{
    "file_diffs": [
        {
            "old_file_content": "\"\"\"\nCorrelations widget\n\"\"\"\nfrom enum import IntEnum\nfrom operator import attrgetter\nfrom types import SimpleNamespace\nfrom itertools import combinations, groupby, chain\n\nimport numpy as np\nfrom scipy.stats import spearmanr, pearsonr\nfrom sklearn.cluster import KMeans\n\nfrom AnyQt.QtCore import Qt, QItemSelectionModel, QItemSelection, \\\n    QSize, pyqtSignal as Signal\nfrom AnyQt.QtGui import QStandardItem\nfrom AnyQt.QtWidgets import QHeaderView\n\nfrom Orange.data import Table, Domain, ContinuousVariable, StringVariable\nfrom Orange.preprocess import SklImpute, Normalize, Remove\nfrom Orange.statistics.util import FDR\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting, ContextSetting, \\\n    DomainContextHandler\nfrom Orange.widgets.utils import vartype\nfrom Orange.widgets.utils.itemmodels import DomainModel\nfrom Orange.widgets.utils.signals import Input, Output\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.visualize.utils import VizRankDialogAttrPair\nfrom Orange.widgets.widget import OWWidget, AttributeList, Msg\n\nNAN = 2\nSIZE_LIMIT = 1000000\n\n\nclass CorrelationType(IntEnum):\n    \"\"\"\n    Correlation type enumerator. Possible correlations: Pearson, Spearman.\n    \"\"\"\n    PEARSON, SPEARMAN = 0, 1\n\n    @staticmethod\n    def items():\n        \"\"\"\n        Texts for correlation types. Can be used in gui controls (eg. combobox).\n        \"\"\"\n        return [\"Pearson correlation\", \"Spearman correlation\"]\n\n\nclass Cluster(SimpleNamespace):\n    instances = None  # type: Optional[List]\n    centroid = None  # type: Optional[np.ndarray]\n\n\nclass KMeansCorrelationHeuristic:\n    \"\"\"\n    Heuristic to obtain the most promising attribute pairs, when there are too\n    many attributes to calculate correlations for all possible pairs.\n    \"\"\"\n    def __init__(self, data):\n        self.n_attributes = len(data.domain.attributes)\n        self.data = data\n        self.clusters = None\n        self.n_clusters = int(np.sqrt(self.n_attributes))\n\n    def get_clusters_of_attributes(self):\n        \"\"\"\n        Generates groupes of attribute IDs, grouped by cluster. Clusters are\n        obtained by KMeans algorithm.\n\n        :return: generator of attributes grouped by cluster\n        \"\"\"\n        data = Normalize()(self.data).X.T\n        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)\n        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])\n        return [Cluster(instances=list(pair[1] for pair in group),\n                        centroid=kmeans.cluster_centers_[l])\n                for l, group in groupby(labels_attrs, key=lambda x: x[0])]\n\n    def get_states(self, initial_state):\n        \"\"\"\n        Generates states (attribute pairs) - the most promising first, i.e.\n        states within clusters, following by states among clusters.\n\n        :param initial_state: initial state; None if this is the first call\n        :return: generator of tuples of states\n        \"\"\"\n        if self.clusters is None:\n            self.clusters = self.get_clusters_of_attributes()\n        clusters = self.clusters\n\n        # combinations within clusters\n        states0 = chain.from_iterable(combinations(cluster.instances, 2)\n                                      for cluster in clusters)\n        if self.n_clusters == 1:\n            return states0\n\n        # combinations among clusters - closest clusters first\n        centroids = [c.centroid for c in clusters]\n        centroids_combs = np.array(list(combinations(centroids, 2)))\n        distances = np.linalg.norm((centroids_combs[:, 0] -\n                                    centroids_combs[:, 1]), axis=1)\n        cluster_combs = list(combinations(range(len(clusters)), 2))\n        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)\n                  for c1 in clusters[cluster_combs[i][0]].instances\n                  for c2 in clusters[cluster_combs[i][1]].instances)\n        states = chain(states0, states)\n\n        if initial_state is not None:\n            while next(states) != initial_state:\n                pass\n            return chain([initial_state], states)\n        return states\n\n\nclass CorrelationRank(VizRankDialogAttrPair):\n    \"\"\"\n    Correlations rank widget.\n    \"\"\"\n    threadStopped = Signal()\n    PValRole = next(gui.OrangeUserRole)\n\n    def __init__(self, *args):\n        super().__init__(*args)\n        self.heuristic = None\n        self.use_heuristic = False\n        self.sel_feature_index = None\n\n    def initialize(self):\n        super().initialize()\n        data = self.master.cont_data\n        self.attrs = data and data.domain.attributes\n        self.model_proxy.setFilterKeyColumn(-1)\n        self.heuristic = None\n        self.use_heuristic = False\n        if self.master.feature is not None:\n            self.sel_feature_index = data.domain.index(self.master.feature)\n        else:\n            self.sel_feature_index = None\n        if data:\n            # use heuristic if data is too big\n            self.use_heuristic = len(data) * len(self.attrs) ** 2 > SIZE_LIMIT \\\n                and self.sel_feature_index is None\n            if self.use_heuristic:\n                self.heuristic = KMeansCorrelationHeuristic(data)\n\n    def compute_score(self, state):\n        (attr1, attr2), corr_type = state, self.master.correlation_type\n        data = self.master.cont_data.X\n        corr = pearsonr if corr_type == CorrelationType.PEARSON else spearmanr\n        r, p_value = corr(data[:, attr1], data[:, attr2])\n        return -abs(r) if not np.isnan(r) else NAN, r, p_value\n\n    def row_for_state(self, score, state):\n        attrs = sorted((self.attrs[x] for x in state), key=attrgetter(\"name\"))\n        attr_items = []\n        for attr in attrs:\n            item = QStandardItem(attr.name)\n            item.setData(attrs, self._AttrRole)\n            item.setData(Qt.AlignLeft + Qt.AlignCenter, Qt.TextAlignmentRole)\n            item.setToolTip(attr.name)\n            attr_items.append(item)\n        correlation_item = QStandardItem(\"{:+.3f}\".format(score[1]))\n        correlation_item.setData(score[2], self.PValRole)\n        correlation_item.setData(attrs, self._AttrRole)\n        correlation_item.setData(\n            self.NEGATIVE_COLOR if score[1] < 0 else self.POSITIVE_COLOR,\n            gui.TableBarItem.BarColorRole)\n        return [correlation_item] + attr_items\n\n    def check_preconditions(self):\n        return self.master.cont_data is not None\n\n    def iterate_states(self, initial_state):\n        if self.sel_feature_index is not None:\n            return self.iterate_states_by_feature()\n        elif self.use_heuristic:\n            return self.heuristic.get_states(initial_state)\n        else:\n            return super().iterate_states(initial_state)\n\n    def iterate_states_by_feature(self):\n        for j in range(len(self.attrs)):\n            if j != self.sel_feature_index:\n                yield self.sel_feature_index, j\n\n    def state_count(self):\n        n = len(self.attrs)\n        return n * (n - 1) / 2 if self.sel_feature_index is None else n - 1\n\n    @staticmethod\n    def bar_length(score):\n        return abs(score[1])\n\n    def stopped(self):\n        self.threadStopped.emit()\n        header = self.rank_table.horizontalHeader()\n        header.setSectionResizeMode(1, QHeaderView.Stretch)\n\n    def start(self, task, *args, **kwargs):\n        self._set_empty_status()\n        super().start(task, *args, **kwargs)\n        self.__set_state_busy()\n\n    def cancel(self):\n        super().cancel()\n        self.__set_state_ready()\n\n    def _connect_signals(self, state):\n        super()._connect_signals(state)\n        state.progress_changed.connect(self.master.progressBarSet)\n        state.status_changed.connect(self.master.setStatusMessage)\n\n    def _disconnect_signals(self, state):\n        super()._disconnect_signals(state)\n        state.progress_changed.disconnect(self.master.progressBarSet)\n        state.status_changed.disconnect(self.master.setStatusMessage)\n\n    def _on_task_done(self, future):\n        super()._on_task_done(future)\n        self.__set_state_ready()\n\n    def __set_state_ready(self):\n        self._set_empty_status()\n        self.master.setBlocking(False)\n\n    def __set_state_busy(self):\n        self.master.progressBarInit()\n        self.master.setBlocking(True)\n\n    def _set_empty_status(self):\n        self.master.progressBarFinished()\n        self.master.setStatusMessage(\"\")\n\n\nclass OWCorrelations(OWWidget):\n    name = \"Correlations\"\n    description = \"Compute all pairwise attribute correlations.\"\n    icon = \"icons/Correlations.svg\"\n    priority = 1106\n    category = \"Unsupervised\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        data = Output(\"Data\", Table)\n        features = Output(\"Features\", AttributeList)\n        correlations = Output(\"Correlations\", Table)\n\n    want_main_area = False\n    want_control_area = True\n\n    correlation_type: int\n\n    settings_version = 3\n    settingsHandler = DomainContextHandler()\n    selection = ContextSetting([])\n    feature = ContextSetting(None)\n    correlation_type = Setting(0)\n\n    class Information(OWWidget.Information):\n        removed_cons_feat = Msg(\"Constant features have been removed.\")\n\n    class Warning(OWWidget.Warning):\n        not_enough_vars = Msg(\"At least two numeric features are needed.\")\n        not_enough_inst = Msg(\"At least two instances are needed.\")\n\n    def __init__(self):\n        super().__init__()\n        self.data = None  # type: Table\n        self.cont_data = None  # type: Table\n\n        # GUI\n        box = gui.vBox(self.controlArea)\n        self.correlation_combo = gui.comboBox(\n            box, self, \"correlation_type\", items=CorrelationType.items(),\n            orientation=Qt.Horizontal, callback=self._correlation_combo_changed\n        )\n\n        self.feature_model = DomainModel(\n            order=DomainModel.ATTRIBUTES, separators=False,\n            placeholder=\"(All combinations)\", valid_types=ContinuousVariable)\n        gui.comboBox(\n            box, self, \"feature\", callback=self._feature_combo_changed,\n            model=self.feature_model, searchable=True\n        )\n\n        self.vizrank, _ = CorrelationRank.add_vizrank(\n            None, self, None, self._vizrank_selection_changed)\n        self.vizrank.button.setEnabled(False)\n        self.vizrank.threadStopped.connect(self._vizrank_stopped)\n\n        gui.separator(box)\n        box.layout().addWidget(self.vizrank.filter)\n        box.layout().addWidget(self.vizrank.rank_table)\n\n        button_box = gui.hBox(self.buttonsArea)\n        button_box.layout().addWidget(self.vizrank.button)\n\n    @staticmethod\n    def sizeHint():\n        return QSize(350, 400)\n\n    def _correlation_combo_changed(self):\n        self.apply()\n\n    def _feature_combo_changed(self):\n        self.apply()\n\n    def _vizrank_selection_changed(self, *args):\n        self.selection = list(args)\n        self.commit()\n\n    def _vizrank_stopped(self):\n        self._vizrank_select()\n\n    def _vizrank_select(self):\n        model = self.vizrank.rank_table.model()\n        if not model.rowCount():\n            return\n        selection = QItemSelection()\n\n        # This flag is needed because data in the model could be\n        # filtered by a feature and therefore selection could not be found\n        selection_in_model = False\n        if self.selection:\n            sel_names = sorted(var.name for var in self.selection)\n            for i in range(model.rowCount()):\n                # pylint: disable=protected-access\n                names = sorted(x.name for x in model.data(\n                    model.index(i, 0), CorrelationRank._AttrRole))\n                if names == sel_names:\n                    selection.select(model.index(i, 0),\n                                     model.index(i, model.columnCount() - 1))\n                    selection_in_model = True\n                    break\n        if not selection_in_model:\n            selection.select(model.index(0, 0),\n                             model.index(0, model.columnCount() - 1))\n        self.vizrank.rank_table.selectionModel().select(\n            selection, QItemSelectionModel.ClearAndSelect)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.closeContext()\n        self.clear_messages()\n        self.data = data\n        self.cont_data = None\n        self.selection = []\n        if data is not None:\n            if len(data) < 2:\n                self.Warning.not_enough_inst()\n            else:\n                domain = data.domain\n                cont_vars = [a for a in domain.class_vars + domain.metas +\n                             domain.attributes if a.is_continuous]\n                cont_data = Table.from_table(Domain(cont_vars), data)\n                remover = Remove(Remove.RemoveConstant)\n                cont_data = remover(cont_data)\n                if remover.attr_results[\"removed\"]:\n                    self.Information.removed_cons_feat()\n                if len(cont_data.domain.attributes) < 2:\n                    self.Warning.not_enough_vars()\n                else:\n                    self.cont_data = SklImpute()(cont_data)\n        self.set_feature_model()\n        self.openContext(self.cont_data)\n        self.apply()\n        self.vizrank.button.setEnabled(self.cont_data is not None)\n\n    def set_feature_model(self):\n        self.feature_model.set_domain(\n            self.cont_data.domain if self.cont_data else None)\n        data = self.data\n        if self.cont_data and data.domain.has_continuous_class:\n            self.feature = self.cont_data.domain[data.domain.class_var.name]\n        else:\n            self.feature = None\n\n    def apply(self):\n        self.vizrank.initialize()\n        if self.cont_data is not None:\n            # this triggers self.commit() by changing vizrank selection\n            self.vizrank.toggle()\n        else:\n            self.commit()\n\n    def commit(self):\n        self.Outputs.data.send(self.data)\n\n        if self.data is None or self.cont_data is None:\n            self.Outputs.features.send(None)\n            self.Outputs.correlations.send(None)\n            return\n\n        attrs = [ContinuousVariable(\"Correlation\"),\n                 ContinuousVariable(\"uncorrected p\"),\n                 ContinuousVariable(\"FDR\")]\n        metas = [StringVariable(\"Feature 1\"), StringVariable(\"Feature 2\")]\n        domain = Domain(attrs, metas=metas)\n        model = self.vizrank.rank_model\n        count = model.rowCount()\n        index = model.index\n        corr = np.array([float(index(row, 0).data())\n                         for row in range(count)])\n        p = np.array([index(row, 0).data(CorrelationRank.PValRole)\n                      for row in range(count)])\n        fdr = FDR(p)\n        x = np.vstack((corr, p, fdr)).T\n        # pylint: disable=protected-access\n        m = np.array([[a.name\n                       for a in index(row, 0).data(CorrelationRank._AttrRole)]\n                      for row in range(count)], dtype=object)\n        corr_table = Table(domain, x, metas=m)\n        corr_table.name = \"Correlations\"\n\n        # data has been imputed; send original attributes\n        self.Outputs.features.send(AttributeList(\n            [self.data.domain[var.name] for var in self.selection]))\n        self.Outputs.correlations.send(corr_table)\n\n    def send_report(self):\n        self.report_table(CorrelationType.items()[self.correlation_type],\n                          self.vizrank.rank_table)\n\n    @classmethod\n    def migrate_context(cls, context, version):\n        if version < 2:\n            sel = context.values[\"selection\"]\n            context.values[\"selection\"] = [(var.name, vartype(var))\n                                           for var in sel[0]]\n        if version < 3:\n            sel = context.values[\"selection\"]\n            context.values[\"selection\"] = ([(name, vtype + 100)\n                                            for name, vtype in sel], -3)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWCorrelations).run(Table(\"iris\"))\n",
            "new_file_content": "\"\"\"\nCorrelations widget\n\"\"\"\nfrom enum import IntEnum\nfrom operator import attrgetter\nfrom types import SimpleNamespace\nfrom itertools import combinations, groupby, chain\n\nimport numpy as np\nfrom scipy.stats import spearmanr, pearsonr\nfrom sklearn.cluster import KMeans\n\nfrom AnyQt.QtCore import Qt, QItemSelectionModel, QItemSelection, \\\n    QSize, pyqtSignal as Signal\nfrom AnyQt.QtGui import QStandardItem\nfrom AnyQt.QtWidgets import QHeaderView\n\nfrom Orange.data import Table, Domain, ContinuousVariable, StringVariable\nfrom Orange.preprocess import SklImpute, Normalize, Remove\nfrom Orange.statistics.util import FDR\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting, ContextSetting, \\\n    DomainContextHandler\nfrom Orange.widgets.utils import vartype\nfrom Orange.widgets.utils.itemmodels import DomainModel\nfrom Orange.widgets.utils.signals import Input, Output\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.visualize.utils import VizRankDialogAttrPair\nfrom Orange.widgets.widget import OWWidget, AttributeList, Msg\n\nNAN = 2\nSIZE_LIMIT = 1000000\n\n\nclass CorrelationType(IntEnum):\n    \"\"\"\n    Correlation type enumerator. Possible correlations: Pearson, Spearman.\n    \"\"\"\n    PEARSON, SPEARMAN = 0, 1\n\n    @staticmethod\n    def items():\n        \"\"\"\n        Texts for correlation types. Can be used in gui controls (eg. combobox).\n        \"\"\"\n        return [\"Pearson correlation\", \"Spearman correlation\"]\n\n\nclass Cluster(SimpleNamespace):\n    instances = None  # type: Optional[List]\n    centroid = None  # type: Optional[np.ndarray]\n\n\nclass KMeansCorrelationHeuristic:\n    \"\"\"\n    Heuristic to obtain the most promising attribute pairs, when there are too\n    many attributes to calculate correlations for all possible pairs.\n    \"\"\"\n    def __init__(self, data):\n        self.n_attributes = len(data.domain.attributes)\n        self.data = data\n        self.clusters = None\n        self.n_clusters = int(np.sqrt(self.n_attributes))\n\n    def get_clusters_of_attributes(self):\n        \"\"\"\n        Generates groupes of attribute IDs, grouped by cluster. Clusters are\n        obtained by KMeans algorithm.\n\n        :return: generator of attributes grouped by cluster\n        \"\"\"\n        data = Normalize()(self.data).X.T\n        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0, n_init=1).fit(data)\n        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])\n        return [Cluster(instances=list(pair[1] for pair in group),\n                        centroid=kmeans.cluster_centers_[l])\n                for l, group in groupby(labels_attrs, key=lambda x: x[0])]\n\n    def get_states(self, initial_state):\n        \"\"\"\n        Generates states (attribute pairs) - the most promising first, i.e.\n        states within clusters, following by states among clusters.\n\n        :param initial_state: initial state; None if this is the first call\n        :return: generator of tuples of states\n        \"\"\"\n        if self.clusters is None:\n            self.clusters = self.get_clusters_of_attributes()\n        clusters = self.clusters\n\n        # combinations within clusters\n        states0 = chain.from_iterable(combinations(cluster.instances, 2)\n                                      for cluster in clusters)\n        if self.n_clusters == 1:\n            return states0\n\n        # combinations among clusters - closest clusters first\n        centroids = [c.centroid for c in clusters]\n        centroids_combs = np.array(list(combinations(centroids, 2)))\n        distances = np.linalg.norm((centroids_combs[:, 0] -\n                                    centroids_combs[:, 1]), axis=1)\n        cluster_combs = list(combinations(range(len(clusters)), 2))\n        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)\n                  for c1 in clusters[cluster_combs[i][0]].instances\n                  for c2 in clusters[cluster_combs[i][1]].instances)\n        states = chain(states0, states)\n\n        if initial_state is not None:\n            while next(states) != initial_state:\n                pass\n            return chain([initial_state], states)\n        return states\n\n\nclass CorrelationRank(VizRankDialogAttrPair):\n    \"\"\"\n    Correlations rank widget.\n    \"\"\"\n    threadStopped = Signal()\n    PValRole = next(gui.OrangeUserRole)\n\n    def __init__(self, *args):\n        super().__init__(*args)\n        self.heuristic = None\n        self.use_heuristic = False\n        self.sel_feature_index = None\n\n    def initialize(self):\n        super().initialize()\n        data = self.master.cont_data\n        self.attrs = data and data.domain.attributes\n        self.model_proxy.setFilterKeyColumn(-1)\n        self.heuristic = None\n        self.use_heuristic = False\n        if self.master.feature is not None:\n            self.sel_feature_index = data.domain.index(self.master.feature)\n        else:\n            self.sel_feature_index = None\n        if data:\n            # use heuristic if data is too big\n            self.use_heuristic = len(data) * len(self.attrs) ** 2 > SIZE_LIMIT \\\n                and self.sel_feature_index is None\n            if self.use_heuristic:\n                self.heuristic = KMeansCorrelationHeuristic(data)\n\n    def compute_score(self, state):\n        (attr1, attr2), corr_type = state, self.master.correlation_type\n        data = self.master.cont_data.X\n        corr = pearsonr if corr_type == CorrelationType.PEARSON else spearmanr\n        r, p_value = corr(data[:, attr1], data[:, attr2])\n        return -abs(r) if not np.isnan(r) else NAN, r, p_value\n\n    def row_for_state(self, score, state):\n        attrs = sorted((self.attrs[x] for x in state), key=attrgetter(\"name\"))\n        attr_items = []\n        for attr in attrs:\n            item = QStandardItem(attr.name)\n            item.setData(attrs, self._AttrRole)\n            item.setData(Qt.AlignLeft + Qt.AlignCenter, Qt.TextAlignmentRole)\n            item.setToolTip(attr.name)\n            attr_items.append(item)\n        correlation_item = QStandardItem(\"{:+.3f}\".format(score[1]))\n        correlation_item.setData(score[2], self.PValRole)\n        correlation_item.setData(attrs, self._AttrRole)\n        correlation_item.setData(\n            self.NEGATIVE_COLOR if score[1] < 0 else self.POSITIVE_COLOR,\n            gui.TableBarItem.BarColorRole)\n        return [correlation_item] + attr_items\n\n    def check_preconditions(self):\n        return self.master.cont_data is not None\n\n    def iterate_states(self, initial_state):\n        if self.sel_feature_index is not None:\n            return self.iterate_states_by_feature()\n        elif self.use_heuristic:\n            return self.heuristic.get_states(initial_state)\n        else:\n            return super().iterate_states(initial_state)\n\n    def iterate_states_by_feature(self):\n        for j in range(len(self.attrs)):\n            if j != self.sel_feature_index:\n                yield self.sel_feature_index, j\n\n    def state_count(self):\n        n = len(self.attrs)\n        return n * (n - 1) / 2 if self.sel_feature_index is None else n - 1\n\n    @staticmethod\n    def bar_length(score):\n        return abs(score[1])\n\n    def stopped(self):\n        self.threadStopped.emit()\n        header = self.rank_table.horizontalHeader()\n        header.setSectionResizeMode(1, QHeaderView.Stretch)\n\n    def start(self, task, *args, **kwargs):\n        self._set_empty_status()\n        super().start(task, *args, **kwargs)\n        self.__set_state_busy()\n\n    def cancel(self):\n        super().cancel()\n        self.__set_state_ready()\n\n    def _connect_signals(self, state):\n        super()._connect_signals(state)\n        state.progress_changed.connect(self.master.progressBarSet)\n        state.status_changed.connect(self.master.setStatusMessage)\n\n    def _disconnect_signals(self, state):\n        super()._disconnect_signals(state)\n        state.progress_changed.disconnect(self.master.progressBarSet)\n        state.status_changed.disconnect(self.master.setStatusMessage)\n\n    def _on_task_done(self, future):\n        super()._on_task_done(future)\n        self.__set_state_ready()\n\n    def __set_state_ready(self):\n        self._set_empty_status()\n        self.master.setBlocking(False)\n\n    def __set_state_busy(self):\n        self.master.progressBarInit()\n        self.master.setBlocking(True)\n\n    def _set_empty_status(self):\n        self.master.progressBarFinished()\n        self.master.setStatusMessage(\"\")\n\n\nclass OWCorrelations(OWWidget):\n    name = \"Correlations\"\n    description = \"Compute all pairwise attribute correlations.\"\n    icon = \"icons/Correlations.svg\"\n    priority = 1106\n    category = \"Unsupervised\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        data = Output(\"Data\", Table)\n        features = Output(\"Features\", AttributeList)\n        correlations = Output(\"Correlations\", Table)\n\n    want_main_area = False\n    want_control_area = True\n\n    correlation_type: int\n\n    settings_version = 3\n    settingsHandler = DomainContextHandler()\n    selection = ContextSetting([])\n    feature = ContextSetting(None)\n    correlation_type = Setting(0)\n\n    class Information(OWWidget.Information):\n        removed_cons_feat = Msg(\"Constant features have been removed.\")\n\n    class Warning(OWWidget.Warning):\n        not_enough_vars = Msg(\"At least two numeric features are needed.\")\n        not_enough_inst = Msg(\"At least two instances are needed.\")\n\n    def __init__(self):\n        super().__init__()\n        self.data = None  # type: Table\n        self.cont_data = None  # type: Table\n\n        # GUI\n        box = gui.vBox(self.controlArea)\n        self.correlation_combo = gui.comboBox(\n            box, self, \"correlation_type\", items=CorrelationType.items(),\n            orientation=Qt.Horizontal, callback=self._correlation_combo_changed\n        )\n\n        self.feature_model = DomainModel(\n            order=DomainModel.ATTRIBUTES, separators=False,\n            placeholder=\"(All combinations)\", valid_types=ContinuousVariable)\n        gui.comboBox(\n            box, self, \"feature\", callback=self._feature_combo_changed,\n            model=self.feature_model, searchable=True\n        )\n\n        self.vizrank, _ = CorrelationRank.add_vizrank(\n            None, self, None, self._vizrank_selection_changed)\n        self.vizrank.button.setEnabled(False)\n        self.vizrank.threadStopped.connect(self._vizrank_stopped)\n\n        gui.separator(box)\n        box.layout().addWidget(self.vizrank.filter)\n        box.layout().addWidget(self.vizrank.rank_table)\n\n        button_box = gui.hBox(self.buttonsArea)\n        button_box.layout().addWidget(self.vizrank.button)\n\n    @staticmethod\n    def sizeHint():\n        return QSize(350, 400)\n\n    def _correlation_combo_changed(self):\n        self.apply()\n\n    def _feature_combo_changed(self):\n        self.apply()\n\n    def _vizrank_selection_changed(self, *args):\n        self.selection = list(args)\n        self.commit()\n\n    def _vizrank_stopped(self):\n        self._vizrank_select()\n\n    def _vizrank_select(self):\n        model = self.vizrank.rank_table.model()\n        if not model.rowCount():\n            return\n        selection = QItemSelection()\n\n        # This flag is needed because data in the model could be\n        # filtered by a feature and therefore selection could not be found\n        selection_in_model = False\n        if self.selection:\n            sel_names = sorted(var.name for var in self.selection)\n            for i in range(model.rowCount()):\n                # pylint: disable=protected-access\n                names = sorted(x.name for x in model.data(\n                    model.index(i, 0), CorrelationRank._AttrRole))\n                if names == sel_names:\n                    selection.select(model.index(i, 0),\n                                     model.index(i, model.columnCount() - 1))\n                    selection_in_model = True\n                    break\n        if not selection_in_model:\n            selection.select(model.index(0, 0),\n                             model.index(0, model.columnCount() - 1))\n        self.vizrank.rank_table.selectionModel().select(\n            selection, QItemSelectionModel.ClearAndSelect)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.closeContext()\n        self.clear_messages()\n        self.data = data\n        self.cont_data = None\n        self.selection = []\n        if data is not None:\n            if len(data) < 2:\n                self.Warning.not_enough_inst()\n            else:\n                domain = data.domain\n                cont_vars = [a for a in domain.class_vars + domain.metas +\n                             domain.attributes if a.is_continuous]\n                cont_data = Table.from_table(Domain(cont_vars), data)\n                remover = Remove(Remove.RemoveConstant)\n                cont_data = remover(cont_data)\n                if remover.attr_results[\"removed\"]:\n                    self.Information.removed_cons_feat()\n                if len(cont_data.domain.attributes) < 2:\n                    self.Warning.not_enough_vars()\n                else:\n                    self.cont_data = SklImpute()(cont_data)\n        self.set_feature_model()\n        self.openContext(self.cont_data)\n        self.apply()\n        self.vizrank.button.setEnabled(self.cont_data is not None)\n\n    def set_feature_model(self):\n        self.feature_model.set_domain(\n            self.cont_data.domain if self.cont_data else None)\n        data = self.data\n        if self.cont_data and data.domain.has_continuous_class:\n            self.feature = self.cont_data.domain[data.domain.class_var.name]\n        else:\n            self.feature = None\n\n    def apply(self):\n        self.vizrank.initialize()\n        if self.cont_data is not None:\n            # this triggers self.commit() by changing vizrank selection\n            self.vizrank.toggle()\n        else:\n            self.commit()\n\n    def commit(self):\n        self.Outputs.data.send(self.data)\n\n        if self.data is None or self.cont_data is None:\n            self.Outputs.features.send(None)\n            self.Outputs.correlations.send(None)\n            return\n\n        attrs = [ContinuousVariable(\"Correlation\"),\n                 ContinuousVariable(\"uncorrected p\"),\n                 ContinuousVariable(\"FDR\")]\n        metas = [StringVariable(\"Feature 1\"), StringVariable(\"Feature 2\")]\n        domain = Domain(attrs, metas=metas)\n        model = self.vizrank.rank_model\n        count = model.rowCount()\n        index = model.index\n        corr = np.array([float(index(row, 0).data())\n                         for row in range(count)])\n        p = np.array([index(row, 0).data(CorrelationRank.PValRole)\n                      for row in range(count)])\n        fdr = FDR(p)\n        x = np.vstack((corr, p, fdr)).T\n        # pylint: disable=protected-access\n        m = np.array([[a.name\n                       for a in index(row, 0).data(CorrelationRank._AttrRole)]\n                      for row in range(count)], dtype=object)\n        corr_table = Table(domain, x, metas=m)\n        corr_table.name = \"Correlations\"\n\n        # data has been imputed; send original attributes\n        self.Outputs.features.send(AttributeList(\n            [self.data.domain[var.name] for var in self.selection]))\n        self.Outputs.correlations.send(corr_table)\n\n    def send_report(self):\n        self.report_table(CorrelationType.items()[self.correlation_type],\n                          self.vizrank.rank_table)\n\n    @classmethod\n    def migrate_context(cls, context, version):\n        if version < 2:\n            sel = context.values[\"selection\"]\n            context.values[\"selection\"] = [(var.name, vartype(var))\n                                           for var in sel[0]]\n        if version < 3:\n            sel = context.values[\"selection\"]\n            context.values[\"selection\"] = ([(name, vtype + 100)\n                                            for name, vtype in sel], -3)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWCorrelations).run(Table(\"iris\"))\n",
            "header": {
                "file": {
                    "path": "Orange/widgets/data/owcorrelations.py"
                },
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            "index_line": {
                "old_commit_hash": "4779ad8a8",
                "new_commit_hash": "b1e969228",
                "mode": "100644"
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                            "start": 70,
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                        "section": "class KMeansCorrelationHeuristic:"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        :return: generator of attributes grouped by cluster",
                                "type": "context"
                            },
                            {
                                "content": "        \"\"\"",
                                "type": "context"
                            },
                            {
                                "content": "        data = Normalize()(self.data).X.T",
                                "type": "context"
                            },
                            {
                                "content": "        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)",
                                "type": "deleted"
                            },
                            {
                                "content": "        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0, n_init=1).fit(data)",
                                "type": "added"
                            },
                            {
                                "content": "        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])",
                                "type": "context"
                            },
                            {
                                "content": "        return [Cluster(instances=list(pair[1] for pair in group),",
                                "type": "context"
                            },
                            {
                                "content": "                        centroid=kmeans.cluster_centers_[l])",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/data/owcorrelations.py",
                            "type": "class",
                            "name": "KMeansCorrelationHeuristic",
                            "content": "class KMeansCorrelationHeuristic:\n    \"\"\"\n    Heuristic to obtain the most promising attribute pairs, when there are too\n    many attributes to calculate correlations for all possible pairs.\n    \"\"\"\n    def __init__(self, data):\n        self.n_attributes = len(data.domain.attributes)\n        self.data = data\n        self.clusters = None\n        self.n_clusters = int(np.sqrt(self.n_attributes))\n\n    def get_clusters_of_attributes(self):\n        \"\"\"\n        Generates groupes of attribute IDs, grouped by cluster. Clusters are\n        obtained by KMeans algorithm.\n\n        :return: generator of attributes grouped by cluster\n        \"\"\"\n        data = Normalize()(self.data).X.T\n        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)\n        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])\n        return [Cluster(instances=list(pair[1] for pair in group),\n                        centroid=kmeans.cluster_centers_[l])\n                for l, group in groupby(labels_attrs, key=lambda x: x[0])]\n\n    def get_states(self, initial_state):\n        \"\"\"\n        Generates states (attribute pairs) - the most promising first, i.e.\n        states within clusters, following by states among clusters.\n\n        :param initial_state: initial state; None if this is the first call\n        :return: generator of tuples of states\n        \"\"\"\n        if self.clusters is None:\n            self.clusters = self.get_clusters_of_attributes()\n        clusters = self.clusters\n\n        # combinations within clusters\n        states0 = chain.from_iterable(combinations(cluster.instances, 2)\n                                      for cluster in clusters)\n        if self.n_clusters == 1:\n            return states0\n\n        # combinations among clusters - closest clusters first\n        centroids = [c.centroid for c in clusters]\n        centroids_combs = np.array(list(combinations(centroids, 2)))\n        distances = np.linalg.norm((centroids_combs[:, 0] -\n                                    centroids_combs[:, 1]), axis=1)\n        cluster_combs = list(combinations(range(len(clusters)), 2))\n        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)\n                  for c1 in clusters[cluster_combs[i][0]].instances\n                  for c2 in clusters[cluster_combs[i][1]].instances)\n        states = chain(states0, states)\n\n        if initial_state is not None:\n            while next(states) != initial_state:\n                pass\n            return chain([initial_state], states)\n        return states",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 54,
                            "end_lineno": 112,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/data/owcorrelations.py",
                            "type": "method",
                            "name": "KMeansCorrelationHeuristic.get_clusters_of_attributes",
                            "content": "class KMeansCorrelationHeuristic:\n    \"\"\"\n    Heuristic to obtain the most promising attribute pairs, when there are too\n    many attributes to calculate correlations for all possible pairs.\n    \"\"\"\n    def __init__(self, data):\n        self.n_attributes = len(data.domain.attributes)\n        self.data = data\n        self.clusters = None\n        self.n_clusters = int(np.sqrt(self.n_attributes))\n\n    def get_clusters_of_attributes(self):\n        \"\"\"\n        Generates groupes of attribute IDs, grouped by cluster. Clusters are\n        obtained by KMeans algorithm.\n\n        :return: generator of attributes grouped by cluster\n        \"\"\"\n        data = Normalize()(self.data).X.T\n        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)\n        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])\n        return [Cluster(instances=list(pair[1] for pair in group),\n                        centroid=kmeans.cluster_centers_[l])\n                for l, group in groupby(labels_attrs, key=lambda x: x[0])]\n\n    def get_states(self, initial_state):\n        \"\"\"\n        Generates states (attribute pairs) - the most promising first, i.e.\n        states within clusters, following by states among clusters.\n\n        :param initial_state: initial state; None if this is the first call\n        :return: generator of tuples of states\n        \"\"\"\n        if self.clusters is None:\n            self.clusters = self.get_clusters_of_attributes()\n        clusters = self.clusters\n\n        # combinations within clusters\n        states0 = chain.from_iterable(combinations(cluster.instances, 2)\n                                      for cluster in clusters)\n        if self.n_clusters == 1:\n            return states0\n\n        # combinations among clusters - closest clusters first\n        centroids = [c.centroid for c in clusters]\n        centroids_combs = np.array(list(combinations(centroids, 2)))\n        distances = np.linalg.norm((centroids_combs[:, 0] -\n                                    centroids_combs[:, 1]), axis=1)\n        cluster_combs = list(combinations(range(len(clusters)), 2))\n        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)\n                  for c1 in clusters[cluster_combs[i][0]].instances\n                  for c2 in clusters[cluster_combs[i][1]].instances)\n        states = chain(states0, states)\n\n        if initial_state is not None:\n            while next(states) != initial_state:\n                pass\n            return chain([initial_state], states)\n        return states",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 65,
                            "end_lineno": 77,
                            "parent": {
                                "file_name": "Orange/widgets/data/owcorrelations.py",
                                "type": "class",
                                "name": "KMeansCorrelationHeuristic",
                                "content": "class KMeansCorrelationHeuristic:\n    \"\"\"\n    Heuristic to obtain the most promising attribute pairs, when there are too\n    many attributes to calculate correlations for all possible pairs.\n    \"\"\"\n    def __init__(self, data):\n        self.n_attributes = len(data.domain.attributes)\n        self.data = data\n        self.clusters = None\n        self.n_clusters = int(np.sqrt(self.n_attributes))\n\n    def get_clusters_of_attributes(self):\n        \"\"\"\n        Generates groupes of attribute IDs, grouped by cluster. Clusters are\n        obtained by KMeans algorithm.\n\n        :return: generator of attributes grouped by cluster\n        \"\"\"\n        data = Normalize()(self.data).X.T\n        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)\n        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])\n        return [Cluster(instances=list(pair[1] for pair in group),\n                        centroid=kmeans.cluster_centers_[l])\n                for l, group in groupby(labels_attrs, key=lambda x: x[0])]\n\n    def get_states(self, initial_state):\n        \"\"\"\n        Generates states (attribute pairs) - the most promising first, i.e.\n        states within clusters, following by states among clusters.\n\n        :param initial_state: initial state; None if this is the first call\n        :return: generator of tuples of states\n        \"\"\"\n        if self.clusters is None:\n            self.clusters = self.get_clusters_of_attributes()\n        clusters = self.clusters\n\n        # combinations within clusters\n        states0 = chain.from_iterable(combinations(cluster.instances, 2)\n                                      for cluster in clusters)\n        if self.n_clusters == 1:\n            return states0\n\n        # combinations among clusters - closest clusters first\n        centroids = [c.centroid for c in clusters]\n        centroids_combs = np.array(list(combinations(centroids, 2)))\n        distances = np.linalg.norm((centroids_combs[:, 0] -\n                                    centroids_combs[:, 1]), axis=1)\n        cluster_combs = list(combinations(range(len(clusters)), 2))\n        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)\n                  for c1 in clusters[cluster_combs[i][0]].instances\n                  for c2 in clusters[cluster_combs[i][1]].instances)\n        states = chain(states0, states)\n\n        if initial_state is not None:\n            while next(states) != initial_state:\n                pass\n            return chain([initial_state], states)\n        return states",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 54,
                                "end_lineno": 112,
                                "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, unsubscriptable-object\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\nimport numpy.testing as npt\n\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable\nfrom Orange.tests import test_filename\nfrom Orange.widgets.data.owcorrelations import (\n    OWCorrelations, KMeansCorrelationHeuristic, CorrelationRank,\n    CorrelationType\n)\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import simulate\nfrom Orange.widgets.visualize.owscatterplot import OWScatterPlot\nfrom Orange.widgets.widget import AttributeList\n\n\nclass TestOWCorrelations(WidgetTest):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.data_cont = Table(\"iris\")\n        cls.data_disc = Table(\"zoo\")\n        cls.data_mixed = Table(\"heart_disease\")\n        cls.housing = Table(\"housing\")\n\n    def setUp(self):\n        self.widget = self.create_widget(OWCorrelations)\n\n    def test_input_data_cont(self):\n        \"\"\"Check correlation table for dataset with continuous attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        n_attrs = len(self.data_cont.domain.attributes)\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 3)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(),\n                         n_attrs * (n_attrs - 1) / 2)\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 0)\n\n    def test_input_data_disc(self):\n        \"\"\"Check correlation table for dataset with discrete attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_disc)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_input_data_mixed(self):\n        \"\"\"Check correlation table for dataset with continuous and discrete\n        attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        domain = self.data_mixed.domain\n        n_attrs = len([a for a in domain.attributes if a.is_continuous])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 3)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(),\n                         n_attrs * (n_attrs - 1) / 2)\n\n    def test_input_data_one_feature(self):\n        \"\"\"Check correlation table for dataset with one attribute\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont[:, [0, 4]])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_input_data_one_instance(self):\n        \"\"\"Check correlation table for dataset with one instance\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont[:1])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n        self.assertTrue(self.widget.Warning.not_enough_inst.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_inst.is_shown())\n\n    def test_input_data_with_constant_features(self):\n        \"\"\"Check correlation table for dataset with constant columns\"\"\"\n        np.random.seed(0)\n        # pylint: disable=no-member\n        X = np.random.randint(3, size=(4, 3)).astype(float)\n        X[:, 2] = 1\n\n        domain = Domain([ContinuousVariable(\"c1\"), ContinuousVariable(\"c2\"),\n                         DiscreteVariable(\"d1\")])\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 1)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 1)\n        self.assertTrue(self.widget.Information.removed_cons_feat.is_shown())\n\n        X = np.ones((4, 3), dtype=float)\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.assertTrue(self.widget.Information.removed_cons_feat.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n\n    def test_input_data_cont_target(self):\n        \"\"\"Check correlation table for dataset with continuous class variable\"\"\"\n        data = self.housing[:5, 11:]\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 2)\n        self.assertEqual(self.widget.controls.feature.count(), 4)\n        self.assertEqual(self.widget.controls.feature.currentText(), \"MEDV\")\n\n        data = self.housing[:5, 13:]\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_output_data(self):\n        \"\"\"Check dataset on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(self.data_cont, output)\n\n    def test_output_features(self):\n        \"\"\"Check features on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        features = self.get_output(self.widget.Outputs.features)\n        self.assertIsInstance(features, AttributeList)\n        self.assertEqual(len(features), 2)\n\n    def test_output_correlations(self):\n        \"\"\"Check correlation table on on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        correlations = self.get_output(self.widget.Outputs.correlations)\n        self.assertIsInstance(correlations, Table)\n        self.assertEqual(len(correlations), 6)\n        self.assertEqual(len(correlations.domain.metas), 2)\n        self.assertListEqual([\"Correlation\", \"uncorrected p\", \"FDR\"],\n                             [m.name for m in correlations.domain.attributes])\n        array = np.array([[0.963, 0, 0],\n                          [0.872, 0, 0],\n                          [0.818, 0, 0],\n                          [-0.421, 0, 0],\n                          [-0.357, 7.52e-6, 0.000009],\n                          [-0.109, 0.1827652, 0.1827652]])\n        npt.assert_almost_equal(correlations.X, array)\n\n    def test_input_changed(self):\n        \"\"\"Check whether changing input emits commit\"\"\"\n        self.widget.commit = Mock()\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.widget.commit.assert_called_once()\n\n        self.widget.commit.reset_mock()\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        self.wait_until_finished()\n        self.process_events()\n        self.widget.commit.assert_called_once()\n\n    def test_saved_selection(self):\n        \"\"\"Select row from settings\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        attrs = self.widget.cont_data.domain.attributes\n        self.widget._vizrank_selection_changed(attrs[3], attrs[1])\n        settings = self.widget.settingsHandler.pack_data(self.widget)\n\n        w = self.create_widget(OWCorrelations, stored_settings=settings)\n        self.send_signal(self.widget.Inputs.data, self.data_cont, widget=w)\n        self.wait_until_finished(w)\n        self.process_events()\n        sel_row = w.vizrank.rank_table.selectionModel().selectedRows()[0].row()\n        self.assertEqual(sel_row, 4)\n\n    def test_scatterplot_input_features(self):\n        \"\"\"Check if attributes have been set after sent to scatterplot\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        spw = self.create_widget(OWScatterPlot)\n        attrs = self.widget.cont_data.domain.attributes\n        self.widget._vizrank_selection_changed(attrs[2], attrs[3])\n        features = self.get_output(self.widget.Outputs.features)\n        self.send_signal(self.widget.Inputs.data, self.data_cont, widget=spw)\n        self.send_signal(spw.Inputs.features, features, widget=spw)\n        self.assertIs(spw.attr_x, self.data_cont.domain[2])\n        self.assertIs(spw.attr_y, self.data_cont.domain[3])\n\n    def test_heuristic(self):\n        \"\"\"Check attribute pairs got by heuristic\"\"\"\n        heuristic = KMeansCorrelationHeuristic(self.data_cont)\n        heuristic.n_clusters = 2\n        self.assertListEqual(list(heuristic.get_states(None)),\n                             [(0, 2), (0, 3), (2, 3), (0, 1), (1, 2), (1, 3)])\n\n    def test_heuristic_get_states(self):\n        \"\"\"Check attribute pairs after the widget has been paused\"\"\"\n        heuristic = KMeansCorrelationHeuristic(self.data_cont)\n        heuristic.n_clusters = 2\n        states = heuristic.get_states(None)\n        _ = next(states)\n        self.assertListEqual(list(heuristic.get_states(next(states))),\n                             [(0, 3), (2, 3), (0, 1), (1, 2), (1, 3)])\n\n    def test_correlation_type(self):\n        c_type = self.widget.controls.correlation_type\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        pearson_corr = self.get_output(self.widget.Outputs.correlations)\n\n        simulate.combobox_activate_item(c_type, \"Spearman correlation\")\n        self.wait_until_finished()\n        self.process_events()\n        sperman_corr = self.get_output(self.widget.Outputs.correlations)\n        self.assertFalse((pearson_corr.X == sperman_corr.X).all())\n\n    def test_feature_combo(self):\n        \"\"\"Check content of feature selection combobox\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        cont_attributes = [attr for attr in self.data_mixed.domain.attributes\n                           if attr.is_continuous]\n        self.assertEqual(len(feature_combo.model()), len(cont_attributes) + 1)\n\n        self.wait_until_stop_blocking()\n        self.send_signal(self.widget.Inputs.data, self.housing)\n        self.assertEqual(len(feature_combo.model()), 15)\n\n    def test_select_feature(self):\n        \"\"\"Test feature selection\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n        self.assertListEqual([\"petal length\", \"petal width\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n        simulate.combobox_activate_index(feature_combo, 1)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 3)\n        self.assertListEqual([\"petal length\", \"sepal length\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n        simulate.combobox_activate_index(feature_combo, 0)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n        self.assertListEqual([\"petal length\", \"sepal length\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n    @patch(\"Orange.widgets.data.owcorrelations.SIZE_LIMIT\", 2000)\n    def test_vizrank_use_heuristic(self):\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertTrue(self.widget.vizrank.use_heuristic)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n\n    @patch(\"Orange.widgets.data.owcorrelations.SIZE_LIMIT\", 2000)\n    def test_select_feature_against_heuristic(self):\n        \"\"\"Never use heuristic if feature is selected\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        simulate.combobox_activate_index(feature_combo, 2)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 3)\n\n    def test_send_report(self):\n        \"\"\"Test report \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.widget.report_button.click()\n        self.wait_until_stop_blocking()\n        self.send_signal(self.widget.Inputs.data, None)\n        self.widget.report_button.click()\n\n\nclass TestCorrelationRank(WidgetTest):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")\n        cls.attrs = cls.iris.domain.attributes\n\n    def setUp(self):\n        self.vizrank = CorrelationRank(None)\n        self.vizrank.attrs = self.attrs\n\n    def test_compute_score(self):\n        self.vizrank.master = Mock()\n        self.vizrank.master.cont_data = self.iris\n        self.vizrank.master.correlation_type = CorrelationType.PEARSON\n        npt.assert_almost_equal(self.vizrank.compute_score((1, 0)),\n                                [-0.1094, -0.1094, 0.1828], 4)\n\n    def test_row_for_state(self):\n        row = self.vizrank.row_for_state((-0.2, 0.2, 0.1), (1, 0))\n        self.assertEqual(row[0].data(Qt.DisplayRole), \"+0.200\")\n        self.assertEqual(row[0].data(CorrelationRank.PValRole), 0.1)\n        self.assertEqual(row[1].data(Qt.DisplayRole), self.attrs[0].name)\n        self.assertEqual(row[2].data(Qt.DisplayRole), self.attrs[1].name)\n\n    def test_iterate_states(self):\n        self.assertListEqual(list(self.vizrank.iterate_states(None)),\n                             [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2)])\n        self.assertListEqual(list(self.vizrank.iterate_states((1, 0))),\n                             [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2)])\n        self.assertListEqual(list(self.vizrank.iterate_states((2, 1))),\n                             [(2, 1), (3, 0), (3, 1), (3, 2)])\n\n    def test_iterate_states_by_feature(self):\n        self.vizrank.sel_feature_index = 2\n        states = self.vizrank.iterate_states_by_feature()\n        self.assertListEqual([(2, 0), (2, 1), (2, 3)], list(states))\n\n    def test_state_count(self):\n        self.assertEqual(self.vizrank.state_count(), 6)\n        self.vizrank.sel_feature_index = 2\n        self.assertEqual(self.vizrank.state_count(), 3)\n\n\nclass TestKMeansCorrelationHeuristic(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/breast-cancer-wisconsin\"))\n        cls.heuristic = KMeansCorrelationHeuristic(cls.data)\n\n    def test_n_clusters(self):\n        self.assertEqual(self.heuristic.n_clusters, 3)\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n        self.assertListEqual([[0], [1, 2, 3, 4, 5, 6, 7], [8]],\n                             result)\n\n    def test_get_states(self):\n        n_attrs = len(self.data.domain.attributes)\n        states = set(self.heuristic.get_states(None))\n        self.assertEqual(len(states), n_attrs * (n_attrs - 1) / 2)\n        self.assertSetEqual(set((min(i, j), max(i, j)) for i in\n                                range(n_attrs) for j in range(i)), states)\n\n    def test_get_states_one_cluster(self):\n        heuristic = KMeansCorrelationHeuristic(Table(\"iris\")[:, :2])\n        states = set(heuristic.get_states(None))\n        self.assertEqual(len(states), 1)\n        self.assertSetEqual(states, {(0, 1)})\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access, unsubscriptable-object\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\nimport numpy.testing as npt\n\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable\nfrom Orange.tests import test_filename\nfrom Orange.widgets.data.owcorrelations import (\n    OWCorrelations, KMeansCorrelationHeuristic, CorrelationRank,\n    CorrelationType\n)\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import simulate\nfrom Orange.widgets.visualize.owscatterplot import OWScatterPlot\nfrom Orange.widgets.widget import AttributeList\n\n\nclass TestOWCorrelations(WidgetTest):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.data_cont = Table(\"iris\")\n        cls.data_disc = Table(\"zoo\")\n        cls.data_mixed = Table(\"heart_disease\")\n        cls.housing = Table(\"housing\")\n\n    def setUp(self):\n        self.widget = self.create_widget(OWCorrelations)\n\n    def test_input_data_cont(self):\n        \"\"\"Check correlation table for dataset with continuous attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        n_attrs = len(self.data_cont.domain.attributes)\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 3)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(),\n                         n_attrs * (n_attrs - 1) / 2)\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 0)\n\n    def test_input_data_disc(self):\n        \"\"\"Check correlation table for dataset with discrete attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_disc)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_input_data_mixed(self):\n        \"\"\"Check correlation table for dataset with continuous and discrete\n        attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        domain = self.data_mixed.domain\n        n_attrs = len([a for a in domain.attributes if a.is_continuous])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 3)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(),\n                         n_attrs * (n_attrs - 1) / 2)\n\n    def test_input_data_one_feature(self):\n        \"\"\"Check correlation table for dataset with one attribute\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont[:, [0, 4]])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_input_data_one_instance(self):\n        \"\"\"Check correlation table for dataset with one instance\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont[:1])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n        self.assertTrue(self.widget.Warning.not_enough_inst.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_inst.is_shown())\n\n    def test_input_data_with_constant_features(self):\n        \"\"\"Check correlation table for dataset with constant columns\"\"\"\n        np.random.seed(0)\n        # pylint: disable=no-member\n        X = np.random.randint(3, size=(4, 3)).astype(float)\n        X[:, 2] = 1\n\n        domain = Domain([ContinuousVariable(\"c1\"), ContinuousVariable(\"c2\"),\n                         DiscreteVariable(\"d1\")])\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 1)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 1)\n        self.assertTrue(self.widget.Information.removed_cons_feat.is_shown())\n\n        X = np.ones((4, 3), dtype=float)\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.assertTrue(self.widget.Information.removed_cons_feat.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n\n    def test_input_data_cont_target(self):\n        \"\"\"Check correlation table for dataset with continuous class variable\"\"\"\n        data = self.housing[:5, 11:]\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 2)\n        self.assertEqual(self.widget.controls.feature.count(), 4)\n        self.assertEqual(self.widget.controls.feature.currentText(), \"MEDV\")\n\n        data = self.housing[:5, 13:]\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_output_data(self):\n        \"\"\"Check dataset on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(self.data_cont, output)\n\n    def test_output_features(self):\n        \"\"\"Check features on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        features = self.get_output(self.widget.Outputs.features)\n        self.assertIsInstance(features, AttributeList)\n        self.assertEqual(len(features), 2)\n\n    def test_output_correlations(self):\n        \"\"\"Check correlation table on on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        correlations = self.get_output(self.widget.Outputs.correlations)\n        self.assertIsInstance(correlations, Table)\n        self.assertEqual(len(correlations), 6)\n        self.assertEqual(len(correlations.domain.metas), 2)\n        self.assertListEqual([\"Correlation\", \"uncorrected p\", \"FDR\"],\n                             [m.name for m in correlations.domain.attributes])\n        array = np.array([[0.963, 0, 0],\n                          [0.872, 0, 0],\n                          [0.818, 0, 0],\n                          [-0.421, 0, 0],\n                          [-0.357, 7.52e-6, 0.000009],\n                          [-0.109, 0.1827652, 0.1827652]])\n        npt.assert_almost_equal(correlations.X, array)\n\n    def test_input_changed(self):\n        \"\"\"Check whether changing input emits commit\"\"\"\n        self.widget.commit = Mock()\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.widget.commit.assert_called_once()\n\n        self.widget.commit.reset_mock()\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        self.wait_until_finished()\n        self.process_events()\n        self.widget.commit.assert_called_once()\n\n    def test_saved_selection(self):\n        \"\"\"Select row from settings\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        attrs = self.widget.cont_data.domain.attributes\n        self.widget._vizrank_selection_changed(attrs[3], attrs[1])\n        settings = self.widget.settingsHandler.pack_data(self.widget)\n\n        w = self.create_widget(OWCorrelations, stored_settings=settings)\n        self.send_signal(self.widget.Inputs.data, self.data_cont, widget=w)\n        self.wait_until_finished(w)\n        self.process_events()\n        sel_row = w.vizrank.rank_table.selectionModel().selectedRows()[0].row()\n        self.assertEqual(sel_row, 4)\n\n    def test_scatterplot_input_features(self):\n        \"\"\"Check if attributes have been set after sent to scatterplot\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        spw = self.create_widget(OWScatterPlot)\n        attrs = self.widget.cont_data.domain.attributes\n        self.widget._vizrank_selection_changed(attrs[2], attrs[3])\n        features = self.get_output(self.widget.Outputs.features)\n        self.send_signal(self.widget.Inputs.data, self.data_cont, widget=spw)\n        self.send_signal(spw.Inputs.features, features, widget=spw)\n        self.assertIs(spw.attr_x, self.data_cont.domain[2])\n        self.assertIs(spw.attr_y, self.data_cont.domain[3])\n\n    def test_heuristic(self):\n        \"\"\"Check attribute pairs got by heuristic\"\"\"\n        heuristic = KMeansCorrelationHeuristic(self.data_cont)\n        heuristic.n_clusters = 2\n        self.assertListEqual(list(heuristic.get_states(None)),\n                             [(0, 2), (0, 3), (2, 3), (0, 1), (1, 2), (1, 3)])\n\n    def test_heuristic_get_states(self):\n        \"\"\"Check attribute pairs after the widget has been paused\"\"\"\n        heuristic = KMeansCorrelationHeuristic(self.data_cont)\n        heuristic.n_clusters = 2\n        states = heuristic.get_states(None)\n        _ = next(states)\n        self.assertListEqual(list(heuristic.get_states(next(states))),\n                             [(0, 3), (2, 3), (0, 1), (1, 2), (1, 3)])\n\n    def test_correlation_type(self):\n        c_type = self.widget.controls.correlation_type\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        pearson_corr = self.get_output(self.widget.Outputs.correlations)\n\n        simulate.combobox_activate_item(c_type, \"Spearman correlation\")\n        self.wait_until_finished()\n        self.process_events()\n        sperman_corr = self.get_output(self.widget.Outputs.correlations)\n        self.assertFalse((pearson_corr.X == sperman_corr.X).all())\n\n    def test_feature_combo(self):\n        \"\"\"Check content of feature selection combobox\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        cont_attributes = [attr for attr in self.data_mixed.domain.attributes\n                           if attr.is_continuous]\n        self.assertEqual(len(feature_combo.model()), len(cont_attributes) + 1)\n\n        self.wait_until_stop_blocking()\n        self.send_signal(self.widget.Inputs.data, self.housing)\n        self.assertEqual(len(feature_combo.model()), 15)\n\n    def test_select_feature(self):\n        \"\"\"Test feature selection\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n        self.assertListEqual([\"petal length\", \"petal width\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n        simulate.combobox_activate_index(feature_combo, 1)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 3)\n        self.assertListEqual([\"petal length\", \"sepal length\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n        simulate.combobox_activate_index(feature_combo, 0)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n        self.assertListEqual([\"petal length\", \"sepal length\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n    @patch(\"Orange.widgets.data.owcorrelations.SIZE_LIMIT\", 2000)\n    def test_vizrank_use_heuristic(self):\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertTrue(self.widget.vizrank.use_heuristic)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n\n    @patch(\"Orange.widgets.data.owcorrelations.SIZE_LIMIT\", 2000)\n    def test_select_feature_against_heuristic(self):\n        \"\"\"Never use heuristic if feature is selected\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        simulate.combobox_activate_index(feature_combo, 2)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 3)\n\n    def test_send_report(self):\n        \"\"\"Test report \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.widget.report_button.click()\n        self.wait_until_stop_blocking()\n        self.send_signal(self.widget.Inputs.data, None)\n        self.widget.report_button.click()\n\n\nclass TestCorrelationRank(WidgetTest):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")\n        cls.attrs = cls.iris.domain.attributes\n\n    def setUp(self):\n        self.vizrank = CorrelationRank(None)\n        self.vizrank.attrs = self.attrs\n\n    def test_compute_score(self):\n        self.vizrank.master = Mock()\n        self.vizrank.master.cont_data = self.iris\n        self.vizrank.master.correlation_type = CorrelationType.PEARSON\n        npt.assert_almost_equal(self.vizrank.compute_score((1, 0)),\n                                [-0.1094, -0.1094, 0.1828], 4)\n\n    def test_row_for_state(self):\n        row = self.vizrank.row_for_state((-0.2, 0.2, 0.1), (1, 0))\n        self.assertEqual(row[0].data(Qt.DisplayRole), \"+0.200\")\n        self.assertEqual(row[0].data(CorrelationRank.PValRole), 0.1)\n        self.assertEqual(row[1].data(Qt.DisplayRole), self.attrs[0].name)\n        self.assertEqual(row[2].data(Qt.DisplayRole), self.attrs[1].name)\n\n    def test_iterate_states(self):\n        self.assertListEqual(list(self.vizrank.iterate_states(None)),\n                             [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2)])\n        self.assertListEqual(list(self.vizrank.iterate_states((1, 0))),\n                             [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2)])\n        self.assertListEqual(list(self.vizrank.iterate_states((2, 1))),\n                             [(2, 1), (3, 0), (3, 1), (3, 2)])\n\n    def test_iterate_states_by_feature(self):\n        self.vizrank.sel_feature_index = 2\n        states = self.vizrank.iterate_states_by_feature()\n        self.assertListEqual([(2, 0), (2, 1), (2, 3)], list(states))\n\n    def test_state_count(self):\n        self.assertEqual(self.vizrank.state_count(), 6)\n        self.vizrank.sel_feature_index = 2\n        self.assertEqual(self.vizrank.state_count(), 3)\n\n\nclass TestKMeansCorrelationHeuristic(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/breast-cancer-wisconsin\"))\n        cls.heuristic = KMeansCorrelationHeuristic(cls.data)\n\n    def test_n_clusters(self):\n        self.assertEqual(self.heuristic.n_clusters, 3)\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n        self.assertListEqual([[0, 3, 5], [1, 2, 6, 7], [4, 8]],\n                             result)\n\n    def test_get_states(self):\n        n_attrs = len(self.data.domain.attributes)\n        states = set(self.heuristic.get_states(None))\n        self.assertEqual(len(states), n_attrs * (n_attrs - 1) / 2)\n        self.assertSetEqual(set((min(i, j), max(i, j)) for i in\n                                range(n_attrs) for j in range(i)), states)\n\n    def test_get_states_one_cluster(self):\n        heuristic = KMeansCorrelationHeuristic(Table(\"iris\")[:, :2])\n        states = set(heuristic.get_states(None))\n        self.assertEqual(len(states), 1)\n        self.assertSetEqual(states, {(0, 1)})\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
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                            "start": 362,
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                        "section": "class TestKMeansCorrelationHeuristic(unittest.TestCase):"
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                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        clusters = self.heuristic.get_clusters_of_attributes()",
                                "type": "context"
                            },
                            {
                                "content": "        # results depend on scikit-learn k-means implementation",
                                "type": "context"
                            },
                            {
                                "content": "        result = sorted([c.instances for c in clusters])",
                                "type": "context"
                            },
                            {
                                "content": "        self.assertListEqual([[0], [1, 2, 3, 4, 5, 6, 7], [8]],",
                                "type": "deleted"
                            },
                            {
                                "content": "        self.assertListEqual([[0, 3, 5], [1, 2, 6, 7], [4, 8]],",
                                "type": "added"
                            },
                            {
                                "content": "                             result)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def test_get_states(self):",
                                "type": "context"
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                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/widgets/data/tests/test_owcorrelations.py",
                            "type": "class",
                            "name": "TestKMeansCorrelationHeuristic",
                            "content": "class TestKMeansCorrelationHeuristic(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/breast-cancer-wisconsin\"))\n        cls.heuristic = KMeansCorrelationHeuristic(cls.data)\n\n    def test_n_clusters(self):\n        self.assertEqual(self.heuristic.n_clusters, 3)\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n        self.assertListEqual([[0], [1, 2, 3, 4, 5, 6, 7], [8]],\n                             result)\n\n    def test_get_states(self):\n        n_attrs = len(self.data.domain.attributes)\n        states = set(self.heuristic.get_states(None))\n        self.assertEqual(len(states), n_attrs * (n_attrs - 1) / 2)\n        self.assertSetEqual(set((min(i, j), max(i, j)) for i in\n                                range(n_attrs) for j in range(i)), states)\n\n    def test_get_states_one_cluster(self):\n        heuristic = KMeansCorrelationHeuristic(Table(\"iris\")[:, :2])\n        states = set(heuristic.get_states(None))\n        self.assertEqual(len(states), 1)\n        self.assertSetEqual(states, {(0, 1)})",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 352,
                            "end_lineno": 379,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/widgets/data/tests/test_owcorrelations.py",
                            "type": "method",
                            "name": "TestKMeansCorrelationHeuristic.test_get_clusters_of_attributes",
                            "content": "class TestKMeansCorrelationHeuristic(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/breast-cancer-wisconsin\"))\n        cls.heuristic = KMeansCorrelationHeuristic(cls.data)\n\n    def test_n_clusters(self):\n        self.assertEqual(self.heuristic.n_clusters, 3)\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n        self.assertListEqual([[0], [1, 2, 3, 4, 5, 6, 7], [8]],\n                             result)\n\n    def test_get_states(self):\n        n_attrs = len(self.data.domain.attributes)\n        states = set(self.heuristic.get_states(None))\n        self.assertEqual(len(states), n_attrs * (n_attrs - 1) / 2)\n        self.assertSetEqual(set((min(i, j), max(i, j)) for i in\n                                range(n_attrs) for j in range(i)), states)\n\n    def test_get_states_one_cluster(self):\n        heuristic = KMeansCorrelationHeuristic(Table(\"iris\")[:, :2])\n        states = set(heuristic.get_states(None))\n        self.assertEqual(len(states), 1)\n        self.assertSetEqual(states, {(0, 1)})",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 361,
                            "end_lineno": 366,
                            "parent": {
                                "file_name": "Orange/widgets/data/tests/test_owcorrelations.py",
                                "type": "class",
                                "name": "TestKMeansCorrelationHeuristic",
                                "content": "class TestKMeansCorrelationHeuristic(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/breast-cancer-wisconsin\"))\n        cls.heuristic = KMeansCorrelationHeuristic(cls.data)\n\n    def test_n_clusters(self):\n        self.assertEqual(self.heuristic.n_clusters, 3)\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n        self.assertListEqual([[0], [1, 2, 3, 4, 5, 6, 7], [8]],\n                             result)\n\n    def test_get_states(self):\n        n_attrs = len(self.data.domain.attributes)\n        states = set(self.heuristic.get_states(None))\n        self.assertEqual(len(states), n_attrs * (n_attrs - 1) / 2)\n        self.assertSetEqual(set((min(i, j), max(i, j)) for i in\n                                range(n_attrs) for j in range(i)), states)\n\n    def test_get_states_one_cluster(self):\n        heuristic = KMeansCorrelationHeuristic(Table(\"iris\")[:, :2])\n        states = set(heuristic.get_states(None))\n        self.assertEqual(len(states), 1)\n        self.assertSetEqual(states, {(0, 1)})",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 352,
                                "end_lineno": 379,
                                "parent": null
                            }
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb^",
    "new_commit_hash": "8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb",
    "commit_message": "KMeansCorrelationHeuristic: KMeans uses n_init=1\n\nDefault values of KMeans change in scikit-learn 1.4. I adapted a failing\ntest (and code) for the new default.\n\nFrom the reference:\n\nWhen n_init='auto', the number of runs depends on the value of init: 10 if using init='random' or init is a callable; 1 if using init='k-means++' or init is an array-like.\n\nChanged in version 1.4: Default value for n_init will change from 10 to 'auto' in version 1.4.",
    "commit_date": "2023-11-27T13:25:11+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access, unsubscriptable-object\nimport unittest\nfrom unittest.mock import patch, Mock\n\nimport numpy as np\nimport numpy.testing as npt\n\nfrom AnyQt.QtCore import Qt\n\nfrom Orange.data import Table, Domain, ContinuousVariable, DiscreteVariable\nfrom Orange.tests import test_filename\nfrom Orange.widgets.data.owcorrelations import (\n    OWCorrelations, KMeansCorrelationHeuristic, CorrelationRank,\n    CorrelationType\n)\nfrom Orange.widgets.tests.base import WidgetTest\nfrom Orange.widgets.tests.utils import simulate\nfrom Orange.widgets.visualize.owscatterplot import OWScatterPlot\nfrom Orange.widgets.widget import AttributeList\n\n\nclass TestOWCorrelations(WidgetTest):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.data_cont = Table(\"iris\")\n        cls.data_disc = Table(\"zoo\")\n        cls.data_mixed = Table(\"heart_disease\")\n        cls.housing = Table(\"housing\")\n\n    def setUp(self):\n        self.widget = self.create_widget(OWCorrelations)\n\n    def test_input_data_cont(self):\n        \"\"\"Check correlation table for dataset with continuous attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        n_attrs = len(self.data_cont.domain.attributes)\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 3)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(),\n                         n_attrs * (n_attrs - 1) / 2)\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 0)\n\n    def test_input_data_disc(self):\n        \"\"\"Check correlation table for dataset with discrete attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_disc)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_input_data_mixed(self):\n        \"\"\"Check correlation table for dataset with continuous and discrete\n        attributes\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        domain = self.data_mixed.domain\n        n_attrs = len([a for a in domain.attributes if a.is_continuous])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 3)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(),\n                         n_attrs * (n_attrs - 1) / 2)\n\n    def test_input_data_one_feature(self):\n        \"\"\"Check correlation table for dataset with one attribute\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont[:, [0, 4]])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_input_data_one_instance(self):\n        \"\"\"Check correlation table for dataset with one instance\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont[:1])\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n        self.assertTrue(self.widget.Warning.not_enough_inst.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.not_enough_inst.is_shown())\n\n    def test_input_data_with_constant_features(self):\n        \"\"\"Check correlation table for dataset with constant columns\"\"\"\n        np.random.seed(0)\n        # pylint: disable=no-member\n        X = np.random.randint(3, size=(4, 3)).astype(float)\n        X[:, 2] = 1\n\n        domain = Domain([ContinuousVariable(\"c1\"), ContinuousVariable(\"c2\"),\n                         DiscreteVariable(\"d1\")])\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 1)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 1)\n        self.assertTrue(self.widget.Information.removed_cons_feat.is_shown())\n\n        X = np.ones((4, 3), dtype=float)\n        self.send_signal(self.widget.Inputs.data, Table(domain, X))\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.columnCount(), 0)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n        self.assertTrue(self.widget.Information.removed_cons_feat.is_shown())\n\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Information.removed_cons_feat.is_shown())\n\n    def test_input_data_cont_target(self):\n        \"\"\"Check correlation table for dataset with continuous class variable\"\"\"\n        data = self.housing[:5, 11:]\n        self.send_signal(self.widget.Inputs.data, data)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 2)\n        self.assertEqual(self.widget.controls.feature.count(), 4)\n        self.assertEqual(self.widget.controls.feature.currentText(), \"MEDV\")\n\n        data = self.housing[:5, 13:]\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertTrue(self.widget.Warning.not_enough_vars.is_shown())\n\n    def test_output_data(self):\n        \"\"\"Check dataset on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        output = self.get_output(self.widget.Outputs.data)\n        self.assertEqual(self.data_cont, output)\n\n    def test_output_features(self):\n        \"\"\"Check features on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        features = self.get_output(self.widget.Outputs.features)\n        self.assertIsInstance(features, AttributeList)\n        self.assertEqual(len(features), 2)\n\n    def test_output_correlations(self):\n        \"\"\"Check correlation table on on output\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        correlations = self.get_output(self.widget.Outputs.correlations)\n        self.assertIsInstance(correlations, Table)\n        self.assertEqual(len(correlations), 6)\n        self.assertEqual(len(correlations.domain.metas), 2)\n        self.assertListEqual([\"Correlation\", \"uncorrected p\", \"FDR\"],\n                             [m.name for m in correlations.domain.attributes])\n        array = np.array([[0.963, 0, 0],\n                          [0.872, 0, 0],\n                          [0.818, 0, 0],\n                          [-0.421, 0, 0],\n                          [-0.357, 7.52e-6, 0.000009],\n                          [-0.109, 0.1827652, 0.1827652]])\n        npt.assert_almost_equal(correlations.X, array)\n\n    def test_input_changed(self):\n        \"\"\"Check whether changing input emits commit\"\"\"\n        self.widget.commit = Mock()\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.widget.commit.assert_called_once()\n\n        self.widget.commit.reset_mock()\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        self.wait_until_finished()\n        self.process_events()\n        self.widget.commit.assert_called_once()\n\n    def test_saved_selection(self):\n        \"\"\"Select row from settings\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        attrs = self.widget.cont_data.domain.attributes\n        self.widget._vizrank_selection_changed(attrs[3], attrs[1])\n        settings = self.widget.settingsHandler.pack_data(self.widget)\n\n        w = self.create_widget(OWCorrelations, stored_settings=settings)\n        self.send_signal(self.widget.Inputs.data, self.data_cont, widget=w)\n        self.wait_until_finished(w)\n        self.process_events()\n        sel_row = w.vizrank.rank_table.selectionModel().selectedRows()[0].row()\n        self.assertEqual(sel_row, 4)\n\n    def test_scatterplot_input_features(self):\n        \"\"\"Check if attributes have been set after sent to scatterplot\"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        spw = self.create_widget(OWScatterPlot)\n        attrs = self.widget.cont_data.domain.attributes\n        self.widget._vizrank_selection_changed(attrs[2], attrs[3])\n        features = self.get_output(self.widget.Outputs.features)\n        self.send_signal(self.widget.Inputs.data, self.data_cont, widget=spw)\n        self.send_signal(spw.Inputs.features, features, widget=spw)\n        self.assertIs(spw.attr_x, self.data_cont.domain[2])\n        self.assertIs(spw.attr_y, self.data_cont.domain[3])\n\n    def test_heuristic(self):\n        \"\"\"Check attribute pairs got by heuristic\"\"\"\n        heuristic = KMeansCorrelationHeuristic(self.data_cont)\n        heuristic.n_clusters = 2\n        self.assertListEqual(list(heuristic.get_states(None)),\n                             [(0, 2), (0, 3), (2, 3), (0, 1), (1, 2), (1, 3)])\n\n    def test_heuristic_get_states(self):\n        \"\"\"Check attribute pairs after the widget has been paused\"\"\"\n        heuristic = KMeansCorrelationHeuristic(self.data_cont)\n        heuristic.n_clusters = 2\n        states = heuristic.get_states(None)\n        _ = next(states)\n        self.assertListEqual(list(heuristic.get_states(next(states))),\n                             [(0, 3), (2, 3), (0, 1), (1, 2), (1, 3)])\n\n    def test_correlation_type(self):\n        c_type = self.widget.controls.correlation_type\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        pearson_corr = self.get_output(self.widget.Outputs.correlations)\n\n        simulate.combobox_activate_item(c_type, \"Spearman correlation\")\n        self.wait_until_finished()\n        self.process_events()\n        sperman_corr = self.get_output(self.widget.Outputs.correlations)\n        self.assertFalse((pearson_corr.X == sperman_corr.X).all())\n\n    def test_feature_combo(self):\n        \"\"\"Check content of feature selection combobox\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_mixed)\n        cont_attributes = [attr for attr in self.data_mixed.domain.attributes\n                           if attr.is_continuous]\n        self.assertEqual(len(feature_combo.model()), len(cont_attributes) + 1)\n\n        self.wait_until_stop_blocking()\n        self.send_signal(self.widget.Inputs.data, self.housing)\n        self.assertEqual(len(feature_combo.model()), 15)\n\n    def test_select_feature(self):\n        \"\"\"Test feature selection\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n        self.assertListEqual([\"petal length\", \"petal width\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n        simulate.combobox_activate_index(feature_combo, 1)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 3)\n        self.assertListEqual([\"petal length\", \"sepal length\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n        simulate.combobox_activate_index(feature_combo, 0)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n        self.assertListEqual([\"petal length\", \"sepal length\"],\n                             [a.name for a in self.get_output(\n                                 self.widget.Outputs.features)])\n\n    @patch(\"Orange.widgets.data.owcorrelations.SIZE_LIMIT\", 2000)\n    def test_vizrank_use_heuristic(self):\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertTrue(self.widget.vizrank.use_heuristic)\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 6)\n\n    @patch(\"Orange.widgets.data.owcorrelations.SIZE_LIMIT\", 2000)\n    def test_select_feature_against_heuristic(self):\n        \"\"\"Never use heuristic if feature is selected\"\"\"\n        feature_combo = self.widget.controls.feature\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        simulate.combobox_activate_index(feature_combo, 2)\n        self.wait_until_finished()\n        self.process_events()\n        self.assertEqual(self.widget.vizrank.rank_model.rowCount(), 3)\n\n    def test_send_report(self):\n        \"\"\"Test report \"\"\"\n        self.send_signal(self.widget.Inputs.data, self.data_cont)\n        self.widget.report_button.click()\n        self.wait_until_stop_blocking()\n        self.send_signal(self.widget.Inputs.data, None)\n        self.widget.report_button.click()\n\n\nclass TestCorrelationRank(WidgetTest):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.iris = Table(\"iris\")\n        cls.attrs = cls.iris.domain.attributes\n\n    def setUp(self):\n        self.vizrank = CorrelationRank(None)\n        self.vizrank.attrs = self.attrs\n\n    def test_compute_score(self):\n        self.vizrank.master = Mock()\n        self.vizrank.master.cont_data = self.iris\n        self.vizrank.master.correlation_type = CorrelationType.PEARSON\n        npt.assert_almost_equal(self.vizrank.compute_score((1, 0)),\n                                [-0.1094, -0.1094, 0.1828], 4)\n\n    def test_row_for_state(self):\n        row = self.vizrank.row_for_state((-0.2, 0.2, 0.1), (1, 0))\n        self.assertEqual(row[0].data(Qt.DisplayRole), \"+0.200\")\n        self.assertEqual(row[0].data(CorrelationRank.PValRole), 0.1)\n        self.assertEqual(row[1].data(Qt.DisplayRole), self.attrs[0].name)\n        self.assertEqual(row[2].data(Qt.DisplayRole), self.attrs[1].name)\n\n    def test_iterate_states(self):\n        self.assertListEqual(list(self.vizrank.iterate_states(None)),\n                             [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2)])\n        self.assertListEqual(list(self.vizrank.iterate_states((1, 0))),\n                             [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2)])\n        self.assertListEqual(list(self.vizrank.iterate_states((2, 1))),\n                             [(2, 1), (3, 0), (3, 1), (3, 2)])\n\n    def test_iterate_states_by_feature(self):\n        self.vizrank.sel_feature_index = 2\n        states = self.vizrank.iterate_states_by_feature()\n        self.assertListEqual([(2, 0), (2, 1), (2, 3)], list(states))\n\n    def test_state_count(self):\n        self.assertEqual(self.vizrank.state_count(), 6)\n        self.vizrank.sel_feature_index = 2\n        self.assertEqual(self.vizrank.state_count(), 3)\n\n\nclass TestKMeansCorrelationHeuristic(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/breast-cancer-wisconsin\"))\n        cls.heuristic = KMeansCorrelationHeuristic(cls.data)\n\n    def test_n_clusters(self):\n        self.assertEqual(self.heuristic.n_clusters, 3)\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n        self.assertListEqual([[0, 3, 5], [1, 2, 6, 7], [4, 8]],\n                             result)\n\n    def test_get_states(self):\n        n_attrs = len(self.data.domain.attributes)\n        states = set(self.heuristic.get_states(None))\n        self.assertEqual(len(states), n_attrs * (n_attrs - 1) / 2)\n        self.assertSetEqual(set((min(i, j), max(i, j)) for i in\n                                range(n_attrs) for j in range(i)), states)\n\n    def test_get_states_one_cluster(self):\n        heuristic = KMeansCorrelationHeuristic(Table(\"iris\")[:, :2])\n        states = set(heuristic.get_states(None))\n        self.assertEqual(len(states), 1)\n        self.assertSetEqual(states, {(0, 1)})\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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.37.0.dev0+8351d7f to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb\nProcessing dependencies for Orange3==3.37.0.dev0+8351d7f\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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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-2aekvcy4/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-2aekvcy4/serverfiles-0.3.1/egg-dist-tmp-smbm51i8\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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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/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.37.0.dev0+8351d7f to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb\nProcessing dependencies for Orange3==3.37.0.dev0+8351d7f\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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages\nSearching for setuptools==75.3.0\nBest match: setuptools 75.3.0\nAdding setuptools 75.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.37.0.dev0+8351d7f\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 185ms\nPrepared 18 packages in 1ms\nInstalled 18 packages in 193ms\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 70ms\nInstalled 2 packages in 45ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 18ms\nInstalled 4 packages in 728ms\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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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-kyhyen29/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 157ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 37ms\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 65 packages in 107ms\nInstalled 61 packages in 694ms\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 64 packages in 69ms\nInstalled 41 packages in 133ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.12.3\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.3\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 2ms\nInstalled 2 packages in 31ms\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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/.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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 40 items\n\nr2e_tests/test_1.py Esss.........................s..ss......             [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/Orange/tests/__init__.py, line 68\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/Orange/tests/__init__.py:68\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_correlation_type\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_feature_combo\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_heuristic\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_heuristic_get_states\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_image_export\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_changed\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_cont\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_cont_target\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_disc\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_mixed\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_one_feature\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_one_instance\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_with_constant_features\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_output_correlations\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_output_data\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_output_features\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_saved_selection\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_scatterplot_input_features\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_select_feature\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_select_feature_against_heuristic\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_send_report\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_vizrank_use_heuristic\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_compute_score\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_iterate_states\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_iterate_states_by_feature\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_row_for_state\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_state_count\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_get_clusters_of_attributes\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_get_states\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_get_states_one_cluster\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_n_clusters\nSKIPPED [2] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [2] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [2] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nERROR r2e_tests/test_1.py::test_filename\n==================== 33 passed, 6 skipped, 1 error in 6.08s ====================\nWidgets left: 0    Max widgets: 2227 \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_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 40 items\n\nr2e_tests/test_1.py Esss.........................s..ss..F...             [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/Orange/tests/__init__.py, line 68\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_8351d7f3069ab53c9c2bc15bfb1a8e4ed1fcdbdb/Orange/tests/__init__.py:68\n=================================== FAILURES ===================================\n________ TestKMeansCorrelationHeuristic.test_get_clusters_of_attributes ________\n\nself = <r2e_tests.test_1.TestKMeansCorrelationHeuristic testMethod=test_get_clusters_of_attributes>\n\n    def test_get_clusters_of_attributes(self):\n        clusters = self.heuristic.get_clusters_of_attributes()\n        # results depend on scikit-learn k-means implementation\n        result = sorted([c.instances for c in clusters])\n>       self.assertListEqual([[0, 3, 5], [1, 2, 6, 7], [4, 8]],\n                             result)\nE       AssertionError: Lists differ: [[0, 3, 5], [1, 2, 6, 7], [4, 8]] != [[0], [1, 2, 3, 4, 5, 6, 7], [8]]\nE       \nE       First differing element 0:\nE       [0, 3, 5]\nE       [0]\nE       \nE       - [[0, 3, 5], [1, 2, 6, 7], [4, 8]]\nE       + [[0], [1, 2, 3, 4, 5, 6, 7], [8]]\n\nr2e_tests/test_1.py:365: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_correlation_type\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_feature_combo\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_heuristic\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_heuristic_get_states\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_image_export\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_changed\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_cont\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_cont_target\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_disc\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_mixed\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_one_feature\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_one_instance\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_input_data_with_constant_features\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_minimum_size\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_msg_base_class\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_output_correlations\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_output_data\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_output_features\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_saved_selection\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_scatterplot_input_features\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_select_feature\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_select_feature_against_heuristic\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_send_report\nPASSED r2e_tests/test_1.py::TestOWCorrelations::test_vizrank_use_heuristic\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_compute_score\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_iterate_states\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_iterate_states_by_feature\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_row_for_state\nPASSED r2e_tests/test_1.py::TestCorrelationRank::test_state_count\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_get_states\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_get_states_one_cluster\nPASSED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_n_clusters\nSKIPPED [2] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:244: image exporting not tested as .widget was not set\nSKIPPED [2] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:238: minimum size not tested as .widget was not set\nSKIPPED [2] .venv/lib/python3.8/site-packages/orangewidget/tests/base.py:250: msg base class not tested as .widget was not set\nERROR r2e_tests/test_1.py::test_filename\nFAILED r2e_tests/test_1.py::TestKMeansCorrelationHeuristic::test_get_clusters_of_attributes\n=============== 1 failed, 32 passed, 6 skipped, 1 error in 4.05s ===============\nWidgets left: 0    Max widgets: 2227 \n\n",
    "old_commit_res_stderr": ""
}
Orange/widgets/data/owcorrelations.py Orange/widgets/data/tests/test_owcorrelations.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** KMeansCorrelationHeuristic Fails After scikit-learn 1.4 Update Due to `n_init` Default Change

**Description:**
After updating to scikit-learn version 1.4, the `KMeansCorrelationHeuristic` is producing unexpected clustering results. This issue arises because the default value for the `n_init` parameter in KMeans has changed from `10` to `'auto'`. As a result, tests that verify the attribute clusters are failing since the clustering behavior differs from the expected outcome.

**Example Code:**
```python
def test_get_clusters_of_attributes():
    heuristic = KMeansCorrelationHeuristic(data)
    clusters = heuristic.get_clusters_of_attributes()
    result = sorted([c.instances for c in clusters])
    print(result)
    # Expected: [[0], [1, 2, 3, 4, 5, 6, 7], [8]]
    # Actual: [[0, 3, 5], [1, 2, 6, 7], [4, 8]]
```

**Expected Behavior:**
The `get_clusters_of_attributes` method should consistently group attributes into the predefined clusters, such as `[[0], [1, 2, 3, 4, 5, 6, 7], [8]]`, regardless of changes in external library defaults.

**Actual Behavior:**
With the `n_init` parameter set to `'auto'`, the clustering results have changed to `[[0, 3, 5], [1, 2, 6, 7], [4, 8]]`, causing the tests to fail because the clusters no longer match the expected groupings.

[/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/data/owcorrelations.py
"""
Correlations widget
"""
from enum import IntEnum
from operator import attrgetter
from types import SimpleNamespace
from itertools import combinations, groupby, chain

import numpy as np
from scipy.stats import spearmanr, pearsonr
from sklearn.cluster import KMeans

from AnyQt.QtCore import Qt, QItemSelectionModel, QItemSelection, \
    QSize, pyqtSignal as Signal
from AnyQt.QtGui import QStandardItem
from AnyQt.QtWidgets import QHeaderView

from Orange.data import Table, Domain, ContinuousVariable, StringVariable
from Orange.preprocess import SklImpute, Normalize, Remove
from Orange.statistics.util import FDR
from Orange.widgets import gui
from Orange.widgets.settings import Setting, ContextSetting, \
    DomainContextHandler
from Orange.widgets.utils import vartype
from Orange.widgets.utils.itemmodels import DomainModel
from Orange.widgets.utils.signals import Input, Output
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.visualize.utils import VizRankDialogAttrPair
from Orange.widgets.widget import OWWidget, AttributeList, Msg

NAN = 2
SIZE_LIMIT = 1000000


class CorrelationType(IntEnum):
    """
    Correlation type enumerator. Possible correlations: Pearson, Spearman.
    """
    PEARSON, SPEARMAN = 0, 1

    @staticmethod
    def items():
        """
        Texts for correlation types. Can be used in gui controls (eg. combobox).
        """
        return ["Pearson correlation", "Spearman correlation"]


class Cluster(SimpleNamespace):
    instances = None  # type: Optional[List]
    centroid = None  # type: Optional[np.ndarray]


class KMeansCorrelationHeuristic:
    """
    Heuristic to obtain the most promising attribute pairs, when there are too
    many attributes to calculate correlations for all possible pairs.
    """
    def __init__(self, data):
        self.n_attributes = len(data.domain.attributes)
        self.data = data
        self.clusters = None
        self.n_clusters = int(np.sqrt(self.n_attributes))

    def get_clusters_of_attributes(self):
        """
        Generates groupes of attribute IDs, grouped by cluster. Clusters are
        obtained by KMeans algorithm.

        :return: generator of attributes grouped by cluster
        """
        data = Normalize()(self.data).X.T
        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)
        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])
        return [Cluster(instances=list(pair[1] for pair in group),
                        centroid=kmeans.cluster_centers_[l])
                for l, group in groupby(labels_attrs, key=lambda x: x[0])]

    def get_states(self, initial_state):
        """
        Generates states (attribute pairs) - the most promising first, i.e.
        states within clusters, following by states among clusters.

        :param initial_state: initial state; None if this is the first call
        :return: generator of tuples of states
        """
        if self.clusters is None:
            self.clusters = self.get_clusters_of_attributes()
        clusters = self.clusters

        # combinations within clusters
        states0 = chain.from_iterable(combinations(cluster.instances, 2)
                                      for cluster in clusters)
        if self.n_clusters == 1:
            return states0

        # combinations among clusters - closest clusters first
        centroids = [c.centroid for c in clusters]
        centroids_combs = np.array(list(combinations(centroids, 2)))
        distances = np.linalg.norm((centroids_combs[:, 0] -
                                    centroids_combs[:, 1]), axis=1)
        cluster_combs = list(combinations(range(len(clusters)), 2))
        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)
                  for c1 in clusters[cluster_combs[i][0]].instances
                  for c2 in clusters[cluster_combs[i][1]].instances)
        states = chain(states0, states)

        if initial_state is not None:
            while next(states) != initial_state:
                pass
            return chain([initial_state], states)
        return states


class CorrelationRank(VizRankDialogAttrPair):
    """
    Correlations rank widget.
    """
    threadStopped = Signal()
    PValRole = next(gui.OrangeUserRole)

    def __init__(self, *args):
        super().__init__(*args)
        self.heuristic = None
        self.use_heuristic = False
        self.sel_feature_index = None

    def initialize(self):
        super().initialize()
        data = self.master.cont_data
        self.attrs = data and data.domain.attributes
        self.model_proxy.setFilterKeyColumn(-1)
        self.heuristic = None
        self.use_heuristic = False
        if self.master.feature is not None:
            self.sel_feature_index = data.domain.index(self.master.feature)
        else:
            self.sel_feature_index = None
        if data:
            # use heuristic if data is too big
            self.use_heuristic = len(data) * len(self.attrs) ** 2 > SIZE_LIMIT \
                and self.sel_feature_index is None
            if self.use_heuristic:
                self.heuristic = KMeansCorrelationHeuristic(data)

    def compute_score(self, state):
        (attr1, attr2), corr_type = state, self.master.correlation_type
        data = self.master.cont_data.X
        corr = pearsonr if corr_type == CorrelationType.PEARSON else spearmanr
        r, p_value = corr(data[:, attr1], data[:, attr2])
        return -abs(r) if not np.isnan(r) else NAN, r, p_value

    def row_for_state(self, score, state):
        attrs = sorted((self.attrs[x] for x in state), key=attrgetter("name"))
        attr_items = []
        for attr in attrs:
            item = QStandardItem(attr.name)
            item.setData(attrs, self._AttrRole)
            item.setData(Qt.AlignLeft + Qt.AlignCenter, Qt.TextAlignmentRole)
            item.setToolTip(attr.name)
            attr_items.append(item)
        correlation_item = QStandardItem("{:+.3f}".format(score[1]))
        correlation_item.setData(score[2], self.PValRole)
        correlation_item.setData(attrs, self._AttrRole)
        correlation_item.setData(
            self.NEGATIVE_COLOR if score[1] < 0 else self.POSITIVE_COLOR,
            gui.TableBarItem.BarColorRole)
        return [correlation_item] + attr_items

    def check_preconditions(self):
        return self.master.cont_data is not None

    def iterate_states(self, initial_state):
        if self.sel_feature_index is not None:
            return self.iterate_states_by_feature()
        elif self.use_heuristic:
            return self.heuristic.get_states(initial_state)
        else:
            return super().iterate_states(initial_state)

    def iterate_states_by_feature(self):
        for j in range(len(self.attrs)):
            if j != self.sel_feature_index:
                yield self.sel_feature_index, j

    def state_count(self):
        n = len(self.attrs)
        return n * (n - 1) / 2 if self.sel_feature_index is None else n - 1

    @staticmethod
    def bar_length(score):
        return abs(score[1])

    def stopped(self):
        self.threadStopped.emit()
        header = self.rank_table.horizontalHeader()
        header.setSectionResizeMode(1, QHeaderView.Stretch)

    def start(self, task, *args, **kwargs):
        self._set_empty_status()
        super().start(task, *args, **kwargs)
        self.__set_state_busy()

    def cancel(self):
        super().cancel()
        self.__set_state_ready()

    def _connect_signals(self, state):
        super()._connect_signals(state)
        state.progress_changed.connect(self.master.progressBarSet)
        state.status_changed.connect(self.master.setStatusMessage)

    def _disconnect_signals(self, state):
        super()._disconnect_signals(state)
        state.progress_changed.disconnect(self.master.progressBarSet)
        state.status_changed.disconnect(self.master.setStatusMessage)

    def _on_task_done(self, future):
        super()._on_task_done(future)
        self.__set_state_ready()

    def __set_state_ready(self):
        self._set_empty_status()
        self.master.setBlocking(False)

    def __set_state_busy(self):
        self.master.progressBarInit()
        self.master.setBlocking(True)

    def _set_empty_status(self):
        self.master.progressBarFinished()
        self.master.setStatusMessage("")


class OWCorrelations(OWWidget):
    name = "Correlations"
    description = "Compute all pairwise attribute correlations."
    icon = "icons/Correlations.svg"
    priority = 1106
    category = "Unsupervised"

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

    class Outputs:
        data = Output("Data", Table)
        features = Output("Features", AttributeList)
        correlations = Output("Correlations", Table)

    want_main_area = False
    want_control_area = True

    correlation_type: int

    settings_version = 3
    settingsHandler = DomainContextHandler()
    selection = ContextSetting([])
    feature = ContextSetting(None)
    correlation_type = Setting(0)

    class Information(OWWidget.Information):
        removed_cons_feat = Msg("Constant features have been removed.")

    class Warning(OWWidget.Warning):
        not_enough_vars = Msg("At least two numeric features are needed.")
        not_enough_inst = Msg("At least two instances are needed.")

    def __init__(self):
        super().__init__()
        self.data = None  # type: Table
        self.cont_data = None  # type: Table

        # GUI
        box = gui.vBox(self.controlArea)
        self.correlation_combo = gui.comboBox(
            box, self, "correlation_type", items=CorrelationType.items(),
            orientation=Qt.Horizontal, callback=self._correlation_combo_changed
        )

        self.feature_model = DomainModel(
            order=DomainModel.ATTRIBUTES, separators=False,
            placeholder="(All combinations)", valid_types=ContinuousVariable)
        gui.comboBox(
            box, self, "feature", callback=self._feature_combo_changed,
            model=self.feature_model, searchable=True
        )

        self.vizrank, _ = CorrelationRank.add_vizrank(
            None, self, None, self._vizrank_selection_changed)
        self.vizrank.button.setEnabled(False)
        self.vizrank.threadStopped.connect(self._vizrank_stopped)

        gui.separator(box)
        box.layout().addWidget(self.vizrank.filter)
        box.layout().addWidget(self.vizrank.rank_table)

        button_box = gui.hBox(self.buttonsArea)
        button_box.layout().addWidget(self.vizrank.button)

    @staticmethod
    def sizeHint():
        return QSize(350, 400)

    def _correlation_combo_changed(self):
        self.apply()

    def _feature_combo_changed(self):
        self.apply()

    def _vizrank_selection_changed(self, *args):
        self.selection = list(args)
        self.commit()

    def _vizrank_stopped(self):
        self._vizrank_select()

    def _vizrank_select(self):
        model = self.vizrank.rank_table.model()
        if not model.rowCount():
            return
        selection = QItemSelection()

        # This flag is needed because data in the model could be
        # filtered by a feature and therefore selection could not be found
        selection_in_model = False
        if self.selection:
            sel_names = sorted(var.name for var in self.selection)
            for i in range(model.rowCount()):
                # pylint: disable=protected-access
                names = sorted(x.name for x in model.data(
                    model.index(i, 0), CorrelationRank._AttrRole))
                if names == sel_names:
                    selection.select(model.index(i, 0),
                                     model.index(i, model.columnCount() - 1))
                    selection_in_model = True
                    break
        if not selection_in_model:
            selection.select(model.index(0, 0),
                             model.index(0, model.columnCount() - 1))
        self.vizrank.rank_table.selectionModel().select(
            selection, QItemSelectionModel.ClearAndSelect)

    @Inputs.data
    def set_data(self, data):
        self.closeContext()
        self.clear_messages()
        self.data = data
        self.cont_data = None
        self.selection = []
        if data is not None:
            if len(data) < 2:
                self.Warning.not_enough_inst()
            else:
                domain = data.domain
                cont_vars = [a for a in domain.class_vars + domain.metas +
                             domain.attributes if a.is_continuous]
                cont_data = Table.from_table(Domain(cont_vars), data)
                remover = Remove(Remove.RemoveConstant)
                cont_data = remover(cont_data)
                if remover.attr_results["removed"]:
                    self.Information.removed_cons_feat()
                if len(cont_data.domain.attributes) < 2:
                    self.Warning.not_enough_vars()
                else:
                    self.cont_data = SklImpute()(cont_data)
        self.set_feature_model()
        self.openContext(self.cont_data)
        self.apply()
        self.vizrank.button.setEnabled(self.cont_data is not None)

    def set_feature_model(self):
        self.feature_model.set_domain(
            self.cont_data.domain if self.cont_data else None)
        data = self.data
        if self.cont_data and data.domain.has_continuous_class:
            self.feature = self.cont_data.domain[data.domain.class_var.name]
        else:
            self.feature = None

    def apply(self):
        self.vizrank.initialize()
        if self.cont_data is not None:
            # this triggers self.commit() by changing vizrank selection
            self.vizrank.toggle()
        else:
            self.commit()

    def commit(self):
        self.Outputs.data.send(self.data)

        if self.data is None or self.cont_data is None:
            self.Outputs.features.send(None)
            self.Outputs.correlations.send(None)
            return

        attrs = [ContinuousVariable("Correlation"),
                 ContinuousVariable("uncorrected p"),
                 ContinuousVariable("FDR")]
        metas = [StringVariable("Feature 1"), StringVariable("Feature 2")]
        domain = Domain(attrs, metas=metas)
        model = self.vizrank.rank_model
        count = model.rowCount()
        index = model.index
        corr = np.array([float(index(row, 0).data())
                         for row in range(count)])
        p = np.array([index(row, 0).data(CorrelationRank.PValRole)
                      for row in range(count)])
        fdr = FDR(p)
        x = np.vstack((corr, p, fdr)).T
        # pylint: disable=protected-access
        m = np.array([[a.name
                       for a in index(row, 0).data(CorrelationRank._AttrRole)]
                      for row in range(count)], dtype=object)
        corr_table = Table(domain, x, metas=m)
        corr_table.name = "Correlations"

        # data has been imputed; send original attributes
        self.Outputs.features.send(AttributeList(
            [self.data.domain[var.name] for var in self.selection]))
        self.Outputs.correlations.send(corr_table)

    def send_report(self):
        self.report_table(CorrelationType.items()[self.correlation_type],
                          self.vizrank.rank_table)

    @classmethod
    def migrate_context(cls, context, version):
        if version < 2:
            sel = context.values["selection"]
            context.values["selection"] = [(var.name, vartype(var))
                                           for var in sel[0]]
        if version < 3:
            sel = context.values["selection"]
            context.values["selection"] = ([(name, vtype + 100)
                                            for name, vtype in sel], -3)


if __name__ == "__main__":  # pragma: no cover
    WidgetPreview(OWCorrelations).run(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.
[ISSUE]
**Title:** KMeansCorrelationHeuristic Fails After scikit-learn 1.4 Update Due to `n_init` Default Change

**Description:**
After updating to scikit-learn version 1.4, the `KMeansCorrelationHeuristic` is producing unexpected clustering results. This issue arises because the default value for the `n_init` parameter in KMeans has changed from `10` to `'auto'`. As a result, tests that verify the attribute clusters are failing since the clustering behavior differs from the expected outcome.

**Example Code:**
```python
def test_get_clusters_of_attributes():
    heuristic = KMeansCorrelationHeuristic(data)
    clusters = heuristic.get_clusters_of_attributes()
    result = sorted([c.instances for c in clusters])
    print(result)
    # Expected: [[0], [1, 2, 3, 4, 5, 6, 7], [8]]
    # Actual: [[0, 3, 5], [1, 2, 6, 7], [4, 8]]
```

**Expected Behavior:**
The `get_clusters_of_attributes` method should consistently group attributes into the predefined clusters, such as `[[0], [1, 2, 3, 4, 5, 6, 7], [8]]`, regardless of changes in external library defaults.

**Actual Behavior:**
With the `n_init` parameter set to `'auto'`, the clustering results have changed to `[[0, 3, 5], [1, 2, 6, 7], [4, 8]]`, causing the tests to fail because the clusters no longer match the expected groupings.

[/ISSUE]
{
    "TestOWCorrelations.test_correlation_type": "PASSED",
    "TestOWCorrelations.test_feature_combo": "PASSED",
    "TestOWCorrelations.test_heuristic": "PASSED",
    "TestOWCorrelations.test_heuristic_get_states": "PASSED",
    "TestOWCorrelations.test_image_export": "PASSED",
    "TestOWCorrelations.test_input_changed": "PASSED",
    "TestOWCorrelations.test_input_data_cont": "PASSED",
    "TestOWCorrelations.test_input_data_cont_target": "PASSED",
    "TestOWCorrelations.test_input_data_disc": "PASSED",
    "TestOWCorrelations.test_input_data_mixed": "PASSED",
    "TestOWCorrelations.test_input_data_one_feature": "PASSED",
    "TestOWCorrelations.test_input_data_one_instance": "PASSED",
    "TestOWCorrelations.test_input_data_with_constant_features": "PASSED",
    "TestOWCorrelations.test_minimum_size": "PASSED",
    "TestOWCorrelations.test_msg_base_class": "PASSED",
    "TestOWCorrelations.test_output_correlations": "PASSED",
    "TestOWCorrelations.test_output_data": "PASSED",
    "TestOWCorrelations.test_output_features": "PASSED",
    "TestOWCorrelations.test_saved_selection": "PASSED",
    "TestOWCorrelations.test_scatterplot_input_features": "PASSED",
    "TestOWCorrelations.test_select_feature": "PASSED",
    "TestOWCorrelations.test_select_feature_against_heuristic": "PASSED",
    "TestOWCorrelations.test_send_report": "PASSED",
    "TestOWCorrelations.test_vizrank_use_heuristic": "PASSED",
    "TestCorrelationRank.test_compute_score": "PASSED",
    "TestCorrelationRank.test_iterate_states": "PASSED",
    "TestCorrelationRank.test_iterate_states_by_feature": "PASSED",
    "TestCorrelationRank.test_row_for_state": "PASSED",
    "TestCorrelationRank.test_state_count": "PASSED",
    "TestKMeansCorrelationHeuristic.test_get_clusters_of_attributes": "PASSED",
    "TestKMeansCorrelationHeuristic.test_get_states": "PASSED",
    "TestKMeansCorrelationHeuristic.test_get_states_one_cluster": "PASSED",
    "TestKMeansCorrelationHeuristic.test_n_clusters": "PASSED",
    "test_filename": "ERROR"
}
diff --git a/Orange/widgets/data/owcorrelations.py b/Orange/widgets/data/owcorrelations.py
index 4779ad8a8..b1e969228 100644
--- a/Orange/widgets/data/owcorrelations.py
+++ b/Orange/widgets/data/owcorrelations.py
@@ -70,7 +70,7 @@ class KMeansCorrelationHeuristic:
         :return: generator of attributes grouped by cluster
         """
         data = Normalize()(self.data).X.T
-        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)
+        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0, n_init=1).fit(data)
         labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])
         return [Cluster(instances=list(pair[1] for pair in group),
                         centroid=kmeans.cluster_centers_[l])

{"Orange/widgets/data/owcorrelations.py": "\"\"\"\nCorrelations widget\n\"\"\"\nfrom enum import IntEnum\nfrom operator import attrgetter\nfrom types import SimpleNamespace\nfrom itertools import combinations, groupby, chain\n\nimport numpy as np\nfrom scipy.stats import spearmanr, pearsonr\nfrom sklearn.cluster import KMeans\n\nfrom AnyQt.QtCore import Qt, QItemSelectionModel, QItemSelection, \\\n    QSize, pyqtSignal as Signal\nfrom AnyQt.QtGui import QStandardItem\nfrom AnyQt.QtWidgets import QHeaderView\n\nfrom Orange.data import Table, Domain, ContinuousVariable, StringVariable\nfrom Orange.preprocess import SklImpute, Normalize, Remove\nfrom Orange.statistics.util import FDR\nfrom Orange.widgets import gui\nfrom Orange.widgets.settings import Setting, ContextSetting, \\\n    DomainContextHandler\nfrom Orange.widgets.utils import vartype\nfrom Orange.widgets.utils.itemmodels import DomainModel\nfrom Orange.widgets.utils.signals import Input, Output\nfrom Orange.widgets.utils.widgetpreview import WidgetPreview\nfrom Orange.widgets.visualize.utils import VizRankDialogAttrPair\nfrom Orange.widgets.widget import OWWidget, AttributeList, Msg\n\nNAN = 2\nSIZE_LIMIT = 1000000\n\n\nclass CorrelationType(IntEnum):\n    \"\"\"\n    Correlation type enumerator. Possible correlations: Pearson, Spearman.\n    \"\"\"\n    PEARSON, SPEARMAN = 0, 1\n\n    @staticmethod\n    def items():\n        \"\"\"\n        Texts for correlation types. Can be used in gui controls (eg. combobox).\n        \"\"\"\n        return [\"Pearson correlation\", \"Spearman correlation\"]\n\n\nclass Cluster(SimpleNamespace):\n    instances = None  # type: Optional[List]\n    centroid = None  # type: Optional[np.ndarray]\n\n\nclass KMeansCorrelationHeuristic:\n    \"\"\"\n    Heuristic to obtain the most promising attribute pairs, when there are too\n    many attributes to calculate correlations for all possible pairs.\n    \"\"\"\n    def __init__(self, data):\n        self.n_attributes = len(data.domain.attributes)\n        self.data = data\n        self.clusters = None\n        self.n_clusters = int(np.sqrt(self.n_attributes))\n\n    def get_clusters_of_attributes(self):\n        \"\"\"\n        Generates groupes of attribute IDs, grouped by cluster. Clusters are\n        obtained by KMeans algorithm.\n\n        :return: generator of attributes grouped by cluster\n        \"\"\"\n        data = Normalize()(self.data).X.T\n        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)\n        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])\n        return [Cluster(instances=list(pair[1] for pair in group),\n                        centroid=kmeans.cluster_centers_[l])\n                for l, group in groupby(labels_attrs, key=lambda x: x[0])]\n\n    def get_states(self, initial_state):\n        \"\"\"\n        Generates states (attribute pairs) - the most promising first, i.e.\n        states within clusters, following by states among clusters.\n\n        :param initial_state: initial state; None if this is the first call\n        :return: generator of tuples of states\n        \"\"\"\n        if self.clusters is None:\n            self.clusters = self.get_clusters_of_attributes()\n        clusters = self.clusters\n\n        # combinations within clusters\n        states0 = chain.from_iterable(combinations(cluster.instances, 2)\n                                      for cluster in clusters)\n        if self.n_clusters == 1:\n            return states0\n\n        # combinations among clusters - closest clusters first\n        centroids = [c.centroid for c in clusters]\n        centroids_combs = np.array(list(combinations(centroids, 2)))\n        distances = np.linalg.norm((centroids_combs[:, 0] -\n                                    centroids_combs[:, 1]), axis=1)\n        cluster_combs = list(combinations(range(len(clusters)), 2))\n        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)\n                  for c1 in clusters[cluster_combs[i][0]].instances\n                  for c2 in clusters[cluster_combs[i][1]].instances)\n        states = chain(states0, states)\n\n        if initial_state is not None:\n            while next(states) != initial_state:\n                pass\n            return chain([initial_state], states)\n        return states\n\n\nclass CorrelationRank(VizRankDialogAttrPair):\n    \"\"\"\n    Correlations rank widget.\n    \"\"\"\n    threadStopped = Signal()\n    PValRole = next(gui.OrangeUserRole)\n\n    def __init__(self, *args):\n        super().__init__(*args)\n        self.heuristic = None\n        self.use_heuristic = False\n        self.sel_feature_index = None\n\n    def initialize(self):\n        super().initialize()\n        data = self.master.cont_data\n        self.attrs = data and data.domain.attributes\n        self.model_proxy.setFilterKeyColumn(-1)\n        self.heuristic = None\n        self.use_heuristic = False\n        if self.master.feature is not None:\n            self.sel_feature_index = data.domain.index(self.master.feature)\n        else:\n            self.sel_feature_index = None\n        if data:\n            # use heuristic if data is too big\n            self.use_heuristic = len(data) * len(self.attrs) ** 2 > SIZE_LIMIT \\\n                and self.sel_feature_index is None\n            if self.use_heuristic:\n                self.heuristic = KMeansCorrelationHeuristic(data)\n\n    def compute_score(self, state):\n        (attr1, attr2), corr_type = state, self.master.correlation_type\n        data = self.master.cont_data.X\n        corr = pearsonr if corr_type == CorrelationType.PEARSON else spearmanr\n        r, p_value = corr(data[:, attr1], data[:, attr2])\n        return -abs(r) if not np.isnan(r) else NAN, r, p_value\n\n    def row_for_state(self, score, state):\n        attrs = sorted((self.attrs[x] for x in state), key=attrgetter(\"name\"))\n        attr_items = []\n        for attr in attrs:\n            item = QStandardItem(attr.name)\n            item.setData(attrs, self._AttrRole)\n            item.setData(Qt.AlignLeft + Qt.AlignCenter, Qt.TextAlignmentRole)\n            item.setToolTip(attr.name)\n            attr_items.append(item)\n        correlation_item = QStandardItem(\"{:+.3f}\".format(score[1]))\n        correlation_item.setData(score[2], self.PValRole)\n        correlation_item.setData(attrs, self._AttrRole)\n        correlation_item.setData(\n            self.NEGATIVE_COLOR if score[1] < 0 else self.POSITIVE_COLOR,\n            gui.TableBarItem.BarColorRole)\n        return [correlation_item] + attr_items\n\n    def check_preconditions(self):\n        return self.master.cont_data is not None\n\n    def iterate_states(self, initial_state):\n        if self.sel_feature_index is not None:\n            return self.iterate_states_by_feature()\n        elif self.use_heuristic:\n            return self.heuristic.get_states(initial_state)\n        else:\n            return super().iterate_states(initial_state)\n\n    def iterate_states_by_feature(self):\n        for j in range(len(self.attrs)):\n            if j != self.sel_feature_index:\n                yield self.sel_feature_index, j\n\n    def state_count(self):\n        n = len(self.attrs)\n        return n * (n - 1) / 2 if self.sel_feature_index is None else n - 1\n\n    @staticmethod\n    def bar_length(score):\n        return abs(score[1])\n\n    def stopped(self):\n        self.threadStopped.emit()\n        header = self.rank_table.horizontalHeader()\n        header.setSectionResizeMode(1, QHeaderView.Stretch)\n\n    def start(self, task, *args, **kwargs):\n        self._set_empty_status()\n        super().start(task, *args, **kwargs)\n        self.__set_state_busy()\n\n    def cancel(self):\n        super().cancel()\n        self.__set_state_ready()\n\n    def _connect_signals(self, state):\n        super()._connect_signals(state)\n        state.progress_changed.connect(self.master.progressBarSet)\n        state.status_changed.connect(self.master.setStatusMessage)\n\n    def _disconnect_signals(self, state):\n        super()._disconnect_signals(state)\n        state.progress_changed.disconnect(self.master.progressBarSet)\n        state.status_changed.disconnect(self.master.setStatusMessage)\n\n    def _on_task_done(self, future):\n        super()._on_task_done(future)\n        self.__set_state_ready()\n\n    def __set_state_ready(self):\n        self._set_empty_status()\n        self.master.setBlocking(False)\n\n    def __set_state_busy(self):\n        self.master.progressBarInit()\n        self.master.setBlocking(True)\n\n    def _set_empty_status(self):\n        self.master.progressBarFinished()\n        self.master.setStatusMessage(\"\")\n\n\nclass OWCorrelations(OWWidget):\n    name = \"Correlations\"\n    description = \"Compute all pairwise attribute correlations.\"\n    icon = \"icons/Correlations.svg\"\n    priority = 1106\n    category = \"Unsupervised\"\n\n    class Inputs:\n        data = Input(\"Data\", Table)\n\n    class Outputs:\n        data = Output(\"Data\", Table)\n        features = Output(\"Features\", AttributeList)\n        correlations = Output(\"Correlations\", Table)\n\n    want_main_area = False\n    want_control_area = True\n\n    correlation_type: int\n\n    settings_version = 3\n    settingsHandler = DomainContextHandler()\n    selection = ContextSetting([])\n    feature = ContextSetting(None)\n    correlation_type = Setting(0)\n\n    class Information(OWWidget.Information):\n        removed_cons_feat = Msg(\"Constant features have been removed.\")\n\n    class Warning(OWWidget.Warning):\n        not_enough_vars = Msg(\"At least two numeric features are needed.\")\n        not_enough_inst = Msg(\"At least two instances are needed.\")\n\n    def __init__(self):\n        super().__init__()\n        self.data = None  # type: Table\n        self.cont_data = None  # type: Table\n\n        # GUI\n        box = gui.vBox(self.controlArea)\n        self.correlation_combo = gui.comboBox(\n            box, self, \"correlation_type\", items=CorrelationType.items(),\n            orientation=Qt.Horizontal, callback=self._correlation_combo_changed\n        )\n\n        self.feature_model = DomainModel(\n            order=DomainModel.ATTRIBUTES, separators=False,\n            placeholder=\"(All combinations)\", valid_types=ContinuousVariable)\n        gui.comboBox(\n            box, self, \"feature\", callback=self._feature_combo_changed,\n            model=self.feature_model, searchable=True\n        )\n\n        self.vizrank, _ = CorrelationRank.add_vizrank(\n            None, self, None, self._vizrank_selection_changed)\n        self.vizrank.button.setEnabled(False)\n        self.vizrank.threadStopped.connect(self._vizrank_stopped)\n\n        gui.separator(box)\n        box.layout().addWidget(self.vizrank.filter)\n        box.layout().addWidget(self.vizrank.rank_table)\n\n        button_box = gui.hBox(self.buttonsArea)\n        button_box.layout().addWidget(self.vizrank.button)\n\n    @staticmethod\n    def sizeHint():\n        return QSize(350, 400)\n\n    def _correlation_combo_changed(self):\n        self.apply()\n\n    def _feature_combo_changed(self):\n        self.apply()\n\n    def _vizrank_selection_changed(self, *args):\n        self.selection = list(args)\n        self.commit()\n\n    def _vizrank_stopped(self):\n        self._vizrank_select()\n\n    def _vizrank_select(self):\n        model = self.vizrank.rank_table.model()\n        if not model.rowCount():\n            return\n        selection = QItemSelection()\n\n        # This flag is needed because data in the model could be\n        # filtered by a feature and therefore selection could not be found\n        selection_in_model = False\n        if self.selection:\n            sel_names = sorted(var.name for var in self.selection)\n            for i in range(model.rowCount()):\n                # pylint: disable=protected-access\n                names = sorted(x.name for x in model.data(\n                    model.index(i, 0), CorrelationRank._AttrRole))\n                if names == sel_names:\n                    selection.select(model.index(i, 0),\n                                     model.index(i, model.columnCount() - 1))\n                    selection_in_model = True\n                    break\n        if not selection_in_model:\n            selection.select(model.index(0, 0),\n                             model.index(0, model.columnCount() - 1))\n        self.vizrank.rank_table.selectionModel().select(\n            selection, QItemSelectionModel.ClearAndSelect)\n\n    @Inputs.data\n    def set_data(self, data):\n        self.closeContext()\n        self.clear_messages()\n        self.data = data\n        self.cont_data = None\n        self.selection = []\n        if data is not None:\n            if len(data) < 2:\n                self.Warning.not_enough_inst()\n            else:\n                domain = data.domain\n                cont_vars = [a for a in domain.class_vars + domain.metas +\n                             domain.attributes if a.is_continuous]\n                cont_data = Table.from_table(Domain(cont_vars), data)\n                remover = Remove(Remove.RemoveConstant)\n                cont_data = remover(cont_data)\n                if remover.attr_results[\"removed\"]:\n                    self.Information.removed_cons_feat()\n                if len(cont_data.domain.attributes) < 2:\n                    self.Warning.not_enough_vars()\n                else:\n                    self.cont_data = SklImpute()(cont_data)\n        self.set_feature_model()\n        self.openContext(self.cont_data)\n        self.apply()\n        self.vizrank.button.setEnabled(self.cont_data is not None)\n\n    def set_feature_model(self):\n        self.feature_model.set_domain(\n            self.cont_data.domain if self.cont_data else None)\n        data = self.data\n        if self.cont_data and data.domain.has_continuous_class:\n            self.feature = self.cont_data.domain[data.domain.class_var.name]\n        else:\n            self.feature = None\n\n    def apply(self):\n        self.vizrank.initialize()\n        if self.cont_data is not None:\n            # this triggers self.commit() by changing vizrank selection\n            self.vizrank.toggle()\n        else:\n            self.commit()\n\n    def commit(self):\n        self.Outputs.data.send(self.data)\n\n        if self.data is None or self.cont_data is None:\n            self.Outputs.features.send(None)\n            self.Outputs.correlations.send(None)\n            return\n\n        attrs = [ContinuousVariable(\"Correlation\"),\n                 ContinuousVariable(\"uncorrected p\"),\n                 ContinuousVariable(\"FDR\")]\n        metas = [StringVariable(\"Feature 1\"), StringVariable(\"Feature 2\")]\n        domain = Domain(attrs, metas=metas)\n        model = self.vizrank.rank_model\n        count = model.rowCount()\n        index = model.index\n        corr = np.array([float(index(row, 0).data())\n                         for row in range(count)])\n        p = np.array([index(row, 0).data(CorrelationRank.PValRole)\n                      for row in range(count)])\n        fdr = FDR(p)\n        x = np.vstack((corr, p, fdr)).T\n        # pylint: disable=protected-access\n        m = np.array([[a.name\n                       for a in index(row, 0).data(CorrelationRank._AttrRole)]\n                      for row in range(count)], dtype=object)\n        corr_table = Table(domain, x, metas=m)\n        corr_table.name = \"Correlations\"\n\n        # data has been imputed; send original attributes\n        self.Outputs.features.send(AttributeList(\n            [self.data.domain[var.name] for var in self.selection]))\n        self.Outputs.correlations.send(corr_table)\n\n    def send_report(self):\n        self.report_table(CorrelationType.items()[self.correlation_type],\n                          self.vizrank.rank_table)\n\n    @classmethod\n    def migrate_context(cls, context, version):\n        if version < 2:\n            sel = context.values[\"selection\"]\n            context.values[\"selection\"] = [(var.name, vartype(var))\n                                           for var in sel[0]]\n        if version < 3:\n            sel = context.values[\"selection\"]\n            context.values[\"selection\"] = ([(name, vtype + 100)\n                                            for name, vtype in sel], -3)\n\n\nif __name__ == \"__main__\":  # pragma: no cover\n    WidgetPreview(OWCorrelations).run(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 ---
[ISSUE]
**Title:** KMeansCorrelationHeuristic Fails After scikit-learn 1.4 Update Due to `n_init` Default Change

**Description:**
After updating to scikit-learn version 1.4, the `KMeansCorrelationHeuristic` is producing unexpected clustering results. This issue arises because the default value for the `n_init` parameter in KMeans has changed from `10` to `'auto'`. As a result, tests that verify the attribute clusters are failing since the clustering behavior differs from the expected outcome.

**Example Code:**
```python
def test_get_clusters_of_attributes():
    heuristic = KMeansCorrelationHeuristic(data)
    clusters = heuristic.get_clusters_of_attributes()
    result = sorted([c.instances for c in clusters])
    print(result)
    # Expected: [[0], [1, 2, 3, 4, 5, 6, 7], [8]]
    # Actual: [[0, 3, 5], [1, 2, 6, 7], [4, 8]]
```

**Expected Behavior:**
The `get_clusters_of_attributes` method should consistently group attributes into the predefined clusters, such as `[[0], [1, 2, 3, 4, 5, 6, 7], [8]]`, regardless of changes in external library defaults.

**Actual Behavior:**
With the `n_init` parameter set to `'auto'`, the clustering results have changed to `[[0, 3, 5], [1, 2, 6, 7], [4, 8]]`, causing the tests to fail because the clusters no longer match the expected groupings.

[/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/data/owcorrelations.py
"""
Correlations widget
"""
from enum import IntEnum
from operator import attrgetter
from types import SimpleNamespace
from itertools import combinations, groupby, chain

import numpy as np
from scipy.stats import spearmanr, pearsonr
from sklearn.cluster import KMeans

from AnyQt.QtCore import Qt, QItemSelectionModel, QItemSelection, \
    QSize, pyqtSignal as Signal
from AnyQt.QtGui import QStandardItem
from AnyQt.QtWidgets import QHeaderView

from Orange.data import Table, Domain, ContinuousVariable, StringVariable
from Orange.preprocess import SklImpute, Normalize, Remove
from Orange.statistics.util import FDR
from Orange.widgets import gui
from Orange.widgets.settings import Setting, ContextSetting, \
    DomainContextHandler
from Orange.widgets.utils import vartype
from Orange.widgets.utils.itemmodels import DomainModel
from Orange.widgets.utils.signals import Input, Output
from Orange.widgets.utils.widgetpreview import WidgetPreview
from Orange.widgets.visualize.utils import VizRankDialogAttrPair
from Orange.widgets.widget import OWWidget, AttributeList, Msg

NAN = 2
SIZE_LIMIT = 1000000


class CorrelationType(IntEnum):
    """
    Correlation type enumerator. Possible correlations: Pearson, Spearman.
    """
    PEARSON, SPEARMAN = 0, 1

    @staticmethod
    def items():
        """
        Texts for correlation types. Can be used in gui controls (eg. combobox).
        """
        return ["Pearson correlation", "Spearman correlation"]


class Cluster(SimpleNamespace):
    instances = None  # type: Optional[List]
    centroid = None  # type: Optional[np.ndarray]


class KMeansCorrelationHeuristic:
    """
    Heuristic to obtain the most promising attribute pairs, when there are too
    many attributes to calculate correlations for all possible pairs.
    """
    def __init__(self, data):
        self.n_attributes = len(data.domain.attributes)
        self.data = data
        self.clusters = None
        self.n_clusters = int(np.sqrt(self.n_attributes))

    def get_clusters_of_attributes(self):
        """
        Generates groupes of attribute IDs, grouped by cluster. Clusters are
        obtained by KMeans algorithm.

        :return: generator of attributes grouped by cluster
        """
        data = Normalize()(self.data).X.T
        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)
        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])
        return [Cluster(instances=list(pair[1] for pair in group),
                        centroid=kmeans.cluster_centers_[l])
                for l, group in groupby(labels_attrs, key=lambda x: x[0])]

    def get_states(self, initial_state):
        """
        Generates states (attribute pairs) - the most promising first, i.e.
        states within clusters, following by states among clusters.

        :param initial_state: initial state; None if this is the first call
        :return: generator of tuples of states
        """
        if self.clusters is None:
            self.clusters = self.get_clusters_of_attributes()
        clusters = self.clusters

        # combinations within clusters
        states0 = chain.from_iterable(combinations(cluster.instances, 2)
                                      for cluster in clusters)
        if self.n_clusters == 1:
            return states0

        # combinations among clusters - closest clusters first
        centroids = [c.centroid for c in clusters]
        centroids_combs = np.array(list(combinations(centroids, 2)))
        distances = np.linalg.norm((centroids_combs[:, 0] -
                                    centroids_combs[:, 1]), axis=1)
        cluster_combs = list(combinations(range(len(clusters)), 2))
        states = ((min((c1, c2)), max((c1, c2))) for i in np.argsort(distances)
                  for c1 in clusters[cluster_combs[i][0]].instances
                  for c2 in clusters[cluster_combs[i][1]].instances)
        states = chain(states0, states)

        if initial_state is not None:
            while next(states) != initial_state:
                pass
            return chain([initial_state], states)
        return states


class CorrelationRank(VizRankDialogAttrPair):
    """
    Correlations rank widget.
    """
    threadStopped = Signal()
    PValRole = next(gui.OrangeUserRole)

    def __init__(self, *args):
        super().__init__(*args)
        self.heuristic = None
        self.use_heuristic = False
        self.sel_feature_index = None

    def initialize(self):
        super().initialize()
        data = self.master.cont_data
        self.attrs = data and data.domain.attributes
        self.model_proxy.setFilterKeyColumn(-1)
        self.heuristic = None
        self.use_heuristic = False
        if self.master.feature is not None:
            self.sel_feature_index = data.domain.index(self.master.feature)
        else:
            self.sel_feature_index = None
        if data:
            # use heuristic if data is too big
            self.use_heuristic = len(data) * len(self.attrs) ** 2 > SIZE_LIMIT \
                and self.sel_feature_index is None
            if self.use_heuristic:
                self.heuristic = KMeansCorrelationHeuristic(data)

    def compute_score(self, state):
        (attr1, attr2), corr_type = state, self.master.correlation_type
        data = self.master.cont_data.X
        corr = pearsonr if corr_type == CorrelationType.PEARSON else spearmanr
        r, p_value = corr(data[:, attr1], data[:, attr2])
        return -abs(r) if not np.isnan(r) else NAN, r, p_value

    def row_for_state(self, score, state):
        attrs = sorted((self.attrs[x] for x in state), key=attrgetter("name"))
        attr_items = []
        for attr in attrs:
            item = QStandardItem(attr.name)
            item.setData(attrs, self._AttrRole)
            item.setData(Qt.AlignLeft + Qt.AlignCenter, Qt.TextAlignmentRole)
            item.setToolTip(attr.name)
            attr_items.append(item)
        correlation_item = QStandardItem("{:+.3f}".format(score[1]))
        correlation_item.setData(score[2], self.PValRole)
        correlation_item.setData(attrs, self._AttrRole)
        correlation_item.setData(
            self.NEGATIVE_COLOR if score[1] < 0 else self.POSITIVE_COLOR,
            gui.TableBarItem.BarColorRole)
        return [correlation_item] + attr_items

    def check_preconditions(self):
        return self.master.cont_data is not None

    def iterate_states(self, initial_state):
        if self.sel_feature_index is not None:
            return self.iterate_states_by_feature()
        elif self.use_heuristic:
            return self.heuristic.get_states(initial_state)
        else:
            return super().iterate_states(initial_state)

    def iterate_states_by_feature(self):
        for j in range(len(self.attrs)):
            if j != self.sel_feature_index:
                yield self.sel_feature_index, j

    def state_count(self):
        n = len(self.attrs)
        return n * (n - 1) / 2 if self.sel_feature_index is None else n - 1

    @staticmethod
    def bar_length(score):
        return abs(score[1])

    def stopped(self):
        self.threadStopped.emit()
        header = self.rank_table.horizontalHeader()
        header.setSectionResizeMode(1, QHeaderView.Stretch)

    def start(self, task, *args, **kwargs):
        self._set_empty_status()
        super().start(task, *args, **kwargs)
        self.__set_state_busy()

    def cancel(self):
        super().cancel()
        self.__set_state_ready()

    def _connect_signals(self, state):
        super()._connect_signals(state)
        state.progress_changed.connect(self.master.progressBarSet)
        state.status_changed.connect(self.master.setStatusMessage)

    def _disconnect_signals(self, state):
        super()._disconnect_signals(state)
        state.progress_changed.disconnect(self.master.progressBarSet)
        state.status_changed.disconnect(self.master.setStatusMessage)

    def _on_task_done(self, future):
        super()._on_task_done(future)
        self.__set_state_ready()

    def __set_state_ready(self):
        self._set_empty_status()
        self.master.setBlocking(False)

    def __set_state_busy(self):
        self.master.progressBarInit()
        self.master.setBlocking(True)

    def _set_empty_status(self):
        self.master.progressBarFinished()
        self.master.setStatusMessage("")


class OWCorrelations(OWWidget):
    name = "Correlations"
    description = "Compute all pairwise attribute correlations."
    icon = "icons/Correlations.svg"
    priority = 1106
    category = "Unsupervised"

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

    class Outputs:
        data = Output("Data", Table)
        features = Output("Features", AttributeList)
        correlations = Output("Correlations", Table)

    want_main_area = False
    want_control_area = True

    correlation_type: int

    settings_version = 3
    settingsHandler = DomainContextHandler()
    selection = ContextSetting([])
    feature = ContextSetting(None)
    correlation_type = Setting(0)

    class Information(OWWidget.Information):
        removed_cons_feat = Msg("Constant features have been removed.")

    class Warning(OWWidget.Warning):
        not_enough_vars = Msg("At least two numeric features are needed.")
        not_enough_inst = Msg("At least two instances are needed.")

    def __init__(self):
        super().__init__()
        self.data = None  # type: Table
        self.cont_data = None  # type: Table

        # GUI
        box = gui.vBox(self.controlArea)
        self.correlation_combo = gui.comboBox(
            box, self, "correlation_type", items=CorrelationType.items(),
            orientation=Qt.Horizontal, callback=self._correlation_combo_changed
        )

        self.feature_model = DomainModel(
            order=DomainModel.ATTRIBUTES, separators=False,
            placeholder="(All combinations)", valid_types=ContinuousVariable)
        gui.comboBox(
            box, self, "feature", callback=self._feature_combo_changed,
            model=self.feature_model, searchable=True
        )

        self.vizrank, _ = CorrelationRank.add_vizrank(
            None, self, None, self._vizrank_selection_changed)
        self.vizrank.button.setEnabled(False)
        self.vizrank.threadStopped.connect(self._vizrank_stopped)

        gui.separator(box)
        box.layout().addWidget(self.vizrank.filter)
        box.layout().addWidget(self.vizrank.rank_table)

        button_box = gui.hBox(self.buttonsArea)
        button_box.layout().addWidget(self.vizrank.button)

    @staticmethod
    def sizeHint():
        return QSize(350, 400)

    def _correlation_combo_changed(self):
        self.apply()

    def _feature_combo_changed(self):
        self.apply()

    def _vizrank_selection_changed(self, *args):
        self.selection = list(args)
        self.commit()

    def _vizrank_stopped(self):
        self._vizrank_select()

    def _vizrank_select(self):
        model = self.vizrank.rank_table.model()
        if not model.rowCount():
            return
        selection = QItemSelection()

        # This flag is needed because data in the model could be
        # filtered by a feature and therefore selection could not be found
        selection_in_model = False
        if self.selection:
            sel_names = sorted(var.name for var in self.selection)
            for i in range(model.rowCount()):
                # pylint: disable=protected-access
                names = sorted(x.name for x in model.data(
                    model.index(i, 0), CorrelationRank._AttrRole))
                if names == sel_names:
                    selection.select(model.index(i, 0),
                                     model.index(i, model.columnCount() - 1))
                    selection_in_model = True
                    break
        if not selection_in_model:
            selection.select(model.index(0, 0),
                             model.index(0, model.columnCount() - 1))
        self.vizrank.rank_table.selectionModel().select(
            selection, QItemSelectionModel.ClearAndSelect)

    @Inputs.data
    def set_data(self, data):
        self.closeContext()
        self.clear_messages()
        self.data = data
        self.cont_data = None
        self.selection = []
        if data is not None:
            if len(data) < 2:
                self.Warning.not_enough_inst()
            else:
                domain = data.domain
                cont_vars = [a for a in domain.class_vars + domain.metas +
                             domain.attributes if a.is_continuous]
                cont_data = Table.from_table(Domain(cont_vars), data)
                remover = Remove(Remove.RemoveConstant)
                cont_data = remover(cont_data)
                if remover.attr_results["removed"]:
                    self.Information.removed_cons_feat()
                if len(cont_data.domain.attributes) < 2:
                    self.Warning.not_enough_vars()
                else:
                    self.cont_data = SklImpute()(cont_data)
        self.set_feature_model()
        self.openContext(self.cont_data)
        self.apply()
        self.vizrank.button.setEnabled(self.cont_data is not None)

    def set_feature_model(self):
        self.feature_model.set_domain(
            self.cont_data.domain if self.cont_data else None)
        data = self.data
        if self.cont_data and data.domain.has_continuous_class:
            self.feature = self.cont_data.domain[data.domain.class_var.name]
        else:
            self.feature = None

    def apply(self):
        self.vizrank.initialize()
        if self.cont_data is not None:
            # this triggers self.commit() by changing vizrank selection
            self.vizrank.toggle()
        else:
            self.commit()

    def commit(self):
        self.Outputs.data.send(self.data)

        if self.data is None or self.cont_data is None:
            self.Outputs.features.send(None)
            self.Outputs.correlations.send(None)
            return

        attrs = [ContinuousVariable("Correlation"),
                 ContinuousVariable("uncorrected p"),
                 ContinuousVariable("FDR")]
        metas = [StringVariable("Feature 1"), StringVariable("Feature 2")]
        domain = Domain(attrs, metas=metas)
        model = self.vizrank.rank_model
        count = model.rowCount()
        index = model.index
        corr = np.array([float(index(row, 0).data())
                         for row in range(count)])
        p = np.array([index(row, 0).data(CorrelationRank.PValRole)
                      for row in range(count)])
        fdr = FDR(p)
        x = np.vstack((corr, p, fdr)).T
        # pylint: disable=protected-access
        m = np.array([[a.name
                       for a in index(row, 0).data(CorrelationRank._AttrRole)]
                      for row in range(count)], dtype=object)
        corr_table = Table(domain, x, metas=m)
        corr_table.name = "Correlations"

        # data has been imputed; send original attributes
        self.Outputs.features.send(AttributeList(
            [self.data.domain[var.name] for var in self.selection]))
        self.Outputs.correlations.send(corr_table)

    def send_report(self):
        self.report_table(CorrelationType.items()[self.correlation_type],
                          self.vizrank.rank_table)

    @classmethod
    def migrate_context(cls, context, version):
        if version < 2:
            sel = context.values["selection"]
            context.values["selection"] = [(var.name, vartype(var))
                                           for var in sel[0]]
        if version < 3:
            sel = context.values["selection"]
            context.values["selection"] = ([(name, vtype + 100)
                                            for name, vtype in sel], -3)


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