{
    "file_diffs": [
        {
            "old_file_content": "import numbers\n\nimport numpy\nfrom AnyQt.QtWidgets import QFormLayout\nfrom AnyQt.QtCore import Qt\n\nfrom orangewidget.report import bool_str\n\nfrom Orange.data import Table, Domain, StringVariable, ContinuousVariable\nfrom Orange.data.util import get_unique_names\nfrom Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT\nfrom Orange.preprocess import preprocess\nfrom Orange.projection import PCA\nfrom Orange.widgets import widget, gui, settings\nfrom Orange.widgets.utils.concurrent import ConcurrentWidgetMixin\nfrom Orange.widgets.utils.slidergraph import SliderGraph\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Input, Output\n\n# Maximum number of PCA components that we can set in the widget\nMAX_COMPONENTS = 100\nLINE_NAMES = [\"component variance\", \"cumulative variance\"]\n\n\nclass OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWPCA).run(Table(\"housing\"))\n",
            "new_file_content": "import numbers\n\nimport numpy\nfrom AnyQt.QtWidgets import QFormLayout\nfrom AnyQt.QtCore import Qt\n\nfrom orangewidget.report import bool_str\nfrom orangewidget.settings import Setting\n\nfrom Orange.data import Table, Domain, StringVariable, ContinuousVariable\nfrom Orange.data.util import get_unique_names\nfrom Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT\nfrom Orange.preprocess import preprocess\nfrom Orange.projection import PCA\nfrom Orange.widgets import widget, gui\nfrom Orange.widgets.utils.annotated_data import add_columns\nfrom Orange.widgets.utils.concurrent import ConcurrentWidgetMixin\nfrom Orange.widgets.utils.slidergraph import SliderGraph\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Input, Output\n\n# Maximum number of PCA components that we can set in the widget\nMAX_COMPONENTS = 100\nLINE_NAMES = [\"component variance\", \"cumulative variance\"]\n\n\nclass OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = Setting(2)\n    variance_covered = Setting(100)\n    auto_commit = Setting(True)\n    normalize = Setting(True)\n    maxp = Setting(20)\n    axis_labels = Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if self.ncomponents in (components, 0):\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array(\n                [[f\"PC{i + 1}\"for i in range(self.ncomponents)]], dtype=object\n            ).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = add_columns(self.data.domain, metas=domain.attributes)\n            data = self.data.transform(data_dom)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", f\"{self.variance_covered:.3f} %\")\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWPCA).run(Table(\"housing\"))\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/unsupervised/owpca.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "29611146f",
                "new_commit_hash": "9cc833230",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/unsupervised/owpca.py"
            },
            "plus_file": {
                "path": "b/Orange/widgets/unsupervised/owpca.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 5,
                            "length": 13
                        },
                        "new_range": {
                            "start": 5,
                            "length": 15
                        },
                        "section": "from AnyQt.QtWidgets import QFormLayout"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "from AnyQt.QtCore import Qt",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "from orangewidget.report import bool_str",
                                "type": "context"
                            },
                            {
                                "content": "from orangewidget.settings import Setting",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data import Table, Domain, StringVariable, ContinuousVariable",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data.util import get_unique_names",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.preprocess import preprocess",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.projection import PCA",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.widgets import widget, gui, settings",
                                "type": "deleted"
                            },
                            {
                                "content": "from Orange.widgets import widget, gui",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.widgets.utils.annotated_data import add_columns",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.widgets.utils.concurrent import ConcurrentWidgetMixin",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.widgets.utils.slidergraph import SliderGraph",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.widgets.utils.widgetpreview import WidgetPreview",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.widgets.utils.annotated_data import add_columns",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 16,
                            "end_lineno": 16,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from orangewidget.settings import Setting",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 8,
                            "end_lineno": 8,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.widgets import widget, gui",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 15,
                            "end_lineno": 15,
                            "parent": null
                        }
                    ],
                    "deleted_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.widgets import widget, gui, settings",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 14,
                            "end_lineno": 14,
                            "parent": null
                        }
                    ]
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 38,
                            "length": 12
                        },
                        "new_range": {
                            "start": 40,
                            "length": 12
                        },
                        "section": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        components = Output(\"Components\", Table)",
                                "type": "context"
                            },
                            {
                                "content": "        pca = Output(\"PCA\", PCA, dynamic=False)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    ncomponents = settings.Setting(2)",
                                "type": "deleted"
                            },
                            {
                                "content": "    variance_covered = settings.Setting(100)",
                                "type": "deleted"
                            },
                            {
                                "content": "    auto_commit = settings.Setting(True)",
                                "type": "deleted"
                            },
                            {
                                "content": "    normalize = settings.Setting(True)",
                                "type": "deleted"
                            },
                            {
                                "content": "    maxp = settings.Setting(20)",
                                "type": "deleted"
                            },
                            {
                                "content": "    axis_labels = settings.Setting(10)",
                                "type": "deleted"
                            },
                            {
                                "content": "    ncomponents = Setting(2)",
                                "type": "added"
                            },
                            {
                                "content": "    variance_covered = Setting(100)",
                                "type": "added"
                            },
                            {
                                "content": "    auto_commit = Setting(True)",
                                "type": "added"
                            },
                            {
                                "content": "    normalize = Setting(True)",
                                "type": "added"
                            },
                            {
                                "content": "    maxp = Setting(20)",
                                "type": "added"
                            },
                            {
                                "content": "    axis_labels = Setting(10)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "class",
                            "name": "OWPCA",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 25,
                            "end_lineno": 399,
                            "parent": null
                        }
                    ],
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                            "start": 222,
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                            "start": 224,
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                        "section": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):"
                    },
                    "line_group": {
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                                "content": "        self._update_axis()",
                                "type": "context"
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                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def _on_cut_changed(self, components):",
                                "type": "context"
                            },
                            {
                                "content": "        if components == self.ncomponents \\",
                                "type": "deleted"
                            },
                            {
                                "content": "                or self.ncomponents == 0:",
                                "type": "deleted"
                            },
                            {
                                "content": "        if self.ncomponents in (components, 0):",
                                "type": "added"
                            },
                            {
                                "content": "            return",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        self.ncomponents = components",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "class",
                            "name": "OWPCA",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 25,
                            "end_lineno": 399,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "method",
                            "name": "OWPCA._on_cut_changed",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 224,
                            "end_lineno": 235,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owpca.py",
                                "type": "class",
                                "name": "OWPCA",
                                "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 25,
                                "end_lineno": 399,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 333,
                            "length": 9
                        },
                        "new_range": {
                            "start": 334,
                            "length": 9
                        },
                        "section": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "            proposed = [a.name for a in self._pca.orig_domain.attributes]",
                                "type": "context"
                            },
                            {
                                "content": "            meta_name = get_unique_names(proposed, 'components')",
                                "type": "context"
                            },
                            {
                                "content": "            meta_vars = [StringVariable(name=meta_name)]",
                                "type": "context"
                            },
                            {
                                "content": "            metas = numpy.array([['PC{}'.format(i + 1)",
                                "type": "deleted"
                            },
                            {
                                "content": "                                  for i in range(self.ncomponents)]],",
                                "type": "deleted"
                            },
                            {
                                "content": "                                dtype=object).T",
                                "type": "deleted"
                            },
                            {
                                "content": "            metas = numpy.array(",
                                "type": "added"
                            },
                            {
                                "content": "                [[f\"PC{i + 1}\"for i in range(self.ncomponents)]], dtype=object",
                                "type": "added"
                            },
                            {
                                "content": "            ).T",
                                "type": "added"
                            },
                            {
                                "content": "            if self._variance_ratio is not None:",
                                "type": "context"
                            },
                            {
                                "content": "                variance_name = get_unique_names(proposed, \"variance\")",
                                "type": "context"
                            },
                            {
                                "content": "                meta_vars.append(ContinuousVariable(variance_name))",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "class",
                            "name": "OWPCA",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 25,
                            "end_lineno": 399,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "method",
                            "name": "OWPCA.commit",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 312,
                            "end_lineno": 366,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owpca.py",
                                "type": "class",
                                "name": "OWPCA",
                                "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 25,
                                "end_lineno": 399,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 351,
                            "length": 14
                        },
                        "new_range": {
                            "start": 352,
                            "length": 8
                        },
                        "section": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "                               metas=metas)",
                                "type": "context"
                            },
                            {
                                "content": "            components.name = 'components'",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "            data_dom = Domain(",
                                "type": "deleted"
                            },
                            {
                                "content": "                self.data.domain.attributes,",
                                "type": "deleted"
                            },
                            {
                                "content": "                self.data.domain.class_vars,",
                                "type": "deleted"
                            },
                            {
                                "content": "                self.data.domain.metas + domain.attributes)",
                                "type": "deleted"
                            },
                            {
                                "content": "            data = Table.from_numpy(",
                                "type": "deleted"
                            },
                            {
                                "content": "                data_dom, self.data.X, self.data.Y,",
                                "type": "deleted"
                            },
                            {
                                "content": "                numpy.hstack((self.data.metas, transformed.X)),",
                                "type": "deleted"
                            },
                            {
                                "content": "                ids=self.data.ids)",
                                "type": "deleted"
                            },
                            {
                                "content": "            data_dom = add_columns(self.data.domain, metas=domain.attributes)",
                                "type": "added"
                            },
                            {
                                "content": "            data = self.data.transform(data_dom)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        self.Outputs.transformed_data.send(transformed)",
                                "type": "context"
                            },
                            {
                                "content": "        self.Outputs.components.send(components)",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "class",
                            "name": "OWPCA",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 25,
                            "end_lineno": 399,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "method",
                            "name": "OWPCA.commit",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 312,
                            "end_lineno": 366,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owpca.py",
                                "type": "class",
                                "name": "OWPCA",
                                "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 25,
                                "end_lineno": 399,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 371,
                            "length": 7
                        },
                        "new_range": {
                            "start": 366,
                            "length": 7
                        },
                        "section": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        self.report_items((",
                                "type": "context"
                            },
                            {
                                "content": "            (\"Normalize data\", bool_str(self.normalize)),",
                                "type": "context"
                            },
                            {
                                "content": "            (\"Selected components\", self.ncomponents),",
                                "type": "context"
                            },
                            {
                                "content": "            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))",
                                "type": "deleted"
                            },
                            {
                                "content": "            (\"Explained variance\", f\"{self.variance_covered:.3f} %\")",
                                "type": "added"
                            },
                            {
                                "content": "        ))",
                                "type": "context"
                            },
                            {
                                "content": "        self.report_plot()",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "class",
                            "name": "OWPCA",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 25,
                            "end_lineno": 399,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "method",
                            "name": "OWPCA.send_report",
                            "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 368,
                            "end_lineno": 376,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owpca.py",
                                "type": "class",
                                "name": "OWPCA",
                                "content": "class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 25,
                                "end_lineno": 399,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse, possible_duplicate_table\nfrom Orange.widgets.unsupervised.owpca import OWPCA\nfrom Orange.tests import test_filename\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.extmath import svd_flip\n\n\nclass TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = dict(ncomponents=10)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = dict(ncomponents=101)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = dict(variance_covered=17.5)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = dict(variance_covered=float('nan'))\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.extmath import svd_flip\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse, possible_duplicate_table\nfrom Orange.widgets.unsupervised.owpca import OWPCA\nfrom Orange.tests import test_filename\n\n\nclass TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = {\"ncomponents\": 10}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = {\"ncomponents\": 101}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = {\"variance_covered\": 17.5}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = {\"variance_covered\": float('nan')}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_table_subclass(self):\n        \"\"\"\n        When input table is instance of Table's subclass (e.g. Corpus) resulting\n        tables should also be an instance subclasses\n        \"\"\"\n        class TableSub(Table):  # pylint: disable=abstract-method\n            pass\n\n        table_subclass = TableSub(self.iris)\n        self.send_signal(self.widget.Inputs.data, table_subclass)\n        data_out = self.get_output(self.widget.Outputs.data)\n        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertIsInstance(data_out, TableSub)\n        self.assertIsInstance(trans_data_out, TableSub)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 4,
                            "length": 6
                        },
                        "new_range": {
                            "start": 4,
                            "length": 8
                        },
                        "section": "import unittest"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "from unittest.mock import patch, Mock",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "import numpy as np",
                                "type": "context"
                            },
                            {
                                "content": "from sklearn.utils import check_random_state",
                                "type": "added"
                            },
                            {
                                "content": "from sklearn.utils.extmath import svd_flip",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data import Table, Domain, ContinuousVariable, TimeVariable",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.preprocess import preprocess",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from sklearn.utils import check_random_state",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 7,
                            "end_lineno": 7,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from sklearn.utils.extmath import svd_flip",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 8,
                            "end_lineno": 8,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 11,
                            "length": 8
                        },
                        "new_range": {
                            "start": 13,
                            "length": 6
                        },
                        "section": "from Orange.widgets.tests.base import WidgetTest"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "from Orange.widgets.tests.utils import table_dense_sparse, possible_duplicate_table",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.widgets.unsupervised.owpca import OWPCA",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.tests import test_filename",
                                "type": "context"
                            },
                            {
                                "content": "from sklearn.utils import check_random_state",
                                "type": "deleted"
                            },
                            {
                                "content": "from sklearn.utils.extmath import svd_flip",
                                "type": "deleted"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "class TestOWPCA(WidgetTest):",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from sklearn.utils import check_random_state",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 14,
                            "end_lineno": 14,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from sklearn.utils.extmath import svd_flip",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 15,
                            "end_lineno": 15,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 63,
                            "length": 19
                        },
                        "new_range": {
                            "start": 63,
                            "length": 19
                        },
                        "section": "class TestOWPCA(WidgetTest):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "            self.widget._setup_plot()  # pylint: disable=protected-access",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_migrate_settings_limits_components(self):",
                                "type": "context"
                            },
                            {
                                "content": "        settings = dict(ncomponents=10)",
                                "type": "deleted"
                            },
                            {
                                "content": "        settings = {\"ncomponents\": 10}",
                                "type": "added"
                            },
                            {
                                "content": "        OWPCA.migrate_settings(settings, 0)",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(settings['ncomponents'], 10)",
                                "type": "context"
                            },
                            {
                                "content": "        settings = dict(ncomponents=101)",
                                "type": "deleted"
                            },
                            {
                                "content": "        settings = {\"ncomponents\": 101}",
                                "type": "added"
                            },
                            {
                                "content": "        OWPCA.migrate_settings(settings, 0)",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(settings['ncomponents'], 100)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_migrate_settings_changes_variance_covered_to_int(self):",
                                "type": "context"
                            },
                            {
                                "content": "        settings = dict(variance_covered=17.5)",
                                "type": "deleted"
                            },
                            {
                                "content": "        settings = {\"variance_covered\": 17.5}",
                                "type": "added"
                            },
                            {
                                "content": "        OWPCA.migrate_settings(settings, 0)",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(settings[\"variance_covered\"], 17)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        settings = dict(variance_covered=float('nan'))",
                                "type": "deleted"
                            },
                            {
                                "content": "        settings = {\"variance_covered\": float('nan')}",
                                "type": "added"
                            },
                            {
                                "content": "        OWPCA.migrate_settings(settings, 0)",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(settings[\"variance_covered\"], 100)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "class",
                            "name": "TestOWPCA",
                            "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = dict(ncomponents=10)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = dict(ncomponents=101)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = dict(variance_covered=17.5)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = dict(variance_covered=float('nan'))\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 18,
                            "end_lineno": 278,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "method",
                            "name": "TestOWPCA.test_migrate_settings_changes_variance_covered_to_int",
                            "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = dict(ncomponents=10)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = dict(ncomponents=101)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = dict(variance_covered=17.5)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = dict(variance_covered=float('nan'))\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 73,
                            "end_lineno": 80,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                                "type": "class",
                                "name": "TestOWPCA",
                                "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = dict(ncomponents=10)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = dict(ncomponents=101)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = dict(variance_covered=17.5)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = dict(variance_covered=float('nan'))\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 18,
                                "end_lineno": 278,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "method",
                            "name": "TestOWPCA.test_migrate_settings_limits_components",
                            "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = dict(ncomponents=10)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = dict(ncomponents=101)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = dict(variance_covered=17.5)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = dict(variance_covered=float('nan'))\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 65,
                            "end_lineno": 71,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                                "type": "class",
                                "name": "TestOWPCA",
                                "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = dict(ncomponents=10)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = dict(ncomponents=101)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = dict(variance_covered=17.5)\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = dict(variance_covered=float('nan'))\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 18,
                                "end_lineno": 278,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 277,
                            "length": 6
                        },
                        "new_range": {
                            "start": 277,
                            "length": 21
                        },
                        "section": "class TestOWPCA(WidgetTest):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        output = self.get_output(widget.Outputs.data)",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertIsNone(output)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_table_subclass(self):",
                                "type": "added"
                            },
                            {
                                "content": "        \"\"\"",
                                "type": "added"
                            },
                            {
                                "content": "        When input table is instance of Table's subclass (e.g. Corpus) resulting",
                                "type": "added"
                            },
                            {
                                "content": "        tables should also be an instance subclasses",
                                "type": "added"
                            },
                            {
                                "content": "        \"\"\"",
                                "type": "added"
                            },
                            {
                                "content": "        class TableSub(Table):  # pylint: disable=abstract-method",
                                "type": "added"
                            },
                            {
                                "content": "            pass",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        table_subclass = TableSub(self.iris)",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.data, table_subclass)",
                                "type": "added"
                            },
                            {
                                "content": "        data_out = self.get_output(self.widget.Outputs.data)",
                                "type": "added"
                            },
                            {
                                "content": "        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsInstance(data_out, TableSub)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsInstance(trans_data_out, TableSub)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "context"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "class",
                            "name": "TestOWPCA",
                            "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = {\"ncomponents\": 10}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = {\"ncomponents\": 101}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = {\"variance_covered\": 17.5}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = {\"variance_covered\": float('nan')}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_table_subclass(self):\n        \"\"\"\n        When input table is instance of Table's subclass (e.g. Corpus) resulting\n        tables should also be an instance subclasses\n        \"\"\"\n        class TableSub(Table):  # pylint: disable=abstract-method\n            pass\n\n        table_subclass = TableSub(self.iris)\n        self.send_signal(self.widget.Inputs.data, table_subclass)\n        data_out = self.get_output(self.widget.Outputs.data)\n        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertIsInstance(data_out, TableSub)\n        self.assertIsInstance(trans_data_out, TableSub)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 18,
                            "end_lineno": 293,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "method",
                            "name": "TestOWPCA.test_table_subclass",
                            "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = {\"ncomponents\": 10}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = {\"ncomponents\": 101}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = {\"variance_covered\": 17.5}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = {\"variance_covered\": float('nan')}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_table_subclass(self):\n        \"\"\"\n        When input table is instance of Table's subclass (e.g. Corpus) resulting\n        tables should also be an instance subclasses\n        \"\"\"\n        class TableSub(Table):  # pylint: disable=abstract-method\n            pass\n\n        table_subclass = TableSub(self.iris)\n        self.send_signal(self.widget.Inputs.data, table_subclass)\n        data_out = self.get_output(self.widget.Outputs.data)\n        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertIsInstance(data_out, TableSub)\n        self.assertIsInstance(trans_data_out, TableSub)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 280,
                            "end_lineno": 293,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                                "type": "class",
                                "name": "TestOWPCA",
                                "content": "class TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = {\"ncomponents\": 10}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = {\"ncomponents\": 101}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = {\"variance_covered\": 17.5}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = {\"variance_covered\": float('nan')}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_table_subclass(self):\n        \"\"\"\n        When input table is instance of Table's subclass (e.g. Corpus) resulting\n        tables should also be an instance subclasses\n        \"\"\"\n        class TableSub(Table):  # pylint: disable=abstract-method\n            pass\n\n        table_subclass = TableSub(self.iris)\n        self.send_signal(self.widget.Inputs.data, table_subclass)\n        data_out = self.get_output(self.widget.Outputs.data)\n        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertIsInstance(data_out, TableSub)\n        self.assertIsInstance(trans_data_out, TableSub)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 18,
                                "end_lineno": 293,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "1725baa9d30f2d91485462e2c4a458dc4cd99931^",
    "new_commit_hash": "1725baa9d30f2d91485462e2c4a458dc4cd99931",
    "commit_message": "Merge pull request #6536 from PrimozGodec/fix-pca\n\n[FIX] PCA - Output instance of table subclass when instance of table subclass on input",
    "commit_date": "2023-08-18T10:31:26+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "1725baa9d30f2d91485462e2c4a458dc4cd99931",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.extmath import svd_flip\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse, possible_duplicate_table\nfrom Orange.widgets.unsupervised.owpca import OWPCA\nfrom Orange.tests import test_filename\n\n\nclass TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5].copy()\n        with data.unlocked():\n            data.X[:, :] = 1.0\n        # Ignore the warning: the test checks whether the widget shows\n        # Warning.trivial_components when this happens\n        with np.errstate(invalid=\"ignore\"):\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.assertTrue(self.widget.Warning.trivial_components.is_shown())\n        self.assertIsNone(self.get_output(self.widget.Outputs.transformed_data))\n        self.assertIsNone(self.get_output(self.widget.Outputs.components))\n\n    def test_empty_data(self):\n        \"\"\" Check widget for dataset with no rows and for dataset with no attributes \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris[:0])\n        self.assertTrue(self.widget.Error.no_instances.is_shown())\n\n        domain = Domain([], None, self.iris.domain.variables)\n        new_data = Table.from_table(domain, self.iris)\n        self.send_signal(self.widget.Inputs.data, new_data)\n        self.assertTrue(self.widget.Error.no_features.is_shown())\n        self.assertFalse(self.widget.Error.no_instances.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_features.is_shown())\n\n    def test_limit_components(self):\n        X = np.random.RandomState(0).rand(101, 101)\n        data = Table.from_numpy(None, X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        tran = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertEqual(len(tran.domain.attributes), 100)\n        self.widget.ncomponents = 101  # should not be accesible\n        with self.assertRaises(IndexError):\n            self.widget._setup_plot()  # pylint: disable=protected-access\n\n    def test_migrate_settings_limits_components(self):\n        settings = {\"ncomponents\": 10}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 10)\n        settings = {\"ncomponents\": 101}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings['ncomponents'], 100)\n\n    def test_migrate_settings_changes_variance_covered_to_int(self):\n        settings = {\"variance_covered\": 17.5}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 17)\n\n        settings = {\"variance_covered\": float('nan')}\n        OWPCA.migrate_settings(settings, 0)\n        self.assertEqual(settings[\"variance_covered\"], 100)\n\n    def test_variance_shown(self):\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.widget.maxp = 2\n        self.widget._setup_plot()\n        self.wait_until_finished()\n        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        self.wait_until_finished()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\n\n    def test_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_variance_attr(self):\n        self.widget.ncomponents = 2\n        self.send_signal(self.widget.Inputs.data, self.iris, wait=5000)\n        self.wait_until_stop_blocking()\n        self.widget._variance_ratio = np.array([0.5, 0.25, 0.2, 0.05])\n        self.widget.commit.now()\n        self.wait_until_finished()\n\n        result = self.get_output(self.widget.Outputs.transformed_data)\n        pc1, pc2 = result.domain.attributes\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.data)\n        pc1, pc2 = result.domain.metas\n        self.assertEqual(pc1.attributes[\"variance\"], 0.5)\n        self.assertEqual(pc2.attributes[\"variance\"], 0.25)\n\n        result = self.get_output(self.widget.Outputs.components)\n        np.testing.assert_almost_equal(result.get_column(\"variance\"), [0.5, 0.25])\n\n    def test_sparse_data(self):\n        \"\"\"Check that PCA returns the same results for both dense and sparse data.\"\"\"\n        dense_data, sparse_data = self.iris, self.iris.to_sparse()\n\n        def _compute_projection(data):\n            self.send_signal(self.widget.Inputs.data, data)\n            self.wait_until_stop_blocking()\n            result = self.get_output(self.widget.Outputs.transformed_data)\n            self.send_signal(self.widget.Inputs.data, None)\n            return result\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        dense_pca = _compute_projection(dense_data)\n        sparse_pca = _compute_projection(sparse_data)\n        np.testing.assert_almost_equal(dense_pca.X, sparse_pca.X)\n\n    def test_all_components_continuous(self):\n        data = Table(test_filename(\"datasets/cyber-security-breaches.tab\"))\n        # GH-2329 only occurred on TimeVariables when normalize=False\n        self.assertTrue(any(isinstance(a, TimeVariable)\n                            for a in data.domain.attributes))\n\n        self.widget.normalize = False\n        self.widget._update_normalize()     # pylint: disable=protected-access\n        self.widget.set_data(data)\n\n        components = self.get_output(self.widget.Outputs.components)\n        self.assertTrue(all(type(a) is ContinuousVariable   # pylint: disable=unidiomatic-typecheck\n                            for a in components.domain.attributes),\n                        \"Some variables aren't of type ContinuousVariable\")\n\n    @table_dense_sparse\n    def test_normalize_data(self, prepare_table):\n        \"\"\"Check that normalization is called at the proper times.\"\"\"\n        data = prepare_table(self.iris)\n\n        # Enable checkbox\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_called_once()\n\n        # Disable checkbox\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.controls.normalize.isChecked())\n        with patch.object(preprocess.Normalize, \"__call__\", wraps=lambda x: x) as normalize:\n            self.send_signal(self.widget.Inputs.data, data, wait=5000)\n            self.wait_until_stop_blocking()\n            self.assertTrue(self.widget.controls.normalize.isEnabled())\n            normalize.assert_not_called()\n\n    @table_dense_sparse\n    def test_normalization_variance(self, prepare_table):\n        data = prepare_table(self.iris)\n        self.widget.ncomponents = 2\n\n        # Enable normalization\n        self.widget.controls.normalize.setChecked(True)\n        self.assertTrue(self.widget.normalize)\n        self.send_signal(self.widget.Inputs.data, data, wait=5000)\n        self.wait_until_stop_blocking()\n        variance_normalized = self.widget.variance_covered\n\n        # Disable normalization\n        self.widget.controls.normalize.setChecked(False)\n        self.assertFalse(self.widget.normalize)\n        self.wait_until_finished()\n        self.wait_until_stop_blocking()\n        variance_unnormalized = self.widget.variance_covered\n\n        # normalized data will have lower covered variance\n        self.assertLess(variance_normalized, variance_unnormalized)\n\n    @table_dense_sparse\n    def test_normalized_gives_correct_result(self, prepare_table):\n        \"\"\"Make sure that normalization through widget gives correct result.\"\"\"\n        # Randomly set some values to zero\n        random_state = check_random_state(42)\n        mask = random_state.beta(1, 2, size=self.iris.X.shape) > 0.5\n        with self.iris.unlocked():\n            self.iris.X[mask] = 0\n\n        data = prepare_table(self.iris)\n\n        # Enable normalization and run data through widget\n        self.widget.controls.normalize.setChecked(True)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_stop_blocking()\n        widget_result = self.get_output(self.widget.Outputs.transformed_data)\n\n        # Compute the correct embedding\n        x = self.iris.X\n        x = (x - x.mean(0)) / x.std(0)\n        U, S, Va = np.linalg.svd(x)\n        U, S, Va = U[:, :2], S[:2], Va[:2]\n        U, Va = svd_flip(U, Va)\n        pca_embedding = U * S\n\n        np.testing.assert_almost_equal(widget_result.X, pca_embedding)\n\n    def test_do_not_mask_features(self):\n        # the widget used to replace cached variables when creating the\n        # components output (until 20170726)\n        data = Table(\"iris.tab\")\n        self.widget.set_data(data)\n        ndata = Table(\"iris.tab\")\n        self.assertEqual(data.domain[0], ndata.domain[0])\n\n    def test_on_cut_changed(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        invalidate = widget._invalidate_selection = Mock()\n        widget._on_cut_changed(2)\n        invalidate.assert_not_called()\n        widget._on_cut_changed(3)\n        invalidate.assert_called()\n\n        widget.ncomponents = 0  # Take all components\n        invalidate.reset_mock()\n        widget._on_cut_changed(1)\n        invalidate.assert_not_called()\n        self.assertEqual(widget.ncomponents, 0)\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n    def test_table_subclass(self):\n        \"\"\"\n        When input table is instance of Table's subclass (e.g. Corpus) resulting\n        tables should also be an instance subclasses\n        \"\"\"\n        class TableSub(Table):  # pylint: disable=abstract-method\n            pass\n\n        table_subclass = TableSub(self.iris)\n        self.send_signal(self.widget.Inputs.data, table_subclass)\n        data_out = self.get_output(self.widget.Outputs.data)\n        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)\n        self.assertIsInstance(data_out, TableSub)\n        self.assertIsInstance(trans_data_out, TableSub)\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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.36.0.dev0+1725baa to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931\nProcessing dependencies for Orange3==3.36.0.dev0+1725baa\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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.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-br0cen2h/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-br0cen2h/serverfiles-0.3.1/egg-dist-tmp-d882e3ly\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.36.0.dev0+1725baa to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931\nProcessing dependencies for Orange3==3.36.0.dev0+1725baa\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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages\nSearching for setuptools==75.3.0\nBest match: setuptools 75.3.0\nAdding setuptools 75.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.1.3\nBest match: scikit-learn 1.1.3\nAdding scikit-learn 1.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.36.0.dev0+1725baa\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 114ms\nPrepared 18 packages in 12ms\nInstalled 18 packages in 107ms\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          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n      depend on Python>=3.8,<3.12, we can conclude that all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n       cannot be used.\n      And because only the following versions of scipy are available:\n          scipy<=1.9.0\n          scipy==1.9.1\n          scipy==1.9.2\n          scipy==1.9.3\n          scipy==1.10.0\n          scipy==1.10.1\n          scipy==1.11.0\n          scipy==1.11.1\n          scipy==1.11.2\n          scipy==1.11.3\n          scipy==1.11.4\n          scipy==1.12.0\n          scipy==1.13.0\n          scipy==1.13.1\n          scipy==1.14.0\n          scipy==1.14.1\n          scipy==1.15.0\n      we can conclude that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.9.2,<=1.9.3 depends on Python>=3.8,\n      we can conclude that scipy>=1.9.2,<=1.9.3 cannot be used.\n      And because we know from (1) that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used, we can conclude that scipy>=1.9.0,<1.11.0 cannot be used.\n      And because scipy==1.11.0 was yanked (reason: License Violation), we can conclude that scipy>=1.9.0,<1.11.1 cannot be\n      used. (2)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.1,<=1.11.3 depends on\n      Python>=3.9,<3.13, we can conclude that scipy>=1.11.1,<=1.11.3 cannot be used.\n      And because we know from (2) that scipy>=1.9.0,<1.11.1 cannot be used, we can conclude that scipy>=1.9.0,<1.11.4 cannot\n      be used. (3)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.4,<=1.13.1 depends on\n      Python>=3.9, we can conclude that scipy>=1.11.4,<=1.13.1 cannot be used.\n      And because we know from (3) that scipy>=1.9.0,<1.11.4 cannot be used, we can conclude that scipy>=1.9.0,<1.14.0 cannot\n      be used. (4)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.14.0 depends on Python>=3.10, we can\n      conclude that scipy>=1.14.0 cannot be used.\n      And because we know from (4) that scipy>=1.9.0,<1.14.0 cannot be used, we can conclude that scipy>=1.9.0 cannot be used.\n      And because you require scipy>=1.9, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scipy` in the requested range (e.g., 1.15.0rc2), 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 16ms\nInstalled 2 packages in 31ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 198ms\nInstalled 16 packages in 1.15s\n + catboost==1.2.7\n + cycler==0.11.0\n + fonttools==4.38.0\n + graphviz==0.20.1\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + pandas==1.3.5\n + pillow==9.5.0\n + plotly==5.18.0\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + pytz==2024.2\n + scipy==1.7.3\n + six==1.17.0\n + tenacity==8.2.3\n + xgboost==1.6.2\nResolved 5 packages in 13ms\nInstalled 3 packages in 28ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + threadpoolctl==3.1.0\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   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:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   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:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   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:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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-0s5bmlh2/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 87ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 25ms\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 59 packages in 78ms\nInstalled 55 packages in 55ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\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 + 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 + 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 + 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==1.1.3\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 + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.2.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 64 packages in 54ms\nInstalled 41 packages in 71ms\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==0.1.35\n + orange-widget-base==4.22.0\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 3ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nResolved 23 packages in 44ms\nInstalled 6 packages in 237ms\n + catboost==1.2.7\n + graphviz==0.20.3\n + nvidia-nccl-cu12==2.24.3\n + plotly==5.24.1\n + tenacity==9.0.0\n + xgboost==2.1.3\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   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:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   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:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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_1725baa9d30f2d91485462e2c4a458dc4cd99931/.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": 1,
    "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_1725baa9d30f2d91485462e2c4a458dc4cd99931\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 25 items\n\nr2e_tests/test_1.py sssE.....................                            [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/Orange/tests/__init__.py, line 68\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/Orange/tests/__init__.py:68\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWPCA::test_all_components_continuous\nPASSED r2e_tests/test_1.py::TestOWPCA::test_constant_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_do_not_mask_features\nPASSED r2e_tests/test_1.py::TestOWPCA::test_empty_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_image_export\nPASSED r2e_tests/test_1.py::TestOWPCA::test_limit_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_changes_variance_covered_to_int\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_limits_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWPCA::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalization_variance\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalize_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalized_gives_correct_result\nPASSED r2e_tests/test_1.py::TestOWPCA::test_on_cut_changed\nPASSED r2e_tests/test_1.py::TestOWPCA::test_output_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_set_variance100\nPASSED r2e_tests/test_1.py::TestOWPCA::test_sparse_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_table_subclass\nPASSED r2e_tests/test_1.py::TestOWPCA::test_unique_domain_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_attr\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_shown\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\nERROR r2e_tests/test_1.py::test_filename\n==================== 21 passed, 3 skipped, 1 error in 6.93s ====================\nWidgets left: 0    Max widgets: 2780 \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_1725baa9d30f2d91485462e2c4a458dc4cd99931\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 25 items\n\nr2e_tests/test_1.py sssE.................F...                            [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/Orange/tests/__init__.py, line 68\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_1725baa9d30f2d91485462e2c4a458dc4cd99931/Orange/tests/__init__.py:68\n=================================== FAILURES ===================================\n________________________ TestOWPCA.test_table_subclass _________________________\n\nself = <r2e_tests.test_1.TestOWPCA testMethod=test_table_subclass>\n\n    def test_table_subclass(self):\n        \"\"\"\n        When input table is instance of Table's subclass (e.g. Corpus) resulting\n        tables should also be an instance subclasses\n        \"\"\"\n        class TableSub(Table):  # pylint: disable=abstract-method\n            pass\n    \n        table_subclass = TableSub(self.iris)\n        self.send_signal(self.widget.Inputs.data, table_subclass)\n        data_out = self.get_output(self.widget.Outputs.data)\n        trans_data_out = self.get_output(self.widget.Outputs.transformed_data)\n>       self.assertIsInstance(data_out, TableSub)\nE       AssertionError: [[5.1, 3.5, 1.4, 0.2 | Iris-setosa] {-2.26454, 0.505704},\nE        [4.9, 3.0, 1.4, 0.2 | Iris-setosa] {-2.08643, -0.655405},\nE        [4.7, 3.2, 1.3, 0.2 | Iris-setosa] {-2.36795, -0.318477},\nE        [4.6, 3.1, 1.5, 0.2 | Iris-setosa] {-2.3042, -0.575368},\nE        [5.0, 3.6, 1.4, 0.2 | Iris-setosa] {-2.38878, 0.674767},\nE        ...\nE       ] is not an instance of <class 'r2e_tests.test_1.TestOWPCA.test_table_subclass.<locals>.TableSub'>\n\nr2e_tests/test_1.py:292: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWPCA::test_all_components_continuous\nPASSED r2e_tests/test_1.py::TestOWPCA::test_constant_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_do_not_mask_features\nPASSED r2e_tests/test_1.py::TestOWPCA::test_empty_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_image_export\nPASSED r2e_tests/test_1.py::TestOWPCA::test_limit_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_changes_variance_covered_to_int\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_limits_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWPCA::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalization_variance\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalize_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalized_gives_correct_result\nPASSED r2e_tests/test_1.py::TestOWPCA::test_on_cut_changed\nPASSED r2e_tests/test_1.py::TestOWPCA::test_output_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_set_variance100\nPASSED r2e_tests/test_1.py::TestOWPCA::test_sparse_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_unique_domain_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_attr\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_shown\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\nERROR r2e_tests/test_1.py::test_filename\nFAILED r2e_tests/test_1.py::TestOWPCA::test_table_subclass - AssertionError: ...\n=============== 1 failed, 20 passed, 3 skipped, 1 error in 4.99s ===============\nWidgets left: 0    Max widgets: 2780 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/unsupervised/owpca.py Orange/widgets/unsupervised/tests/test_owpca.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** PCA Widget Does Not Preserve Table Subclasses in Output

**Description:**
When providing an input table that is an instance of a subclass of `Table` (e.g., `TableSub`), the PCA widget fails to maintain the subclass in its output tables. Instead, it returns instances of the base `Table` class. This behavior is unexpected and can lead to issues when downstream processes rely on the specific subclass type.

**Example Code:**
```python
from Orange.data import Table, Domain

class TableSub(Table):
    pass

# Create an instance of the subclass
table_subclass = TableSub("iris")

# Send the subclass instance to the PCA widget
pca_widget.send_input(table_subclass)

# Retrieve the output tables
data_out = pca_widget.get_output("data")
trans_data_out = pca_widget.get_output("transformed_data")

# Expecting instances of TableSub
print(isinstance(data_out, TableSub))          # Should be True
print(isinstance(trans_data_out, TableSub))    # Should be True
```

**Expected Behavior:**
When the PCA widget receives an input table that is a subclass of `Table`, both the transformed data and components outputs should also be instances of that subclass. This ensures consistency and preserves any additional functionalities or attributes defined in the subclass.

**Actual Behavior:**
The output tables from the PCA widget are instances of the base `Table` class instead of the provided subclass. This results in an `AssertionError` when checking the instance type, as shown below:

```
AssertionError: [<TableSub instance>] is not an instance of <class 'TableSub'>
```

[/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/unsupervised/owpca.py
import numbers

import numpy
from AnyQt.QtWidgets import QFormLayout
from AnyQt.QtCore import Qt

from orangewidget.report import bool_str

from Orange.data import Table, Domain, StringVariable, ContinuousVariable
from Orange.data.util import get_unique_names
from Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT
from Orange.preprocess import preprocess
from Orange.projection import PCA
from Orange.widgets import widget, gui, settings
from Orange.widgets.utils.concurrent import ConcurrentWidgetMixin
from Orange.widgets.utils.slidergraph import SliderGraph
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Input, Output

# Maximum number of PCA components that we can set in the widget
MAX_COMPONENTS = 100
LINE_NAMES = ["component variance", "cumulative variance"]


class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
    name = "PCA"
    description = "Principal component analysis with a scree-diagram."
    icon = "icons/PCA.svg"
    priority = 3050
    keywords = "pca, principal component analysis, linear transformation"

    class Inputs:
        data = Input("Data", Table)

    class Outputs:
        transformed_data = Output("Transformed Data", Table, replaces=["Transformed data"])
        data = Output("Data", Table, default=True)
        components = Output("Components", Table)
        pca = Output("PCA", PCA, dynamic=False)

    ncomponents = settings.Setting(2)
    variance_covered = settings.Setting(100)
    auto_commit = settings.Setting(True)
    normalize = settings.Setting(True)
    maxp = settings.Setting(20)
    axis_labels = settings.Setting(10)

    graph_name = "plot.plotItem"  # QGraphicsView (pg.PlotWidget -> SliderGraph)

    class Warning(widget.OWWidget.Warning):
        trivial_components = widget.Msg(
            "All components of the PCA are trivial (explain 0 variance). "
            "Input data is constant (or near constant).")

    class Error(widget.OWWidget.Error):
        no_features = widget.Msg("At least 1 feature is required")
        no_instances = widget.Msg("At least 1 data instance is required")

    def __init__(self):
        super().__init__()
        ConcurrentWidgetMixin.__init__(self)

        self.data = None
        self._pca = None
        self._transformed = None
        self._variance_ratio = None
        self._cumulative = None

        # Components Selection
        form = QFormLayout()
        box = gui.widgetBox(self.controlArea, "Components Selection",
                            orientation=form)

        self.components_spin = gui.spin(
            box, self, "ncomponents", 1, MAX_COMPONENTS,
            callback=self._update_selection_component_spin,
            keyboardTracking=False, addToLayout=False
        )
        self.components_spin.setSpecialValueText("All")

        self.variance_spin = gui.spin(
            box, self, "variance_covered", 1, 100,
            callback=self._update_selection_variance_spin,
            keyboardTracking=False, addToLayout=False
        )
        self.variance_spin.setSuffix("%")

        form.addRow("Components:", self.components_spin)
        form.addRow("Explained variance:", self.variance_spin)

        # Options
        self.options_box = gui.vBox(self.controlArea, "Options")
        self.normalize_box = gui.checkBox(
            self.options_box, self, "normalize",
            "Normalize variables", callback=self._update_normalize,
            attribute=Qt.WA_LayoutUsesWidgetRect
        )

        self.maxp_spin = gui.spin(
            self.options_box, self, "maxp", 1, MAX_COMPONENTS,
            label="Show only first", callback=self._setup_plot,
            keyboardTracking=False
        )

        gui.rubber(self.controlArea)

        gui.auto_apply(self.buttonsArea, self, "auto_commit")

        self.plot = SliderGraph(
            "Principal Components", "Proportion of variance",
            self._on_cut_changed)

        self.mainArea.layout().addWidget(self.plot)
        self._update_normalize()

    @Inputs.data
    def set_data(self, data):
        self.cancel()
        self.clear_messages()
        self.clear()
        self.information()
        self.data = None
        if not data:
            self.clear_outputs()
        if isinstance(data, SqlTable):
            if data.approx_len() < AUTO_DL_LIMIT:
                data = Table(data)
            else:
                self.information("Data has been sampled")
                data_sample = data.sample_time(1, no_cache=True)
                data_sample.download_data(2000, partial=True)
                data = Table(data_sample)
        if isinstance(data, Table):
            if not data.domain.attributes:
                self.Error.no_features()
                self.clear_outputs()
                return
            if not data:
                self.Error.no_instances()
                self.clear_outputs()
                return

        self.data = data
        self.fit()

    def fit(self):
        self.cancel()
        self.clear()
        self.Warning.trivial_components.clear()
        if self.data is None:
            return

        data = self.data

        projector = self._create_projector()

        if not isinstance(data, SqlTable):
            self.start(self._call_projector, data, projector)

    @staticmethod
    def _call_projector(data: Table, projector, state):

        def callback(i: float, status=""):
            state.set_progress_value(i * 100)
            if status:
                state.set_status(status)
            if state.is_interruption_requested():
                raise Exception  # pylint: disable=broad-exception-raised

        return projector(data, progress_callback=callback)

    def on_done(self, result):
        pca = result
        variance_ratio = pca.explained_variance_ratio_
        cumulative = numpy.cumsum(variance_ratio)

        if numpy.isfinite(cumulative[-1]):
            self.components_spin.setRange(0, len(cumulative))
            self._pca = pca
            self._variance_ratio = variance_ratio
            self._cumulative = cumulative
            self._setup_plot()
        else:
            self.Warning.trivial_components()

        self.commit.now()

    def on_partial_result(self, result):
        pass

    def onDeleteWidget(self):
        self.shutdown()
        super().onDeleteWidget()

    def clear(self):
        self._pca = None
        self._transformed = None
        self._variance_ratio = None
        self._cumulative = None
        self.plot.clear_plot()

    def clear_outputs(self):
        self.Outputs.transformed_data.send(None)
        self.Outputs.data.send(None)
        self.Outputs.components.send(None)
        self.Outputs.pca.send(self._create_projector())

    def _setup_plot(self):
        if self._pca is None:
            self.plot.clear_plot()
            return

        explained_ratio = self._variance_ratio
        explained = self._cumulative
        cutpos = self._nselected_components()
        p = min(len(self._variance_ratio), self.maxp)

        self.plot.update(
            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],
            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)

        self._update_axis()

    def _on_cut_changed(self, components):
        if components == self.ncomponents \
                or self.ncomponents == 0:
            return

        self.ncomponents = components
        if self._pca is not None:
            var = self._cumulative[components - 1]
            if numpy.isfinite(var):
                self.variance_covered = int(var * 100)

        self._invalidate_selection()

    def _update_selection_component_spin(self):
        # cut changed by "ncomponents" spin.
        if self._pca is None:
            self._invalidate_selection()
            return

        if self.ncomponents == 0:
            # Special "All" value
            cut = len(self._variance_ratio)
        else:
            cut = self.ncomponents

        var = self._cumulative[cut - 1]
        if numpy.isfinite(var):
            self.variance_covered = int(var * 100)

        self.plot.set_cut_point(cut)
        self._invalidate_selection()

    def _update_selection_variance_spin(self):
        # cut changed by "max variance" spin.
        if self._pca is None:
            return

        cut = numpy.searchsorted(self._cumulative,
                                 self.variance_covered / 100.0) + 1
        cut = min(cut, len(self._cumulative))
        self.ncomponents = cut
        self.plot.set_cut_point(cut)
        self._invalidate_selection()

    def _update_normalize(self):
        self.fit()
        if self.data is None:
            self._invalidate_selection()

    def _create_projector(self):
        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)
        projector.component = self.ncomponents  # for use as a Scorer
        if self.normalize:
            projector.preprocessors = \
                PCA.preprocessors + [preprocess.Normalize(center=False)]
        return projector

    def _nselected_components(self):
        """Return the number of selected components."""
        if self._pca is None:
            return 0

        if self.ncomponents == 0:
            # Special "All" value
            max_comp = len(self._variance_ratio)
        else:
            max_comp = self.ncomponents

        var_max = self._cumulative[max_comp - 1]
        if var_max != numpy.floor(self.variance_covered / 100.0):
            cut = max_comp
            assert numpy.isfinite(var_max)
            self.variance_covered = int(var_max * 100)
        else:
            self.ncomponents = cut = numpy.searchsorted(
                self._cumulative, self.variance_covered / 100.0) + 1
        return cut

    def _invalidate_selection(self):
        self.commit.deferred()

    def _update_axis(self):
        p = min(len(self._variance_ratio), self.maxp)
        axis = self.plot.getAxis("bottom")
        d = max((p-1)//(self.axis_labels-1), 1)
        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])

    @gui.deferred
    def commit(self):
        transformed = data = components = None
        if self._pca is not None:
            if self._transformed is None:
                # Compute the full transform (MAX_COMPONENTS components) once.
                self._transformed = self._pca(self.data)
            transformed = self._transformed

            if self._variance_ratio is not None:
                for var, explvar in zip(
                        transformed.domain.attributes,
                        self._variance_ratio[:self.ncomponents]):
                    var.attributes["variance"] = round(explvar, 6)
            domain = Domain(
                transformed.domain.attributes[:self.ncomponents],
                self.data.domain.class_vars,
                self.data.domain.metas
            )
            transformed = transformed.from_table(domain, transformed)

            # prevent caching new features by defining compute_value
            proposed = [a.name for a in self._pca.orig_domain.attributes]
            meta_name = get_unique_names(proposed, 'components')
            meta_vars = [StringVariable(name=meta_name)]
            metas = numpy.array([['PC{}'.format(i + 1)
                                  for i in range(self.ncomponents)]],
                                dtype=object).T
            if self._variance_ratio is not None:
                variance_name = get_unique_names(proposed, "variance")
                meta_vars.append(ContinuousVariable(variance_name))
                metas = numpy.hstack(
                    (metas,
                     self._variance_ratio[:self.ncomponents, None]))

            dom = Domain(
                [ContinuousVariable(name, compute_value=lambda _: None)
                 for name in proposed],
                metas=meta_vars)
            components = Table(dom, self._pca.components_[:self.ncomponents],
                               metas=metas)
            components.name = 'components'

            data_dom = Domain(
                self.data.domain.attributes,
                self.data.domain.class_vars,
                self.data.domain.metas + domain.attributes)
            data = Table.from_numpy(
                data_dom, self.data.X, self.data.Y,
                numpy.hstack((self.data.metas, transformed.X)),
                ids=self.data.ids)

        self.Outputs.transformed_data.send(transformed)
        self.Outputs.components.send(components)
        self.Outputs.data.send(data)
        self.Outputs.pca.send(self._create_projector())

    def send_report(self):
        if self.data is None:
            return
        self.report_items((
            ("Normalize data", bool_str(self.normalize)),
            ("Selected components", self.ncomponents),
            ("Explained variance", "{:.3f} %".format(self.variance_covered))
        ))
        self.report_plot()

    @classmethod
    def migrate_settings(cls, settings, version):
        if "variance_covered" in settings:
            # Due to the error in gh-1896 the variance_covered was persisted
            # as a NaN value, causing a TypeError in the widgets `__init__`.
            vc = settings["variance_covered"]
            if isinstance(vc, numbers.Real):
                if numpy.isfinite(vc):
                    vc = int(vc)
                else:
                    vc = 100
                settings["variance_covered"] = vc
        if settings.get("ncomponents", 0) > MAX_COMPONENTS:
            settings["ncomponents"] = MAX_COMPONENTS

        # Remove old `decomposition_idx` when SVD was still included
        settings.pop("decomposition_idx", None)

        # Remove RemotePCA settings
        settings.pop("batch_size", None)
        settings.pop("address", None)
        settings.pop("auto_update", None)


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWPCA).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:** PCA Widget Does Not Preserve Table Subclasses in Output

**Description:**
When providing an input table that is an instance of a subclass of `Table` (e.g., `TableSub`), the PCA widget fails to maintain the subclass in its output tables. Instead, it returns instances of the base `Table` class. This behavior is unexpected and can lead to issues when downstream processes rely on the specific subclass type.

**Example Code:**
```python
from Orange.data import Table, Domain

class TableSub(Table):
    pass

# Create an instance of the subclass
table_subclass = TableSub("iris")

# Send the subclass instance to the PCA widget
pca_widget.send_input(table_subclass)

# Retrieve the output tables
data_out = pca_widget.get_output("data")
trans_data_out = pca_widget.get_output("transformed_data")

# Expecting instances of TableSub
print(isinstance(data_out, TableSub))          # Should be True
print(isinstance(trans_data_out, TableSub))    # Should be True
```

**Expected Behavior:**
When the PCA widget receives an input table that is a subclass of `Table`, both the transformed data and components outputs should also be instances of that subclass. This ensures consistency and preserves any additional functionalities or attributes defined in the subclass.

**Actual Behavior:**
The output tables from the PCA widget are instances of the base `Table` class instead of the provided subclass. This results in an `AssertionError` when checking the instance type, as shown below:

```
AssertionError: [<TableSub instance>] is not an instance of <class 'TableSub'>
```

[/ISSUE]
{
    "TestOWPCA.test_all_components_continuous": "PASSED",
    "TestOWPCA.test_constant_data": "PASSED",
    "TestOWPCA.test_do_not_mask_features": "PASSED",
    "TestOWPCA.test_empty_data": "PASSED",
    "TestOWPCA.test_image_export": "PASSED",
    "TestOWPCA.test_limit_components": "PASSED",
    "TestOWPCA.test_migrate_settings_changes_variance_covered_to_int": "PASSED",
    "TestOWPCA.test_migrate_settings_limits_components": "PASSED",
    "TestOWPCA.test_minimum_size": "PASSED",
    "TestOWPCA.test_msg_base_class": "PASSED",
    "TestOWPCA.test_normalization_variance": "PASSED",
    "TestOWPCA.test_normalize_data": "PASSED",
    "TestOWPCA.test_normalized_gives_correct_result": "PASSED",
    "TestOWPCA.test_on_cut_changed": "PASSED",
    "TestOWPCA.test_output_data": "PASSED",
    "TestOWPCA.test_set_variance100": "PASSED",
    "TestOWPCA.test_sparse_data": "PASSED",
    "TestOWPCA.test_table_subclass": "PASSED",
    "TestOWPCA.test_unique_domain_components": "PASSED",
    "TestOWPCA.test_variance_attr": "PASSED",
    "TestOWPCA.test_variance_shown": "PASSED",
    "test_filename": "ERROR"
}
diff --git a/Orange/widgets/unsupervised/owpca.py b/Orange/widgets/unsupervised/owpca.py
index 29611146f..9cc833230 100644
--- a/Orange/widgets/unsupervised/owpca.py
+++ b/Orange/widgets/unsupervised/owpca.py
@@ -5,13 +5,15 @@ from AnyQt.QtWidgets import QFormLayout
 from AnyQt.QtCore import Qt
 
 from orangewidget.report import bool_str
+from orangewidget.settings import Setting
 
 from Orange.data import Table, Domain, StringVariable, ContinuousVariable
 from Orange.data.util import get_unique_names
 from Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT
 from Orange.preprocess import preprocess
 from Orange.projection import PCA
-from Orange.widgets import widget, gui, settings
+from Orange.widgets import widget, gui
+from Orange.widgets.utils.annotated_data import add_columns
 from Orange.widgets.utils.concurrent import ConcurrentWidgetMixin
 from Orange.widgets.utils.slidergraph import SliderGraph
 from Orange.widgets.utils.widgetpreview import WidgetPreview
@@ -38,12 +40,12 @@ class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
         components = Output("Components", Table)
         pca = Output("PCA", PCA, dynamic=False)
 
-    ncomponents = settings.Setting(2)
-    variance_covered = settings.Setting(100)
-    auto_commit = settings.Setting(True)
-    normalize = settings.Setting(True)
-    maxp = settings.Setting(20)
-    axis_labels = settings.Setting(10)
+    ncomponents = Setting(2)
+    variance_covered = Setting(100)
+    auto_commit = Setting(True)
+    normalize = Setting(True)
+    maxp = Setting(20)
+    axis_labels = Setting(10)
 
     graph_name = "plot.plotItem"  # QGraphicsView (pg.PlotWidget -> SliderGraph)
 
@@ -222,8 +224,7 @@ class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
         self._update_axis()
 
     def _on_cut_changed(self, components):
-        if components == self.ncomponents \
-                or self.ncomponents == 0:
+        if self.ncomponents in (components, 0):
             return
 
         self.ncomponents = components
@@ -333,9 +334,9 @@ class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
             proposed = [a.name for a in self._pca.orig_domain.attributes]
             meta_name = get_unique_names(proposed, 'components')
             meta_vars = [StringVariable(name=meta_name)]
-            metas = numpy.array([['PC{}'.format(i + 1)
-                                  for i in range(self.ncomponents)]],
-                                dtype=object).T
+            metas = numpy.array(
+                [[f"PC{i + 1}"for i in range(self.ncomponents)]], dtype=object
+            ).T
             if self._variance_ratio is not None:
                 variance_name = get_unique_names(proposed, "variance")
                 meta_vars.append(ContinuousVariable(variance_name))
@@ -351,14 +352,8 @@ class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
                                metas=metas)
             components.name = 'components'
 
-            data_dom = Domain(
-                self.data.domain.attributes,
-                self.data.domain.class_vars,
-                self.data.domain.metas + domain.attributes)
-            data = Table.from_numpy(
-                data_dom, self.data.X, self.data.Y,
-                numpy.hstack((self.data.metas, transformed.X)),
-                ids=self.data.ids)
+            data_dom = add_columns(self.data.domain, metas=domain.attributes)
+            data = self.data.transform(data_dom)
 
         self.Outputs.transformed_data.send(transformed)
         self.Outputs.components.send(components)
@@ -371,7 +366,7 @@ class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
         self.report_items((
             ("Normalize data", bool_str(self.normalize)),
             ("Selected components", self.ncomponents),
-            ("Explained variance", "{:.3f} %".format(self.variance_covered))
+            ("Explained variance", f"{self.variance_covered:.3f} %")
         ))
         self.report_plot()
 

{"Orange/widgets/unsupervised/owpca.py": "import numbers\n\nimport numpy\nfrom AnyQt.QtWidgets import QFormLayout\nfrom AnyQt.QtCore import Qt\n\nfrom orangewidget.report import bool_str\n\nfrom Orange.data import Table, Domain, StringVariable, ContinuousVariable\nfrom Orange.data.util import get_unique_names\nfrom Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT\nfrom Orange.preprocess import preprocess\nfrom Orange.projection import PCA\nfrom Orange.widgets import widget, gui, settings\nfrom Orange.widgets.utils.concurrent import ConcurrentWidgetMixin\nfrom Orange.widgets.utils.slidergraph import SliderGraph\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Input, Output\n\n# Maximum number of PCA components that we can set in the widget\nMAX_COMPONENTS = 100\nLINE_NAMES = [\"component variance\", \"cumulative variance\"]\n\n\nclass OWPCA(widget.OWWidget, ConcurrentWidgetMixin):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = \"pca, principal component analysis, linear transformation\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        transformed_data = Output(\"Transformed Data\", Table, replaces=[\"Transformed data\"])\n        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"  # QGraphicsView (pg.PlotWidget -> SliderGraph)\n\n    class Warning(widget.OWWidget.Warning):\n        trivial_components = widget.Msg(\n            \"All components of the PCA are trivial (explain 0 variance). \"\n            \"Input data is constant (or near constant).\")\n\n    class Error(widget.OWWidget.Error):\n        no_features = widget.Msg(\"At least 1 feature is required\")\n        no_instances = widget.Msg(\"At least 1 data instance is required\")\n\n    def __init__(self):\n        super().__init__()\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n\n        # Components Selection\n        form = QFormLayout()\n        box = gui.widgetBox(self.controlArea, \"Components Selection\",\n                            orientation=form)\n\n        self.components_spin = gui.spin(\n            box, self, \"ncomponents\", 1, MAX_COMPONENTS,\n            callback=self._update_selection_component_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.components_spin.setSpecialValueText(\"All\")\n\n        self.variance_spin = gui.spin(\n            box, self, \"variance_covered\", 1, 100,\n            callback=self._update_selection_variance_spin,\n            keyboardTracking=False, addToLayout=False\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize,\n            attribute=Qt.WA_LayoutUsesWidgetRect\n        )\n\n        self.maxp_spin = gui.spin(\n            self.options_box, self, \"maxp\", 1, MAX_COMPONENTS,\n            label=\"Show only first\", callback=self._setup_plot,\n            keyboardTracking=False\n        )\n\n        gui.rubber(self.controlArea)\n\n        gui.auto_apply(self.buttonsArea, self, \"auto_commit\")\n\n        self.plot = SliderGraph(\n            \"Principal Components\", \"Proportion of variance\",\n            self._on_cut_changed)\n\n        self.mainArea.layout().addWidget(self.plot)\n        self._update_normalize()\n\n    @Inputs.data\n    def set_data(self, data):\n        self.cancel()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.cancel()\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        projector = self._create_projector()\n\n        if not isinstance(data, SqlTable):\n            self.start(self._call_projector, data, projector)\n\n    @staticmethod\n    def _call_projector(data: Table, projector, state):\n\n        def callback(i: float, status=\"\"):\n            state.set_progress_value(i * 100)\n            if status:\n                state.set_status(status)\n            if state.is_interruption_requested():\n                raise Exception  # pylint: disable=broad-exception-raised\n\n        return projector(data, progress_callback=callback)\n\n    def on_done(self, result):\n        pca = result\n        variance_ratio = pca.explained_variance_ratio_\n        cumulative = numpy.cumsum(variance_ratio)\n\n        if numpy.isfinite(cumulative[-1]):\n            self.components_spin.setRange(0, len(cumulative))\n            self._pca = pca\n            self._variance_ratio = variance_ratio\n            self._cumulative = cumulative\n            self._setup_plot()\n        else:\n            self.Warning.trivial_components()\n\n        self.commit.now()\n\n    def on_partial_result(self, result):\n        pass\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def clear(self):\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self.plot.clear_plot()\n\n    def clear_outputs(self):\n        self.Outputs.transformed_data.send(None)\n        self.Outputs.data.send(None)\n        self.Outputs.components.send(None)\n        self.Outputs.pca.send(self._create_projector())\n\n    def _setup_plot(self):\n        if self._pca is None:\n            self.plot.clear_plot()\n            return\n\n        explained_ratio = self._variance_ratio\n        explained = self._cumulative\n        cutpos = self._nselected_components()\n        p = min(len(self._variance_ratio), self.maxp)\n\n        self.plot.update(\n            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],\n            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0:\n            return\n\n        self.ncomponents = components\n        if self._pca is not None:\n            var = self._cumulative[components - 1]\n            if numpy.isfinite(var):\n                self.variance_covered = int(var * 100)\n\n        self._invalidate_selection()\n\n    def _update_selection_component_spin(self):\n        # cut changed by \"ncomponents\" spin.\n        if self._pca is None:\n            self._invalidate_selection()\n            return\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            cut = len(self._variance_ratio)\n        else:\n            cut = self.ncomponents\n\n        var = self._cumulative[cut - 1]\n        if numpy.isfinite(var):\n            self.variance_covered = int(var * 100)\n\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_selection_variance_spin(self):\n        # cut changed by \"max variance\" spin.\n        if self._pca is None:\n            return\n\n        cut = numpy.searchsorted(self._cumulative,\n                                 self.variance_covered / 100.0) + 1\n        cut = min(cut, len(self._cumulative))\n        self.ncomponents = cut\n        self.plot.set_cut_point(cut)\n        self._invalidate_selection()\n\n    def _update_normalize(self):\n        self.fit()\n        if self.data is None:\n            self._invalidate_selection()\n\n    def _create_projector(self):\n        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        projector.component = self.ncomponents  # for use as a Scorer\n        if self.normalize:\n            projector.preprocessors = \\\n                PCA.preprocessors + [preprocess.Normalize(center=False)]\n        return projector\n\n    def _nselected_components(self):\n        \"\"\"Return the number of selected components.\"\"\"\n        if self._pca is None:\n            return 0\n\n        if self.ncomponents == 0:\n            # Special \"All\" value\n            max_comp = len(self._variance_ratio)\n        else:\n            max_comp = self.ncomponents\n\n        var_max = self._cumulative[max_comp - 1]\n        if var_max != numpy.floor(self.variance_covered / 100.0):\n            cut = max_comp\n            assert numpy.isfinite(var_max)\n            self.variance_covered = int(var_max * 100)\n        else:\n            self.ncomponents = cut = numpy.searchsorted(\n                self._cumulative, self.variance_covered / 100.0) + 1\n        return cut\n\n    def _invalidate_selection(self):\n        self.commit.deferred()\n\n    def _update_axis(self):\n        p = min(len(self._variance_ratio), self.maxp)\n        axis = self.plot.getAxis(\"bottom\")\n        d = max((p-1)//(self.axis_labels-1), 1)\n        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])\n\n    @gui.deferred\n    def commit(self):\n        transformed = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            if self._variance_ratio is not None:\n                for var, explvar in zip(\n                        transformed.domain.attributes,\n                        self._variance_ratio[:self.ncomponents]):\n                    var.attributes[\"variance\"] = round(explvar, 6)\n            domain = Domain(\n                transformed.domain.attributes[:self.ncomponents],\n                self.data.domain.class_vars,\n                self.data.domain.metas\n            )\n            transformed = transformed.from_table(domain, transformed)\n\n            # prevent caching new features by defining compute_value\n            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            meta_vars = [StringVariable(name=meta_name)]\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            if self._variance_ratio is not None:\n                variance_name = get_unique_names(proposed, \"variance\")\n                meta_vars.append(ContinuousVariable(variance_name))\n                metas = numpy.hstack(\n                    (metas,\n                     self._variance_ratio[:self.ncomponents, None]))\n\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=meta_vars)\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._create_projector())\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", bool_str(self.normalize)),\n            (\"Selected components\", self.ncomponents),\n            (\"Explained variance\", \"{:.3f} %\".format(self.variance_covered))\n        ))\n        self.report_plot()\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if \"variance_covered\" in settings:\n            # Due to the error in gh-1896 the variance_covered was persisted\n            # as a NaN value, causing a TypeError in the widgets `__init__`.\n            vc = settings[\"variance_covered\"]\n            if isinstance(vc, numbers.Real):\n                if numpy.isfinite(vc):\n                    vc = int(vc)\n                else:\n                    vc = 100\n                settings[\"variance_covered\"] = vc\n        if settings.get(\"ncomponents\", 0) > MAX_COMPONENTS:\n            settings[\"ncomponents\"] = MAX_COMPONENTS\n\n        # Remove old `decomposition_idx` when SVD was still included\n        settings.pop(\"decomposition_idx\", None)\n\n        # Remove RemotePCA settings\n        settings.pop(\"batch_size\", None)\n        settings.pop(\"address\", None)\n        settings.pop(\"auto_update\", None)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWPCA).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:** PCA Widget Does Not Preserve Table Subclasses in Output

**Description:**
When providing an input table that is an instance of a subclass of `Table` (e.g., `TableSub`), the PCA widget fails to maintain the subclass in its output tables. Instead, it returns instances of the base `Table` class. This behavior is unexpected and can lead to issues when downstream processes rely on the specific subclass type.

**Example Code:**
```python
from Orange.data import Table, Domain

class TableSub(Table):
    pass

# Create an instance of the subclass
table_subclass = TableSub("iris")

# Send the subclass instance to the PCA widget
pca_widget.send_input(table_subclass)

# Retrieve the output tables
data_out = pca_widget.get_output("data")
trans_data_out = pca_widget.get_output("transformed_data")

# Expecting instances of TableSub
print(isinstance(data_out, TableSub))          # Should be True
print(isinstance(trans_data_out, TableSub))    # Should be True
```

**Expected Behavior:**
When the PCA widget receives an input table that is a subclass of `Table`, both the transformed data and components outputs should also be instances of that subclass. This ensures consistency and preserves any additional functionalities or attributes defined in the subclass.

**Actual Behavior:**
The output tables from the PCA widget are instances of the base `Table` class instead of the provided subclass. This results in an `AssertionError` when checking the instance type, as shown below:

```
AssertionError: [<TableSub instance>] is not an instance of <class 'TableSub'>
```

[/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/unsupervised/owpca.py
import numbers

import numpy
from AnyQt.QtWidgets import QFormLayout
from AnyQt.QtCore import Qt

from orangewidget.report import bool_str

from Orange.data import Table, Domain, StringVariable, ContinuousVariable
from Orange.data.util import get_unique_names
from Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT
from Orange.preprocess import preprocess
from Orange.projection import PCA
from Orange.widgets import widget, gui, settings
from Orange.widgets.utils.concurrent import ConcurrentWidgetMixin
from Orange.widgets.utils.slidergraph import SliderGraph
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Input, Output

# Maximum number of PCA components that we can set in the widget
MAX_COMPONENTS = 100
LINE_NAMES = ["component variance", "cumulative variance"]


class OWPCA(widget.OWWidget, ConcurrentWidgetMixin):
    name = "PCA"
    description = "Principal component analysis with a scree-diagram."
    icon = "icons/PCA.svg"
    priority = 3050
    keywords = "pca, principal component analysis, linear transformation"

    class Inputs:
        data = Input("Data", Table)

    class Outputs:
        transformed_data = Output("Transformed Data", Table, replaces=["Transformed data"])
        data = Output("Data", Table, default=True)
        components = Output("Components", Table)
        pca = Output("PCA", PCA, dynamic=False)

    ncomponents = settings.Setting(2)
    variance_covered = settings.Setting(100)
    auto_commit = settings.Setting(True)
    normalize = settings.Setting(True)
    maxp = settings.Setting(20)
    axis_labels = settings.Setting(10)

    graph_name = "plot.plotItem"  # QGraphicsView (pg.PlotWidget -> SliderGraph)

    class Warning(widget.OWWidget.Warning):
        trivial_components = widget.Msg(
            "All components of the PCA are trivial (explain 0 variance). "
            "Input data is constant (or near constant).")

    class Error(widget.OWWidget.Error):
        no_features = widget.Msg("At least 1 feature is required")
        no_instances = widget.Msg("At least 1 data instance is required")

    def __init__(self):
        super().__init__()
        ConcurrentWidgetMixin.__init__(self)

        self.data = None
        self._pca = None
        self._transformed = None
        self._variance_ratio = None
        self._cumulative = None

        # Components Selection
        form = QFormLayout()
        box = gui.widgetBox(self.controlArea, "Components Selection",
                            orientation=form)

        self.components_spin = gui.spin(
            box, self, "ncomponents", 1, MAX_COMPONENTS,
            callback=self._update_selection_component_spin,
            keyboardTracking=False, addToLayout=False
        )
        self.components_spin.setSpecialValueText("All")

        self.variance_spin = gui.spin(
            box, self, "variance_covered", 1, 100,
            callback=self._update_selection_variance_spin,
            keyboardTracking=False, addToLayout=False
        )
        self.variance_spin.setSuffix("%")

        form.addRow("Components:", self.components_spin)
        form.addRow("Explained variance:", self.variance_spin)

        # Options
        self.options_box = gui.vBox(self.controlArea, "Options")
        self.normalize_box = gui.checkBox(
            self.options_box, self, "normalize",
            "Normalize variables", callback=self._update_normalize,
            attribute=Qt.WA_LayoutUsesWidgetRect
        )

        self.maxp_spin = gui.spin(
            self.options_box, self, "maxp", 1, MAX_COMPONENTS,
            label="Show only first", callback=self._setup_plot,
            keyboardTracking=False
        )

        gui.rubber(self.controlArea)

        gui.auto_apply(self.buttonsArea, self, "auto_commit")

        self.plot = SliderGraph(
            "Principal Components", "Proportion of variance",
            self._on_cut_changed)

        self.mainArea.layout().addWidget(self.plot)
        self._update_normalize()

    @Inputs.data
    def set_data(self, data):
        self.cancel()
        self.clear_messages()
        self.clear()
        self.information()
        self.data = None
        if not data:
            self.clear_outputs()
        if isinstance(data, SqlTable):
            if data.approx_len() < AUTO_DL_LIMIT:
                data = Table(data)
            else:
                self.information("Data has been sampled")
                data_sample = data.sample_time(1, no_cache=True)
                data_sample.download_data(2000, partial=True)
                data = Table(data_sample)
        if isinstance(data, Table):
            if not data.domain.attributes:
                self.Error.no_features()
                self.clear_outputs()
                return
            if not data:
                self.Error.no_instances()
                self.clear_outputs()
                return

        self.data = data
        self.fit()

    def fit(self):
        self.cancel()
        self.clear()
        self.Warning.trivial_components.clear()
        if self.data is None:
            return

        data = self.data

        projector = self._create_projector()

        if not isinstance(data, SqlTable):
            self.start(self._call_projector, data, projector)

    @staticmethod
    def _call_projector(data: Table, projector, state):

        def callback(i: float, status=""):
            state.set_progress_value(i * 100)
            if status:
                state.set_status(status)
            if state.is_interruption_requested():
                raise Exception  # pylint: disable=broad-exception-raised

        return projector(data, progress_callback=callback)

    def on_done(self, result):
        pca = result
        variance_ratio = pca.explained_variance_ratio_
        cumulative = numpy.cumsum(variance_ratio)

        if numpy.isfinite(cumulative[-1]):
            self.components_spin.setRange(0, len(cumulative))
            self._pca = pca
            self._variance_ratio = variance_ratio
            self._cumulative = cumulative
            self._setup_plot()
        else:
            self.Warning.trivial_components()

        self.commit.now()

    def on_partial_result(self, result):
        pass

    def onDeleteWidget(self):
        self.shutdown()
        super().onDeleteWidget()

    def clear(self):
        self._pca = None
        self._transformed = None
        self._variance_ratio = None
        self._cumulative = None
        self.plot.clear_plot()

    def clear_outputs(self):
        self.Outputs.transformed_data.send(None)
        self.Outputs.data.send(None)
        self.Outputs.components.send(None)
        self.Outputs.pca.send(self._create_projector())

    def _setup_plot(self):
        if self._pca is None:
            self.plot.clear_plot()
            return

        explained_ratio = self._variance_ratio
        explained = self._cumulative
        cutpos = self._nselected_components()
        p = min(len(self._variance_ratio), self.maxp)

        self.plot.update(
            numpy.arange(1, p+1), [explained_ratio[:p], explained[:p]],
            [Qt.red, Qt.darkYellow], cutpoint_x=cutpos, names=LINE_NAMES)

        self._update_axis()

    def _on_cut_changed(self, components):
        if components == self.ncomponents \
                or self.ncomponents == 0:
            return

        self.ncomponents = components
        if self._pca is not None:
            var = self._cumulative[components - 1]
            if numpy.isfinite(var):
                self.variance_covered = int(var * 100)

        self._invalidate_selection()

    def _update_selection_component_spin(self):
        # cut changed by "ncomponents" spin.
        if self._pca is None:
            self._invalidate_selection()
            return

        if self.ncomponents == 0:
            # Special "All" value
            cut = len(self._variance_ratio)
        else:
            cut = self.ncomponents

        var = self._cumulative[cut - 1]
        if numpy.isfinite(var):
            self.variance_covered = int(var * 100)

        self.plot.set_cut_point(cut)
        self._invalidate_selection()

    def _update_selection_variance_spin(self):
        # cut changed by "max variance" spin.
        if self._pca is None:
            return

        cut = numpy.searchsorted(self._cumulative,
                                 self.variance_covered / 100.0) + 1
        cut = min(cut, len(self._cumulative))
        self.ncomponents = cut
        self.plot.set_cut_point(cut)
        self._invalidate_selection()

    def _update_normalize(self):
        self.fit()
        if self.data is None:
            self._invalidate_selection()

    def _create_projector(self):
        projector = PCA(n_components=MAX_COMPONENTS, random_state=0)
        projector.component = self.ncomponents  # for use as a Scorer
        if self.normalize:
            projector.preprocessors = \
                PCA.preprocessors + [preprocess.Normalize(center=False)]
        return projector

    def _nselected_components(self):
        """Return the number of selected components."""
        if self._pca is None:
            return 0

        if self.ncomponents == 0:
            # Special "All" value
            max_comp = len(self._variance_ratio)
        else:
            max_comp = self.ncomponents

        var_max = self._cumulative[max_comp - 1]
        if var_max != numpy.floor(self.variance_covered / 100.0):
            cut = max_comp
            assert numpy.isfinite(var_max)
            self.variance_covered = int(var_max * 100)
        else:
            self.ncomponents = cut = numpy.searchsorted(
                self._cumulative, self.variance_covered / 100.0) + 1
        return cut

    def _invalidate_selection(self):
        self.commit.deferred()

    def _update_axis(self):
        p = min(len(self._variance_ratio), self.maxp)
        axis = self.plot.getAxis("bottom")
        d = max((p-1)//(self.axis_labels-1), 1)
        axis.setTicks([[(i, str(i)) for i in range(1, p + 1, d)]])

    @gui.deferred
    def commit(self):
        transformed = data = components = None
        if self._pca is not None:
            if self._transformed is None:
                # Compute the full transform (MAX_COMPONENTS components) once.
                self._transformed = self._pca(self.data)
            transformed = self._transformed

            if self._variance_ratio is not None:
                for var, explvar in zip(
                        transformed.domain.attributes,
                        self._variance_ratio[:self.ncomponents]):
                    var.attributes["variance"] = round(explvar, 6)
            domain = Domain(
                transformed.domain.attributes[:self.ncomponents],
                self.data.domain.class_vars,
                self.data.domain.metas
            )
            transformed = transformed.from_table(domain, transformed)

            # prevent caching new features by defining compute_value
            proposed = [a.name for a in self._pca.orig_domain.attributes]
            meta_name = get_unique_names(proposed, 'components')
            meta_vars = [StringVariable(name=meta_name)]
            metas = numpy.array([['PC{}'.format(i + 1)
                                  for i in range(self.ncomponents)]],
                                dtype=object).T
            if self._variance_ratio is not None:
                variance_name = get_unique_names(proposed, "variance")
                meta_vars.append(ContinuousVariable(variance_name))
                metas = numpy.hstack(
                    (metas,
                     self._variance_ratio[:self.ncomponents, None]))

            dom = Domain(
                [ContinuousVariable(name, compute_value=lambda _: None)
                 for name in proposed],
                metas=meta_vars)
            components = Table(dom, self._pca.components_[:self.ncomponents],
                               metas=metas)
            components.name = 'components'

            data_dom = Domain(
                self.data.domain.attributes,
                self.data.domain.class_vars,
                self.data.domain.metas + domain.attributes)
            data = Table.from_numpy(
                data_dom, self.data.X, self.data.Y,
                numpy.hstack((self.data.metas, transformed.X)),
                ids=self.data.ids)

        self.Outputs.transformed_data.send(transformed)
        self.Outputs.components.send(components)
        self.Outputs.data.send(data)
        self.Outputs.pca.send(self._create_projector())

    def send_report(self):
        if self.data is None:
            return
        self.report_items((
            ("Normalize data", bool_str(self.normalize)),
            ("Selected components", self.ncomponents),
            ("Explained variance", "{:.3f} %".format(self.variance_covered))
        ))
        self.report_plot()

    @classmethod
    def migrate_settings(cls, settings, version):
        if "variance_covered" in settings:
            # Due to the error in gh-1896 the variance_covered was persisted
            # as a NaN value, causing a TypeError in the widgets `__init__`.
            vc = settings["variance_covered"]
            if isinstance(vc, numbers.Real):
                if numpy.isfinite(vc):
                    vc = int(vc)
                else:
                    vc = 100
                settings["variance_covered"] = vc
        if settings.get("ncomponents", 0) > MAX_COMPONENTS:
            settings["ncomponents"] = MAX_COMPONENTS

        # Remove old `decomposition_idx` when SVD was still included
        settings.pop("decomposition_idx", None)

        # Remove RemotePCA settings
        settings.pop("batch_size", None)
        settings.pop("address", None)
        settings.pop("auto_update", None)


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWPCA).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