{
    "file_diffs": [
        {
            "old_file_content": "import numbers\n\nimport numpy\nfrom AnyQt.QtWidgets import QFormLayout\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, StringVariable, ContinuousVariable\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.slidergraph import SliderGraph\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Input, Output\n\n\n# Maximum number of PCA components that we can set in the widget\nMAX_COMPONENTS = 100\n\n\nclass OWPCA(widget.OWWidget):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = [\"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        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    settingsHandler = settings.DomainContextHandler()\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.ContextSetting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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        self.data = None\n\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self._init_projector()\n\n        # Components Selection\n        box = gui.vBox(self.controlArea, \"Components Selection\")\n        form = QFormLayout()\n        box.layout().addLayout(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\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\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Variance covered:\", 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 data\", callback=self._update_normalize\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        self.controlArea.layout().addStretch()\n\n        gui.auto_apply(self.controlArea, 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.closeContext()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\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.openContext(data)\n        self._init_projector()\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        if self.normalize:\n            self._pca_projector.preprocessors = \\\n                self._pca_preprocessors + [preprocess.Normalize(center=False)]\n        else:\n            self._pca_projector.preprocessors = self._pca_preprocessors\n\n        if not isinstance(data, SqlTable):\n            pca = self._pca_projector(data)\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.unconditional_commit()\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.components.send(None)\n        self.Outputs.pca.send(self._pca_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)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n\n        if not (self.ncomponents == 0 and\n                components == len(self._variance_ratio)):\n            self.ncomponents = components\n\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        if components != self._nselected_components():\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 _init_projector(self):\n        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        self._pca_projector.component = self.ncomponents\n        self._pca_preprocessors = PCA.preprocessors\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()\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    def commit(self):\n        transformed = 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            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            # prevent caching new features by defining compute_value\n            dom = Domain(\n                [ContinuousVariable(a.name, compute_value=lambda _: None)\n                 for a in self._pca.orig_domain.attributes],\n                metas=[StringVariable(name='component')])\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.pca.send(self._pca_projector)\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", 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 Orange.data import Table, Domain, StringVariable, ContinuousVariable\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.slidergraph import SliderGraph\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Input, Output\n\n\n# Maximum number of PCA components that we can set in the widget\nMAX_COMPONENTS = 100\n\n\nclass OWPCA(widget.OWWidget):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = [\"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        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    settingsHandler = settings.DomainContextHandler()\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.ContextSetting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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        self.data = None\n\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self._init_projector()\n\n        # Components Selection\n        box = gui.vBox(self.controlArea, \"Components Selection\")\n        form = QFormLayout()\n        box.layout().addLayout(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\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\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Variance covered:\", 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 data\", callback=self._update_normalize\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        self.controlArea.layout().addStretch()\n\n        gui.auto_apply(self.controlArea, 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.closeContext()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\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.openContext(data)\n        self._init_projector()\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        if self.normalize:\n            self._pca_projector.preprocessors = \\\n                self._pca_preprocessors + [preprocess.Normalize(center=False)]\n        else:\n            self._pca_projector.preprocessors = self._pca_preprocessors\n\n        if not isinstance(data, SqlTable):\n            pca = self._pca_projector(data)\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.unconditional_commit()\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.components.send(None)\n        self.Outputs.pca.send(self._pca_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)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                or self._pca is not None \\\n                and components == len(self._variance_ratio):\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 _init_projector(self):\n        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        self._pca_projector.component = self.ncomponents\n        self._pca_preprocessors = PCA.preprocessors\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()\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    def commit(self):\n        transformed = 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            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            # prevent caching new features by defining compute_value\n            dom = Domain(\n                [ContinuousVariable(a.name, compute_value=lambda _: None)\n                 for a in self._pca.orig_domain.attributes],\n                metas=[StringVariable(name='component')])\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.pca.send(self._pca_projector)\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", 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",
            "header": {
                "file": {
                    "path": "Orange/widgets/unsupervised/owpca.py"
                },
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            },
            "index_line": {
                "old_commit_hash": "76936fe8f",
                "new_commit_hash": "1bb96df1c",
                "mode": "100644"
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            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/unsupervised/owpca.py"
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            "plus_file": {
                "path": "b/Orange/widgets/unsupervised/owpca.py"
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                        "old_range": {
                            "start": 199,
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                        "section": "class OWPCA(widget.OWWidget):"
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                                "content": "        self._update_axis()",
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                            {
                                "content": "    def _on_cut_changed(self, components):",
                                "type": "context"
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                                "content": "        if components == self.ncomponents \\",
                                "type": "added"
                            },
                            {
                                "content": "                or self.ncomponents == 0 \\",
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                            {
                                "content": "                or self._pca is not None \\",
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                                "content": "                and components == len(self._variance_ratio):",
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                                "content": "            return",
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                                "content": "",
                                "type": "context"
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                            {
                                "content": "        if not (self.ncomponents == 0 and",
                                "type": "deleted"
                            },
                            {
                                "content": "                components == len(self._variance_ratio)):",
                                "type": "deleted"
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                            {
                                "content": "            self.ncomponents = components",
                                "type": "deleted"
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                                "content": "",
                                "type": "deleted"
                            },
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                                "content": "        self.ncomponents = components",
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                                "content": "        if self._pca is not None:",
                                "type": "context"
                            },
                            {
                                "content": "            var = self._cumulative[components - 1]",
                                "type": "context"
                            },
                            {
                                "content": "            if numpy.isfinite(var):",
                                "type": "context"
                            },
                            {
                                "content": "                self.variance_covered = int(var * 100)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        if components != self._nselected_components():",
                                "type": "deleted"
                            },
                            {
                                "content": "            self._invalidate_selection()",
                                "type": "deleted"
                            },
                            {
                                "content": "        self._invalidate_selection()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def _update_selection_component_spin(self):",
                                "type": "context"
                            },
                            {
                                "content": "        # cut changed by \"ncomponents\" spin.",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "class",
                            "name": "OWPCA",
                            "content": "class OWPCA(widget.OWWidget):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = [\"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        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    settingsHandler = settings.DomainContextHandler()\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.ContextSetting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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        self.data = None\n\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self._init_projector()\n\n        # Components Selection\n        box = gui.vBox(self.controlArea, \"Components Selection\")\n        form = QFormLayout()\n        box.layout().addLayout(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\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\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Variance covered:\", 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 data\", callback=self._update_normalize\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        self.controlArea.layout().addStretch()\n\n        gui.auto_apply(self.controlArea, 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.closeContext()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\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.openContext(data)\n        self._init_projector()\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        if self.normalize:\n            self._pca_projector.preprocessors = \\\n                self._pca_preprocessors + [preprocess.Normalize(center=False)]\n        else:\n            self._pca_projector.preprocessors = self._pca_preprocessors\n\n        if not isinstance(data, SqlTable):\n            pca = self._pca_projector(data)\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.unconditional_commit()\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.components.send(None)\n        self.Outputs.pca.send(self._pca_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)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n\n        if not (self.ncomponents == 0 and\n                components == len(self._variance_ratio)):\n            self.ncomponents = components\n\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        if components != self._nselected_components():\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 _init_projector(self):\n        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        self._pca_projector.component = self.ncomponents\n        self._pca_preprocessors = PCA.preprocessors\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()\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    def commit(self):\n        transformed = 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            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            # prevent caching new features by defining compute_value\n            dom = Domain(\n                [ContinuousVariable(a.name, compute_value=lambda _: None)\n                 for a in self._pca.orig_domain.attributes],\n                metas=[StringVariable(name='component')])\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.pca.send(self._pca_projector)\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", 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": 21,
                            "end_lineno": 348,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "method",
                            "name": "OWPCA._on_cut_changed",
                            "content": "class OWPCA(widget.OWWidget):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = [\"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        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    settingsHandler = settings.DomainContextHandler()\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.ContextSetting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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        self.data = None\n\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self._init_projector()\n\n        # Components Selection\n        box = gui.vBox(self.controlArea, \"Components Selection\")\n        form = QFormLayout()\n        box.layout().addLayout(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\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\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Variance covered:\", 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 data\", callback=self._update_normalize\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        self.controlArea.layout().addStretch()\n\n        gui.auto_apply(self.controlArea, 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.closeContext()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\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.openContext(data)\n        self._init_projector()\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        if self.normalize:\n            self._pca_projector.preprocessors = \\\n                self._pca_preprocessors + [preprocess.Normalize(center=False)]\n        else:\n            self._pca_projector.preprocessors = self._pca_preprocessors\n\n        if not isinstance(data, SqlTable):\n            pca = self._pca_projector(data)\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.unconditional_commit()\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.components.send(None)\n        self.Outputs.pca.send(self._pca_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)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n\n        if not (self.ncomponents == 0 and\n                components == len(self._variance_ratio)):\n            self.ncomponents = components\n\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        if components != self._nselected_components():\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 _init_projector(self):\n        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        self._pca_projector.component = self.ncomponents\n        self._pca_preprocessors = PCA.preprocessors\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()\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    def commit(self):\n        transformed = 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            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            # prevent caching new features by defining compute_value\n            dom = Domain(\n                [ContinuousVariable(a.name, compute_value=lambda _: None)\n                 for a in self._pca.orig_domain.attributes],\n                metas=[StringVariable(name='component')])\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.pca.send(self._pca_projector)\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", 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": 201,
                            "end_lineno": 213,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owpca.py",
                                "type": "class",
                                "name": "OWPCA",
                                "content": "class OWPCA(widget.OWWidget):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = [\"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        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    settingsHandler = settings.DomainContextHandler()\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.ContextSetting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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        self.data = None\n\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self._init_projector()\n\n        # Components Selection\n        box = gui.vBox(self.controlArea, \"Components Selection\")\n        form = QFormLayout()\n        box.layout().addLayout(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\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\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Variance covered:\", 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 data\", callback=self._update_normalize\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        self.controlArea.layout().addStretch()\n\n        gui.auto_apply(self.controlArea, 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.closeContext()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\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.openContext(data)\n        self._init_projector()\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        if self.normalize:\n            self._pca_projector.preprocessors = \\\n                self._pca_preprocessors + [preprocess.Normalize(center=False)]\n        else:\n            self._pca_projector.preprocessors = self._pca_preprocessors\n\n        if not isinstance(data, SqlTable):\n            pca = self._pca_projector(data)\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.unconditional_commit()\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.components.send(None)\n        self.Outputs.pca.send(self._pca_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)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n\n        if not (self.ncomponents == 0 and\n                components == len(self._variance_ratio)):\n            self.ncomponents = components\n\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        if components != self._nselected_components():\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 _init_projector(self):\n        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        self._pca_projector.component = self.ncomponents\n        self._pca_preprocessors = PCA.preprocessors\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()\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    def commit(self):\n        transformed = 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            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            # prevent caching new features by defining compute_value\n            dom = Domain(\n                [ContinuousVariable(a.name, compute_value=lambda _: None)\n                 for a in self._pca.orig_domain.attributes],\n                metas=[StringVariable(name='component')])\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.pca.send(self._pca_projector)\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", 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": 21,
                                "end_lineno": 348,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\nfrom unittest.mock import patch\n\nimport numpy as np\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.preprocess.preprocess import Normalize\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse\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]\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)\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(X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data)\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.send_signal(self.widget.Inputs.data, data)\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        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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)\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_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        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",
            "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\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.preprocess.preprocess import Normalize\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse\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]\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)\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(X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data)\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.send_signal(self.widget.Inputs.data, data)\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        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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)\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_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        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\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/unsupervised/tests/test_owpca.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "6a1c50485",
                "new_commit_hash": "4d6b47f91",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/unsupervised/tests/test_owpca.py"
            },
            "plus_file": {
                "path": "b/Orange/widgets/unsupervised/tests/test_owpca.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 1,
                            "length": 6
                        },
                        "new_range": {
                            "start": 1,
                            "length": 7
                        },
                        "section": ""
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "# Test methods with long descriptive names can omit docstrings",
                                "type": "context"
                            },
                            {
                                "content": "# pylint: disable=missing-docstring",
                                "type": "deleted"
                            },
                            {
                                "content": "from unittest.mock import patch",
                                "type": "deleted"
                            },
                            {
                                "content": "# pylint: disable=missing-docstring, protected-access",
                                "type": "added"
                            },
                            {
                                "content": "import unittest",
                                "type": "added"
                            },
                            {
                                "content": "from unittest.mock import patch, Mock",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "import numpy as np",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "import",
                            "name": "unittest",
                            "content": "import unittest",
                            "ast_type_str": "Import",
                            "start_lineno": 3,
                            "end_lineno": 3,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from unittest.mock import patch, Mock",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 4,
                            "end_lineno": 4,
                            "parent": null
                        }
                    ],
                    "deleted_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "from unittest.mock import patch",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 3,
                            "end_lineno": 3,
                            "parent": null
                        }
                    ]
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 203,
                            "length": 3
                        },
                        "new_range": {
                            "start": 204,
                            "length": 22
                        },
                        "section": "class TestOWPCA(WidgetTest):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        self.widget.set_data(data)",
                                "type": "context"
                            },
                            {
                                "content": "        ndata = Table(\"iris.tab\")",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(data.domain[0], ndata.domain[0])",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    def test_on_cut_changed(self):",
                                "type": "added"
                            },
                            {
                                "content": "        widget = self.widget",
                                "type": "added"
                            },
                            {
                                "content": "        widget.ncomponents = 2",
                                "type": "added"
                            },
                            {
                                "content": "        invalidate = widget._invalidate_selection = Mock()",
                                "type": "added"
                            },
                            {
                                "content": "        widget._on_cut_changed(2)",
                                "type": "added"
                            },
                            {
                                "content": "        invalidate.assert_not_called()",
                                "type": "added"
                            },
                            {
                                "content": "        widget._on_cut_changed(3)",
                                "type": "added"
                            },
                            {
                                "content": "        invalidate.assert_called()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        widget.ncomponents = 0  # Take all components",
                                "type": "added"
                            },
                            {
                                "content": "        invalidate.reset_mock()",
                                "type": "added"
                            },
                            {
                                "content": "        widget._on_cut_changed(1)",
                                "type": "added"
                            },
                            {
                                "content": "        invalidate.assert_not_called()",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(widget.ncomponents, 0)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "added"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "added"
                            },
                            {
                                "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]\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)\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(X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data)\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.send_signal(self.widget.Inputs.data, data)\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        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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)\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_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        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)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 19,
                            "end_lineno": 221,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "method",
                            "name": "TestOWPCA.test_on_cut_changed",
                            "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]\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)\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(X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data)\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.send_signal(self.widget.Inputs.data, data)\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        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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)\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_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        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)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 208,
                            "end_lineno": 221,
                            "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]\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)\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(X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data)\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.send_signal(self.widget.Inputs.data, data)\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        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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)\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_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        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)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 19,
                                "end_lineno": 221,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "statement",
                            "name": "",
                            "content": "if __name__ == \"__main__\":\n    unittest.main()",
                            "ast_type_str": "If",
                            "start_lineno": 224,
                            "end_lineno": 225,
                            "parent": null
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "c5b28736a5ace4f481d38e9f94c47f0bcff91985^",
    "new_commit_hash": "c5b28736a5ace4f481d38e9f94c47f0bcff91985",
    "commit_message": "OWPCA: Fix invalid callback on treshold drag",
    "commit_date": "2019-09-22T15:16:20+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "c5b28736a5ace4f481d38e9f94c47f0bcff91985",
    "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\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.preprocess.preprocess import Normalize\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse\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]\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)\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(X)\n        self.widget.ncomponents = 100\n        self.send_signal(self.widget.Inputs.data, data)\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.send_signal(self.widget.Inputs.data, data)\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        var2 = self.widget.variance_covered\n        self.widget.ncomponents = 3\n        self.widget._update_selection_component_spin()\n        var3 = self.widget.variance_covered\n        self.assertGreater(var3, var2)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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\", wraps=Normalize) as normalize:\n            self.send_signal(self.widget.Inputs.data, data)\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)\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_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        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\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...\nrunning build_ext\nrunning build_src\nbuild_src\nbuilding extension \"Orange.classification._simple_tree\" sources\nbuilding extension \"Orange.classification._tree_scorers\" sources\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._contingency\" sources\nbuilding extension \"Orange.data._io\" sources\nbuilding extension \"Orange.data._variable\" sources\nbuilding extension \"Orange.distance._distance\" sources\nbuilding extension \"Orange.preprocess._discretize\" sources\nbuilding extension \"Orange.preprocess._relieff\" sources\nbuilding extension \"Orange.projection._som\" sources\nbuilding extension \"Orange.widgets.utils._grid_density\" sources\nbuild_src: building npy-pkg config files\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmp1f1ciyve/tmp\ncreating /tmp/tmp1f1ciyve/tmp/tmp1f1ciyve\ncompile options: '-MMD -MF /tmp/tmp1f1ciyve/file.c.d -c'\ncc: /tmp/tmp1f1ciyve/file.c\ncustomize UnixCCompiler using build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpojwo5_gr/tmp\ncreating /tmp/tmpojwo5_gr/tmp/tmpojwo5_gr\ncompile options: '-MMD -MF /tmp/tmpojwo5_gr/file.c.d -c'\ncc: /tmp/tmpojwo5_gr/file.c\ncustomize UnixCCompiler using build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/classification/_simple_tree.c\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 Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/classification/_tree_scorers.c\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 Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_valuecount.c\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 Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_contingency.c\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 Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_io.c\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 Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_variable.c\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 Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/distance/_distance.c\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 Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/preprocess/_discretize.c\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 Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncompiling C++ sources\nC compiler: c++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\nc++: Orange/preprocess/_relieff.cpp\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 Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/projection/_som.c\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 Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncompiling C++ sources\nC compiler: c++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\nc++: Orange/widgets/utils/_grid_density.cpp\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 Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\nrunning develop\nrunning build_scripts\nrunning egg_info\nrunning build_src\nbuild_src\nbuilding extension \"Orange.classification._simple_tree\" sources\nbuilding extension \"Orange.classification._tree_scorers\" sources\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._contingency\" sources\nbuilding extension \"Orange.data._io\" sources\nbuilding extension \"Orange.data._variable\" sources\nbuilding extension \"Orange.distance._distance\" sources\nbuilding extension \"Orange.preprocess._discretize\" sources\nbuilding extension \"Orange.preprocess._relieff\" sources\nbuilding extension \"Orange.projection._som\" sources\nbuilding extension \"Orange.widgets.utils._grid_density\" sources\nbuild_src: building npy-pkg config files\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'\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 '*.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 'COPYING'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmp3phc96_h/tmp\ncreating /tmp/tmp3phc96_h/tmp/tmp3phc96_h\ncompile options: '-MMD -MF /tmp/tmp3phc96_h/file.c.d -c'\ncc: /tmp/tmp3phc96_h/file.c\ncustomize UnixCCompiler using build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmp18bj3mao/tmp\ncreating /tmp/tmp18bj3mao/tmp/tmp18bj3mao\ncompile options: '-MMD -MF /tmp/tmp18bj3mao/file.c.d -c'\ncc: /tmp/tmp18bj3mao/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.24.0.dev0+c5b2873 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985\nProcessing dependencies for Orange3==3.24.0.dev0+c5b2873\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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for setuptools==59.8.0\nBest match: setuptools 59.8.0\nAdding setuptools 59.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for scipy==1.7.3\nBest match: scipy 1.7.3\nAdding scipy 1.7.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for scikit-learn==1.0.2\nBest match: scikit-learn 1.0.2\nAdding scikit-learn 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for requests==2.31.0\nBest match: requests 2.31.0\nAdding requests 2.31.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for PyYAML==6.0.1\nBest match: PyYAML 6.0.1\nAdding PyYAML 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for pyqtgraph==0.12.4\nBest match: pyqtgraph 0.12.4\nAdding pyqtgraph 0.12.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for pip==24.0\nBest match: pip 24.0\nAdding pip 24.0 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for pandas==1.3.5\nBest match: pandas 1.3.5\nAdding pandas 1.3.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for orange-widget-base==4.0.1\nBest match: orange-widget-base 4.0.1\nAdding orange-widget-base 4.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for numpy==1.17.5\nBest match: numpy 1.17.5\nAdding numpy 1.17.5 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for networkx==2.6.3\nBest match: networkx 2.6.3\nAdding networkx 2.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for matplotlib==3.5.3\nBest match: matplotlib 3.5.3\nAdding matplotlib 3.5.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for keyrings.alt==4.2.0\nBest match: keyrings.alt 4.2.0\nAdding keyrings.alt 4.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for keyring==24.1.1\nBest match: keyring 24.1.1\nAdding keyring 24.1.1 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for joblib==1.3.2\nBest match: joblib 1.3.2\nAdding joblib 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for threadpoolctl==3.1.0\nBest match: threadpoolctl 3.1.0\nAdding threadpoolctl 3.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for urllib3==2.0.7\nBest match: urllib3 2.0.7\nAdding urllib3 2.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for packaging==24.0\nBest match: packaging 24.0\nAdding packaging 24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for importlib-metadata==6.7.0\nBest match: importlib-metadata 6.7.0\nAdding importlib-metadata 6.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for qasync==0.25.0\nBest match: qasync 0.25.0\nAdding qasync 0.25.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for requests-cache==1.1.1\nBest match: requests-cache 1.1.1\nAdding requests-cache 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for fonttools==4.38.0\nBest match: fonttools 4.38.0\nAdding fonttools 4.38.0 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for Pillow==9.5.0\nBest match: Pillow 9.5.0\nAdding Pillow 9.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for cycler==0.11.0\nBest match: cycler 0.11.0\nAdding cycler 0.11.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for kiwisolver==1.4.5\nBest match: kiwisolver 1.4.5\nAdding kiwisolver 1.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for jaraco.classes==3.2.3\nBest match: jaraco.classes 3.2.3\nAdding jaraco.classes 3.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for importlib-resources==5.12.0\nBest match: importlib-resources 5.12.0\nAdding importlib-resources 5.12.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for zipp==3.15.0\nBest match: zipp 3.15.0\nAdding zipp 3.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for typing-extensions==4.7.1\nBest match: typing-extensions 4.7.1\nAdding typing-extensions 4.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for cattrs==23.1.2\nBest match: cattrs 23.1.2\nAdding cattrs 23.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for attrs==24.2.0\nBest match: attrs 24.2.0\nAdding attrs 24.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for platformdirs==4.0.0\nBest match: platformdirs 4.0.0\nAdding platformdirs 4.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for more-itertools==9.1.0\nBest match: more-itertools 9.1.0\nAdding more-itertools 9.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for cffi==1.15.1\nBest match: cffi 1.15.1\nAdding cffi 1.15.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nSearching for pycparser==2.21\nBest match: pycparser 2.21\nAdding pycparser 2.21 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.24.0.dev0+c5b2873\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.7\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 135ms\nPrepared 18 packages in 22ms\nInstalled 18 packages in 123ms\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\nResolved 38 packages in 210ms\nInstalled 33 packages in 602ms\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.15.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + cryptography==44.0.0\n + idna==3.10\n + importlib-resources==5.12.0\n + jaraco-classes==3.2.3\n + jeepney==0.8.0\n + joblib==1.3.2\n + keyring==24.1.1\n + keyrings-alt==4.2.0\n + more-itertools==9.1.0\n + networkx==2.6.3\n + opentsne==1.0.1\n + pandas==1.3.5\n + pip==24.0\n + pycparser==2.21\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.1\n + requests==2.31.0\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 34 packages in 146ms\nInstalled 19 packages in 223ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + cattrs==23.1.2\n + commonmark==0.9.1\n + cycler==0.11.0\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + fonttools==4.38.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.0.1\n + pillow==9.5.0\n + platformdirs==4.0.0\n + pyparsing==3.1.4\n + pyqtgraph==0.12.4\n + qasync==0.25.0\n + requests-cache==1.1.1\n + url-normalize==1.4.3\nResolved 2 packages in 3ms\nInstalled 2 packages in 13ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nAudited 1 package in 14ms\nAudited 2 packages in 1ms\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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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:3115:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3115 |   __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:4619:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4619 |   __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:5396:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5396 |           for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5517:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5517 |           __pyx_t_27 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5523:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5523 |           __pyx_t_27 = (((__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:7051:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7051 |         for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7741:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7741 |         for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7801:143: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7801 |               __pyx_t_17 = (((*((__pyx_t_5numpy_int32_t *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_18 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                               ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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:3881:24: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3881 |           __pyx_v_decs = (__pyx_v_decs / 10.0);\n      |           ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:4070:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4070 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 221, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:480:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  480 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1301:7: note: ‘__pyx_v_attr_index’ was declared here\n 1301 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:2935:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2935 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:581:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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:3655:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3655 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3654:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3654 |   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:4547:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4547 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_38, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_39, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4114:7: note: ‘__pyx_v_win_y’ was declared here\n 4114 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4536:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4536 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_36, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_37, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4113:7: note: ‘__pyx_v_win_x’ was declared here\n 4113 |   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:4746:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4746 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4745:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4745 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3141:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3141 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:2964:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2964 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2665:7: note: ‘__pyx_v_win_y’ was declared here\n 2665 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:2953:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2953 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_25, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2664:7: note: ‘__pyx_v_win_x’ was declared here\n 2664 |   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:5515:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5515 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5514:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5514 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/.venv/lib/python3.7/site-packages/setuptools/command/egg_info.py:628: SetuptoolsDeprecationWarning: Custom 'build_py' does not implement 'get_data_files_without_manifest'.\nPlease extend command classes from setuptools instead of distutils.\n  SetuptoolsDeprecationWarning\n",
    "new_commit_res_code": 1,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985\ncollected 17 items\n\nr2e_tests/test_1.py E................                                    [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/Orange/tests/__init__.py, line 65\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: 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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/Orange/tests/__init__.py:65\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_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_set_variance100\nPASSED r2e_tests/test_1.py::TestOWPCA::test_sparse_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_shown\nERROR r2e_tests/test_1.py::test_filename\n========================= 16 passed, 1 error in 5.36s ==========================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985\ncollected 17 items\n\nr2e_tests/test_1.py E............F...                                    [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c5b28736a5ace4f481d38e9f94c47f0bcff91985/Orange/tests/__init__.py, line 65\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: 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_c5b28736a5ace4f481d38e9f94c47f0bcff91985/Orange/tests/__init__.py:65\n=================================== FAILURES ===================================\n________________________ TestOWPCA.test_on_cut_changed _________________________\n\nself = <r2e_tests.test_1.TestOWPCA testMethod=test_on_cut_changed>\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\nr2e_tests/test_1.py:213: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\n_mock_self = <Mock id='123167107947088'>\n\n    def assert_not_called(_mock_self):\n        \"\"\"assert that the mock was never called.\n        \"\"\"\n        self = _mock_self\n        if self.call_count != 0:\n            msg = (\"Expected '%s' to not have been called. Called %s times.\" %\n                   (self._mock_name or 'mock', self.call_count))\n>           raise AssertionError(msg)\nE           AssertionError: Expected 'mock' to not have been called. Called 1 times.\n\n../../../../.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib/python3.7/unittest/mock.py:841: 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_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_set_variance100\nPASSED r2e_tests/test_1.py::TestOWPCA::test_sparse_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_shown\nERROR r2e_tests/test_1.py::test_filename\nFAILED r2e_tests/test_1.py::TestOWPCA::test_on_cut_changed - AssertionError: ...\n==================== 1 failed, 15 passed, 1 error in 3.86s =====================\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:** Changing PCA components incorrectly invalidates selection under certain conditions

**Description:**
When adjusting the number of PCA components, setting it to the current number of components or to zero should not invalidate the current selection. However, attempting to do so inadvertently triggers the selection invalidation, leading to unnecessary processing and potential errors.

**Example Code:**
```python
widget = OWPCA()
widget.ncomponents = 2
invalidate = widget._invalidate_selection = Mock()

# Setting to the same number of components should not invalidate the selection
widget._on_cut_changed(2)
invalidate.assert_not_called()

# Changing to a different number should invalidate the selection
widget._on_cut_changed(3)
invalidate.assert_called()

# Setting to zero (take all components) should not invalidate the selection
widget.ncomponents = 0
invalidate.reset_mock()
widget._on_cut_changed(1)
invalidate.assert_not_called()
```

**Expected Behavior:**
- Setting `ncomponents` to its current value or to zero should leave the selection unchanged and not trigger any invalidation callbacks.

**Actual Behavior:**
- The selection is incorrectly invalidated even when setting `ncomponents` to its current value or to zero, disrupting the workflow and causing unexpected behavior.

[/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 Orange.data import Table, Domain, StringVariable, ContinuousVariable
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.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


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

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

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

    settingsHandler = settings.DomainContextHandler()

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

    graph_name = "plot.plotItem"

    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__()
        self.data = None

        self._pca = None
        self._transformed = None
        self._variance_ratio = None
        self._cumulative = None
        self._init_projector()

        # Components Selection
        box = gui.vBox(self.controlArea, "Components Selection")
        form = QFormLayout()
        box.layout().addLayout(form)

        self.components_spin = gui.spin(
            box, self, "ncomponents", 1, MAX_COMPONENTS,
            callback=self._update_selection_component_spin,
            keyboardTracking=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
        )
        self.variance_spin.setSuffix("%")

        form.addRow("Components:", self.components_spin)
        form.addRow("Variance covered:", self.variance_spin)

        # Options
        self.options_box = gui.vBox(self.controlArea, "Options")
        self.normalize_box = gui.checkBox(
            self.options_box, self, "normalize",
            "Normalize data", callback=self._update_normalize
        )

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

        self.controlArea.layout().addStretch()

        gui.auto_apply(self.controlArea, 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.closeContext()
        self.clear_messages()
        self.clear()
        self.information()
        self.data = None
        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.openContext(data)
        self._init_projector()

        self.data = data
        self.fit()

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

        data = self.data

        if self.normalize:
            self._pca_projector.preprocessors = \
                self._pca_preprocessors + [preprocess.Normalize(center=False)]
        else:
            self._pca_projector.preprocessors = self._pca_preprocessors

        if not isinstance(data, SqlTable):
            pca = self._pca_projector(data)
            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.unconditional_commit()

    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.components.send(None)
        self.Outputs.pca.send(self._pca_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)

        self._update_axis()

    def _on_cut_changed(self, components):

        if not (self.ncomponents == 0 and
                components == len(self._variance_ratio)):
            self.ncomponents = components

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

        if components != self._nselected_components():
            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 _init_projector(self):
        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)
        self._pca_projector.component = self.ncomponents
        self._pca_preprocessors = PCA.preprocessors

    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()

    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)]])

    def commit(self):
        transformed = 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

            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
            dom = Domain(
                [ContinuousVariable(a.name, compute_value=lambda _: None)
                 for a in self._pca.orig_domain.attributes],
                metas=[StringVariable(name='component')])
            metas = numpy.array([['PC{}'.format(i + 1)
                                  for i in range(self.ncomponents)]],
                                dtype=object).T
            components = Table(dom, self._pca.components_[:self.ncomponents],
                               metas=metas)
            components.name = 'components'

        self._pca_projector.component = self.ncomponents
        self.Outputs.transformed_data.send(transformed)
        self.Outputs.components.send(components)
        self.Outputs.pca.send(self._pca_projector)

    def send_report(self):
        if self.data is None:
            return
        self.report_items((
            ("Normalize data", 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:** Changing PCA components incorrectly invalidates selection under certain conditions

**Description:**
When adjusting the number of PCA components, setting it to the current number of components or to zero should not invalidate the current selection. However, attempting to do so inadvertently triggers the selection invalidation, leading to unnecessary processing and potential errors.

**Example Code:**
```python
widget = OWPCA()
widget.ncomponents = 2
invalidate = widget._invalidate_selection = Mock()

# Setting to the same number of components should not invalidate the selection
widget._on_cut_changed(2)
invalidate.assert_not_called()

# Changing to a different number should invalidate the selection
widget._on_cut_changed(3)
invalidate.assert_called()

# Setting to zero (take all components) should not invalidate the selection
widget.ncomponents = 0
invalidate.reset_mock()
widget._on_cut_changed(1)
invalidate.assert_not_called()
```

**Expected Behavior:**
- Setting `ncomponents` to its current value or to zero should leave the selection unchanged and not trigger any invalidation callbacks.

**Actual Behavior:**
- The selection is incorrectly invalidated even when setting `ncomponents` to its current value or to zero, disrupting the workflow and causing unexpected behavior.

[/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_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_set_variance100": "PASSED",
    "TestOWPCA.test_sparse_data": "PASSED",
    "TestOWPCA.test_variance_shown": "PASSED",
    "test_filename": "ERROR"
}
diff --git a/Orange/widgets/unsupervised/owpca.py b/Orange/widgets/unsupervised/owpca.py
index 76936fe8f..1bb96df1c 100644
--- a/Orange/widgets/unsupervised/owpca.py
+++ b/Orange/widgets/unsupervised/owpca.py
@@ -199,18 +199,19 @@ class OWPCA(widget.OWWidget):
         self._update_axis()
 
     def _on_cut_changed(self, components):
+        if components == self.ncomponents \
+                or self.ncomponents == 0 \
+                or self._pca is not None \
+                and components == len(self._variance_ratio):
+            return
 
-        if not (self.ncomponents == 0 and
-                components == len(self._variance_ratio)):
-            self.ncomponents = components
-
+        self.ncomponents = components
         if self._pca is not None:
             var = self._cumulative[components - 1]
             if numpy.isfinite(var):
                 self.variance_covered = int(var * 100)
 
-        if components != self._nselected_components():
-            self._invalidate_selection()
+        self._invalidate_selection()
 
     def _update_selection_component_spin(self):
         # cut changed by "ncomponents" spin.

{"Orange/widgets/unsupervised/owpca.py": "import numbers\n\nimport numpy\nfrom AnyQt.QtWidgets import QFormLayout\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, StringVariable, ContinuousVariable\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.slidergraph import SliderGraph\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import Input, Output\n\n\n# Maximum number of PCA components that we can set in the widget\nMAX_COMPONENTS = 100\n\n\nclass OWPCA(widget.OWWidget):\n    name = \"PCA\"\n    description = \"Principal component analysis with a scree-diagram.\"\n    icon = \"icons/PCA.svg\"\n    priority = 3050\n    keywords = [\"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        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    settingsHandler = settings.DomainContextHandler()\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.ContextSetting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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        self.data = None\n\n        self._pca = None\n        self._transformed = None\n        self._variance_ratio = None\n        self._cumulative = None\n        self._init_projector()\n\n        # Components Selection\n        box = gui.vBox(self.controlArea, \"Components Selection\")\n        form = QFormLayout()\n        box.layout().addLayout(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\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\n        )\n        self.variance_spin.setSuffix(\"%\")\n\n        form.addRow(\"Components:\", self.components_spin)\n        form.addRow(\"Variance covered:\", 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 data\", callback=self._update_normalize\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        self.controlArea.layout().addStretch()\n\n        gui.auto_apply(self.controlArea, 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.closeContext()\n        self.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\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.openContext(data)\n        self._init_projector()\n\n        self.data = data\n        self.fit()\n\n    def fit(self):\n        self.clear()\n        self.Warning.trivial_components.clear()\n        if self.data is None:\n            return\n\n        data = self.data\n\n        if self.normalize:\n            self._pca_projector.preprocessors = \\\n                self._pca_preprocessors + [preprocess.Normalize(center=False)]\n        else:\n            self._pca_projector.preprocessors = self._pca_preprocessors\n\n        if not isinstance(data, SqlTable):\n            pca = self._pca_projector(data)\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.unconditional_commit()\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.components.send(None)\n        self.Outputs.pca.send(self._pca_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)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n\n        if not (self.ncomponents == 0 and\n                components == len(self._variance_ratio)):\n            self.ncomponents = components\n\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        if components != self._nselected_components():\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 _init_projector(self):\n        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)\n        self._pca_projector.component = self.ncomponents\n        self._pca_preprocessors = PCA.preprocessors\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()\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    def commit(self):\n        transformed = 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            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            # prevent caching new features by defining compute_value\n            dom = Domain(\n                [ContinuousVariable(a.name, compute_value=lambda _: None)\n                 for a in self._pca.orig_domain.attributes],\n                metas=[StringVariable(name='component')])\n            metas = numpy.array([['PC{}'.format(i + 1)\n                                  for i in range(self.ncomponents)]],\n                                dtype=object).T\n            components = Table(dom, self._pca.components_[:self.ncomponents],\n                               metas=metas)\n            components.name = 'components'\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.pca.send(self._pca_projector)\n\n    def send_report(self):\n        if self.data is None:\n            return\n        self.report_items((\n            (\"Normalize data\", 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:** Changing PCA components incorrectly invalidates selection under certain conditions

**Description:**
When adjusting the number of PCA components, setting it to the current number of components or to zero should not invalidate the current selection. However, attempting to do so inadvertently triggers the selection invalidation, leading to unnecessary processing and potential errors.

**Example Code:**
```python
widget = OWPCA()
widget.ncomponents = 2
invalidate = widget._invalidate_selection = Mock()

# Setting to the same number of components should not invalidate the selection
widget._on_cut_changed(2)
invalidate.assert_not_called()

# Changing to a different number should invalidate the selection
widget._on_cut_changed(3)
invalidate.assert_called()

# Setting to zero (take all components) should not invalidate the selection
widget.ncomponents = 0
invalidate.reset_mock()
widget._on_cut_changed(1)
invalidate.assert_not_called()
```

**Expected Behavior:**
- Setting `ncomponents` to its current value or to zero should leave the selection unchanged and not trigger any invalidation callbacks.

**Actual Behavior:**
- The selection is incorrectly invalidated even when setting `ncomponents` to its current value or to zero, disrupting the workflow and causing unexpected behavior.

[/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 Orange.data import Table, Domain, StringVariable, ContinuousVariable
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.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


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

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

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

    settingsHandler = settings.DomainContextHandler()

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

    graph_name = "plot.plotItem"

    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__()
        self.data = None

        self._pca = None
        self._transformed = None
        self._variance_ratio = None
        self._cumulative = None
        self._init_projector()

        # Components Selection
        box = gui.vBox(self.controlArea, "Components Selection")
        form = QFormLayout()
        box.layout().addLayout(form)

        self.components_spin = gui.spin(
            box, self, "ncomponents", 1, MAX_COMPONENTS,
            callback=self._update_selection_component_spin,
            keyboardTracking=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
        )
        self.variance_spin.setSuffix("%")

        form.addRow("Components:", self.components_spin)
        form.addRow("Variance covered:", self.variance_spin)

        # Options
        self.options_box = gui.vBox(self.controlArea, "Options")
        self.normalize_box = gui.checkBox(
            self.options_box, self, "normalize",
            "Normalize data", callback=self._update_normalize
        )

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

        self.controlArea.layout().addStretch()

        gui.auto_apply(self.controlArea, 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.closeContext()
        self.clear_messages()
        self.clear()
        self.information()
        self.data = None
        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.openContext(data)
        self._init_projector()

        self.data = data
        self.fit()

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

        data = self.data

        if self.normalize:
            self._pca_projector.preprocessors = \
                self._pca_preprocessors + [preprocess.Normalize(center=False)]
        else:
            self._pca_projector.preprocessors = self._pca_preprocessors

        if not isinstance(data, SqlTable):
            pca = self._pca_projector(data)
            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.unconditional_commit()

    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.components.send(None)
        self.Outputs.pca.send(self._pca_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)

        self._update_axis()

    def _on_cut_changed(self, components):

        if not (self.ncomponents == 0 and
                components == len(self._variance_ratio)):
            self.ncomponents = components

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

        if components != self._nselected_components():
            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 _init_projector(self):
        self._pca_projector = PCA(n_components=MAX_COMPONENTS, random_state=0)
        self._pca_projector.component = self.ncomponents
        self._pca_preprocessors = PCA.preprocessors

    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()

    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)]])

    def commit(self):
        transformed = 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

            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
            dom = Domain(
                [ContinuousVariable(a.name, compute_value=lambda _: None)
                 for a in self._pca.orig_domain.attributes],
                metas=[StringVariable(name='component')])
            metas = numpy.array([['PC{}'.format(i + 1)
                                  for i in range(self.ncomponents)]],
                                dtype=object).T
            components = Table(dom, self._pca.components_[:self.ncomponents],
                               metas=metas)
            components.name = 'components'

        self._pca_projector.component = self.ncomponents
        self.Outputs.transformed_data.send(transformed)
        self.Outputs.components.send(components)
        self.Outputs.pca.send(self._pca_projector)

    def send_report(self):
        if self.data is None:
            return
        self.report_items((
            ("Normalize data", 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