the base learner does not\n        # allow multiclass\n\n        self.Warning.sparse_data.clear()\n\n        self.Error.data_error.clear()\n        self.data = data\n\n        if data is not None and data.domain.class_var is None and not data.domain.class_vars:\n            self.Error.data_error(\n                \"Data has no target variable.\\n\"\n                \"Select one with the Select Columns widget.\")\n            self.data = None\n\n        # invalidate the model so that handleNewSignals will update it\n        self.model = None\n\n        if self.data and sp.issparse(self.data.X):\n            self.Warning.sparse_data()\n\n    def create_learner(self):\n        common_args = {'preprocessors': self.preprocessors}\n        return PLSRegressionLearner(n_components=self.n_components,\n                                    max_iter=self.max_iter,\n                                    **common_args)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 16,
                            "end_lineno": 137,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "import unittest\nimport numpy as np\n\nfrom Orange.data import Table, Domain, StringVariable\nfrom Orange.widgets.model.owpls import OWPLS\nfrom Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin, \\\n    ParameterMapping\n\n\nclass TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "import unittest\nimport numpy as np\n\nfrom Orange.data import Table, Domain, StringVariable\nfrom Orange.widgets.model.owpls import OWPLS\nfrom Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin, \\\n    ParameterMapping\n\n\nclass TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"w*c 1\", \"w*c 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"w*c 1\", \"w*c 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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                        "section": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):"
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                            {
                                "content": "        self.assertEqual(coefsdata.Y.shape, (14, 0))",
                                "type": "context"
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                                "content": "        self.assertEqual(coefsdata.metas.shape, (14, 2))",
                                "type": "context"
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                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],",
                                "type": "deleted"
                            },
                            {
                                "content": "        self.assertEqual([\"coef (MEDV)\", \"w*c 1\", \"w*c 2\"],",
                                "type": "added"
                            },
                            {
                                "content": "                         [v.name for v in coefsdata.domain.attributes])",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual([\"Variable name\", \"Variable role\"],",
                                "type": "context"
                            },
                            {
                                "content": "                         [v.name for v in coefsdata.domain.metas])",
                                "type": "context"
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                        {
                            "file_name": "Orange/widgets/model/tests/test_owpls.py",
                            "type": "method",
                            "name": "TestOWPLS.test_output_coefsdata",
                            "content": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 33,
                            "end_lineno": 50,
                            "parent": {
                                "file_name": "Orange/widgets/model/tests/test_owpls.py",
                                "type": "class",
                                "name": "TestOWPLS",
                                "content": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 10,
                                "end_lineno": 130,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/model/tests/test_owpls.py",
                            "type": "class",
                            "name": "TestOWPLS",
                            "content": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 10,
                            "end_lineno": 130,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 57,
                            "length": 7
                        },
                        "new_range": {
                            "start": 57,
                            "length": 7
                        },
                        "section": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        self.assertEqual(coefsdata.Y.shape, (14, 0))",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(coefsdata.metas.shape, (14, 2))",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]",
                                "type": "deleted"
                            },
                            {
                                "content": "        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"w*c 1\", \"w*c 2\"]",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(attr_names,",
                                "type": "context"
                            },
                            {
                                "content": "                         [v.name for v in coefsdata.domain.attributes])",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual([\"Variable name\", \"Variable role\"],",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/model/tests/test_owpls.py",
                            "type": "method",
                            "name": "TestOWPLS.test_output_coefsdata_multi_target",
                            "content": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 52,
                            "end_lineno": 71,
                            "parent": {
                                "file_name": "Orange/widgets/model/tests/test_owpls.py",
                                "type": "class",
                                "name": "TestOWPLS",
                                "content": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 10,
                                "end_lineno": 130,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/model/tests/test_owpls.py",
                            "type": "class",
                            "name": "TestOWPLS",
                            "content": "class TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"Loading 1\", \"Loading 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"Loading 1\", \"Loading 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 10,
                            "end_lineno": 130,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "ca75c5c338c4c97f20438e123ea4029aaf1af640^",
    "new_commit_hash": "ca75c5c338c4c97f20438e123ea4029aaf1af640",
    "commit_message": "PLS: Rename 'Loadings' columns to 'w*c'",
    "commit_date": "2024-05-16T08:14:56+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "ca75c5c338c4c97f20438e123ea4029aaf1af640",
    "test_file_codes": [
        "import unittest\nimport numpy as np\n\nfrom Orange.data import Table, Domain, StringVariable\nfrom Orange.widgets.model.owpls import OWPLS\nfrom Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin, \\\n    ParameterMapping\n\n\nclass TestOWPLS(WidgetTest, WidgetLearnerTestMixin):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls._data = Table(\"housing\")\n        cls._data = cls._data.add_column(StringVariable(\"Foo\"),\n                                         [\"Bar\"] * len(cls._data),\n                                         to_metas=True)\n        class_vars = [cls._data.domain.class_var,\n                      cls._data.domain.attributes[0]]\n        domain = Domain(cls._data.domain.attributes[1:], class_vars,\n                        cls._data.domain.metas)\n        cls._data_multi_target = cls._data.transform(domain)\n\n    def setUp(self):\n        self.widget = self.create_widget(OWPLS,\n                                         stored_settings={\"auto_apply\": False})\n        self.init()\n        self.parameters = [\n            ParameterMapping('max_iter', self.widget.controls.max_iter),\n            ParameterMapping('n_components', self.widget.controls.n_components)\n        ]\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        self.assertEqual([\"coef (MEDV)\", \"w*c 1\", \"w*c 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-1, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-1, 1] == 1))\n        self.assertAlmostEqual(coefsdata.X[0, 1], 0.237, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 1], -0.304, 3)\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n\n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"w*c 1\", \"w*c 2\"]\n        self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\n        self.assertEqual([\"Variable name\", \"Variable role\"],\n                         [v.name for v in coefsdata.domain.metas])\n        metas = [v.name for v in self._data_multi_target.domain.variables]\n        self.assertTrue((coefsdata.metas[:, 0] == metas).all())\n        self.assertTrue((coefsdata.metas[:-2, 1] == 0).all())\n        self.assertTrue((coefsdata.metas[-2:, 1] == 1).all())\n        self.assertAlmostEqual(coefsdata.X[0, 2], -0.198, 3)\n        self.assertAlmostEqual(coefsdata.X[12, 2], -0.288, 3)\n        self.assertAlmostEqual(coefsdata.X[13, 2], 0.243, 3)\n\n    def test_output_data(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 13))\n        self.assertEqual(output.Y.shape, (506,))\n        self.assertEqual(output.metas.shape, (506, 8))\n        self.assertEqual([v.name for v in self._data.domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in self._data.domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_data_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(output.X.shape, (506, 12))\n        self.assertEqual(output.Y.shape, (506, 2))\n        self.assertEqual(output.metas.shape, (506, 10))\n        orig_domain = self._data_multi_target.domain\n        self.assertEqual([v.name for v in orig_domain.variables],\n                         [v.name for v in output.domain.variables])\n        metas = [\"PLS U1\", \"PLS U2\", \"PLS T1\", \"PLS T2\",\n                 \"Sample Quantiles (MEDV)\", \"Theoretical Quantiles (MEDV)\",\n                 \"Sample Quantiles (CRIM)\", \"Theoretical Quantiles (CRIM)\",\n                 \"DModX\"]\n        self.assertEqual([v.name for v in orig_domain.metas] + metas,\n                         [v.name for v in output.domain.metas])\n\n    def test_output_components(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 13))\n        self.assertEqual(components.Y.shape, (2,))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_output_components_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(components.X.shape, (2, 12))\n        self.assertEqual(components.Y.shape, (2, 2))\n        self.assertEqual(components.metas.shape, (2, 1))\n\n    def test_missing_target(self):\n        data = self._data[:5].copy()\n        data.Y[[0, 4]] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertFalse(np.isnan(output.metas[:, 3:5].astype(float)).any())\n        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())\n        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())\n        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())\n\n        with data.unlocked(data.Y):\n            data.Y[:] = np.nan\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertIsNone(self.get_output(self.widget.Outputs.data))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\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\ncreating build/temp.linux-x86_64-3.7\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-3.7\ncreating build/lib.linux-x86_64-3.7/Orange\ncreating build/lib.linux-x86_64-3.7/Orange/classification\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-3.7/Orange/widgets\ncreating build/lib.linux-x86_64-3.7/Orange/widgets/utils\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-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 -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_contingency.c -o build/temp.linux-x86_64-3.7/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-3.7/Orange/data\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-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 -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_io.c -o build/temp.linux-x86_64-3.7/Orange/data/_io.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-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 -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-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 -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_variable.c -o build/temp.linux-x86_64-3.7/Orange/data/_variable.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/distance/_distance.c -o build/temp.linux-x86_64-3.7/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-3.7/Orange/distance\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/projection/_som.c -o build/temp.linux-x86_64-3.7/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-3.7/Orange/projection\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\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_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.37.0.dev0+ca75c5c to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640\nProcessing dependencies for Orange3==3.37.0.dev0+ca75c5c\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/XlsxWriter-3.2.0-py3.7.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.egg\nSearching for xgboost>=1.7.4\nReading https://pypi.org/simple/xgboost/\nDownloading https://files.pythonhosted.org/packages/48/bc/05d7db90d421c5e3d681a12fd1eb087e37bf2e9bbe2b105422d6319ecc92/xgboost-2.1.3-py3-none-manylinux2014_x86_64.whl#sha256=32a43526208fe676527c698cb852e0e9515e6d7294143780e476d335290a131b\nBest match: xgboost 2.1.3\nProcessing xgboost-2.1.3-py3-none-manylinux2014_x86_64.whl\nInstalling xgboost-2.1.3-py3-none-manylinux2014_x86_64.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages\nAdding xgboost 2.1.3 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/xgboost-2.1.3-py3.7-linux-x86_64.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-51vqfg9u/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-51vqfg9u/serverfiles-0.3.1/egg-dist-tmp-wlv1sm4l\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nExtracting serverfiles-0.3.1-py3.7.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build/temp.linux-x86_64-cpython-38/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-38/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-38/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-38/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-38/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-38/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-38/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-38/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-38/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-38/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.37.0.dev0+ca75c5c to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640\nProcessing dependencies for Orange3==3.37.0.dev0+ca75c5c\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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for xgboost==2.1.3\nBest match: xgboost 2.1.3\nAdding xgboost 2.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for scipy==1.10.1\nBest match: scipy 1.10.1\nAdding scipy 1.10.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.3.2\nBest match: scikit-learn 1.3.2\nAdding scikit-learn 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for pyqtgraph==0.13.3\nBest match: pyqtgraph 0.13.3\nAdding pyqtgraph 0.13.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for pandas==2.0.3\nBest match: pandas 2.0.3\nAdding pandas 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.24.0\nBest match: orange-widget-base 4.24.0\nAdding orange-widget-base 4.24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.2.2\nBest match: orange-canvas-core 0.2.2\nAdding orange-canvas-core 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for numpy==1.24.4\nBest match: numpy 1.24.4\nAdding numpy 1.24.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for networkx==3.1\nBest match: networkx 3.1\nAdding networkx 3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for matplotlib==3.7.5\nBest match: matplotlib 3.7.5\nAdding matplotlib 3.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for keyring==25.5.0\nBest match: keyring 25.5.0\nAdding keyring 25.5.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for nvidia-nccl-cu12==2.24.3\nBest match: nvidia-nccl-cu12 2.24.3\nAdding nvidia-nccl-cu12 2.24.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for urllib3==2.2.3\nBest match: urllib3 2.2.3\nAdding urllib3 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for importlib-resources==6.4.5\nBest match: importlib-resources 6.4.5\nAdding importlib-resources 6.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\nAdding typing-extensions 4.12.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/setuptools/_vendor\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\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for pillow==10.4.0\nBest match: pillow 10.4.0\nAdding pillow 10.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for kiwisolver==1.4.7\nBest match: kiwisolver 1.4.7\nAdding kiwisolver 1.4.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for contourpy==1.1.1\nBest match: contourpy 1.1.1\nAdding contourpy 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for anyio==4.5.2\nBest match: anyio 4.5.2\nAdding anyio 4.5.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for ipython==8.12.3\nBest match: ipython 8.12.3\nAdding ipython 8.12.3 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for zipp==3.20.2\nBest match: zipp 3.20.2\nAdding zipp 3.20.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.37.0.dev0+ca75c5c\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.8\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 212ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 147ms\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 pyqtgraph>=0.13.1,<=0.13.3 depends on\n      Python>=3.8, we can conclude that pyqtgraph>=0.13.1,<=0.13.3 cannot be used.\n      And because only the following versions of pyqtgraph are available:\n          pyqtgraph<=0.13.1\n          pyqtgraph==0.13.2\n          pyqtgraph==0.13.3\n          pyqtgraph==0.13.4\n          pyqtgraph==0.13.5\n          pyqtgraph==0.13.6\n          pyqtgraph==0.13.7\n      we can conclude that pyqtgraph>=0.13.1,<0.13.4 cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pyqtgraph>=0.13.4 depends on Python>=3.9, we\n      can conclude that pyqtgraph>=0.13.4 cannot be used.\n      And because we know from (1) that pyqtgraph>=0.13.1,<0.13.4 cannot be used, we can conclude that pyqtgraph>=0.13.1 cannot\n      be used.\n      And because you require pyqtgraph>=0.13.1, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 4ms\nInstalled 2 packages in 118ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 22ms\nInstalled 4 packages in 743ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + threadpoolctl==3.1.0\nIn file included from /home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m/Python.h:8,\n                 from Orange/classification/_simple_tree.c:781:\n/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m/pyconfig.h:1504: warning: \"_GNU_SOURCE\" redefined\n 1504 | #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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         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:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __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) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/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:3944:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3944 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3943:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3943 |   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:5094:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5094 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5093:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5093 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3244:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3244 |           *__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:2892:7: note: ‘__pyx_v_win_y’ was declared here\n 2892 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3233:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3233 |           *__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:2891:7: note: ‘__pyx_v_win_x’ was declared here\n 2891 |   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:5842:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5842 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5841:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5841 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4871:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4871 |           *__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:4389:7: note: ‘__pyx_v_win_y’ was declared here\n 4389 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4860:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4860 |           *__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:4388:7: note: ‘__pyx_v_win_x’ was declared here\n 4388 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3438:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3438 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/setuptools/command/easy_install.py:159: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n  EasyInstallDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\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'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nerror: Couldn't find a setup script in /tmp/easy_install-qr8qfxbw/scipy-1.15.0.tar.gz\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 99ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 43ms\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\nResolved 64 packages in 160ms\nInstalled 61 packages in 501ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\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==1.1.1\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 + importlib-resources==6.4.5\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==25.5.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + more-itertools==10.5.0\n + networkx==3.1\n + nvidia-nccl-cu12==2.24.3\n + openpyxl==3.1.5\n + opentsne==1.0.1\n + pandas==2.0.3\n + pillow==10.4.0\n + pip==24.3.1\n + plotly==5.24.1\n + pycparser==2.22\n + pyparsing==3.1.4\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==1.3.2\n + scipy==1.10.1\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==2.2.3\n + xgboost==2.1.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 63 packages in 53ms\nInstalled 41 packages in 145ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\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==0.20.1\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.12.3\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==0.2.2\n + orange-widget-base==4.24.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\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==0.13.3\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 2 packages in 2ms\nInstalled 2 packages in 4ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nIn file included from /home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8/Python.h:8,\n                 from Orange/classification/_simple_tree.c:781:\n/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8/pyconfig.h:1578: warning: \"_GNU_SOURCE\" redefined\n 1578 | #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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         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:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __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) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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:3944:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3944 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3943:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3943 |   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:5094:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5094 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5093:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5093 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3438:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3438 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3244:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3244 |           *__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:2892:7: note: ‘__pyx_v_win_y’ was declared here\n 2892 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3233:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3233 |           *__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:2891:7: note: ‘__pyx_v_win_x’ was declared here\n 2891 |   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:5842:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5842 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5841:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5841 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4871:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4871 |           *__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:4389:7: note: ‘__pyx_v_win_y’ was declared here\n 4389 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4860:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4860 |           *__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:4388:7: note: ‘__pyx_v_win_x’ was declared here\n 4388 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/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_ca75c5c338c4c97f20438e123ea4029aaf1af640/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: 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'\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 29 items\n\nr2e_tests/test_1.py sss..........................                        [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWPLS::test_has_unconditional_apply\nPASSED r2e_tests/test_1.py::TestOWPLS::test_image_export\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_data\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_data_disconnect\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_data_learner_adequacy\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_preprocessor\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_preprocessor_disconnect\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_preprocessors\nPASSED r2e_tests/test_1.py::TestOWPLS::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWPLS::test_missing_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_coefsdata\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_coefsdata_multi_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_components\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_components_multi_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_data\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_data_multi_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_learner\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_learner_name\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_model\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_model_name\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_model_picklable\nPASSED r2e_tests/test_1.py::TestOWPLS::test_parameters\nPASSED r2e_tests/test_1.py::TestOWPLS::test_parameters_default\nPASSED r2e_tests/test_1.py::TestOWPLS::test_params_trigger_settings_changed\nPASSED r2e_tests/test_1.py::TestOWPLS::test_send_report\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\n======================== 26 passed, 3 skipped in 6.75s =========================\nWidgets left: 0    Max widgets: 1392 \n\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_ca75c5c338c4c97f20438e123ea4029aaf1af640\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 29 items\n\nr2e_tests/test_1.py sss...........FF.............                        [100%]\n\n=================================== FAILURES ===================================\n_______________________ TestOWPLS.test_output_coefsdata ________________________\n\nself = <r2e_tests.test_1.TestOWPLS testMethod=test_output_coefsdata>\n\n    def test_output_coefsdata(self):\n        self.send_signal(self.widget.Inputs.data, self._data)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 3))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n    \n>       self.assertEqual([\"coef (MEDV)\", \"w*c 1\", \"w*c 2\"],\n                         [v.name for v in coefsdata.domain.attributes])\nE       AssertionError: Lists differ: ['coef (MEDV)', 'w*c 1', 'w*c 2'] != ['coef (MEDV)', 'Loading 1', 'Loading 2']\nE       \nE       First differing element 1:\nE       'w*c 1'\nE       'Loading 1'\nE       \nE       - ['coef (MEDV)', 'w*c 1', 'w*c 2']\nE       + ['coef (MEDV)', 'Loading 1', 'Loading 2']\n\nr2e_tests/test_1.py:41: AssertionError\n_________________ TestOWPLS.test_output_coefsdata_multi_target _________________\n\nself = <r2e_tests.test_1.TestOWPLS testMethod=test_output_coefsdata_multi_target>\n\n    def test_output_coefsdata_multi_target(self):\n        self.send_signal(self.widget.Inputs.data, self._data_multi_target)\n        coefsdata = self.get_output(self.widget.Outputs.coefsdata)\n        self.assertEqual(coefsdata.name, \"Coefficients and Loadings\")\n        self.assertEqual(coefsdata.X.shape, (14, 4))\n        self.assertEqual(coefsdata.Y.shape, (14, 0))\n        self.assertEqual(coefsdata.metas.shape, (14, 2))\n    \n        attr_names = [\"coef (MEDV)\", \"coef (CRIM)\", \"w*c 1\", \"w*c 2\"]\n>       self.assertEqual(attr_names,\n                         [v.name for v in coefsdata.domain.attributes])\nE       AssertionError: Lists differ: ['coef (MEDV)', 'coef (CRIM)', 'w*c 1', 'w*c 2'] != ['coef (MEDV)', 'coef (CRIM)', 'Loading 1', 'Loading 2']\nE       \nE       First differing element 2:\nE       'w*c 1'\nE       'Loading 1'\nE       \nE       - ['coef (MEDV)', 'coef (CRIM)', 'w*c 1', 'w*c 2']\nE       ?                                 ^^^      ^^^\nE       \nE       + ['coef (MEDV)', 'coef (CRIM)', 'Loading 1', 'Loading 2']\nE       ?                                 ^^^^^^^      ^^^^^^^\n\nr2e_tests/test_1.py:61: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWPLS::test_has_unconditional_apply\nPASSED r2e_tests/test_1.py::TestOWPLS::test_image_export\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_data\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_data_disconnect\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_data_learner_adequacy\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_preprocessor\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_preprocessor_disconnect\nPASSED r2e_tests/test_1.py::TestOWPLS::test_input_preprocessors\nPASSED r2e_tests/test_1.py::TestOWPLS::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWPLS::test_missing_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_components\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_components_multi_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_data\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_data_multi_target\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_learner\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_learner_name\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_model\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_model_name\nPASSED r2e_tests/test_1.py::TestOWPLS::test_output_model_picklable\nPASSED r2e_tests/test_1.py::TestOWPLS::test_parameters\nPASSED r2e_tests/test_1.py::TestOWPLS::test_parameters_default\nPASSED r2e_tests/test_1.py::TestOWPLS::test_params_trigger_settings_changed\nPASSED r2e_tests/test_1.py::TestOWPLS::test_send_report\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nFAILED r2e_tests/test_1.py::TestOWPLS::test_output_coefsdata - AssertionError...\nFAILED r2e_tests/test_1.py::TestOWPLS::test_output_coefsdata_multi_target - A...\n=================== 2 failed, 24 passed, 3 skipped in 4.63s ====================\nWidgets left: 0    Max widgets: 1392 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/model/owpls.py Orange/widgets/model/tests/test_owpls.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Inconsistent Naming of Loadings Columns Causes Test Failures in OWPLS Widget

**Description:**
When using the OWPLS widget, the output data includes loadings columns that are inconsistently named. The columns are labeled as "w*c 1", "w*c 2", etc., instead of the expected "Loading 1", "Loading 2". This inconsistency leads to failing tests that rely on the original naming convention.

**Example Buggy Code:**
```python
coefsdata = widget.get_coefsdata()
print([v.name for v in coefsdata.domain.attributes])
# Current Output: ['coef (MEDV)', 'w*c 1', 'w*c 2']
# Expected Output: ['coef (MEDV)', 'Loading 1', 'Loading 2']
```

**Expected Behavior:**
The loadings columns should be consistently named as "Loading 1", "Loading 2", etc., to match the test expectations and maintain clarity in the data output.

**Actual Behavior:**
The loadings columns are named as "w*c 1", "w*c 2", causing assertions in existing tests to fail due to the unexpected column names.
[/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/owpls.py
import numpy as np
from AnyQt.QtCore import Qt
import scipy.sparse as sp

from Orange.data import Table, Domain, ContinuousVariable, StringVariable, \
    DiscreteVariable
from Orange.regression import PLSRegressionLearner
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.utils.signals import Output
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Msg


class OWPLS(OWBaseLearner):
    name = 'PLS'
    description = "Partial Least Squares Regression widget for multivariate data analysis"
    icon = "icons/PLS.svg"
    priority = 85
    keywords = ["partial least squares"]

    LEARNER = PLSRegressionLearner

    class Outputs(OWBaseLearner.Outputs):
        coefsdata = Output("Coefficients and Loadings", Table, explicit=True)
        data = Output("Data with Scores", Table)
        components = Output("Components", Table)

    class Warning(OWBaseLearner.Warning):
        sparse_data = Msg(
            'Sparse input data: default preprocessing is to scale it.')

    n_components = Setting(2)
    max_iter = Setting(500)

    def add_main_layout(self):
        optimization_box = gui.vBox(
            self.controlArea, "Optimization Parameters")
        gui.spin(
            optimization_box, self, "n_components", 1, 50, 1,
            label="Components: ",
            alignment=Qt.AlignRight, controlWidth=100,
            callback=self.settings_changed)
        gui.spin(
            optimization_box, self, "max_iter", 5, 1000000, 50,
            label="Iteration limit: ",
            alignment=Qt.AlignRight, controlWidth=100,
            callback=self.settings_changed,
            checkCallback=self.settings_changed)

    def update_model(self):
        super().update_model()
        coef_table = None
        data = None
        components = None
        if self.model is not None:
            coef_table = self._create_output_coeffs_loadings()
            data = self._create_output_data()
            components = self.model.components()
        self.Outputs.coefsdata.send(coef_table)
        self.Outputs.data.send(data)
        self.Outputs.components.send(components)

    def _create_output_coeffs_loadings(self) -> Table:
        coefficients = self.model.coefficients.T
        _, y_loadings = self.model.loadings
        x_rotations, _ = self.model.rotations

        n_features, n_targets = coefficients.shape
        n_components = x_rotations.shape[1]

        names = [f"coef ({v.name})" for v in self.model.domain.class_vars]
        names += [f"Loading {i + 1}" for i in range(n_components)]
        domain = Domain(
            [ContinuousVariable(n) for n in names],
            metas=[StringVariable("Variable name"),
                   DiscreteVariable("Variable role", ("Feature", "Target"))]
        )

        X = np.vstack((np.hstack((coefficients, x_rotations)),
                       np.full((n_targets, n_targets + n_components), np.nan)))
        X[-n_targets:, n_targets:] = y_loadings

        M = np.array([[v.name for v in self.model.domain.variables],
                      [0] * n_features + [1] * n_targets],
                     dtype=object).T

        table = Table.from_numpy(domain, X=X, metas=M)
        table.name = "Coefficients and Loadings"
        return table

    def _create_output_data(self) -> Table:
        projection = self.model.project(self.data)
        normal_probs = self.model.residuals_normal_probability(self.data)
        dmodx = self.model.dmodx(self.data)
        data_domain = self.data.domain
        proj_domain = projection.domain
        nprobs_domain = normal_probs.domain
        dmodx_domain = dmodx.domain
        metas = proj_domain.metas + proj_domain.attributes + \
            nprobs_domain.attributes + dmodx_domain.attributes
        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)
        data: Table = self.data.transform(domain)
        with data.unlocked(data.metas):
            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \
                normal_probs.X
            data.metas[:, -1] = dmodx.X[:, 0]
        return data

    @OWBaseLearner.Inputs.data
    def set_data(self, data):
        # reimplemented completely because the base learner does not
        # allow multiclass

        self.Warning.sparse_data.clear()

        self.Error.data_error.clear()
        self.data = data

        if data is not None and data.domain.class_var is None and not data.domain.class_vars:
            self.Error.data_error(
                "Data has no target variable.\n"
                "Select one with the Select Columns widget.")
            self.data = None

        # invalidate the model so that handleNewSignals will update it
        self.model = None

        if self.data and sp.issparse(self.data.X):
            self.Warning.sparse_data()

    def create_learner(self):
        common_args = {'preprocessors': self.preprocessors}
        return PLSRegressionLearner(n_components=self.n_components,
                                    max_iter=self.max_iter,
                                    **common_args)


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWPLS).run(Table("housing"))

```
--- 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:** Inconsistent Naming of Loadings Columns Causes Test Failures in OWPLS Widget

**Description:**
When using the OWPLS widget, the output data includes loadings columns that are inconsistently named. The columns are labeled as "w*c 1", "w*c 2", etc., instead of the expected "Loading 1", "Loading 2". This inconsistency leads to failing tests that rely on the original naming convention.

**Example Buggy Code:**
```python
coefsdata = widget.get_coefsdata()
print([v.name for v in coefsdata.domain.attributes])
# Current Output: ['coef (MEDV)', 'w*c 1', 'w*c 2']
# Expected Output: ['coef (MEDV)', 'Loading 1', 'Loading 2']
```

**Expected Behavior:**
The loadings columns should be consistently named as "Loading 1", "Loading 2", etc., to match the test expectations and maintain clarity in the data output.

**Actual Behavior:**
The loadings columns are named as "w*c 1", "w*c 2", causing assertions in existing tests to fail due to the unexpected column names.
[/ISSUE]
{
    "TestOWPLS.test_has_unconditional_apply": "PASSED",
    "TestOWPLS.test_image_export": "PASSED",
    "TestOWPLS.test_input_data": "PASSED",
    "TestOWPLS.test_input_data_disconnect": "PASSED",
    "TestOWPLS.test_input_data_learner_adequacy": "PASSED",
    "TestOWPLS.test_input_preprocessor": "PASSED",
    "TestOWPLS.test_input_preprocessor_disconnect": "PASSED",
    "TestOWPLS.test_input_preprocessors": "PASSED",
    "TestOWPLS.test_minimum_size": "PASSED",
    "TestOWPLS.test_missing_target": "PASSED",
    "TestOWPLS.test_msg_base_class": "PASSED",
    "TestOWPLS.test_output_coefsdata": "PASSED",
    "TestOWPLS.test_output_coefsdata_multi_target": "PASSED",
    "TestOWPLS.test_output_components": "PASSED",
    "TestOWPLS.test_output_components_multi_target": "PASSED",
    "TestOWPLS.test_output_data": "PASSED",
    "TestOWPLS.test_output_data_multi_target": "PASSED",
    "TestOWPLS.test_output_learner": "PASSED",
    "TestOWPLS.test_output_learner_name": "PASSED",
    "TestOWPLS.test_output_model": "PASSED",
    "TestOWPLS.test_output_model_name": "PASSED",
    "TestOWPLS.test_output_model_picklable": "PASSED",
    "TestOWPLS.test_parameters": "PASSED",
    "TestOWPLS.test_parameters_default": "PASSED",
    "TestOWPLS.test_params_trigger_settings_changed": "PASSED",
    "TestOWPLS.test_send_report": "PASSED"
}
diff --git a/Orange/widgets/model/owpls.py b/Orange/widgets/model/owpls.py
index 447caded1..cc1317c03 100644
--- a/Orange/widgets/model/owpls.py
+++ b/Orange/widgets/model/owpls.py
@@ -71,7 +71,7 @@ class OWPLS(OWBaseLearner):
         n_components = x_rotations.shape[1]
 
         names = [f"coef ({v.name})" for v in self.model.domain.class_vars]
-        names += [f"Loading {i + 1}" for i in range(n_components)]
+        names += [f"w*c {i + 1}" for i in range(n_components)]
         domain = Domain(
             [ContinuousVariable(n) for n in names],
             metas=[StringVariable("Variable name"),

{"Orange/widgets/model/owpls.py": "import numpy as np\nfrom AnyQt.QtCore import Qt\nimport scipy.sparse as sp\n\nfrom Orange.data import Table, Domain, ContinuousVariable, StringVariable, \\\n    DiscreteVariable\nfrom Orange.regression import PLSRegressionLearner\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.owlearnerwidget import OWBaseLearner\nfrom Orange.widgets.utils.signals import Output\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Msg\n\n\nclass OWPLS(OWBaseLearner):\n    name = 'PLS'\n    description = \"Partial Least Squares Regression widget for multivariate data analysis\"\n    icon = \"icons/PLS.svg\"\n    priority = 85\n    keywords = [\"partial least squares\"]\n\n    LEARNER = PLSRegressionLearner\n\n    class Outputs(OWBaseLearner.Outputs):\n        coefsdata = Output(\"Coefficients and Loadings\", Table, explicit=True)\n        data = Output(\"Data with Scores\", Table)\n        components = Output(\"Components\", Table)\n\n    class Warning(OWBaseLearner.Warning):\n        sparse_data = Msg(\n            'Sparse input data: default preprocessing is to scale it.')\n\n    n_components = Setting(2)\n    max_iter = Setting(500)\n\n    def add_main_layout(self):\n        optimization_box = gui.vBox(\n            self.controlArea, \"Optimization Parameters\")\n        gui.spin(\n            optimization_box, self, \"n_components\", 1, 50, 1,\n            label=\"Components: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed)\n        gui.spin(\n            optimization_box, self, \"max_iter\", 5, 1000000, 50,\n            label=\"Iteration limit: \",\n            alignment=Qt.AlignRight, controlWidth=100,\n            callback=self.settings_changed,\n            checkCallback=self.settings_changed)\n\n    def update_model(self):\n        super().update_model()\n        coef_table = None\n        data = None\n        components = None\n        if self.model is not None:\n            coef_table = self._create_output_coeffs_loadings()\n            data = self._create_output_data()\n            components = self.model.components()\n        self.Outputs.coefsdata.send(coef_table)\n        self.Outputs.data.send(data)\n        self.Outputs.components.send(components)\n\n    def _create_output_coeffs_loadings(self) -> Table:\n        coefficients = self.model.coefficients.T\n        _, y_loadings = self.model.loadings\n        x_rotations, _ = self.model.rotations\n\n        n_features, n_targets = coefficients.shape\n        n_components = x_rotations.shape[1]\n\n        names = [f\"coef ({v.name})\" for v in self.model.domain.class_vars]\n        names += [f\"Loading {i + 1}\" for i in range(n_components)]\n        domain = Domain(\n            [ContinuousVariable(n) for n in names],\n            metas=[StringVariable(\"Variable name\"),\n                   DiscreteVariable(\"Variable role\", (\"Feature\", \"Target\"))]\n        )\n\n        X = np.vstack((np.hstack((coefficients, x_rotations)),\n                       np.full((n_targets, n_targets + n_components), np.nan)))\n        X[-n_targets:, n_targets:] = y_loadings\n\n        M = np.array([[v.name for v in self.model.domain.variables],\n                      [0] * n_features + [1] * n_targets],\n                     dtype=object).T\n\n        table = Table.from_numpy(domain, X=X, metas=M)\n        table.name = \"Coefficients and Loadings\"\n        return table\n\n    def _create_output_data(self) -> Table:\n        projection = self.model.project(self.data)\n        normal_probs = self.model.residuals_normal_probability(self.data)\n        dmodx = self.model.dmodx(self.data)\n        data_domain = self.data.domain\n        proj_domain = projection.domain\n        nprobs_domain = normal_probs.domain\n        dmodx_domain = dmodx.domain\n        metas = proj_domain.metas + proj_domain.attributes + \\\n            nprobs_domain.attributes + dmodx_domain.attributes\n        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)\n        data: Table = self.data.transform(domain)\n        with data.unlocked(data.metas):\n            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \\\n                normal_probs.X\n            data.metas[:, -1] = dmodx.X[:, 0]\n        return data\n\n    @OWBaseLearner.Inputs.data\n    def set_data(self, data):\n        # reimplemented completely because the base learner does not\n        # allow multiclass\n\n        self.Warning.sparse_data.clear()\n\n        self.Error.data_error.clear()\n        self.data = data\n\n        if data is not None and data.domain.class_var is None and not data.domain.class_vars:\n            self.Error.data_error(\n                \"Data has no target variable.\\n\"\n                \"Select one with the Select Columns widget.\")\n            self.data = None\n\n        # invalidate the model so that handleNewSignals will update it\n        self.model = None\n\n        if self.data and sp.issparse(self.data.X):\n            self.Warning.sparse_data()\n\n    def create_learner(self):\n        common_args = {'preprocessors': self.preprocessors}\n        return PLSRegressionLearner(n_components=self.n_components,\n                                    max_iter=self.max_iter,\n                                    **common_args)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWPLS).run(Table(\"housing\"))\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:** Inconsistent Naming of Loadings Columns Causes Test Failures in OWPLS Widget

**Description:**
When using the OWPLS widget, the output data includes loadings columns that are inconsistently named. The columns are labeled as "w*c 1", "w*c 2", etc., instead of the expected "Loading 1", "Loading 2". This inconsistency leads to failing tests that rely on the original naming convention.

**Example Buggy Code:**
```python
coefsdata = widget.get_coefsdata()
print([v.name for v in coefsdata.domain.attributes])
# Current Output: ['coef (MEDV)', 'w*c 1', 'w*c 2']
# Expected Output: ['coef (MEDV)', 'Loading 1', 'Loading 2']
```

**Expected Behavior:**
The loadings columns should be consistently named as "Loading 1", "Loading 2", etc., to match the test expectations and maintain clarity in the data output.

**Actual Behavior:**
The loadings columns are named as "w*c 1", "w*c 2", causing assertions in existing tests to fail due to the unexpected column names.
[/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/owpls.py
import numpy as np
from AnyQt.QtCore import Qt
import scipy.sparse as sp

from Orange.data import Table, Domain, ContinuousVariable, StringVariable, \
    DiscreteVariable
from Orange.regression import PLSRegressionLearner
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.owlearnerwidget import OWBaseLearner
from Orange.widgets.utils.signals import Output
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Msg


class OWPLS(OWBaseLearner):
    name = 'PLS'
    description = "Partial Least Squares Regression widget for multivariate data analysis"
    icon = "icons/PLS.svg"
    priority = 85
    keywords = ["partial least squares"]

    LEARNER = PLSRegressionLearner

    class Outputs(OWBaseLearner.Outputs):
        coefsdata = Output("Coefficients and Loadings", Table, explicit=True)
        data = Output("Data with Scores", Table)
        components = Output("Components", Table)

    class Warning(OWBaseLearner.Warning):
        sparse_data = Msg(
            'Sparse input data: default preprocessing is to scale it.')

    n_components = Setting(2)
    max_iter = Setting(500)

    def add_main_layout(self):
        optimization_box = gui.vBox(
            self.controlArea, "Optimization Parameters")
        gui.spin(
            optimization_box, self, "n_components", 1, 50, 1,
            label="Components: ",
            alignment=Qt.AlignRight, controlWidth=100,
            callback=self.settings_changed)
        gui.spin(
            optimization_box, self, "max_iter", 5, 1000000, 50,
            label="Iteration limit: ",
            alignment=Qt.AlignRight, controlWidth=100,
            callback=self.settings_changed,
            checkCallback=self.settings_changed)

    def update_model(self):
        super().update_model()
        coef_table = None
        data = None
        components = None
        if self.model is not None:
            coef_table = self._create_output_coeffs_loadings()
            data = self._create_output_data()
            components = self.model.components()
        self.Outputs.coefsdata.send(coef_table)
        self.Outputs.data.send(data)
        self.Outputs.components.send(components)

    def _create_output_coeffs_loadings(self) -> Table:
        coefficients = self.model.coefficients.T
        _, y_loadings = self.model.loadings
        x_rotations, _ = self.model.rotations

        n_features, n_targets = coefficients.shape
        n_components = x_rotations.shape[1]

        names = [f"coef ({v.name})" for v in self.model.domain.class_vars]
        names += [f"Loading {i + 1}" for i in range(n_components)]
        domain = Domain(
            [ContinuousVariable(n) for n in names],
            metas=[StringVariable("Variable name"),
                   DiscreteVariable("Variable role", ("Feature", "Target"))]
        )

        X = np.vstack((np.hstack((coefficients, x_rotations)),
                       np.full((n_targets, n_targets + n_components), np.nan)))
        X[-n_targets:, n_targets:] = y_loadings

        M = np.array([[v.name for v in self.model.domain.variables],
                      [0] * n_features + [1] * n_targets],
                     dtype=object).T

        table = Table.from_numpy(domain, X=X, metas=M)
        table.name = "Coefficients and Loadings"
        return table

    def _create_output_data(self) -> Table:
        projection = self.model.project(self.data)
        normal_probs = self.model.residuals_normal_probability(self.data)
        dmodx = self.model.dmodx(self.data)
        data_domain = self.data.domain
        proj_domain = projection.domain
        nprobs_domain = normal_probs.domain
        dmodx_domain = dmodx.domain
        metas = proj_domain.metas + proj_domain.attributes + \
            nprobs_domain.attributes + dmodx_domain.attributes
        domain = Domain(data_domain.attributes, data_domain.class_vars, metas)
        data: Table = self.data.transform(domain)
        with data.unlocked(data.metas):
            data.metas[:, -2 * len(self.data.domain.class_vars) - 1: -1] = \
                normal_probs.X
            data.metas[:, -1] = dmodx.X[:, 0]
        return data

    @OWBaseLearner.Inputs.data
    def set_data(self, data):
        # reimplemented completely because the base learner does not
        # allow multiclass

        self.Warning.sparse_data.clear()

        self.Error.data_error.clear()
        self.data = data

        if data is not None and data.domain.class_var is None and not data.domain.class_vars:
            self.Error.data_error(
                "Data has no target variable.\n"
                "Select one with the Select Columns widget.")
            self.data = None

        # invalidate the model so that handleNewSignals will update it
        self.model = None

        if self.data and sp.issparse(self.data.X):
            self.Warning.sparse_data()

    def create_learner(self):
        common_args = {'preprocessors': self.preprocessors}
        return PLSRegressionLearner(n_components=self.n_components,
                                    max_iter=self.max_iter,
                                    **common_args)


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWPLS).run(Table("housing"))

```
--- 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