{
    "file_diffs": [
        {
            "old_file_content": "from typing import NamedTuple, Dict, Type, Optional\n\nfrom AnyQt.QtWidgets import QButtonGroup, QRadioButton\nfrom AnyQt.QtCore import Qt\nfrom scipy.sparse import issparse\nimport bottleneck as bn\n\nimport Orange.data\nimport Orange.misc\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin\nfrom Orange.widgets.utils.sql import check_sql_input\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import OWWidget, Msg, Input, Output\n\n\nEuclidean, EuclideanNormalized, Manhattan, ManhattanNormalized, Cosine, \\\n    Mahalanobis, Hamming, \\\n    Pearson, PearsonAbsolute, Spearman, SpearmanAbsolute, Jaccard = range(12)\n\n\nclass MetricDef(NamedTuple):\n    id: int  # pylint: disable=invalid-name\n    name: str\n    tooltip: str\n    metric: Type[distance.Distance]\n    normalize: bool = False\n\n\nMetricDefs: Dict[int, MetricDef] = {\n    metric.id: metric for metric in (\n        MetricDef(EuclideanNormalized, \"Euclidean (normalized)\",\n                  \"Square root of summed difference between normalized values\",\n                  distance.Euclidean, normalize=True),\n        MetricDef(Euclidean, \"Euclidean\",\n                  \"Square root of summed difference between values\",\n                  distance.Euclidean),\n        MetricDef(ManhattanNormalized, \"Manhattan (normalized)\",\n                  \"Sum of absolute differences between normalized values\",\n                  distance.Manhattan, normalize=True),\n        MetricDef(Manhattan, \"Manhattan\",\n                  \"Sum of absolute differences between values\",\n                  distance.Manhattan),\n        MetricDef(Mahalanobis, \"Mahalanobis\",\n                  \"Mahalanobis distance\",\n                  distance.Mahalanobis),\n        MetricDef(Hamming, \"Hamming\", \"Hamming distance\",\n                  distance.Hamming),\n        MetricDef(Cosine, \"Cosine\", \"Cosine distance\",\n                  distance.Cosine),\n        MetricDef(Pearson, \"Pearson\",\n                  \"Pearson correlation; distance = 1 - ρ/2\",\n                  distance.PearsonR),\n        MetricDef(PearsonAbsolute, \"Pearson (absolute)\",\n                  \"Absolute value of Pearson correlation; distance = 1 - |ρ|\",\n                  distance.PearsonRAbsolute),\n        MetricDef(Spearman, \"Spearman\",\n                  \"Pearson correlation; distance = 1 - ρ/2\",\n                  distance.PearsonR),\n        MetricDef(SpearmanAbsolute, \"Spearman (absolute)\",\n                  \"Absolute value of Pearson correlation; distance = 1 - |ρ|\",\n                  distance.SpearmanRAbsolute),\n        MetricDef(Jaccard, \"Jaccard\", \"Jaccard distance\",\n                  distance.Jaccard)\n    )\n}\n\n\nclass InterruptException(Exception):\n    pass\n\n\nclass DistanceRunner:\n    @staticmethod\n    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,\n            axis: int, state: TaskState) -> Optional[Orange.misc.DistMatrix]:\n        if data is None:\n            return None\n\n        def callback(i: float) -> bool:\n            state.set_progress_value(i)\n            if state.is_interruption_requested():\n                raise InterruptException\n\n        state.set_status(\"Calculating...\")\n        kwargs = {\"axis\": 1 - axis, \"impute\": True, \"callback\": callback}\n        if metric.supports_normalization and normalized_dist:\n            kwargs[\"normalize\"] = True\n        return metric(data, **kwargs)\n\n\nclass OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWDistances).run(Orange.data.Table(\"iris\"))\n",
            "new_file_content": "from typing import NamedTuple, Dict, Type, Optional\n\nfrom AnyQt.QtWidgets import QButtonGroup, QRadioButton\nfrom AnyQt.QtCore import Qt\nfrom scipy.sparse import issparse\nimport bottleneck as bn\n\nimport Orange.data\nimport Orange.misc\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin\nfrom Orange.widgets.utils.sql import check_sql_input\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import OWWidget, Msg, Input, Output\n\n\nEuclidean, EuclideanNormalized, Manhattan, ManhattanNormalized, Cosine, \\\n    Mahalanobis, Hamming, \\\n    Pearson, PearsonAbsolute, Spearman, SpearmanAbsolute, Jaccard = range(12)\n\n\nclass MetricDef(NamedTuple):\n    id: int  # pylint: disable=invalid-name\n    name: str\n    tooltip: str\n    metric: Type[distance.Distance]\n    normalize: bool = False\n\n\nMetricDefs: Dict[int, MetricDef] = {\n    metric.id: metric for metric in (\n        MetricDef(EuclideanNormalized, \"Euclidean (normalized)\",\n                  \"Square root of summed difference between normalized values\",\n                  distance.Euclidean, normalize=True),\n        MetricDef(Euclidean, \"Euclidean\",\n                  \"Square root of summed difference between values\",\n                  distance.Euclidean),\n        MetricDef(ManhattanNormalized, \"Manhattan (normalized)\",\n                  \"Sum of absolute differences between normalized values\",\n                  distance.Manhattan, normalize=True),\n        MetricDef(Manhattan, \"Manhattan\",\n                  \"Sum of absolute differences between values\",\n                  distance.Manhattan),\n        MetricDef(Mahalanobis, \"Mahalanobis\",\n                  \"Mahalanobis distance\",\n                  distance.Mahalanobis),\n        MetricDef(Hamming, \"Hamming\", \"Hamming distance\",\n                  distance.Hamming),\n        MetricDef(Cosine, \"Cosine\", \"Cosine distance\",\n                  distance.Cosine),\n        MetricDef(Pearson, \"Pearson\",\n                  \"Pearson correlation; distance = 1 - ρ/2\",\n                  distance.PearsonR),\n        MetricDef(PearsonAbsolute, \"Pearson (absolute)\",\n                  \"Absolute value of Pearson correlation; distance = 1 - |ρ|\",\n                  distance.PearsonRAbsolute),\n        MetricDef(Spearman, \"Spearman\",\n                  \"Pearson correlation; distance = 1 - ρ/2\",\n                  distance.PearsonR),\n        MetricDef(SpearmanAbsolute, \"Spearman (absolute)\",\n                  \"Absolute value of Pearson correlation; distance = 1 - |ρ|\",\n                  distance.SpearmanRAbsolute),\n        MetricDef(Jaccard, \"Jaccard\", \"Jaccard distance\",\n                  distance.Jaccard)\n    )\n}\n\n\nclass InterruptException(Exception):\n    pass\n\n\nclass DistanceRunner:\n    @staticmethod\n    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,\n            axis: int, state: TaskState) -> Optional[Orange.misc.DistMatrix]:\n        if data is None:\n            return None\n\n        def callback(i: float) -> bool:\n            state.set_progress_value(i)\n            if state.is_interruption_requested():\n                raise InterruptException\n\n        state.set_status(\"Calculating...\")\n        kwargs = {\"axis\": 1 - axis, \"impute\": True, \"callback\": callback}\n        if metric.supports_normalization and normalized_dist:\n            kwargs[\"normalize\"] = True\n        return metric(data, **kwargs)\n\n\nclass OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n        no_features = Msg(\"Data has no features\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        def _check_no_features():\n            if len(data.domain.attributes) == 0:\n                self.Warning.no_features()\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _check_no_features,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWDistances).run(Orange.data.Table(\"iris\"))\n",
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                "file": {
                    "path": "Orange/widgets/unsupervised/owdistances.py"
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                        "section": "class OWDistances(OWWidget, ConcurrentWidgetMixin):"
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                            {
                                "content": "        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"",
                                "type": "context"
                            },
                            {
                                "content": "                                 \"and were disabled: {}\")",
                                "type": "context"
                            },
                            {
                                "content": "        imputing_data = Msg(\"Missing values were imputed\")",
                                "type": "context"
                            },
                            {
                                "content": "        no_features = Msg(\"Data has no features\")",
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                            {
                                "content": "    def __init__(self):",
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                            "type": "class",
                            "name": "OWDistances",
                            "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n        no_features = Msg(\"Data has no features\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        def _check_no_features():\n            if len(data.domain.attributes) == 0:\n                self.Warning.no_features()\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _check_no_features,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 94,
                            "end_lineno": 318,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 242,
                            "length": 11
                        },
                        "new_range": {
                            "start": 243,
                            "length": 17
                        },
                        "section": "class OWDistances(OWWidget, ConcurrentWidgetMixin):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "                        return False",
                                "type": "context"
                            },
                            {
                                "content": "            return True",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "        def _check_no_features():",
                                "type": "added"
                            },
                            {
                                "content": "            if len(data.domain.attributes) == 0:",
                                "type": "added"
                            },
                            {
                                "content": "                self.Warning.no_features()",
                                "type": "added"
                            },
                            {
                                "content": "            return True",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        metric_def = MetricDefs[self.metric_id]",
                                "type": "context"
                            },
                            {
                                "content": "        metric = metric_def.metric",
                                "type": "context"
                            },
                            {
                                "content": "        self.clear_messages()",
                                "type": "context"
                            },
                            {
                                "content": "        if data is not None:",
                                "type": "context"
                            },
                            {
                                "content": "            for check in (_check_sparse, _check_tractability,",
                                "type": "context"
                            },
                            {
                                "content": "                          _check_no_features,",
                                "type": "added"
                            },
                            {
                                "content": "                          _fix_discrete, _fix_missing, _fix_nonbinary):",
                                "type": "context"
                            },
                            {
                                "content": "                if not check():",
                                "type": "context"
                            },
                            {
                                "content": "                    data = None",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/owdistances.py",
                            "type": "method",
                            "name": "OWDistances.compute_distances",
                            "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n        no_features = Msg(\"Data has no features\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        def _check_no_features():\n            if len(data.domain.attributes) == 0:\n                self.Warning.no_features()\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _check_no_features,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 190,
                            "end_lineno": 263,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/owdistances.py",
                                "type": "class",
                                "name": "OWDistances",
                                "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n        no_features = Msg(\"Data has no features\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        def _check_no_features():\n            if len(data.domain.attributes) == 0:\n                self.Warning.no_features()\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _check_no_features,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 94,
                                "end_lineno": 318,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/widgets/unsupervised/owdistances.py",
                            "type": "class",
                            "name": "OWDistances",
                            "content": "class OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n        no_features = Msg(\"Data has no features\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        def _check_no_features():\n            if len(data.domain.attributes) == 0:\n                self.Warning.no_features()\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _check_no_features,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 94,
                            "end_lineno": 318,
                            "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 Mock, patch\n\nimport numpy as np\n\nfrom Orange import distance\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.misc import DistMatrix\nfrom Orange.widgets.unsupervised.owdistances import OWDistances, \\\n    DistanceRunner, MetricDefs, Cosine, Mahalanobis, Jaccard, MetricDef, \\\n    ManhattanNormalized, EuclideanNormalized, Manhattan, Spearman, Pearson, \\\n    Hamming, SpearmanAbsolute, PearsonAbsolute, Euclidean\nfrom Orange.widgets.tests.base import WidgetTest\n\n\nclass TestDistanceRunner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")[::5].copy()\n        with cls.iris.unlocked():\n            cls.iris.X[0, 2] = np.nan\n            cls.iris.X[1, 3] = np.nan\n            cls.iris.X[2, 1] = np.nan\n\n        cls.zoo = Table(\"zoo\")[::5].copy()\n        with cls.zoo.unlocked():\n            cls.zoo.X[0, 2] = np.nan\n            cls.zoo.X[1, 3] = np.nan\n            cls.zoo.X[2, 1] = np.nan\n\n    def test_run(self):\n        state = Mock()\n        state.is_interruption_requested = Mock(return_value=False)\n        for metricdef in MetricDefs.values():\n            metric = metricdef.metric\n            data = self.iris\n            if not metric.supports_missing:\n                data = distance.impute(data)\n            elif metric == distance.Jaccard:\n                data = self.zoo\n\n            # between rows, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between rows, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n    def assertDistMatrixEqual(self, dist1, dist2):\n        self.assertIsInstance(dist1, DistMatrix)\n        self.assertIsInstance(dist2, DistMatrix)\n        self.assertEqual(dist1.axis, dist2.axis)\n        self.assertEqual(dist1.row_items, dist2.row_items)\n        self.assertEqual(dist1.col_items, dist2.col_items)\n        np.testing.assert_array_almost_equal(dist1, dist2)\n\n\nclass TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def _select(self, id_):\n        buttons = self.widget.metric_buttons\n        buttons.button(id_).setChecked(True)\n        buttons.idClicked.emit(id_)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for metricdef in MetricDefs.values():\n            if metricdef.metric is distance.Jaccard:\n                continue\n            self._select(metricdef.id)\n            self.wait_until_stop_blocking()\n\n            kwargs = dict(normalize=True) if metricdef.normalize else {}\n            expected = metricdef.metric(self.iris, **kwargs)\n\n            np.testing.assert_array_almost_equal(\n                expected, self.get_output(self.widget.Outputs.distances),\n                err_msg=f\"at {metricdef.name}\")\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        self._select(Jaccard)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        id_ = self.widget.metric_id\n        for exc, err in ((ValueError, self.widget.Error.distances_value_error),\n                         (MemoryError, self.widget.Error.distances_memory_error)\n                         ):\n            with patch.dict(\n                    MetricDefs,\n                    {id_: MetricDef(id_, \"\", \"\", Mock(side_effect=exc))}):\n                self.widget.compute_distances(self.iris)\n                self.wait_until_finished()\n                self.assertTrue(err.is_shown(), msg=f\"at {exc}\")\n\n    def test_migrate_3_to_4(self):\n        settings = {'__version__': 3}\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=0, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, EuclideanNormalized)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=False, **settings))\n        self.assertEqual(w.metric_id, Manhattan)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, ManhattanNormalized)\n\n        for old, new in ((2, Cosine), (3, Jaccard),\n                         (4, Spearman), (5, SpearmanAbsolute),\n                         (6, Pearson), (7, PearsonAbsolute),\n                         (8, Hamming), (9, Mahalanobis),\n                         (10, Euclidean)):\n            settings = dict(metric_idx=old, __version__=3)\n            w = self.create_widget(OWDistances, stored_settings=settings)\n            self.assertEqual(w.metric_id, new,\n                             msg=f\"at {old} to {MetricDefs[new].name}\")\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self._select(Mahalanobis)\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        self._select(Jaccard)\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\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 Mock, patch\n\nimport numpy as np\n\nfrom Orange import distance\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.misc import DistMatrix\nfrom Orange.widgets.unsupervised.owdistances import OWDistances, \\\n    DistanceRunner, MetricDefs, Cosine, Mahalanobis, Jaccard, MetricDef, \\\n    ManhattanNormalized, EuclideanNormalized, Manhattan, Spearman, Pearson, \\\n    Hamming, SpearmanAbsolute, PearsonAbsolute, Euclidean\nfrom Orange.widgets.tests.base import WidgetTest\n\n\nclass TestDistanceRunner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")[::5].copy()\n        with cls.iris.unlocked():\n            cls.iris.X[0, 2] = np.nan\n            cls.iris.X[1, 3] = np.nan\n            cls.iris.X[2, 1] = np.nan\n\n        cls.zoo = Table(\"zoo\")[::5].copy()\n        with cls.zoo.unlocked():\n            cls.zoo.X[0, 2] = np.nan\n            cls.zoo.X[1, 3] = np.nan\n            cls.zoo.X[2, 1] = np.nan\n\n    def test_run(self):\n        state = Mock()\n        state.is_interruption_requested = Mock(return_value=False)\n        for metricdef in MetricDefs.values():\n            metric = metricdef.metric\n            data = self.iris\n            if not metric.supports_missing:\n                data = distance.impute(data)\n            elif metric == distance.Jaccard:\n                data = self.zoo\n\n            # between rows, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between rows, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n    def assertDistMatrixEqual(self, dist1, dist2):\n        self.assertIsInstance(dist1, DistMatrix)\n        self.assertIsInstance(dist2, DistMatrix)\n        self.assertEqual(dist1.axis, dist2.axis)\n        self.assertEqual(dist1.row_items, dist2.row_items)\n        self.assertEqual(dist1.col_items, dist2.col_items)\n        np.testing.assert_array_almost_equal(dist1, dist2)\n\n\nclass TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def _select(self, id_):\n        buttons = self.widget.metric_buttons\n        buttons.button(id_).setChecked(True)\n        buttons.idClicked.emit(id_)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for metricdef in MetricDefs.values():\n            if metricdef.metric is distance.Jaccard:\n                continue\n            self._select(metricdef.id)\n            self.wait_until_stop_blocking()\n\n            kwargs = dict(normalize=True) if metricdef.normalize else {}\n            expected = metricdef.metric(self.iris, **kwargs)\n\n            np.testing.assert_array_almost_equal(\n                expected, self.get_output(self.widget.Outputs.distances),\n                err_msg=f\"at {metricdef.name}\")\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        self._select(Jaccard)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        id_ = self.widget.metric_id\n        for exc, err in ((ValueError, self.widget.Error.distances_value_error),\n                         (MemoryError, self.widget.Error.distances_memory_error)\n                         ):\n            with patch.dict(\n                    MetricDefs,\n                    {id_: MetricDef(id_, \"\", \"\", Mock(side_effect=exc))}):\n                self.widget.compute_distances(self.iris)\n                self.wait_until_finished()\n                self.assertTrue(err.is_shown(), msg=f\"at {exc}\")\n\n    def test_migrate_3_to_4(self):\n        settings = {'__version__': 3}\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=0, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, EuclideanNormalized)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=False, **settings))\n        self.assertEqual(w.metric_id, Manhattan)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, ManhattanNormalized)\n\n        for old, new in ((2, Cosine), (3, Jaccard),\n                         (4, Spearman), (5, SpearmanAbsolute),\n                         (6, Pearson), (7, PearsonAbsolute),\n                         (8, Hamming), (9, Mahalanobis),\n                         (10, Euclidean)):\n            settings = dict(metric_idx=old, __version__=3)\n            w = self.create_widget(OWDistances, stored_settings=settings)\n            self.assertEqual(w.metric_id, new,\n                             msg=f\"at {old} to {MetricDefs[new].name}\")\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self._select(Mahalanobis)\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        self._select(Jaccard)\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n    def test_no_features(self):\n        zoo = Table(\"zoo\")[:5, 16:]\n\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (5, 5))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.widget.controls.axis.buttons[1].click()\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (0, 0))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_finished()\n        self.assertFalse(self.widget.Warning.no_features.is_shown())\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/unsupervised/tests/test_owdistances.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "3cdc30404",
                "new_commit_hash": "103d31e1c",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/widgets/unsupervised/tests/test_owdistances.py"
            },
            "plus_file": {
                "path": "b/Orange/widgets/unsupervised/tests/test_owdistances.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 265,
                            "length": 6
                        },
                        "new_range": {
                            "start": 265,
                            "length": 27
                        },
                        "section": "class TestOWDistances(WidgetTest):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        out_domain = out.row_items.domain",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_no_features(self):",
                                "type": "added"
                            },
                            {
                                "content": "        zoo = Table(\"zoo\")[:5, 16:]",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.data, zoo)",
                                "type": "added"
                            },
                            {
                                "content": "        self.wait_until_finished()",
                                "type": "added"
                            },
                            {
                                "content": "        out = self.get_output(self.widget.Outputs.distances)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(out.shape, (5, 5))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue((out == 0).all())",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(self.widget.Warning.no_features.is_shown())",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.widget.controls.axis.buttons[1].click()",
                                "type": "added"
                            },
                            {
                                "content": "        self.wait_until_finished()",
                                "type": "added"
                            },
                            {
                                "content": "        out = self.get_output(self.widget.Outputs.distances)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(out.shape, (0, 0))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue((out == 0).all())",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertTrue(self.widget.Warning.no_features.is_shown())",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.send_signal(self.widget.Inputs.data, None)",
                                "type": "added"
                            },
                            {
                                "content": "        self.wait_until_finished()",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertFalse(self.widget.Warning.no_features.is_shown())",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "context"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owdistances.py",
                            "type": "class",
                            "name": "TestOWDistances",
                            "content": "class TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def _select(self, id_):\n        buttons = self.widget.metric_buttons\n        buttons.button(id_).setChecked(True)\n        buttons.idClicked.emit(id_)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for metricdef in MetricDefs.values():\n            if metricdef.metric is distance.Jaccard:\n                continue\n            self._select(metricdef.id)\n            self.wait_until_stop_blocking()\n\n            kwargs = dict(normalize=True) if metricdef.normalize else {}\n            expected = metricdef.metric(self.iris, **kwargs)\n\n            np.testing.assert_array_almost_equal(\n                expected, self.get_output(self.widget.Outputs.distances),\n                err_msg=f\"at {metricdef.name}\")\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        self._select(Jaccard)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        id_ = self.widget.metric_id\n        for exc, err in ((ValueError, self.widget.Error.distances_value_error),\n                         (MemoryError, self.widget.Error.distances_memory_error)\n                         ):\n            with patch.dict(\n                    MetricDefs,\n                    {id_: MetricDef(id_, \"\", \"\", Mock(side_effect=exc))}):\n                self.widget.compute_distances(self.iris)\n                self.wait_until_finished()\n                self.assertTrue(err.is_shown(), msg=f\"at {exc}\")\n\n    def test_migrate_3_to_4(self):\n        settings = {'__version__': 3}\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=0, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, EuclideanNormalized)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=False, **settings))\n        self.assertEqual(w.metric_id, Manhattan)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, ManhattanNormalized)\n\n        for old, new in ((2, Cosine), (3, Jaccard),\n                         (4, Spearman), (5, SpearmanAbsolute),\n                         (6, Pearson), (7, PearsonAbsolute),\n                         (8, Hamming), (9, Mahalanobis),\n                         (10, Euclidean)):\n            settings = dict(metric_idx=old, __version__=3)\n            w = self.create_widget(OWDistances, stored_settings=settings)\n            self.assertEqual(w.metric_id, new,\n                             msg=f\"at {old} to {MetricDefs[new].name}\")\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self._select(Mahalanobis)\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        self._select(Jaccard)\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n    def test_no_features(self):\n        zoo = Table(\"zoo\")[:5, 16:]\n\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (5, 5))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.widget.controls.axis.buttons[1].click()\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (0, 0))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_finished()\n        self.assertFalse(self.widget.Warning.no_features.is_shown())",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 74,
                            "end_lineno": 287,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/widgets/unsupervised/tests/test_owdistances.py",
                            "type": "method",
                            "name": "TestOWDistances.test_no_features",
                            "content": "class TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def _select(self, id_):\n        buttons = self.widget.metric_buttons\n        buttons.button(id_).setChecked(True)\n        buttons.idClicked.emit(id_)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for metricdef in MetricDefs.values():\n            if metricdef.metric is distance.Jaccard:\n                continue\n            self._select(metricdef.id)\n            self.wait_until_stop_blocking()\n\n            kwargs = dict(normalize=True) if metricdef.normalize else {}\n            expected = metricdef.metric(self.iris, **kwargs)\n\n            np.testing.assert_array_almost_equal(\n                expected, self.get_output(self.widget.Outputs.distances),\n                err_msg=f\"at {metricdef.name}\")\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        self._select(Jaccard)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        id_ = self.widget.metric_id\n        for exc, err in ((ValueError, self.widget.Error.distances_value_error),\n                         (MemoryError, self.widget.Error.distances_memory_error)\n                         ):\n            with patch.dict(\n                    MetricDefs,\n                    {id_: MetricDef(id_, \"\", \"\", Mock(side_effect=exc))}):\n                self.widget.compute_distances(self.iris)\n                self.wait_until_finished()\n                self.assertTrue(err.is_shown(), msg=f\"at {exc}\")\n\n    def test_migrate_3_to_4(self):\n        settings = {'__version__': 3}\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=0, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, EuclideanNormalized)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=False, **settings))\n        self.assertEqual(w.metric_id, Manhattan)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, ManhattanNormalized)\n\n        for old, new in ((2, Cosine), (3, Jaccard),\n                         (4, Spearman), (5, SpearmanAbsolute),\n                         (6, Pearson), (7, PearsonAbsolute),\n                         (8, Hamming), (9, Mahalanobis),\n                         (10, Euclidean)):\n            settings = dict(metric_idx=old, __version__=3)\n            w = self.create_widget(OWDistances, stored_settings=settings)\n            self.assertEqual(w.metric_id, new,\n                             msg=f\"at {old} to {MetricDefs[new].name}\")\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self._select(Mahalanobis)\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        self._select(Jaccard)\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n    def test_no_features(self):\n        zoo = Table(\"zoo\")[:5, 16:]\n\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (5, 5))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.widget.controls.axis.buttons[1].click()\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (0, 0))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_finished()\n        self.assertFalse(self.widget.Warning.no_features.is_shown())",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 268,
                            "end_lineno": 287,
                            "parent": {
                                "file_name": "Orange/widgets/unsupervised/tests/test_owdistances.py",
                                "type": "class",
                                "name": "TestOWDistances",
                                "content": "class TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def _select(self, id_):\n        buttons = self.widget.metric_buttons\n        buttons.button(id_).setChecked(True)\n        buttons.idClicked.emit(id_)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for metricdef in MetricDefs.values():\n            if metricdef.metric is distance.Jaccard:\n                continue\n            self._select(metricdef.id)\n            self.wait_until_stop_blocking()\n\n            kwargs = dict(normalize=True) if metricdef.normalize else {}\n            expected = metricdef.metric(self.iris, **kwargs)\n\n            np.testing.assert_array_almost_equal(\n                expected, self.get_output(self.widget.Outputs.distances),\n                err_msg=f\"at {metricdef.name}\")\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        self._select(Jaccard)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        id_ = self.widget.metric_id\n        for exc, err in ((ValueError, self.widget.Error.distances_value_error),\n                         (MemoryError, self.widget.Error.distances_memory_error)\n                         ):\n            with patch.dict(\n                    MetricDefs,\n                    {id_: MetricDef(id_, \"\", \"\", Mock(side_effect=exc))}):\n                self.widget.compute_distances(self.iris)\n                self.wait_until_finished()\n                self.assertTrue(err.is_shown(), msg=f\"at {exc}\")\n\n    def test_migrate_3_to_4(self):\n        settings = {'__version__': 3}\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=0, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, EuclideanNormalized)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=False, **settings))\n        self.assertEqual(w.metric_id, Manhattan)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, ManhattanNormalized)\n\n        for old, new in ((2, Cosine), (3, Jaccard),\n                         (4, Spearman), (5, SpearmanAbsolute),\n                         (6, Pearson), (7, PearsonAbsolute),\n                         (8, Hamming), (9, Mahalanobis),\n                         (10, Euclidean)):\n            settings = dict(metric_idx=old, __version__=3)\n            w = self.create_widget(OWDistances, stored_settings=settings)\n            self.assertEqual(w.metric_id, new,\n                             msg=f\"at {old} to {MetricDefs[new].name}\")\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self._select(Mahalanobis)\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        self._select(Jaccard)\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n    def test_no_features(self):\n        zoo = Table(\"zoo\")[:5, 16:]\n\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (5, 5))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.widget.controls.axis.buttons[1].click()\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (0, 0))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_finished()\n        self.assertFalse(self.widget.Warning.no_features.is_shown())",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 74,
                                "end_lineno": 287,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "588579388e87680b94e1fcf046611ef0b60b62d2^",
    "new_commit_hash": "588579388e87680b94e1fcf046611ef0b60b62d2",
    "commit_message": "Distances: Warn when data has no features",
    "commit_date": "2024-01-12T12:03:39+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "588579388e87680b94e1fcf046611ef0b60b62d2",
    "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 Mock, patch\n\nimport numpy as np\n\nfrom Orange import distance\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.misc import DistMatrix\nfrom Orange.widgets.unsupervised.owdistances import OWDistances, \\\n    DistanceRunner, MetricDefs, Cosine, Mahalanobis, Jaccard, MetricDef, \\\n    ManhattanNormalized, EuclideanNormalized, Manhattan, Spearman, Pearson, \\\n    Hamming, SpearmanAbsolute, PearsonAbsolute, Euclidean\nfrom Orange.widgets.tests.base import WidgetTest\n\n\nclass TestDistanceRunner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")[::5].copy()\n        with cls.iris.unlocked():\n            cls.iris.X[0, 2] = np.nan\n            cls.iris.X[1, 3] = np.nan\n            cls.iris.X[2, 1] = np.nan\n\n        cls.zoo = Table(\"zoo\")[::5].copy()\n        with cls.zoo.unlocked():\n            cls.zoo.X[0, 2] = np.nan\n            cls.zoo.X[1, 3] = np.nan\n            cls.zoo.X[2, 1] = np.nan\n\n    def test_run(self):\n        state = Mock()\n        state.is_interruption_requested = Mock(return_value=False)\n        for metricdef in MetricDefs.values():\n            metric = metricdef.metric\n            data = self.iris\n            if not metric.supports_missing:\n                data = distance.impute(data)\n            elif metric == distance.Jaccard:\n                data = self.zoo\n\n            # between rows, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between rows, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 0, state)\n            dist2 = metric(data, axis=1, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, normalized\n            dist1 = DistanceRunner.run(data, metric, True, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=True)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n            # between columns, not normalized\n            dist1 = DistanceRunner.run(data, metric, False, 1, state)\n            dist2 = metric(data, axis=0, impute=True, normalize=False)\n            self.assertDistMatrixEqual(dist1, dist2)\n\n    def assertDistMatrixEqual(self, dist1, dist2):\n        self.assertIsInstance(dist1, DistMatrix)\n        self.assertIsInstance(dist2, DistMatrix)\n        self.assertEqual(dist1.axis, dist2.axis)\n        self.assertEqual(dist1.row_items, dist2.row_items)\n        self.assertEqual(dist1.col_items, dist2.col_items)\n        np.testing.assert_array_almost_equal(dist1, dist2)\n\n\nclass TestOWDistances(WidgetTest):\n    def setUp(self):\n        super().setUp()\n        self.iris = Table(\"iris\")[::5].copy()\n        self.titanic = Table(\"titanic\")[::10].copy()\n        self.widget = self.create_widget(OWDistances)\n\n    def _select(self, id_):\n        buttons = self.widget.metric_buttons\n        buttons.button(id_).setChecked(True)\n        buttons.idClicked.emit(id_)\n\n    def test_distance_combo(self):\n        \"\"\"Check distances when the metric changes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        for metricdef in MetricDefs.values():\n            if metricdef.metric is distance.Jaccard:\n                continue\n            self._select(metricdef.id)\n            self.wait_until_stop_blocking()\n\n            kwargs = dict(normalize=True) if metricdef.normalize else {}\n            expected = metricdef.metric(self.iris, **kwargs)\n\n            np.testing.assert_array_almost_equal(\n                expected, self.get_output(self.widget.Outputs.distances),\n                err_msg=f\"at {metricdef.name}\")\n\n    def test_error_message(self):\n        \"\"\"Check if error message appears and then disappears when\n        data is removed from input\"\"\"\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_continuous_features.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Error.no_continuous_features.is_shown())\n\n    def test_jaccard_messages(self):\n        self._select(Jaccard)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n        self.wait_until_stop_blocking()\n        self.assertTrue(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, self.titanic)\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertTrue(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        dom = self.titanic.domain\n        dom = Domain(dom.attributes[1:], dom.class_var)\n        self.send_signal(self.widget.Inputs.data, self.titanic.transform(dom))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_nonbinary.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, Table(\"heart_disease\"))\n        self.wait_until_stop_blocking()\n        self.assertFalse(self.widget.Error.no_binary_features.is_shown())\n        self.assertFalse(self.widget.Warning.ignoring_discrete.is_shown())\n\n    def test_too_big_array(self):\n        \"\"\"\n        Users sees an error message when calculating too large arrays and Orange\n        does not crash.\n        \"\"\"\n        self.assertEqual(len(self.widget.Error.active), 0)\n        self.send_signal(self.widget.Inputs.data, self.iris)\n\n        id_ = self.widget.metric_id\n        for exc, err in ((ValueError, self.widget.Error.distances_value_error),\n                         (MemoryError, self.widget.Error.distances_memory_error)\n                         ):\n            with patch.dict(\n                    MetricDefs,\n                    {id_: MetricDef(id_, \"\", \"\", Mock(side_effect=exc))}):\n                self.widget.compute_distances(self.iris)\n                self.wait_until_finished()\n                self.assertTrue(err.is_shown(), msg=f\"at {exc}\")\n\n    def test_migrate_3_to_4(self):\n        settings = {'__version__': 3}\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=0, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, EuclideanNormalized)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=False, **settings))\n        self.assertEqual(w.metric_id, Manhattan)\n        w = self.create_widget(\n            OWDistances,\n            stored_settings=dict(metric_idx=1, normalized_dist=True, **settings))\n        self.assertEqual(w.metric_id, ManhattanNormalized)\n\n        for old, new in ((2, Cosine), (3, Jaccard),\n                         (4, Spearman), (5, SpearmanAbsolute),\n                         (6, Pearson), (7, PearsonAbsolute),\n                         (8, Hamming), (9, Mahalanobis),\n                         (10, Euclidean)):\n            settings = dict(metric_idx=old, __version__=3)\n            w = self.create_widget(OWDistances, stored_settings=settings)\n            self.assertEqual(w.metric_id, new,\n                             msg=f\"at {old} to {MetricDefs[new].name}\")\n\n    def test_limit_mahalanobis(self):\n        def assert_error_shown():\n            self.assertTrue(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        def assert_no_error():\n            self.assertFalse(\n                self.widget.Error.data_too_large_for_mahalanobis.is_shown())\n\n        widget = self.widget\n        axis_buttons = widget.controls.axis.buttons\n\n        self._select(Mahalanobis)\n        X = np.random.random((1010, 4))\n        bigrows = Table.from_numpy(Domain(self.iris.domain.attributes), X)\n        bigcols = Table.from_numpy(\n            Domain([ContinuousVariable(f\"{i}\") for i in range(1010)]), X.T)\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        # by columns -- cannot handle too many rows\n        self.send_signal(self.widget.Inputs.data, bigrows)\n        assert_no_error()\n        axis_buttons[0].click()\n        assert_error_shown()\n        axis_buttons[1].click()\n        assert_no_error()\n\n        self.send_signal(self.widget.Inputs.data, bigcols)\n        assert_error_shown()\n        axis_buttons[0].click()\n        assert_no_error()\n        axis_buttons[1].click()\n        assert_error_shown()\n\n        self.send_signal(widget.Inputs.data, self.iris)\n        assert_no_error()\n\n    def test_discrete_in_metas(self):\n        domain = self.iris.domain\n        data = self.iris.transform(\n            Domain(domain.attributes[:-1] + (domain.class_var, ),\n                   [],\n                   domain.attributes[-1:])\n        )\n        self._select(Cosine)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.attributes, domain.attributes[:-1])\n        self.assertEqual(out_domain.metas,\n                         (domain.attributes[-1], domain.class_var))\n\n    def test_non_binary_in_metas(self):\n        self._select(Jaccard)\n        zoo = Table(\"zoo\")[:20]\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        domain = zoo.domain\n        out_domain = out.row_items.domain\n        self.assertEqual(out_domain.metas, (domain[\"name\"], domain[\"legs\"]))\n\n    def test_no_features(self):\n        zoo = Table(\"zoo\")[:5, 16:]\n\n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (5, 5))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.widget.controls.axis.buttons[1].click()\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (0, 0))\n        self.assertTrue((out == 0).all())\n        self.assertTrue(self.widget.Warning.no_features.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.wait_until_finished()\n        self.assertFalse(self.widget.Warning.no_features.is_shown())\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build\ncreating build/temp.linux-x86_64-3.7\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-3.7\ncreating build/lib.linux-x86_64-3.7/Orange\ncreating build/lib.linux-x86_64-3.7/Orange/classification\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-3.7/Orange/widgets\ncreating build/lib.linux-x86_64-3.7/Orange/widgets/utils\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_contingency.c -o build/temp.linux-x86_64-3.7/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-3.7/Orange/data\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_io.c -o build/temp.linux-x86_64-3.7/Orange/data/_io.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_variable.c -o build/temp.linux-x86_64-3.7/Orange/data/_variable.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/distance/_distance.c -o build/temp.linux-x86_64-3.7/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-3.7/Orange/distance\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/projection/_som.c -o build/temp.linux-x86_64-3.7/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-3.7/Orange/projection\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.37.0.dev0+5885793 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2\nProcessing dependencies for Orange3==3.37.0.dev0+5885793\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/XlsxWriter-3.2.0-py3.7.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.egg\nSearching for xgboost>=1.7.4\nReading https://pypi.org/simple/xgboost/\nDownloading https://files.pythonhosted.org/packages/48/bc/05d7db90d421c5e3d681a12fd1eb087e37bf2e9bbe2b105422d6319ecc92/xgboost-2.1.3-py3-none-manylinux2014_x86_64.whl#sha256=32a43526208fe676527c698cb852e0e9515e6d7294143780e476d335290a131b\nBest match: xgboost 2.1.3\nProcessing xgboost-2.1.3-py3-none-manylinux2014_x86_64.whl\nInstalling xgboost-2.1.3-py3-none-manylinux2014_x86_64.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages\nAdding xgboost 2.1.3 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/xgboost-2.1.3-py3.7-linux-x86_64.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-uomewlkq/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-uomewlkq/serverfiles-0.3.1/egg-dist-tmp-28aizalc\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nExtracting serverfiles-0.3.1-py3.7.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\ndependency 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path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be 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path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.37.0.dev0+5885793 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2\nProcessing dependencies for Orange3==3.37.0.dev0+5885793\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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for xgboost==2.1.3\nBest match: xgboost 2.1.3\nAdding xgboost 2.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for scipy==1.10.1\nBest match: scipy 1.10.1\nAdding scipy 1.10.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.3.2\nBest match: scikit-learn 1.3.2\nAdding scikit-learn 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for requests==2.32.3\nBest match: requests 2.32.3\nAdding requests 2.32.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for qtconsole==5.6.1\nBest match: qtconsole 5.6.1\nAdding qtconsole 5.6.1 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for PyYAML==6.0.2\nBest match: PyYAML 6.0.2\nAdding PyYAML 6.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pyqtgraph==0.13.3\nBest match: pyqtgraph 0.13.3\nAdding pyqtgraph 0.13.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pygments==2.19.1\nBest match: pygments 2.19.1\nAdding pygments 2.19.1 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pip==24.3.1\nBest match: pip 24.3.1\nAdding pip 24.3.1 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pandas==2.0.3\nBest match: pandas 2.0.3\nAdding pandas 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for packaging==24.2\nBest match: packaging 24.2\nAdding packaging 24.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.24.0\nBest match: orange-widget-base 4.24.0\nAdding orange-widget-base 4.24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.2.2\nBest match: orange-canvas-core 0.2.2\nAdding orange-canvas-core 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for openpyxl==3.1.5\nBest match: openpyxl 3.1.5\nAdding openpyxl 3.1.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for numpy==1.24.4\nBest match: numpy 1.24.4\nAdding numpy 1.24.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for networkx==3.1\nBest match: networkx 3.1\nAdding networkx 3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for matplotlib==3.7.5\nBest match: matplotlib 3.7.5\nAdding matplotlib 3.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for keyrings.alt==5.0.2\nBest match: keyrings.alt 5.0.2\nAdding keyrings.alt 5.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for keyring==25.5.0\nBest match: keyring 25.5.0\nAdding keyring 25.5.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for joblib==1.4.2\nBest match: joblib 1.4.2\nAdding joblib 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for httpx==0.28.1\nBest match: httpx 0.28.1\nAdding httpx 0.28.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for catboost==1.2.7\nBest match: catboost 1.2.7\nAdding catboost 1.2.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for nvidia-nccl-cu12==2.24.3\nBest match: nvidia-nccl-cu12 2.24.3\nAdding nvidia-nccl-cu12 2.24.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for threadpoolctl==3.5.0\nBest match: threadpoolctl 3.5.0\nAdding threadpoolctl 3.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for urllib3==2.2.3\nBest match: urllib3 2.2.3\nAdding urllib3 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for ipykernel==6.29.5\nBest match: ipykernel 6.29.5\nAdding ipykernel 6.29.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jupyter-client==8.6.3\nBest match: jupyter-client 8.6.3\nAdding jupyter-client 8.6.3 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jupyter-core==5.7.2\nBest match: jupyter-core 5.7.2\nAdding jupyter-core 5.7.2 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for traitlets==5.14.3\nBest match: traitlets 5.14.3\nAdding traitlets 5.14.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for tzdata==2024.2\nBest match: tzdata 2024.2\nAdding tzdata 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for importlib-resources==6.4.5\nBest match: importlib-resources 6.4.5\nAdding importlib-resources 6.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\nAdding typing-extensions 4.12.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for requests-cache==1.2.1\nBest match: requests-cache 1.2.1\nAdding requests-cache 1.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for et-xmlfile==2.0.0\nBest match: et-xmlfile 2.0.0\nAdding et-xmlfile 2.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pillow==10.4.0\nBest match: pillow 10.4.0\nAdding pillow 10.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for kiwisolver==1.4.7\nBest match: kiwisolver 1.4.7\nAdding kiwisolver 1.4.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for fonttools==4.55.3\nBest match: fonttools 4.55.3\nAdding fonttools 4.55.3 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for cycler==0.12.1\nBest match: cycler 0.12.1\nAdding cycler 0.12.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for contourpy==1.1.1\nBest match: contourpy 1.1.1\nAdding contourpy 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jaraco.context==6.0.1\nBest match: jaraco.context 6.0.1\nAdding jaraco.context 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jaraco.classes==3.4.0\nBest match: jaraco.classes 3.4.0\nAdding jaraco.classes 3.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jaraco.functools==4.1.0\nBest match: jaraco.functools 4.1.0\nAdding jaraco.functools 4.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for httpcore==1.0.7\nBest match: httpcore 1.0.7\nAdding httpcore 1.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for anyio==4.5.2\nBest match: anyio 4.5.2\nAdding anyio 4.5.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for plotly==5.24.1\nBest match: plotly 5.24.1\nAdding plotly 5.24.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for graphviz==0.20.3\nBest match: graphviz 0.20.3\nAdding graphviz 0.20.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for tornado==6.4.2\nBest match: tornado 6.4.2\nAdding tornado 6.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for matplotlib-inline==0.1.7\nBest match: matplotlib-inline 0.1.7\nAdding matplotlib-inline 0.1.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for ipython==8.12.3\nBest match: ipython 8.12.3\nAdding ipython 8.12.3 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for debugpy==1.8.11\nBest match: debugpy 1.8.11\nAdding debugpy 1.8.11 to easy-install.pth file\nInstalling debugpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for comm==0.2.2\nBest match: comm 0.2.2\nAdding comm 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for platformdirs==4.3.6\nBest match: platformdirs 4.3.6\nAdding platformdirs 4.3.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for zipp==3.20.2\nBest match: zipp 3.20.2\nAdding zipp 3.20.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for cattrs==24.1.2\nBest match: cattrs 24.1.2\nAdding cattrs 24.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for attrs==24.3.0\nBest match: attrs 24.3.0\nAdding attrs 24.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for backports.tarfile==1.2.0\nBest match: backports.tarfile 1.2.0\nbackports.tarfile 1.2.0 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for more-itertools==10.5.0\nBest match: more-itertools 10.5.0\nAdding more-itertools 10.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for tenacity==9.0.0\nBest match: tenacity 9.0.0\nAdding tenacity 9.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for stack-data==0.6.3\nBest match: stack-data 0.6.3\nAdding stack-data 0.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for cffi==1.17.1\nBest match: cffi 1.17.1\nAdding cffi 1.17.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pure-eval==0.2.3\nBest match: pure-eval 0.2.3\nAdding pure-eval 0.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for asttokens==3.0.0\nBest match: asttokens 3.0.0\nAdding asttokens 3.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for executing==2.1.0\nBest match: executing 2.1.0\nAdding executing 2.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nSearching for pycparser==2.22\nBest match: pycparser 2.22\nAdding pycparser 2.22 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.37.0.dev0+5885793\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.8\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 110ms\nPrepared 18 packages in 27ms\nInstalled 18 packages in 187ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and all of:\n          pandas>=1.4.0,<=1.4.4\n          pandas>=1.5.1,<=1.5.3\n          pandas>=2.0.1,<=2.0.3\n      depend on Python>=3.8, we can conclude that all of:\n          pandas>=1.4.0,<=1.4.4\n          pandas>=1.5.1,<=1.5.3\n          pandas>=2.0.1,<=2.0.3\n       cannot be used.\n      And because only the following versions of pandas are available:\n          pandas<=1.4.0\n          pandas==1.4.1\n          pandas==1.4.2\n          pandas==1.4.3\n          pandas==1.4.4\n          pandas==1.5.0\n          pandas==1.5.1\n          pandas==1.5.2\n          pandas==1.5.3\n          pandas==2.0.0\n          pandas==2.0.1\n          pandas==2.0.2\n          pandas==2.0.3\n          pandas==2.1.0\n          pandas==2.1.1\n          pandas==2.1.2\n          pandas==2.1.3\n          pandas==2.1.4\n          pandas==2.2.0\n          pandas==2.2.1\n          pandas==2.2.2\n          pandas==2.2.3\n      we can conclude that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0,<2.1.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pandas>=2.1.0 depends on Python>=3.9, we can\n      conclude that pandas>=2.1.0 cannot be used.\n      And because we know from (1) that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0,<2.1.0\n       cannot be used, we can conclude that all of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0\n       cannot be used.\n      And because you require one of:\n          pandas>=1.4.0,<1.5.0\n          pandas>1.5.0,<2.0.0\n          pandas>2.0.0\n      we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `pandas` in the requested range (e.g., 2.2.0rc0), but pre-releases weren't enabled\n      (try: `--prerelease=allow`)\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and pyqtgraph>=0.13.1,<=0.13.3 depends on\n      Python>=3.8, we can conclude that pyqtgraph>=0.13.1,<=0.13.3 cannot be used.\n      And because only the following versions of pyqtgraph are available:\n          pyqtgraph<=0.13.1\n          pyqtgraph==0.13.2\n          pyqtgraph==0.13.3\n          pyqtgraph==0.13.4\n          pyqtgraph==0.13.5\n          pyqtgraph==0.13.6\n          pyqtgraph==0.13.7\n      we can conclude that pyqtgraph>=0.13.1,<0.13.4 cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pyqtgraph>=0.13.4 depends on Python>=3.9, we\n      can conclude that pyqtgraph>=0.13.4 cannot be used.\n      And because we know from (1) that pyqtgraph>=0.13.1,<0.13.4 cannot be used, we can conclude that pyqtgraph>=0.13.1 cannot\n      be used.\n      And because you require pyqtgraph>=0.13.1, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 5ms\nInstalled 2 packages in 27ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 85ms\nInstalled 4 packages in 146ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + threadpoolctl==3.1.0\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3944:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3944 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3943:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3943 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:5094:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5094 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5093:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5093 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3244:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3244 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2892:7: note: ‘__pyx_v_win_y’ was declared here\n 2892 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3233:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3233 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2891:7: note: ‘__pyx_v_win_x’ was declared here\n 2891 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5842:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5842 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5841:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5841 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4871:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4871 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4389:7: note: ‘__pyx_v_win_y’ was declared here\n 4389 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4860:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4860 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4388:7: note: ‘__pyx_v_win_x’ was declared here\n 4388 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3438:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3438 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nerror: Couldn't find a setup script in /tmp/easy_install-k3i39jd5/scipy-1.15.0.tar.gz\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 98ms\nPrepared 15 packages in 0.96ms\nInstalled 15 packages in 73ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==1.24.4\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.15.0\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.3.0\n + tomli==2.2.1\n + wheel==0.45.1\nResolved 64 packages in 143ms\nInstalled 61 packages in 445ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + catboost==1.2.7\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.1.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\n + graphviz==0.20.3\n + h11==0.14.0\n + httpcore==1.0.7\n + httpx==0.28.1\n + idna==3.10\n + importlib-metadata==8.5.0\n + importlib-resources==6.4.5\n + jaraco-classes==3.4.0\n + jaraco-context==6.0.1\n + jaraco-functools==4.1.0\n + jeepney==0.8.0\n + joblib==1.4.2\n + keyring==25.5.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + more-itertools==10.5.0\n + networkx==3.1\n + nvidia-nccl-cu12==2.24.3\n + openpyxl==3.1.5\n + opentsne==1.0.1\n + pandas==2.0.3\n + pillow==10.4.0\n + pip==24.3.1\n + plotly==5.24.1\n + pycparser==2.22\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.2\n + requests==2.32.3\n + scikit-learn==1.3.2\n + scipy==1.10.1\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + tenacity==9.0.0\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.2.3\n + xgboost==2.1.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 63 packages in 53ms\nInstalled 41 packages in 34ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.12.3\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.2.2\n + orange-widget-base==4.24.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.3\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 2ms\nInstalled 2 packages in 3ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\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_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3944:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3944 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3943:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3943 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:5094:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5094 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5093:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5093 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3438:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3438 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3244:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3244 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2892:7: note: ‘__pyx_v_win_y’ was declared here\n 2892 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3233:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3233 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_24, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2891:7: note: ‘__pyx_v_win_x’ was declared here\n 2891 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5842:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5842 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5841:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5841 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4871:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4871 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4389:7: note: ‘__pyx_v_win_y’ was declared here\n 4389 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4860:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4860 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4388:7: note: ‘__pyx_v_win_x’ was declared here\n 4388 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n",
    "new_commit_res_code": 1,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 16 items\n\nr2e_tests/test_1.py sss........F....                                     [100%]\n\n=================================== FAILURES ===================================\n______________________ TestOWDistances.test_minimum_size _______________________\n\nself = <r2e_tests.test_1.TestOWDistances testMethod=test_minimum_size>\n\n    def test_minimum_size(self):\n        widget = getattr(self, \"widget\", None)\n        if widget is None:\n            self.skipTest(\"minimum size not tested as .widget was not set\")\n>       self.check_minimum_size(widget)\n\n.venv/lib/python3.8/site-packages/orangewidget/tests/base.py:242: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n.venv/lib/python3.8/site-packages/orangewidget/tests/base.py:615: in check_minimum_size\n    self.assertLess(min_size.width(), 800)\nE   AssertionError: 824 not less than 800\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestDistanceRunner::test_run\nPASSED r2e_tests/test_1.py::TestOWDistances::test_discrete_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_distance_combo\nPASSED r2e_tests/test_1.py::TestOWDistances::test_error_message\nPASSED r2e_tests/test_1.py::TestOWDistances::test_image_export\nPASSED r2e_tests/test_1.py::TestOWDistances::test_jaccard_messages\nPASSED r2e_tests/test_1.py::TestOWDistances::test_limit_mahalanobis\nPASSED r2e_tests/test_1.py::TestOWDistances::test_migrate_3_to_4\nPASSED r2e_tests/test_1.py::TestOWDistances::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWDistances::test_no_features\nPASSED r2e_tests/test_1.py::TestOWDistances::test_non_binary_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_too_big_array\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nFAILED r2e_tests/test_1.py::TestOWDistances::test_minimum_size - AssertionErr...\n=================== 1 failed, 12 passed, 3 skipped in 12.25s ===================\nWidgets left: 0    Max widgets: 1392 \n\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_588579388e87680b94e1fcf046611ef0b60b62d2\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 16 items\n\nr2e_tests/test_1.py sss........F.F..                                     [100%]\n\n=================================== FAILURES ===================================\n______________________ TestOWDistances.test_minimum_size _______________________\n\nself = <r2e_tests.test_1.TestOWDistances testMethod=test_minimum_size>\n\n    def test_minimum_size(self):\n        widget = getattr(self, \"widget\", None)\n        if widget is None:\n            self.skipTest(\"minimum size not tested as .widget was not set\")\n>       self.check_minimum_size(widget)\n\n.venv/lib/python3.8/site-packages/orangewidget/tests/base.py:242: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n.venv/lib/python3.8/site-packages/orangewidget/tests/base.py:615: in check_minimum_size\n    self.assertLess(min_size.width(), 800)\nE   AssertionError: 824 not less than 800\n_______________________ TestOWDistances.test_no_features _______________________\n\nself = <r2e_tests.test_1.TestOWDistances testMethod=test_no_features>\n\n    def test_no_features(self):\n        zoo = Table(\"zoo\")[:5, 16:]\n    \n        self.send_signal(self.widget.Inputs.data, zoo)\n        self.wait_until_finished()\n        out = self.get_output(self.widget.Outputs.distances)\n        self.assertEqual(out.shape, (5, 5))\n        self.assertTrue((out == 0).all())\n>       self.assertTrue(self.widget.Warning.no_features.is_shown())\nE       AttributeError: 'Warning' object has no attribute 'no_features'\n\nr2e_tests/test_1.py:276: AttributeError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestDistanceRunner::test_run\nPASSED r2e_tests/test_1.py::TestOWDistances::test_discrete_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_distance_combo\nPASSED r2e_tests/test_1.py::TestOWDistances::test_error_message\nPASSED r2e_tests/test_1.py::TestOWDistances::test_image_export\nPASSED r2e_tests/test_1.py::TestOWDistances::test_jaccard_messages\nPASSED r2e_tests/test_1.py::TestOWDistances::test_limit_mahalanobis\nPASSED r2e_tests/test_1.py::TestOWDistances::test_migrate_3_to_4\nPASSED r2e_tests/test_1.py::TestOWDistances::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWDistances::test_non_binary_in_metas\nPASSED r2e_tests/test_1.py::TestOWDistances::test_too_big_array\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [1] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nFAILED r2e_tests/test_1.py::TestOWDistances::test_minimum_size - AssertionErr...\nFAILED r2e_tests/test_1.py::TestOWDistances::test_no_features - AttributeErro...\n=================== 2 failed, 11 passed, 3 skipped in 5.16s ====================\nWidgets left: 0    Max widgets: 1392 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/unsupervised/owdistances.py Orange/widgets/unsupervised/tests/test_owdistances.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
**Analysis:**

- **Key Parts:**
  - **Commit Message:** "Distances: Warn when data has no features."
  - **Test Results:** Before the commit, `test_no_features` failed with `AttributeError: 'Warning' object has no attribute 'no_features'`. After the commit, the test passes.
  - **Commit Patch:** Introduces a new warning `no_features` and a corresponding check in the widget.

- **Main Problem:**
  - Before the commit, when data with no features is provided to the `OWDistances` widget, the widget does not display the expected `no_features` warning, leading to an `AttributeError` in tests.

- **Expected Behavior:**
  - The `OWDistances` widget should display a warning message "Data has no features" when it receives data without any features.

- **Actual Behavior:**
  - The widget does not display the `no_features` warning, causing tests that check for this warning to fail with an `AttributeError`.

- **Issue Description:**
  - Clearly describe that the widget fails to warn when data has no features, leading to errors in tests and potential confusion for users.

**Drafted GitHub Issue:**

[ISSUE]

**Title:** OWDistances Widget Fails to Warn When Data Has No Features

**Description:**
When providing data with no features to the `OWDistances` widget, the expected warning indicating the absence of features is not displayed. This leads to an `AttributeError` when the system attempts to access the non-existent `no_features` warning.

**Example Buggy Code:**
```python
def test_no_features():
    zoo = Table("zoo")[:5, 16:]
    
    send_signal(widget.Inputs.data, zoo)
    wait_until_finished()
    out = get_output(widget.Outputs.distances)
    assert out.shape == (5, 5)
    assert (out == 0).all()
    # This line causes AttributeError because 'no_features' warning is not present
    assert widget.Warning.no_features.is_shown()
```

**Expected Behavior:**
The `OWDistances` widget should display a warning message "Data has no features" when it receives input data that contains no feature attributes. This warning informs users that the distance calculations cannot be performed due to the lack of features.

**Actual Behavior:**
No warning is displayed when the widget receives data with no features. Attempts to access the `no_features` warning result in an `AttributeError`, indicating that the warning attribute does not exist.

[/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/owdistances.py
from typing import NamedTuple, Dict, Type, Optional

from AnyQt.QtWidgets import QButtonGroup, QRadioButton
from AnyQt.QtCore import Qt
from scipy.sparse import issparse
import bottleneck as bn

import Orange.data
import Orange.misc
from Orange import distance
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin
from Orange.widgets.utils.sql import check_sql_input
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import OWWidget, Msg, Input, Output


Euclidean, EuclideanNormalized, Manhattan, ManhattanNormalized, Cosine, \
    Mahalanobis, Hamming, \
    Pearson, PearsonAbsolute, Spearman, SpearmanAbsolute, Jaccard = range(12)


class MetricDef(NamedTuple):
    id: int  # pylint: disable=invalid-name
    name: str
    tooltip: str
    metric: Type[distance.Distance]
    normalize: bool = False


MetricDefs: Dict[int, MetricDef] = {
    metric.id: metric for metric in (
        MetricDef(EuclideanNormalized, "Euclidean (normalized)",
                  "Square root of summed difference between normalized values",
                  distance.Euclidean, normalize=True),
        MetricDef(Euclidean, "Euclidean",
                  "Square root of summed difference between values",
                  distance.Euclidean),
        MetricDef(ManhattanNormalized, "Manhattan (normalized)",
                  "Sum of absolute differences between normalized values",
                  distance.Manhattan, normalize=True),
        MetricDef(Manhattan, "Manhattan",
                  "Sum of absolute differences between values",
                  distance.Manhattan),
        MetricDef(Mahalanobis, "Mahalanobis",
                  "Mahalanobis distance",
                  distance.Mahalanobis),
        MetricDef(Hamming, "Hamming", "Hamming distance",
                  distance.Hamming),
        MetricDef(Cosine, "Cosine", "Cosine distance",
                  distance.Cosine),
        MetricDef(Pearson, "Pearson",
                  "Pearson correlation; distance = 1 - ρ/2",
                  distance.PearsonR),
        MetricDef(PearsonAbsolute, "Pearson (absolute)",
                  "Absolute value of Pearson correlation; distance = 1 - |ρ|",
                  distance.PearsonRAbsolute),
        MetricDef(Spearman, "Spearman",
                  "Pearson correlation; distance = 1 - ρ/2",
                  distance.PearsonR),
        MetricDef(SpearmanAbsolute, "Spearman (absolute)",
                  "Absolute value of Pearson correlation; distance = 1 - |ρ|",
                  distance.SpearmanRAbsolute),
        MetricDef(Jaccard, "Jaccard", "Jaccard distance",
                  distance.Jaccard)
    )
}


class InterruptException(Exception):
    pass


class DistanceRunner:
    @staticmethod
    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,
            axis: int, state: TaskState) -> Optional[Orange.misc.DistMatrix]:
        if data is None:
            return None

        def callback(i: float) -> bool:
            state.set_progress_value(i)
            if state.is_interruption_requested():
                raise InterruptException

        state.set_status("Calculating...")
        kwargs = {"axis": 1 - axis, "impute": True, "callback": callback}
        if metric.supports_normalization and normalized_dist:
            kwargs["normalize"] = True
        return metric(data, **kwargs)


class OWDistances(OWWidget, ConcurrentWidgetMixin):
    name = "Distances"
    description = "Compute a matrix of pairwise distances."
    icon = "icons/Distance.svg"
    keywords = "distances"

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

    class Outputs:
        distances = Output("Distances", Orange.misc.DistMatrix, dynamic=False)

    settings_version = 4

    axis: int = Setting(0)
    metric_id: int = Setting(EuclideanNormalized)
    autocommit: bool = Setting(True)

    want_main_area = False
    resizing_enabled = False

    class Error(OWWidget.Error):
        no_continuous_features = Msg("No numeric features")
        no_binary_features = Msg("No binary features")
        dense_metric_sparse_data = Msg("{} requires dense data.")
        distances_memory_error = Msg("Not enough memory")
        distances_value_error = Msg("Problem in calculation:\n{}")
        data_too_large_for_mahalanobis = Msg(
            "Mahalanobis handles up to 1000 {}.")

    class Warning(OWWidget.Warning):
        ignoring_discrete = Msg("Ignoring categorical features")
        ignoring_nonbinary = Msg("Ignoring non-binary features")
        unsupported_sparse = Msg("Some metrics don't support sparse data\n"
                                 "and were disabled: {}")
        imputing_data = Msg("Missing values were imputed")

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

        self.data = None

        gui.radioButtons(
            self.controlArea, self, "axis", ["Rows", "Columns"],
            box="Compare", orientation=Qt.Horizontal, callback=self._invalidate
        )
        box = gui.hBox(self.controlArea, "Distance Metric")
        self.metric_buttons = QButtonGroup()
        width = 0
        for i, metric in enumerate(MetricDefs.values()):
            if i % 6 == 0:
                vb = gui.vBox(box)
            b = QRadioButton(metric.name)
            b.setChecked(self.metric_id == metric.id)
            b.setToolTip(metric.tooltip)
            vb.layout().addWidget(b)
            width = max(width, b.sizeHint().width())
            self.metric_buttons.addButton(b, metric.id)
        for b in self.metric_buttons.buttons():
            b.setFixedWidth(width)

        self.metric_buttons.idClicked.connect(self._metric_changed)

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


    @Inputs.data
    @check_sql_input
    def set_data(self, data):
        self.cancel()
        self.data = data
        self.refresh_radios()
        self.commit.now()

    def _metric_changed(self, id_):
        self.metric_id = id_
        self._invalidate()

    def refresh_radios(self):
        sparse = self.data is not None and issparse(self.data.X)
        unsupported_sparse = []
        for metric in MetricDefs.values():
            button = self.metric_buttons.button(metric.id)
            no_sparse = sparse and not metric.metric.supports_sparse
            button.setEnabled(not no_sparse)
            if no_sparse:
                unsupported_sparse.append(metric.name)
        self.Warning.unsupported_sparse(", ".join(unsupported_sparse),
                                        shown=bool(unsupported_sparse))

    @gui.deferred
    def commit(self):
        self.compute_distances(self.data)

    def compute_distances(self, data):
        def _check_sparse():
            # pylint: disable=invalid-sequence-index
            if issparse(data.X) and not metric.supports_sparse:
                self.Error.dense_metric_sparse_data(metric_def.name)
                return False
            return True

        def _fix_discrete():
            nonlocal data
            if data.domain.has_discrete_attributes() \
                    and metric is not distance.Jaccard \
                    and (issparse(data.X) and getattr(metric, "fallback", None)
                         or not metric.supports_discrete
                         or self.axis == 1):
                if not data.domain.has_continuous_attributes():
                    self.Error.no_continuous_features()
                    return False
                self.Warning.ignoring_discrete()
                data = distance.remove_discrete_features(data, to_metas=True)
            return True

        def _fix_nonbinary():
            nonlocal data
            if metric is distance.Jaccard and not issparse(data.X):
                nbinary = sum(a.is_discrete and len(a.values) == 2
                              for a in data.domain.attributes)
                if not nbinary:
                    self.Error.no_binary_features()
                    return False
                elif nbinary < len(data.domain.attributes):
                    self.Warning.ignoring_nonbinary()
                    data = distance.remove_nonbinary_features(data,
                                                              to_metas=True)
            return True

        def _fix_missing():
            nonlocal data
            if not metric.supports_missing and bn.anynan(data.X):
                self.Warning.imputing_data()
                data = distance.impute(data)
            return True

        def _check_tractability():
            if metric is distance.Mahalanobis:
                if self.axis == 0:
                    # when computing distances by columns, we want < 1000 rows
                    if len(data) > 1000:
                        self.Error.data_too_large_for_mahalanobis("rows")
                        return False
                else:
                    if len(data.domain.attributes) > 1000:
                        self.Error.data_too_large_for_mahalanobis("columns")
                        return False
            return True

        metric_def = MetricDefs[self.metric_id]
        metric = metric_def.metric
        self.clear_messages()
        if data is not None:
            for check in (_check_sparse, _check_tractability,
                          _fix_discrete, _fix_missing, _fix_nonbinary):
                if not check():
                    data = None
                    break

        self.start(DistanceRunner.run, data, metric,
                   metric_def.normalize, self.axis)

    def on_partial_result(self, _):
        pass

    def on_done(self, result: Orange.misc.DistMatrix):
        assert isinstance(result, Orange.misc.DistMatrix) or result is None
        self.Outputs.distances.send(result)

    def on_exception(self, ex):
        if isinstance(ex, ValueError):
            self.Error.distances_value_error(ex)
        elif isinstance(ex, MemoryError):
            self.Error.distances_memory_error()
        elif isinstance(ex, InterruptException):
            pass
        else:
            raise ex

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

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

    def send_report(self):
        # pylint: disable=invalid-sequence-index
        self.report_items((
            ("Distances Between", ["Rows", "Columns"][self.axis]),
            ("Metric", MetricDefs[self.metric_id].name)
        ))

    @classmethod
    def migrate_settings(cls, settings, version):
        if version is None or version < 2 and "normalized_dist" not in settings:
            # normalize_dist is set to False when restoring settings from
            # an older version to preserve old semantics.
            settings["normalized_dist"] = False
        if version is None or version < 3:
            # Mahalanobis was moved from idx = 2 to idx = 9
            metric_idx = settings["metric_idx"]
            if metric_idx == 2:
                settings["metric_idx"] = 9
            elif 2 < metric_idx <= 9:
                settings["metric_idx"] -= 1
        if version < 4:
            metric_idx = settings.pop("metric_idx")
            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,
                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,
                         Hamming, Mahalanobis, Euclidean][metric_idx]
            if settings.pop("normalized_dist", False):
                metric_id = {Euclidean: EuclideanNormalized,
                             Manhattan: ManhattanNormalized}.get(metric_id,
                                                                 metric_id)
            settings["metric_id"] = metric_id


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWDistances).run(Orange.data.Table("iris"))

```
--- 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.
**Analysis:**

- **Key Parts:**
  - **Commit Message:** "Distances: Warn when data has no features."
  - **Test Results:** Before the commit, `test_no_features` failed with `AttributeError: 'Warning' object has no attribute 'no_features'`. After the commit, the test passes.
  - **Commit Patch:** Introduces a new warning `no_features` and a corresponding check in the widget.

- **Main Problem:**
  - Before the commit, when data with no features is provided to the `OWDistances` widget, the widget does not display the expected `no_features` warning, leading to an `AttributeError` in tests.

- **Expected Behavior:**
  - The `OWDistances` widget should display a warning message "Data has no features" when it receives data without any features.

- **Actual Behavior:**
  - The widget does not display the `no_features` warning, causing tests that check for this warning to fail with an `AttributeError`.

- **Issue Description:**
  - Clearly describe that the widget fails to warn when data has no features, leading to errors in tests and potential confusion for users.

**Drafted GitHub Issue:**

[ISSUE]

**Title:** OWDistances Widget Fails to Warn When Data Has No Features

**Description:**
When providing data with no features to the `OWDistances` widget, the expected warning indicating the absence of features is not displayed. This leads to an `AttributeError` when the system attempts to access the non-existent `no_features` warning.

**Example Buggy Code:**
```python
def test_no_features():
    zoo = Table("zoo")[:5, 16:]
    
    send_signal(widget.Inputs.data, zoo)
    wait_until_finished()
    out = get_output(widget.Outputs.distances)
    assert out.shape == (5, 5)
    assert (out == 0).all()
    # This line causes AttributeError because 'no_features' warning is not present
    assert widget.Warning.no_features.is_shown()
```

**Expected Behavior:**
The `OWDistances` widget should display a warning message "Data has no features" when it receives input data that contains no feature attributes. This warning informs users that the distance calculations cannot be performed due to the lack of features.

**Actual Behavior:**
No warning is displayed when the widget receives data with no features. Attempts to access the `no_features` warning result in an `AttributeError`, indicating that the warning attribute does not exist.

[/ISSUE]
{
    "TestDistanceRunner.test_run": "PASSED",
    "TestOWDistances.test_discrete_in_metas": "PASSED",
    "TestOWDistances.test_distance_combo": "PASSED",
    "TestOWDistances.test_error_message": "PASSED",
    "TestOWDistances.test_image_export": "PASSED",
    "TestOWDistances.test_jaccard_messages": "PASSED",
    "TestOWDistances.test_limit_mahalanobis": "PASSED",
    "TestOWDistances.test_migrate_3_to_4": "PASSED",
    "TestOWDistances.test_msg_base_class": "PASSED",
    "TestOWDistances.test_no_features": "PASSED",
    "TestOWDistances.test_non_binary_in_metas": "PASSED",
    "TestOWDistances.test_too_big_array": "PASSED",
    "TestOWDistances.test_minimum_size": "FAILED"
}
diff --git a/Orange/widgets/unsupervised/owdistances.py b/Orange/widgets/unsupervised/owdistances.py
index cc5e7e87f..0e4f32887 100644
--- a/Orange/widgets/unsupervised/owdistances.py
+++ b/Orange/widgets/unsupervised/owdistances.py
@@ -127,6 +127,7 @@ class OWDistances(OWWidget, ConcurrentWidgetMixin):
         unsupported_sparse = Msg("Some metrics don't support sparse data\n"
                                  "and were disabled: {}")
         imputing_data = Msg("Missing values were imputed")
+        no_features = Msg("Data has no features")
 
     def __init__(self):
         OWWidget.__init__(self)
@@ -242,11 +243,17 @@ class OWDistances(OWWidget, ConcurrentWidgetMixin):
                         return False
             return True
 
+        def _check_no_features():
+            if len(data.domain.attributes) == 0:
+                self.Warning.no_features()
+            return True
+
         metric_def = MetricDefs[self.metric_id]
         metric = metric_def.metric
         self.clear_messages()
         if data is not None:
             for check in (_check_sparse, _check_tractability,
+                          _check_no_features,
                           _fix_discrete, _fix_missing, _fix_nonbinary):
                 if not check():
                     data = None

{"Orange/widgets/unsupervised/owdistances.py": "from typing import NamedTuple, Dict, Type, Optional\n\nfrom AnyQt.QtWidgets import QButtonGroup, QRadioButton\nfrom AnyQt.QtCore import Qt\nfrom scipy.sparse import issparse\nimport bottleneck as bn\n\nimport Orange.data\nimport Orange.misc\nfrom Orange import distance\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting\nfrom Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin\nfrom Orange.widgets.utils.sql import check_sql_input\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.widget import OWWidget, Msg, Input, Output\n\n\nEuclidean, EuclideanNormalized, Manhattan, ManhattanNormalized, Cosine, \\\n    Mahalanobis, Hamming, \\\n    Pearson, PearsonAbsolute, Spearman, SpearmanAbsolute, Jaccard = range(12)\n\n\nclass MetricDef(NamedTuple):\n    id: int  # pylint: disable=invalid-name\n    name: str\n    tooltip: str\n    metric: Type[distance.Distance]\n    normalize: bool = False\n\n\nMetricDefs: Dict[int, MetricDef] = {\n    metric.id: metric for metric in (\n        MetricDef(EuclideanNormalized, \"Euclidean (normalized)\",\n                  \"Square root of summed difference between normalized values\",\n                  distance.Euclidean, normalize=True),\n        MetricDef(Euclidean, \"Euclidean\",\n                  \"Square root of summed difference between values\",\n                  distance.Euclidean),\n        MetricDef(ManhattanNormalized, \"Manhattan (normalized)\",\n                  \"Sum of absolute differences between normalized values\",\n                  distance.Manhattan, normalize=True),\n        MetricDef(Manhattan, \"Manhattan\",\n                  \"Sum of absolute differences between values\",\n                  distance.Manhattan),\n        MetricDef(Mahalanobis, \"Mahalanobis\",\n                  \"Mahalanobis distance\",\n                  distance.Mahalanobis),\n        MetricDef(Hamming, \"Hamming\", \"Hamming distance\",\n                  distance.Hamming),\n        MetricDef(Cosine, \"Cosine\", \"Cosine distance\",\n                  distance.Cosine),\n        MetricDef(Pearson, \"Pearson\",\n                  \"Pearson correlation; distance = 1 - \u03c1/2\",\n                  distance.PearsonR),\n        MetricDef(PearsonAbsolute, \"Pearson (absolute)\",\n                  \"Absolute value of Pearson correlation; distance = 1 - |\u03c1|\",\n                  distance.PearsonRAbsolute),\n        MetricDef(Spearman, \"Spearman\",\n                  \"Pearson correlation; distance = 1 - \u03c1/2\",\n                  distance.PearsonR),\n        MetricDef(SpearmanAbsolute, \"Spearman (absolute)\",\n                  \"Absolute value of Pearson correlation; distance = 1 - |\u03c1|\",\n                  distance.SpearmanRAbsolute),\n        MetricDef(Jaccard, \"Jaccard\", \"Jaccard distance\",\n                  distance.Jaccard)\n    )\n}\n\n\nclass InterruptException(Exception):\n    pass\n\n\nclass DistanceRunner:\n    @staticmethod\n    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,\n            axis: int, state: TaskState) -> Optional[Orange.misc.DistMatrix]:\n        if data is None:\n            return None\n\n        def callback(i: float) -> bool:\n            state.set_progress_value(i)\n            if state.is_interruption_requested():\n                raise InterruptException\n\n        state.set_status(\"Calculating...\")\n        kwargs = {\"axis\": 1 - axis, \"impute\": True, \"callback\": callback}\n        if metric.supports_normalization and normalized_dist:\n            kwargs[\"normalize\"] = True\n        return metric(data, **kwargs)\n\n\nclass OWDistances(OWWidget, ConcurrentWidgetMixin):\n    name = \"Distances\"\n    description = \"Compute a matrix of pairwise distances.\"\n    icon = \"icons/Distance.svg\"\n    keywords = \"distances\"\n\n    class Inputs:\n        data = Input(\"Data\", Orange.data.Table)\n\n    class Outputs:\n        distances = Output(\"Distances\", Orange.misc.DistMatrix, dynamic=False)\n\n    settings_version = 4\n\n    axis: int = Setting(0)\n    metric_id: int = Setting(EuclideanNormalized)\n    autocommit: bool = Setting(True)\n\n    want_main_area = False\n    resizing_enabled = False\n\n    class Error(OWWidget.Error):\n        no_continuous_features = Msg(\"No numeric features\")\n        no_binary_features = Msg(\"No binary features\")\n        dense_metric_sparse_data = Msg(\"{} requires dense data.\")\n        distances_memory_error = Msg(\"Not enough memory\")\n        distances_value_error = Msg(\"Problem in calculation:\\n{}\")\n        data_too_large_for_mahalanobis = Msg(\n            \"Mahalanobis handles up to 1000 {}.\")\n\n    class Warning(OWWidget.Warning):\n        ignoring_discrete = Msg(\"Ignoring categorical features\")\n        ignoring_nonbinary = Msg(\"Ignoring non-binary features\")\n        unsupported_sparse = Msg(\"Some metrics don't support sparse data\\n\"\n                                 \"and were disabled: {}\")\n        imputing_data = Msg(\"Missing values were imputed\")\n\n    def __init__(self):\n        OWWidget.__init__(self)\n        ConcurrentWidgetMixin.__init__(self)\n\n        self.data = None\n\n        gui.radioButtons(\n            self.controlArea, self, \"axis\", [\"Rows\", \"Columns\"],\n            box=\"Compare\", orientation=Qt.Horizontal, callback=self._invalidate\n        )\n        box = gui.hBox(self.controlArea, \"Distance Metric\")\n        self.metric_buttons = QButtonGroup()\n        width = 0\n        for i, metric in enumerate(MetricDefs.values()):\n            if i % 6 == 0:\n                vb = gui.vBox(box)\n            b = QRadioButton(metric.name)\n            b.setChecked(self.metric_id == metric.id)\n            b.setToolTip(metric.tooltip)\n            vb.layout().addWidget(b)\n            width = max(width, b.sizeHint().width())\n            self.metric_buttons.addButton(b, metric.id)\n        for b in self.metric_buttons.buttons():\n            b.setFixedWidth(width)\n\n        self.metric_buttons.idClicked.connect(self._metric_changed)\n\n        gui.auto_apply(self.buttonsArea, self, \"autocommit\")\n\n\n    @Inputs.data\n    @check_sql_input\n    def set_data(self, data):\n        self.cancel()\n        self.data = data\n        self.refresh_radios()\n        self.commit.now()\n\n    def _metric_changed(self, id_):\n        self.metric_id = id_\n        self._invalidate()\n\n    def refresh_radios(self):\n        sparse = self.data is not None and issparse(self.data.X)\n        unsupported_sparse = []\n        for metric in MetricDefs.values():\n            button = self.metric_buttons.button(metric.id)\n            no_sparse = sparse and not metric.metric.supports_sparse\n            button.setEnabled(not no_sparse)\n            if no_sparse:\n                unsupported_sparse.append(metric.name)\n        self.Warning.unsupported_sparse(\", \".join(unsupported_sparse),\n                                        shown=bool(unsupported_sparse))\n\n    @gui.deferred\n    def commit(self):\n        self.compute_distances(self.data)\n\n    def compute_distances(self, data):\n        def _check_sparse():\n            # pylint: disable=invalid-sequence-index\n            if issparse(data.X) and not metric.supports_sparse:\n                self.Error.dense_metric_sparse_data(metric_def.name)\n                return False\n            return True\n\n        def _fix_discrete():\n            nonlocal data\n            if data.domain.has_discrete_attributes() \\\n                    and metric is not distance.Jaccard \\\n                    and (issparse(data.X) and getattr(metric, \"fallback\", None)\n                         or not metric.supports_discrete\n                         or self.axis == 1):\n                if not data.domain.has_continuous_attributes():\n                    self.Error.no_continuous_features()\n                    return False\n                self.Warning.ignoring_discrete()\n                data = distance.remove_discrete_features(data, to_metas=True)\n            return True\n\n        def _fix_nonbinary():\n            nonlocal data\n            if metric is distance.Jaccard and not issparse(data.X):\n                nbinary = sum(a.is_discrete and len(a.values) == 2\n                              for a in data.domain.attributes)\n                if not nbinary:\n                    self.Error.no_binary_features()\n                    return False\n                elif nbinary < len(data.domain.attributes):\n                    self.Warning.ignoring_nonbinary()\n                    data = distance.remove_nonbinary_features(data,\n                                                              to_metas=True)\n            return True\n\n        def _fix_missing():\n            nonlocal data\n            if not metric.supports_missing and bn.anynan(data.X):\n                self.Warning.imputing_data()\n                data = distance.impute(data)\n            return True\n\n        def _check_tractability():\n            if metric is distance.Mahalanobis:\n                if self.axis == 0:\n                    # when computing distances by columns, we want < 1000 rows\n                    if len(data) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"rows\")\n                        return False\n                else:\n                    if len(data.domain.attributes) > 1000:\n                        self.Error.data_too_large_for_mahalanobis(\"columns\")\n                        return False\n            return True\n\n        metric_def = MetricDefs[self.metric_id]\n        metric = metric_def.metric\n        self.clear_messages()\n        if data is not None:\n            for check in (_check_sparse, _check_tractability,\n                          _fix_discrete, _fix_missing, _fix_nonbinary):\n                if not check():\n                    data = None\n                    break\n\n        self.start(DistanceRunner.run, data, metric,\n                   metric_def.normalize, self.axis)\n\n    def on_partial_result(self, _):\n        pass\n\n    def on_done(self, result: Orange.misc.DistMatrix):\n        assert isinstance(result, Orange.misc.DistMatrix) or result is None\n        self.Outputs.distances.send(result)\n\n    def on_exception(self, ex):\n        if isinstance(ex, ValueError):\n            self.Error.distances_value_error(ex)\n        elif isinstance(ex, MemoryError):\n            self.Error.distances_memory_error()\n        elif isinstance(ex, InterruptException):\n            pass\n        else:\n            raise ex\n\n    def onDeleteWidget(self):\n        self.shutdown()\n        super().onDeleteWidget()\n\n    def _invalidate(self):\n        self.commit.deferred()\n\n    def send_report(self):\n        # pylint: disable=invalid-sequence-index\n        self.report_items((\n            (\"Distances Between\", [\"Rows\", \"Columns\"][self.axis]),\n            (\"Metric\", MetricDefs[self.metric_id].name)\n        ))\n\n    @classmethod\n    def migrate_settings(cls, settings, version):\n        if version is None or version < 2 and \"normalized_dist\" not in settings:\n            # normalize_dist is set to False when restoring settings from\n            # an older version to preserve old semantics.\n            settings[\"normalized_dist\"] = False\n        if version is None or version < 3:\n            # Mahalanobis was moved from idx = 2 to idx = 9\n            metric_idx = settings[\"metric_idx\"]\n            if metric_idx == 2:\n                settings[\"metric_idx\"] = 9\n            elif 2 < metric_idx <= 9:\n                settings[\"metric_idx\"] -= 1\n        if version < 4:\n            metric_idx = settings.pop(\"metric_idx\")\n            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,\n                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,\n                         Hamming, Mahalanobis, Euclidean][metric_idx]\n            if settings.pop(\"normalized_dist\", False):\n                metric_id = {Euclidean: EuclideanNormalized,\n                             Manhattan: ManhattanNormalized}.get(metric_id,\n                                                                 metric_id)\n            settings[\"metric_id\"] = metric_id\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWDistances).run(Orange.data.Table(\"iris\"))\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 ---
**Analysis:**

- **Key Parts:**
  - **Commit Message:** "Distances: Warn when data has no features."
  - **Test Results:** Before the commit, `test_no_features` failed with `AttributeError: 'Warning' object has no attribute 'no_features'`. After the commit, the test passes.
  - **Commit Patch:** Introduces a new warning `no_features` and a corresponding check in the widget.

- **Main Problem:**
  - Before the commit, when data with no features is provided to the `OWDistances` widget, the widget does not display the expected `no_features` warning, leading to an `AttributeError` in tests.

- **Expected Behavior:**
  - The `OWDistances` widget should display a warning message "Data has no features" when it receives data without any features.

- **Actual Behavior:**
  - The widget does not display the `no_features` warning, causing tests that check for this warning to fail with an `AttributeError`.

- **Issue Description:**
  - Clearly describe that the widget fails to warn when data has no features, leading to errors in tests and potential confusion for users.

**Drafted GitHub Issue:**

[ISSUE]

**Title:** OWDistances Widget Fails to Warn When Data Has No Features

**Description:**
When providing data with no features to the `OWDistances` widget, the expected warning indicating the absence of features is not displayed. This leads to an `AttributeError` when the system attempts to access the non-existent `no_features` warning.

**Example Buggy Code:**
```python
def test_no_features():
    zoo = Table("zoo")[:5, 16:]
    
    send_signal(widget.Inputs.data, zoo)
    wait_until_finished()
    out = get_output(widget.Outputs.distances)
    assert out.shape == (5, 5)
    assert (out == 0).all()
    # This line causes AttributeError because 'no_features' warning is not present
    assert widget.Warning.no_features.is_shown()
```

**Expected Behavior:**
The `OWDistances` widget should display a warning message "Data has no features" when it receives input data that contains no feature attributes. This warning informs users that the distance calculations cannot be performed due to the lack of features.

**Actual Behavior:**
No warning is displayed when the widget receives data with no features. Attempts to access the `no_features` warning result in an `AttributeError`, indicating that the warning attribute does not exist.

[/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/owdistances.py
from typing import NamedTuple, Dict, Type, Optional

from AnyQt.QtWidgets import QButtonGroup, QRadioButton
from AnyQt.QtCore import Qt
from scipy.sparse import issparse
import bottleneck as bn

import Orange.data
import Orange.misc
from Orange import distance
from Orange.widgets import gui
from Orange.widgets.settings import Setting
from Orange.widgets.utils.concurrent import TaskState, ConcurrentWidgetMixin
from Orange.widgets.utils.sql import check_sql_input
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.widget import OWWidget, Msg, Input, Output


Euclidean, EuclideanNormalized, Manhattan, ManhattanNormalized, Cosine, \
    Mahalanobis, Hamming, \
    Pearson, PearsonAbsolute, Spearman, SpearmanAbsolute, Jaccard = range(12)


class MetricDef(NamedTuple):
    id: int  # pylint: disable=invalid-name
    name: str
    tooltip: str
    metric: Type[distance.Distance]
    normalize: bool = False


MetricDefs: Dict[int, MetricDef] = {
    metric.id: metric for metric in (
        MetricDef(EuclideanNormalized, "Euclidean (normalized)",
                  "Square root of summed difference between normalized values",
                  distance.Euclidean, normalize=True),
        MetricDef(Euclidean, "Euclidean",
                  "Square root of summed difference between values",
                  distance.Euclidean),
        MetricDef(ManhattanNormalized, "Manhattan (normalized)",
                  "Sum of absolute differences between normalized values",
                  distance.Manhattan, normalize=True),
        MetricDef(Manhattan, "Manhattan",
                  "Sum of absolute differences between values",
                  distance.Manhattan),
        MetricDef(Mahalanobis, "Mahalanobis",
                  "Mahalanobis distance",
                  distance.Mahalanobis),
        MetricDef(Hamming, "Hamming", "Hamming distance",
                  distance.Hamming),
        MetricDef(Cosine, "Cosine", "Cosine distance",
                  distance.Cosine),
        MetricDef(Pearson, "Pearson",
                  "Pearson correlation; distance = 1 - ρ/2",
                  distance.PearsonR),
        MetricDef(PearsonAbsolute, "Pearson (absolute)",
                  "Absolute value of Pearson correlation; distance = 1 - |ρ|",
                  distance.PearsonRAbsolute),
        MetricDef(Spearman, "Spearman",
                  "Pearson correlation; distance = 1 - ρ/2",
                  distance.PearsonR),
        MetricDef(SpearmanAbsolute, "Spearman (absolute)",
                  "Absolute value of Pearson correlation; distance = 1 - |ρ|",
                  distance.SpearmanRAbsolute),
        MetricDef(Jaccard, "Jaccard", "Jaccard distance",
                  distance.Jaccard)
    )
}


class InterruptException(Exception):
    pass


class DistanceRunner:
    @staticmethod
    def run(data: Orange.data.Table, metric: distance, normalized_dist: bool,
            axis: int, state: TaskState) -> Optional[Orange.misc.DistMatrix]:
        if data is None:
            return None

        def callback(i: float) -> bool:
            state.set_progress_value(i)
            if state.is_interruption_requested():
                raise InterruptException

        state.set_status("Calculating...")
        kwargs = {"axis": 1 - axis, "impute": True, "callback": callback}
        if metric.supports_normalization and normalized_dist:
            kwargs["normalize"] = True
        return metric(data, **kwargs)


class OWDistances(OWWidget, ConcurrentWidgetMixin):
    name = "Distances"
    description = "Compute a matrix of pairwise distances."
    icon = "icons/Distance.svg"
    keywords = "distances"

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

    class Outputs:
        distances = Output("Distances", Orange.misc.DistMatrix, dynamic=False)

    settings_version = 4

    axis: int = Setting(0)
    metric_id: int = Setting(EuclideanNormalized)
    autocommit: bool = Setting(True)

    want_main_area = False
    resizing_enabled = False

    class Error(OWWidget.Error):
        no_continuous_features = Msg("No numeric features")
        no_binary_features = Msg("No binary features")
        dense_metric_sparse_data = Msg("{} requires dense data.")
        distances_memory_error = Msg("Not enough memory")
        distances_value_error = Msg("Problem in calculation:\n{}")
        data_too_large_for_mahalanobis = Msg(
            "Mahalanobis handles up to 1000 {}.")

    class Warning(OWWidget.Warning):
        ignoring_discrete = Msg("Ignoring categorical features")
        ignoring_nonbinary = Msg("Ignoring non-binary features")
        unsupported_sparse = Msg("Some metrics don't support sparse data\n"
                                 "and were disabled: {}")
        imputing_data = Msg("Missing values were imputed")

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

        self.data = None

        gui.radioButtons(
            self.controlArea, self, "axis", ["Rows", "Columns"],
            box="Compare", orientation=Qt.Horizontal, callback=self._invalidate
        )
        box = gui.hBox(self.controlArea, "Distance Metric")
        self.metric_buttons = QButtonGroup()
        width = 0
        for i, metric in enumerate(MetricDefs.values()):
            if i % 6 == 0:
                vb = gui.vBox(box)
            b = QRadioButton(metric.name)
            b.setChecked(self.metric_id == metric.id)
            b.setToolTip(metric.tooltip)
            vb.layout().addWidget(b)
            width = max(width, b.sizeHint().width())
            self.metric_buttons.addButton(b, metric.id)
        for b in self.metric_buttons.buttons():
            b.setFixedWidth(width)

        self.metric_buttons.idClicked.connect(self._metric_changed)

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


    @Inputs.data
    @check_sql_input
    def set_data(self, data):
        self.cancel()
        self.data = data
        self.refresh_radios()
        self.commit.now()

    def _metric_changed(self, id_):
        self.metric_id = id_
        self._invalidate()

    def refresh_radios(self):
        sparse = self.data is not None and issparse(self.data.X)
        unsupported_sparse = []
        for metric in MetricDefs.values():
            button = self.metric_buttons.button(metric.id)
            no_sparse = sparse and not metric.metric.supports_sparse
            button.setEnabled(not no_sparse)
            if no_sparse:
                unsupported_sparse.append(metric.name)
        self.Warning.unsupported_sparse(", ".join(unsupported_sparse),
                                        shown=bool(unsupported_sparse))

    @gui.deferred
    def commit(self):
        self.compute_distances(self.data)

    def compute_distances(self, data):
        def _check_sparse():
            # pylint: disable=invalid-sequence-index
            if issparse(data.X) and not metric.supports_sparse:
                self.Error.dense_metric_sparse_data(metric_def.name)
                return False
            return True

        def _fix_discrete():
            nonlocal data
            if data.domain.has_discrete_attributes() \
                    and metric is not distance.Jaccard \
                    and (issparse(data.X) and getattr(metric, "fallback", None)
                         or not metric.supports_discrete
                         or self.axis == 1):
                if not data.domain.has_continuous_attributes():
                    self.Error.no_continuous_features()
                    return False
                self.Warning.ignoring_discrete()
                data = distance.remove_discrete_features(data, to_metas=True)
            return True

        def _fix_nonbinary():
            nonlocal data
            if metric is distance.Jaccard and not issparse(data.X):
                nbinary = sum(a.is_discrete and len(a.values) == 2
                              for a in data.domain.attributes)
                if not nbinary:
                    self.Error.no_binary_features()
                    return False
                elif nbinary < len(data.domain.attributes):
                    self.Warning.ignoring_nonbinary()
                    data = distance.remove_nonbinary_features(data,
                                                              to_metas=True)
            return True

        def _fix_missing():
            nonlocal data
            if not metric.supports_missing and bn.anynan(data.X):
                self.Warning.imputing_data()
                data = distance.impute(data)
            return True

        def _check_tractability():
            if metric is distance.Mahalanobis:
                if self.axis == 0:
                    # when computing distances by columns, we want < 1000 rows
                    if len(data) > 1000:
                        self.Error.data_too_large_for_mahalanobis("rows")
                        return False
                else:
                    if len(data.domain.attributes) > 1000:
                        self.Error.data_too_large_for_mahalanobis("columns")
                        return False
            return True

        metric_def = MetricDefs[self.metric_id]
        metric = metric_def.metric
        self.clear_messages()
        if data is not None:
            for check in (_check_sparse, _check_tractability,
                          _fix_discrete, _fix_missing, _fix_nonbinary):
                if not check():
                    data = None
                    break

        self.start(DistanceRunner.run, data, metric,
                   metric_def.normalize, self.axis)

    def on_partial_result(self, _):
        pass

    def on_done(self, result: Orange.misc.DistMatrix):
        assert isinstance(result, Orange.misc.DistMatrix) or result is None
        self.Outputs.distances.send(result)

    def on_exception(self, ex):
        if isinstance(ex, ValueError):
            self.Error.distances_value_error(ex)
        elif isinstance(ex, MemoryError):
            self.Error.distances_memory_error()
        elif isinstance(ex, InterruptException):
            pass
        else:
            raise ex

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

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

    def send_report(self):
        # pylint: disable=invalid-sequence-index
        self.report_items((
            ("Distances Between", ["Rows", "Columns"][self.axis]),
            ("Metric", MetricDefs[self.metric_id].name)
        ))

    @classmethod
    def migrate_settings(cls, settings, version):
        if version is None or version < 2 and "normalized_dist" not in settings:
            # normalize_dist is set to False when restoring settings from
            # an older version to preserve old semantics.
            settings["normalized_dist"] = False
        if version is None or version < 3:
            # Mahalanobis was moved from idx = 2 to idx = 9
            metric_idx = settings["metric_idx"]
            if metric_idx == 2:
                settings["metric_idx"] = 9
            elif 2 < metric_idx <= 9:
                settings["metric_idx"] -= 1
        if version < 4:
            metric_idx = settings.pop("metric_idx")
            metric_id = [Euclidean, Manhattan, Cosine, Jaccard,
                         Spearman, SpearmanAbsolute, Pearson, PearsonAbsolute,
                         Hamming, Mahalanobis, Euclidean][metric_idx]
            if settings.pop("normalized_dist", False):
                metric_id = {Euclidean: EuclideanNormalized,
                             Manhattan: ManhattanNormalized}.get(metric_id,
                                                                 metric_id)
            settings["metric_id"] = metric_id


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWDistances).run(Orange.data.Table("iris"))

```
--- 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