{
    "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.util import get_unique_names\nfrom Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT\nfrom Orange.preprocess import preprocess\nfrom Orange.projection import PCA\nfrom Orange.widgets import widget, gui, settings\nfrom Orange.widgets.utils.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\nLINE_NAMES = [\"component variance\", \"cumulative variance\"]\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        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize\n        )\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.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._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.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, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                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 = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            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            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=[StringVariable(name=meta_name)])\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            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._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.util import get_unique_names\nfrom Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT\nfrom Orange.preprocess import preprocess\nfrom Orange.projection import PCA\nfrom Orange.widgets import widget, gui, settings\nfrom Orange.widgets.utils.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\nLINE_NAMES = [\"component variance\", \"cumulative variance\"]\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        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize\n        )\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.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self._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.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, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                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 = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            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            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=[StringVariable(name=meta_name)])\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            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._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"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "f03b52167",
                "new_commit_hash": "6aced939b",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/unsupervised/owpca.py"
            },
            "plus_file": {
                "path": "b/Orange/widgets/unsupervised/owpca.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 116,
                            "length": 6
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                        "new_range": {
                            "start": 116,
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                        },
                        "section": "class OWPCA(widget.OWWidget):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        self.clear()",
                                "type": "context"
                            },
                            {
                                "content": "        self.information()",
                                "type": "context"
                            },
                            {
                                "content": "        self.data = None",
                                "type": "context"
                            },
                            {
                                "content": "        if not data:",
                                "type": "added"
                            },
                            {
                                "content": "            self.clear_outputs()",
                                "type": "added"
                            },
                            {
                                "content": "        if isinstance(data, SqlTable):",
                                "type": "context"
                            },
                            {
                                "content": "            if data.approx_len() < AUTO_DL_LIMIT:",
                                "type": "context"
                            },
                            {
                                "content": "                data = Table(data)",
                                "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        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize\n        )\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.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self._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.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, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                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 = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            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            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=[StringVariable(name=meta_name)])\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            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._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": 23,
                            "end_lineno": 363,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owpca.py",
                            "type": "method",
                            "name": "OWPCA.set_data",
                            "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        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize\n        )\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.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self._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.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, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                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 = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            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            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=[StringVariable(name=meta_name)])\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            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._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": 114,
                            "end_lineno": 142,
                            "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        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize\n        )\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.clear_messages()\n        self.clear()\n        self.information()\n        self.data = None\n        if not data:\n            self.clear_outputs()\n        if isinstance(data, SqlTable):\n            if data.approx_len() < AUTO_DL_LIMIT:\n                data = Table(data)\n            else:\n                self.information(\"Data has been sampled\")\n                data_sample = data.sample_time(1, no_cache=True)\n                data_sample.download_data(2000, partial=True)\n                data = Table(data_sample)\n        if isinstance(data, Table):\n            if not data.domain.attributes:\n                self.Error.no_features()\n                self.clear_outputs()\n                return\n            if not data:\n                self.Error.no_instances()\n                self.clear_outputs()\n                return\n\n        self._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.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, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                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 = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            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            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=[StringVariable(name=meta_name)])\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            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._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": 23,
                                "end_lineno": 363,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\n\nfrom Orange.data import Table, Domain, ContinuousVariable, TimeVariable\nfrom Orange.preprocess import preprocess\nfrom Orange.preprocess.preprocess import Normalize\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import table_dense_sparse, possible_duplicate_table\nfrom Orange.widgets.unsupervised.owpca import OWPCA\nfrom Orange.tests import test_filename\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.extmath import svd_flip\n\n\nclass TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5]\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.from_numpy(None, 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_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_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    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\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, possible_duplicate_table\nfrom Orange.widgets.unsupervised.owpca import OWPCA\nfrom Orange.tests import test_filename\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.extmath import svd_flip\n\n\nclass TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5]\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.from_numpy(None, 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_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_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    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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                            "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.from_numpy(None, 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_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_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    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
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                            "end_lineno": 253,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owpca.py",
                            "type": "method",
                            "name": "TestOWPCA.test_output_data",
                            "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.from_numpy(None, 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_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_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    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 229,
                            "end_lineno": 253,
                            "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.from_numpy(None, 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_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_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    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 19,
                                "end_lineno": 253,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "b5c62e0ffdb180e11f8e307a600afe1b97552f1d^",
    "new_commit_hash": "b5c62e0ffdb180e11f8e307a600afe1b97552f1d",
    "commit_message": "OWPCA: clear output when input data is removed",
    "commit_date": "2020-09-03T17:48:35+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "b5c62e0ffdb180e11f8e307a600afe1b97552f1d",
    "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, possible_duplicate_table\nfrom Orange.widgets.unsupervised.owpca import OWPCA\nfrom Orange.tests import test_filename\nfrom sklearn.utils import check_random_state\nfrom sklearn.utils.extmath import svd_flip\n\n\nclass TestOWPCA(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(OWPCA)  # type: OWPCA\n        self.iris = Table(\"iris\")  # type: Table\n\n    def test_set_variance100(self):\n        self.widget.set_data(self.iris)\n        self.widget.variance_covered = 100\n        self.widget._update_selection_variance_spin()\n\n    def test_constant_data(self):\n        data = self.iris[::5]\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.from_numpy(None, 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_unique_domain_components(self):\n        table = possible_duplicate_table('components')\n        self.send_signal(self.widget.Inputs.data, table)\n        out = self.get_output(self.widget.Outputs.components)\n        self.assertEqual(out.domain.metas[0].name, 'components (1)')\n\n    def test_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    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n\n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n\n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n        self.assertIsNone(output)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "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/tmpmhbuf_m7/tmp\ncreating /tmp/tmpmhbuf_m7/tmp/tmpmhbuf_m7\ncompile options: '-MMD -MF /tmp/tmpmhbuf_m7/file.c.d -c'\ncc: /tmp/tmpmhbuf_m7/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/tmpcqit0qeq/tmp\ncreating /tmp/tmpcqit0qeq/tmp/tmpcqit0qeq\ncompile options: '-MMD -MF /tmp/tmpcqit0qeq/file.c.d -c'\ncc: /tmp/tmpcqit0qeq/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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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/tmpvedpmx7m/tmp\ncreating /tmp/tmpvedpmx7m/tmp/tmpvedpmx7m\ncompile options: '-MMD -MF /tmp/tmpvedpmx7m/file.c.d -c'\ncc: /tmp/tmpvedpmx7m/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/tmpgc9z35lc/tmp\ncreating /tmp/tmpgc9z35lc/tmp/tmpgc9z35lc\ncompile options: '-MMD -MF /tmp/tmpgc9z35lc/file.c.d -c'\ncc: /tmp/tmpgc9z35lc/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.27.0.dev0+b5c62e0 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d\nProcessing dependencies for Orange3==3.27.0.dev0+b5c62e0\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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for openpyxl==3.1.3\nBest match: openpyxl 3.1.3\nAdding openpyxl 3.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for httpx==0.24.1\nBest match: httpx 0.24.1\nAdding httpx 0.24.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for PyQtWebEngine==5.15.6\nBest match: PyQtWebEngine 5.15.6\nAdding PyQtWebEngine 5.15.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for PyQt5==5.15.10\nBest match: PyQt5 5.15.10\nAdding PyQt5 5.15.10 to easy-install.pth file\nInstalling pylupdate5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling pyrcc5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling pyuic5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for et-xmlfile==1.1.0\nBest match: et-xmlfile 1.1.0\nAdding et-xmlfile 1.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for httpcore==0.17.3\nBest match: httpcore 0.17.3\nAdding httpcore 0.17.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for PyQtWebEngine-Qt5==5.15.16\nBest match: PyQtWebEngine-Qt5 5.15.16\nAdding PyQtWebEngine-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for PyQt5-sip==12.13.0\nBest match: PyQt5-sip 12.13.0\nAdding PyQt5-sip 12.13.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for PyQt5-Qt5==5.15.16\nBest match: PyQt5-Qt5 5.15.16\nAdding PyQt5-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nSearching for anyio==3.7.1\nBest match: anyio 3.7.1\nAdding anyio 3.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.27.0.dev0+b5c62e0\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 123ms\nPrepared 18 packages in 17ms\nInstalled 18 packages in 205ms\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 54 packages in 325ms\nInstalled 47 packages in 426ms\n + anyio==3.7.1\n + baycomp==1.0.3\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 + cycler==0.11.0\n + et-xmlfile==1.1.0\n + fonttools==4.38.0\n + h11==0.14.0\n + httpcore==0.17.3\n + httpx==0.24.1\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 + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + more-itertools==9.1.0\n + networkx==2.6.3\n + openpyxl==3.1.3\n + opentsne==1.0.1\n + pandas==1.3.5\n + pillow==9.5.0\n + pip==24.0\n + pycparser==2.21\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.1\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 + sniffio==1.3.1\n + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 39 packages in 107ms\nInstalled 13 packages in 100ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + cattrs==23.1.2\n + commonmark==0.9.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + platformdirs==4.0.0\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 4ms\nInstalled 2 packages in 27ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nAudited 1 package in 18ms\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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:627:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/.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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d\nplugins: anyio-3.7.1\ncollected 23 items\n\nr2e_tests/test_1.py sssE...................                              [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/Orange/tests/__init__.py, line 65\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/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_image_export\nPASSED r2e_tests/test_1.py::TestOWPCA::test_limit_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_changes_variance_covered_to_int\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_limits_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWPCA::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalization_variance\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalize_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalized_gives_correct_result\nPASSED r2e_tests/test_1.py::TestOWPCA::test_on_cut_changed\nPASSED r2e_tests/test_1.py::TestOWPCA::test_output_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_set_variance100\nPASSED r2e_tests/test_1.py::TestOWPCA::test_sparse_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_unique_domain_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_shown\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nERROR r2e_tests/test_1.py::test_filename\n==================== 19 passed, 3 skipped, 1 error in 5.75s ====================\nWidgets left: 0    Max widgets: 2552 \n\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_b5c62e0ffdb180e11f8e307a600afe1b97552f1d\nplugins: anyio-3.7.1\ncollected 23 items\n\nr2e_tests/test_1.py sssE..............F....                              [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/Orange/tests/__init__.py, line 65\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_b5c62e0ffdb180e11f8e307a600afe1b97552f1d/Orange/tests/__init__.py:65\n=================================== FAILURES ===================================\n__________________________ TestOWPCA.test_output_data __________________________\n\nself = <r2e_tests.test_1.TestOWPCA testMethod=test_output_data>\n\n    def test_output_data(self):\n        widget = self.widget\n        widget.ncomponents = 2\n        domain = Domain(self.iris.domain.attributes[:3],\n                        self.iris.domain.class_var,\n                        self.iris.domain.attributes[3:])\n        iris = self.iris.transform(domain)\n        self.send_signal(widget.Inputs.data, iris)\n        output = self.get_output(widget.Outputs.data)\n        outdom = output.domain\n        self.assertEqual(domain.attributes, outdom.attributes)\n        self.assertEqual(domain.class_var, outdom.class_var)\n        self.assertEqual(domain.metas, outdom.metas[:1])\n        self.assertEqual(len(outdom.metas), 3)\n        np.testing.assert_equal(iris.X, output.X)\n        np.testing.assert_equal(iris.Y, output.Y)\n        np.testing.assert_equal(iris.metas[:, 0], output.metas[:, 0])\n    \n        trans = self.get_output(widget.Outputs.transformed_data)\n        self.assertEqual(trans.domain.attributes, outdom.metas[1:])\n        np.testing.assert_equal(trans.X, output.metas[:, 1:])\n    \n        self.send_signal(widget.Inputs.data, None)\n        output = self.get_output(widget.Outputs.data)\n>       self.assertIsNone(output)\nE       AssertionError: [[5.1, 3.5, 1.4 | Iris-setosa] {0.2, -1.85953, 0.449173},\nE        [4.9, 3.0, 1.4 | Iris-setosa] {0.2, -1.60267, -0.697065},\nE        [4.7, 3.2, 1.3 | Iris-setosa] {0.2, -1.95885, -0.387732},\nE        [4.6, 3.1, 1.5 | Iris-setosa] {0.2, -1.87492, -0.639969},\nE        [5.0, 3.6, 1.4 | Iris-setosa] {0.2, -2.018, 0.605866},\nE        ...\nE       ] is not None\n\nr2e_tests/test_1.py:253: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWPCA::test_all_components_continuous\nPASSED r2e_tests/test_1.py::TestOWPCA::test_constant_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_do_not_mask_features\nPASSED r2e_tests/test_1.py::TestOWPCA::test_empty_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_image_export\nPASSED r2e_tests/test_1.py::TestOWPCA::test_limit_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_changes_variance_covered_to_int\nPASSED r2e_tests/test_1.py::TestOWPCA::test_migrate_settings_limits_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWPCA::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalization_variance\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalize_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_normalized_gives_correct_result\nPASSED r2e_tests/test_1.py::TestOWPCA::test_on_cut_changed\nPASSED r2e_tests/test_1.py::TestOWPCA::test_set_variance100\nPASSED r2e_tests/test_1.py::TestOWPCA::test_sparse_data\nPASSED r2e_tests/test_1.py::TestOWPCA::test_unique_domain_components\nPASSED r2e_tests/test_1.py::TestOWPCA::test_variance_shown\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.7/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nERROR r2e_tests/test_1.py::test_filename\nFAILED r2e_tests/test_1.py::TestOWPCA::test_output_data - AssertionError: [[5...\n=============== 1 failed, 18 passed, 3 skipped, 1 error in 5.15s ===============\nWidgets left: 0    Max widgets: 2552 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/unsupervised/owpca.py Orange/widgets/unsupervised/tests/test_owpca.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** OWPCA widget does not clear outputs when input data is removed

**Description:**
When the input data is removed from the OWPCA widget, the outputs remain populated instead of being cleared. This leads to outdated or inconsistent data being present in the outputs even after the input has been cleared.

**Example Code:**
```python
def test_output_clears_when_data_removed():
    widget = OWPCA()
    widget.set_data(sample_data)
    output = widget.get_output()
    widget.remove_data()  # Simulate removing input data
    output_after_removal = widget.get_output()
    # Expected: output_after_removal is None
```

**Expected Behavior:**
After removing the input data, all outputs from the OWPCA widget should be cleared (set to `None`).

**Actual Behavior:**
The outputs remain populated with data even after the input data is removed.

[/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.util import get_unique_names
from Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT
from Orange.preprocess import preprocess
from Orange.projection import PCA
from Orange.widgets import widget, gui, settings
from Orange.widgets.utils.slidergraph import SliderGraph
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Input, Output


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


class OWPCA(widget.OWWidget):
    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"])
        data = Output("Data", Table, default=True)
        components = Output("Components", Table)
        pca = Output("PCA", PCA, dynamic=False)

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

    graph_name = "plot.plotItem"

    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("Explained variance:", self.variance_spin)

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

        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.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._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.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, names=LINE_NAMES)

        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

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

        self._invalidate_selection()

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

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

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

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

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

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

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

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

            domain = Domain(
                transformed.domain.attributes[:self.ncomponents],
                self.data.domain.class_vars,
                self.data.domain.metas
            )
            transformed = transformed.from_table(domain, transformed)
            # prevent caching new features by defining compute_value
            proposed = [a.name for a in self._pca.orig_domain.attributes]
            meta_name = get_unique_names(proposed, 'components')
            dom = Domain(
                [ContinuousVariable(name, compute_value=lambda _: None)
                 for name in proposed],
                metas=[StringVariable(name=meta_name)])
            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'

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

        self._pca_projector.component = self.ncomponents
        self.Outputs.transformed_data.send(transformed)
        self.Outputs.components.send(components)
        self.Outputs.data.send(data)
        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:** OWPCA widget does not clear outputs when input data is removed

**Description:**
When the input data is removed from the OWPCA widget, the outputs remain populated instead of being cleared. This leads to outdated or inconsistent data being present in the outputs even after the input has been cleared.

**Example Code:**
```python
def test_output_clears_when_data_removed():
    widget = OWPCA()
    widget.set_data(sample_data)
    output = widget.get_output()
    widget.remove_data()  # Simulate removing input data
    output_after_removal = widget.get_output()
    # Expected: output_after_removal is None
```

**Expected Behavior:**
After removing the input data, all outputs from the OWPCA widget should be cleared (set to `None`).

**Actual Behavior:**
The outputs remain populated with data even after the input data is removed.

[/ISSUE]
{
    "TestOWPCA.test_all_components_continuous": "PASSED",
    "TestOWPCA.test_constant_data": "PASSED",
    "TestOWPCA.test_do_not_mask_features": "PASSED",
    "TestOWPCA.test_empty_data": "PASSED",
    "TestOWPCA.test_image_export": "PASSED",
    "TestOWPCA.test_limit_components": "PASSED",
    "TestOWPCA.test_migrate_settings_changes_variance_covered_to_int": "PASSED",
    "TestOWPCA.test_migrate_settings_limits_components": "PASSED",
    "TestOWPCA.test_minimum_size": "PASSED",
    "TestOWPCA.test_msg_base_class": "PASSED",
    "TestOWPCA.test_normalization_variance": "PASSED",
    "TestOWPCA.test_normalize_data": "PASSED",
    "TestOWPCA.test_normalized_gives_correct_result": "PASSED",
    "TestOWPCA.test_on_cut_changed": "PASSED",
    "TestOWPCA.test_output_data": "PASSED",
    "TestOWPCA.test_set_variance100": "PASSED",
    "TestOWPCA.test_sparse_data": "PASSED",
    "TestOWPCA.test_unique_domain_components": "PASSED",
    "TestOWPCA.test_variance_shown": "PASSED",
    "test_filename": "ERROR"
}
diff --git a/Orange/widgets/unsupervised/owpca.py b/Orange/widgets/unsupervised/owpca.py
index f03b52167..6aced939b 100644
--- a/Orange/widgets/unsupervised/owpca.py
+++ b/Orange/widgets/unsupervised/owpca.py
@@ -116,6 +116,8 @@ class OWPCA(widget.OWWidget):
         self.clear()
         self.information()
         self.data = None
+        if not data:
+            self.clear_outputs()
         if isinstance(data, SqlTable):
             if data.approx_len() < AUTO_DL_LIMIT:
                 data = Table(data)

{"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.util import get_unique_names\nfrom Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT\nfrom Orange.preprocess import preprocess\nfrom Orange.projection import PCA\nfrom Orange.widgets import widget, gui, settings\nfrom Orange.widgets.utils.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\nLINE_NAMES = [\"component variance\", \"cumulative variance\"]\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        data = Output(\"Data\", Table, default=True)\n        components = Output(\"Components\", Table)\n        pca = Output(\"PCA\", PCA, dynamic=False)\n\n    ncomponents = settings.Setting(2)\n    variance_covered = settings.Setting(100)\n    auto_commit = settings.Setting(True)\n    normalize = settings.Setting(True)\n    maxp = settings.Setting(20)\n    axis_labels = settings.Setting(10)\n\n    graph_name = \"plot.plotItem\"\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(\"Explained variance:\", self.variance_spin)\n\n        # Options\n        self.options_box = gui.vBox(self.controlArea, \"Options\")\n        self.normalize_box = gui.checkBox(\n            self.options_box, self, \"normalize\",\n            \"Normalize variables\", callback=self._update_normalize\n        )\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.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._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.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, names=LINE_NAMES)\n\n        self._update_axis()\n\n    def _on_cut_changed(self, components):\n        if components == self.ncomponents \\\n                or self.ncomponents == 0 \\\n                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 = data = components = None\n        if self._pca is not None:\n            if self._transformed is None:\n                # Compute the full transform (MAX_COMPONENTS components) once.\n                self._transformed = self._pca(self.data)\n            transformed = self._transformed\n\n            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            proposed = [a.name for a in self._pca.orig_domain.attributes]\n            meta_name = get_unique_names(proposed, 'components')\n            dom = Domain(\n                [ContinuousVariable(name, compute_value=lambda _: None)\n                 for name in proposed],\n                metas=[StringVariable(name=meta_name)])\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            data_dom = Domain(\n                self.data.domain.attributes,\n                self.data.domain.class_vars,\n                self.data.domain.metas + domain.attributes)\n            data = Table.from_numpy(\n                data_dom, self.data.X, self.data.Y,\n                numpy.hstack((self.data.metas, transformed.X)),\n                ids=self.data.ids)\n\n        self._pca_projector.component = self.ncomponents\n        self.Outputs.transformed_data.send(transformed)\n        self.Outputs.components.send(components)\n        self.Outputs.data.send(data)\n        self.Outputs.pca.send(self._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:** OWPCA widget does not clear outputs when input data is removed

**Description:**
When the input data is removed from the OWPCA widget, the outputs remain populated instead of being cleared. This leads to outdated or inconsistent data being present in the outputs even after the input has been cleared.

**Example Code:**
```python
def test_output_clears_when_data_removed():
    widget = OWPCA()
    widget.set_data(sample_data)
    output = widget.get_output()
    widget.remove_data()  # Simulate removing input data
    output_after_removal = widget.get_output()
    # Expected: output_after_removal is None
```

**Expected Behavior:**
After removing the input data, all outputs from the OWPCA widget should be cleared (set to `None`).

**Actual Behavior:**
The outputs remain populated with data even after the input data is removed.

[/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.util import get_unique_names
from Orange.data.sql.table import SqlTable, AUTO_DL_LIMIT
from Orange.preprocess import preprocess
from Orange.projection import PCA
from Orange.widgets import widget, gui, settings
from Orange.widgets.utils.slidergraph import SliderGraph
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import Input, Output


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


class OWPCA(widget.OWWidget):
    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"])
        data = Output("Data", Table, default=True)
        components = Output("Components", Table)
        pca = Output("PCA", PCA, dynamic=False)

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

    graph_name = "plot.plotItem"

    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("Explained variance:", self.variance_spin)

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

        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.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._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.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, names=LINE_NAMES)

        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

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

        self._invalidate_selection()

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

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

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

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

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

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

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

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

            domain = Domain(
                transformed.domain.attributes[:self.ncomponents],
                self.data.domain.class_vars,
                self.data.domain.metas
            )
            transformed = transformed.from_table(domain, transformed)
            # prevent caching new features by defining compute_value
            proposed = [a.name for a in self._pca.orig_domain.attributes]
            meta_name = get_unique_names(proposed, 'components')
            dom = Domain(
                [ContinuousVariable(name, compute_value=lambda _: None)
                 for name in proposed],
                metas=[StringVariable(name=meta_name)])
            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'

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

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