{
    "file_diffs": [
        {
            "old_file_content": "import random\nfrom itertools import takewhile\nfrom operator import itemgetter\n\nimport numpy as np\n\n\nimport Orange\nfrom Orange.util import Reprable\nfrom Orange.preprocess.score import ANOVA, GainRatio, \\\n    UnivariateLinearRegression\n\n__all__ = [\"SelectBestFeatures\", \"SelectRandomFeatures\"]\n\n\nclass SelectBestFeatures(Reprable):\n    \"\"\"\n    A feature selector that builds a new dataset consisting of either the top\n    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`\n    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features\n    are scored using the provided feature scoring `method`. By default it is\n    assumed that feature importance decreases with decreasing scores.\n\n    If both `k` and `threshold` are set, only features satisfying both\n    conditions will be selected.\n\n    If `method` is not set, it is automatically selected when presented with\n    the dataset. Datasets with both continuous and discrete features are\n    scored using a method suitable for the majority of features.\n\n    Parameters\n    ----------\n    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer\n        Univariate feature scoring method.\n\n    k : int or float\n        The number or propotion of top features to select.\n\n    threshold : float\n        A threshold that a feature should meet according to the provided method.\n\n    decreasing : boolean\n        The order of feature importance when sorted from the most to the least\n        important feature.\n    \"\"\"\n\n    def __init__(self, method=None, k=None, threshold=None, decreasing=True):\n        self.method = method\n        self.k = k\n        self.threshold = threshold\n        self.decreasing = decreasing\n\n    def __call__(self, data):\n        n_attrs = len(data.domain.attributes)\n        if isinstance(self.k, float):\n            effective_k = np.round(self.k * n_attrs).astype(int) or 1\n        else:\n            effective_k = self.k\n\n        method = self.method\n        # select default method according to the provided data\n        if method is None:\n            autoMethod = True\n            discr_ratio = (sum(a.is_discrete\n                               for a in data.domain.attributes)\n                           / len(data.domain.attributes))\n            if data.domain.has_discrete_class:\n                if discr_ratio >= 0.5:\n                    method = GainRatio()\n                else:\n                    method = ANOVA()\n            else:\n                method = UnivariateLinearRegression()\n\n        features = data.domain.attributes\n        try:\n            scores = method(data)\n        except ValueError:\n            scores = self.score_only_nice_features(data, method)\n        best = sorted(zip(scores, features), key=itemgetter(0),\n                      reverse=self.decreasing)\n        if self.k:\n            best = best[:effective_k]\n        if self.threshold:\n            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else\n                    (lambda x: x[0] <= self.threshold))\n            best = takewhile(pred, best)\n\n        domain = Orange.data.Domain([f for s, f in best],\n                                    data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def score_only_nice_features(self, data, method):\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes])\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n        all_scores[mask] = scores\n        return all_scores\n\n\nclass SelectRandomFeatures(Reprable):\n    \"\"\"\n    A feature selector that selects random `k` features from an input\n    dataset and returns a dataset with selected features. Parameter\n    `k` is either an integer (number of feature) or float (from 0.0 to\n    1.0, proportion of retained features).\n\n    Parameters\n    ----------\n\n    k : int or float (default = 0.1)\n        The number or proportion of features to retain.\n    \"\"\"\n\n    def __init__(self, k=0.1):\n        self.k = k\n\n    def __call__(self, data):\n        if isinstance(self.k, float):\n            effective_k = int(len(data.domain.attributes) * self.k)\n        else:\n            effective_k = self.k\n\n        domain = Orange.data.Domain(\n            random.sample(data.domain.attributes,\n                          min(effective_k, len(data.domain.attributes))),\n            data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n",
            "new_file_content": "import random\nfrom itertools import takewhile\nfrom operator import itemgetter\n\nimport numpy as np\n\n\nimport Orange\nfrom Orange.util import Reprable\nfrom Orange.preprocess.score import ANOVA, GainRatio, \\\n    UnivariateLinearRegression\n\n__all__ = [\"SelectBestFeatures\", \"SelectRandomFeatures\"]\n\n\nclass SelectBestFeatures(Reprable):\n    \"\"\"\n    A feature selector that builds a new dataset consisting of either the top\n    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`\n    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features\n    are scored using the provided feature scoring `method`. By default it is\n    assumed that feature importance decreases with decreasing scores.\n\n    If both `k` and `threshold` are set, only features satisfying both\n    conditions will be selected.\n\n    If `method` is not set, it is automatically selected when presented with\n    the dataset. Datasets with both continuous and discrete features are\n    scored using a method suitable for the majority of features.\n\n    Parameters\n    ----------\n    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer\n        Univariate feature scoring method.\n\n    k : int or float\n        The number or propotion of top features to select.\n\n    threshold : float\n        A threshold that a feature should meet according to the provided method.\n\n    decreasing : boolean\n        The order of feature importance when sorted from the most to the least\n        important feature.\n    \"\"\"\n\n    def __init__(self, method=None, k=None, threshold=None, decreasing=True):\n        self.method = method\n        self.k = k\n        self.threshold = threshold\n        self.decreasing = decreasing\n\n    def __call__(self, data):\n        n_attrs = len(data.domain.attributes)\n        if isinstance(self.k, float):\n            effective_k = np.round(self.k * n_attrs).astype(int) or 1\n        else:\n            effective_k = self.k\n\n        method = self.method\n        # select default method according to the provided data\n        if method is None:\n            autoMethod = True\n            discr_ratio = (sum(a.is_discrete\n                               for a in data.domain.attributes)\n                           / len(data.domain.attributes))\n            if data.domain.has_discrete_class:\n                if discr_ratio >= 0.5:\n                    method = GainRatio()\n                else:\n                    method = ANOVA()\n            else:\n                method = UnivariateLinearRegression()\n\n        features = data.domain.attributes\n        try:\n            scores = method(data)\n        except ValueError:\n            scores = self.score_only_nice_features(data, method)\n        best = sorted(zip(scores, features), key=itemgetter(0),\n                      reverse=self.decreasing)\n        if self.k:\n            best = best[:effective_k]\n        if self.threshold:\n            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else\n                    (lambda x: x[0] <= self.threshold))\n            best = takewhile(pred, best)\n\n        domain = Orange.data.Domain([f for s, f in best],\n                                    data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def score_only_nice_features(self, data, method):\n        # dtype must be defined because array can be empty\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes], dtype=np.bool)\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n        all_scores[mask] = scores\n        return all_scores\n\n\nclass SelectRandomFeatures(Reprable):\n    \"\"\"\n    A feature selector that selects random `k` features from an input\n    dataset and returns a dataset with selected features. Parameter\n    `k` is either an integer (number of feature) or float (from 0.0 to\n    1.0, proportion of retained features).\n\n    Parameters\n    ----------\n\n    k : int or float (default = 0.1)\n        The number or proportion of features to retain.\n    \"\"\"\n\n    def __init__(self, k=0.1):\n        self.k = k\n\n    def __call__(self, data):\n        if isinstance(self.k, float):\n            effective_k = int(len(data.domain.attributes) * self.k)\n        else:\n            effective_k = self.k\n\n        domain = Orange.data.Domain(\n            random.sample(data.domain.attributes,\n                          min(effective_k, len(data.domain.attributes))),\n            data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n",
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                                "content": "    def score_only_nice_features(self, data, method):",
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                            },
                            {
                                "content": "        # dtype must be defined because array can be empty",
                                "type": "added"
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                            {
                                "content": "        mask = np.array([isinstance(a, method.feature_type)",
                                "type": "context"
                            },
                            {
                                "content": "                         for a in data.domain.attributes])",
                                "type": "deleted"
                            },
                            {
                                "content": "                         for a in data.domain.attributes], dtype=np.bool)",
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                                "content": "        features = [f for f in data.domain.attributes",
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                            },
                            {
                                "content": "                    if isinstance(f, method.feature_type)]",
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                        {
                            "file_name": "Orange/preprocess/fss.py",
                            "type": "method",
                            "name": "SelectBestFeatures.score_only_nice_features",
                            "content": "class SelectBestFeatures(Reprable):\n    \"\"\"\n    A feature selector that builds a new dataset consisting of either the top\n    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`\n    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features\n    are scored using the provided feature scoring `method`. By default it is\n    assumed that feature importance decreases with decreasing scores.\n\n    If both `k` and `threshold` are set, only features satisfying both\n    conditions will be selected.\n\n    If `method` is not set, it is automatically selected when presented with\n    the dataset. Datasets with both continuous and discrete features are\n    scored using a method suitable for the majority of features.\n\n    Parameters\n    ----------\n    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer\n        Univariate feature scoring method.\n\n    k : int or float\n        The number or propotion of top features to select.\n\n    threshold : float\n        A threshold that a feature should meet according to the provided method.\n\n    decreasing : boolean\n        The order of feature importance when sorted from the most to the least\n        important feature.\n    \"\"\"\n\n    def __init__(self, method=None, k=None, threshold=None, decreasing=True):\n        self.method = method\n        self.k = k\n        self.threshold = threshold\n        self.decreasing = decreasing\n\n    def __call__(self, data):\n        n_attrs = len(data.domain.attributes)\n        if isinstance(self.k, float):\n            effective_k = np.round(self.k * n_attrs).astype(int) or 1\n        else:\n            effective_k = self.k\n\n        method = self.method\n        # select default method according to the provided data\n        if method is None:\n            autoMethod = True\n            discr_ratio = (sum(a.is_discrete\n                               for a in data.domain.attributes)\n                           / len(data.domain.attributes))\n            if data.domain.has_discrete_class:\n                if discr_ratio >= 0.5:\n                    method = GainRatio()\n                else:\n                    method = ANOVA()\n            else:\n                method = UnivariateLinearRegression()\n\n        features = data.domain.attributes\n        try:\n            scores = method(data)\n        except ValueError:\n            scores = self.score_only_nice_features(data, method)\n        best = sorted(zip(scores, features), key=itemgetter(0),\n                      reverse=self.decreasing)\n        if self.k:\n            best = best[:effective_k]\n        if self.threshold:\n            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else\n                    (lambda x: x[0] <= self.threshold))\n            best = takewhile(pred, best)\n\n        domain = Orange.data.Domain([f for s, f in best],\n                                    data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def score_only_nice_features(self, data, method):\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes])\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n        all_scores[mask] = scores\n        return all_scores",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 93,
                            "end_lineno": 102,
                            "parent": {
                                "file_name": "Orange/preprocess/fss.py",
                                "type": "class",
                                "name": "SelectBestFeatures",
                                "content": "class SelectBestFeatures(Reprable):\n    \"\"\"\n    A feature selector that builds a new dataset consisting of either the top\n    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`\n    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features\n    are scored using the provided feature scoring `method`. By default it is\n    assumed that feature importance decreases with decreasing scores.\n\n    If both `k` and `threshold` are set, only features satisfying both\n    conditions will be selected.\n\n    If `method` is not set, it is automatically selected when presented with\n    the dataset. Datasets with both continuous and discrete features are\n    scored using a method suitable for the majority of features.\n\n    Parameters\n    ----------\n    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer\n        Univariate feature scoring method.\n\n    k : int or float\n        The number or propotion of top features to select.\n\n    threshold : float\n        A threshold that a feature should meet according to the provided method.\n\n    decreasing : boolean\n        The order of feature importance when sorted from the most to the least\n        important feature.\n    \"\"\"\n\n    def __init__(self, method=None, k=None, threshold=None, decreasing=True):\n        self.method = method\n        self.k = k\n        self.threshold = threshold\n        self.decreasing = decreasing\n\n    def __call__(self, data):\n        n_attrs = len(data.domain.attributes)\n        if isinstance(self.k, float):\n            effective_k = np.round(self.k * n_attrs).astype(int) or 1\n        else:\n            effective_k = self.k\n\n        method = self.method\n        # select default method according to the provided data\n        if method is None:\n            autoMethod = True\n            discr_ratio = (sum(a.is_discrete\n                               for a in data.domain.attributes)\n                           / len(data.domain.attributes))\n            if data.domain.has_discrete_class:\n                if discr_ratio >= 0.5:\n                    method = GainRatio()\n                else:\n                    method = ANOVA()\n            else:\n                method = UnivariateLinearRegression()\n\n        features = data.domain.attributes\n        try:\n            scores = method(data)\n        except ValueError:\n            scores = self.score_only_nice_features(data, method)\n        best = sorted(zip(scores, features), key=itemgetter(0),\n                      reverse=self.decreasing)\n        if self.k:\n            best = best[:effective_k]\n        if self.threshold:\n            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else\n                    (lambda x: x[0] <= self.threshold))\n            best = takewhile(pred, best)\n\n        domain = Orange.data.Domain([f for s, f in best],\n                                    data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def score_only_nice_features(self, data, method):\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes])\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n        all_scores[mask] = scores\n        return all_scores",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 16,
                                "end_lineno": 102,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/preprocess/fss.py",
                            "type": "class",
                            "name": "SelectBestFeatures",
                            "content": "class SelectBestFeatures(Reprable):\n    \"\"\"\n    A feature selector that builds a new dataset consisting of either the top\n    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`\n    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features\n    are scored using the provided feature scoring `method`. By default it is\n    assumed that feature importance decreases with decreasing scores.\n\n    If both `k` and `threshold` are set, only features satisfying both\n    conditions will be selected.\n\n    If `method` is not set, it is automatically selected when presented with\n    the dataset. Datasets with both continuous and discrete features are\n    scored using a method suitable for the majority of features.\n\n    Parameters\n    ----------\n    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer\n        Univariate feature scoring method.\n\n    k : int or float\n        The number or propotion of top features to select.\n\n    threshold : float\n        A threshold that a feature should meet according to the provided method.\n\n    decreasing : boolean\n        The order of feature importance when sorted from the most to the least\n        important feature.\n    \"\"\"\n\n    def __init__(self, method=None, k=None, threshold=None, decreasing=True):\n        self.method = method\n        self.k = k\n        self.threshold = threshold\n        self.decreasing = decreasing\n\n    def __call__(self, data):\n        n_attrs = len(data.domain.attributes)\n        if isinstance(self.k, float):\n            effective_k = np.round(self.k * n_attrs).astype(int) or 1\n        else:\n            effective_k = self.k\n\n        method = self.method\n        # select default method according to the provided data\n        if method is None:\n            autoMethod = True\n            discr_ratio = (sum(a.is_discrete\n                               for a in data.domain.attributes)\n                           / len(data.domain.attributes))\n            if data.domain.has_discrete_class:\n                if discr_ratio >= 0.5:\n                    method = GainRatio()\n                else:\n                    method = ANOVA()\n            else:\n                method = UnivariateLinearRegression()\n\n        features = data.domain.attributes\n        try:\n            scores = method(data)\n        except ValueError:\n            scores = self.score_only_nice_features(data, method)\n        best = sorted(zip(scores, features), key=itemgetter(0),\n                      reverse=self.decreasing)\n        if self.k:\n            best = best[:effective_k]\n        if self.threshold:\n            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else\n                    (lambda x: x[0] <= self.threshold))\n            best = takewhile(pred, best)\n\n        domain = Orange.data.Domain([f for s, f in best],\n                                    data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def score_only_nice_features(self, data, method):\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes])\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n        all_scores[mask] = scores\n        return all_scores",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 16,
                            "end_lineno": 102,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "",
            "new_file_content": "import unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import Domain, Table, DiscreteVariable, ContinuousVariable\nfrom Orange.preprocess import fss\n\n\nclass SelectBestFeaturesTest(unittest.TestCase):\n    def test_no_nice_features(self):\n        method = Mock()\n        method.feature_type = DiscreteVariable\n        selector = fss.SelectBestFeatures(method, 5)\n\n        domain = Domain([])\n        data = Table.from_numpy(domain, np.zeros((100, 0)))\n        selection = selector.score_only_nice_features(data, method)\n        self.assertEqual(selection.size, 0)\n        method.assert_not_called()\n\n        domain = Domain([ContinuousVariable(\"x\")])\n        data = Table.from_numpy(domain, np.zeros((100, 1)))\n        selector.decreasing = True\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('-inf')])\n        method.assert_not_called()\n\n        selector.decreasing = False\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('inf')])\n        method.assert_not_called()\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/preprocess/tests/test_fss.py"
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                    "line_group": {
                        "all_lines": [
                            {
                                "content": "import unittest",
                                "type": "added"
                            },
                            {
                                "content": "from unittest.mock import Mock",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "import numpy as np",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.data import Domain, Table, DiscreteVariable, ContinuousVariable",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.preprocess import fss",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "class SelectBestFeaturesTest(unittest.TestCase):",
                                "type": "added"
                            },
                            {
                                "content": "    def test_no_nice_features(self):",
                                "type": "added"
                            },
                            {
                                "content": "        method = Mock()",
                                "type": "added"
                            },
                            {
                                "content": "        method.feature_type = DiscreteVariable",
                                "type": "added"
                            },
                            {
                                "content": "        selector = fss.SelectBestFeatures(method, 5)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        domain = Domain([])",
                                "type": "added"
                            },
                            {
                                "content": "        data = Table.from_numpy(domain, np.zeros((100, 0)))",
                                "type": "added"
                            },
                            {
                                "content": "        selection = selector.score_only_nice_features(data, method)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(selection.size, 0)",
                                "type": "added"
                            },
                            {
                                "content": "        method.assert_not_called()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        domain = Domain([ContinuousVariable(\"x\")])",
                                "type": "added"
                            },
                            {
                                "content": "        data = Table.from_numpy(domain, np.zeros((100, 1)))",
                                "type": "added"
                            },
                            {
                                "content": "        selector.decreasing = True",
                                "type": "added"
                            },
                            {
                                "content": "        selection = selector.score_only_nice_features(data, method)",
                                "type": "added"
                            },
                            {
                                "content": "        np.testing.assert_equal(selection, [float('-inf')])",
                                "type": "added"
                            },
                            {
                                "content": "        method.assert_not_called()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        selector.decreasing = False",
                                "type": "added"
                            },
                            {
                                "content": "        selection = selector.score_only_nice_features(data, method)",
                                "type": "added"
                            },
                            {
                                "content": "        np.testing.assert_equal(selection, [float('inf')])",
                                "type": "added"
                            },
                            {
                                "content": "        method.assert_not_called()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "added"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "method",
                            "name": "SelectBestFeaturesTest.test_no_nice_features",
                            "content": "class SelectBestFeaturesTest(unittest.TestCase):\n    def test_no_nice_features(self):\n        method = Mock()\n        method.feature_type = DiscreteVariable\n        selector = fss.SelectBestFeatures(method, 5)\n\n        domain = Domain([])\n        data = Table.from_numpy(domain, np.zeros((100, 0)))\n        selection = selector.score_only_nice_features(data, method)\n        self.assertEqual(selection.size, 0)\n        method.assert_not_called()\n\n        domain = Domain([ContinuousVariable(\"x\")])\n        data = Table.from_numpy(domain, np.zeros((100, 1)))\n        selector.decreasing = True\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('-inf')])\n        method.assert_not_called()\n\n        selector.decreasing = False\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('inf')])\n        method.assert_not_called()",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 11,
                            "end_lineno": 32,
                            "parent": {
                                "file_name": "Orange/preprocess/tests/test_fss.py",
                                "type": "class",
                                "name": "SelectBestFeaturesTest",
                                "content": "class SelectBestFeaturesTest(unittest.TestCase):\n    def test_no_nice_features(self):\n        method = Mock()\n        method.feature_type = DiscreteVariable\n        selector = fss.SelectBestFeatures(method, 5)\n\n        domain = Domain([])\n        data = Table.from_numpy(domain, np.zeros((100, 0)))\n        selection = selector.score_only_nice_features(data, method)\n        self.assertEqual(selection.size, 0)\n        method.assert_not_called()\n\n        domain = Domain([ContinuousVariable(\"x\")])\n        data = Table.from_numpy(domain, np.zeros((100, 1)))\n        selector.decreasing = True\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('-inf')])\n        method.assert_not_called()\n\n        selector.decreasing = False\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('inf')])\n        method.assert_not_called()",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 10,
                                "end_lineno": 32,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "statement",
                            "name": "",
                            "content": "from unittest.mock import Mock",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 2,
                            "end_lineno": 2,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "import",
                            "name": "unittest",
                            "content": "import unittest",
                            "ast_type_str": "Import",
                            "start_lineno": 1,
                            "end_lineno": 1,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "class",
                            "name": "SelectBestFeaturesTest",
                            "content": "class SelectBestFeaturesTest(unittest.TestCase):\n    def test_no_nice_features(self):\n        method = Mock()\n        method.feature_type = DiscreteVariable\n        selector = fss.SelectBestFeatures(method, 5)\n\n        domain = Domain([])\n        data = Table.from_numpy(domain, np.zeros((100, 0)))\n        selection = selector.score_only_nice_features(data, method)\n        self.assertEqual(selection.size, 0)\n        method.assert_not_called()\n\n        domain = Domain([ContinuousVariable(\"x\")])\n        data = Table.from_numpy(domain, np.zeros((100, 1)))\n        selector.decreasing = True\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('-inf')])\n        method.assert_not_called()\n\n        selector.decreasing = False\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('inf')])\n        method.assert_not_called()",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 10,
                            "end_lineno": 32,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "import",
                            "name": "numpy",
                            "content": "import numpy as np",
                            "ast_type_str": "Import",
                            "start_lineno": 4,
                            "end_lineno": 4,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "statement",
                            "name": "",
                            "content": "if __name__ == \"__main__\":\n    unittest.main()",
                            "ast_type_str": "If",
                            "start_lineno": 35,
                            "end_lineno": 36,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.data import Domain, Table, DiscreteVariable, ContinuousVariable",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 6,
                            "end_lineno": 6,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_fss.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.preprocess import fss",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 7,
                            "end_lineno": 7,
                            "parent": null
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "89aa26514f0a1dbbf8becc1eea1b54906727b656^",
    "new_commit_hash": "89aa26514f0a1dbbf8becc1eea1b54906727b656",
    "commit_message": "Merge pull request #4207 from janezd/fss-no-nice-features\n\n[FIX] Fix crash in Preprocess' Select Relevant Features when there are no features",
    "commit_date": "2019-11-22T09:59:42+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "89aa26514f0a1dbbf8becc1eea1b54906727b656",
    "test_file_codes": [
        "import unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\n\nfrom Orange.data import Domain, Table, DiscreteVariable, ContinuousVariable\nfrom Orange.preprocess import fss\n\n\nclass SelectBestFeaturesTest(unittest.TestCase):\n    def test_no_nice_features(self):\n        method = Mock()\n        method.feature_type = DiscreteVariable\n        selector = fss.SelectBestFeatures(method, 5)\n\n        domain = Domain([])\n        data = Table.from_numpy(domain, np.zeros((100, 0)))\n        selection = selector.score_only_nice_features(data, method)\n        self.assertEqual(selection.size, 0)\n        method.assert_not_called()\n\n        domain = Domain([ContinuousVariable(\"x\")])\n        data = Table.from_numpy(domain, np.zeros((100, 1)))\n        selector.decreasing = True\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('-inf')])\n        method.assert_not_called()\n\n        selector.decreasing = False\n        selection = selector.score_only_nice_features(data, method)\n        np.testing.assert_equal(selection, [float('inf')])\n        method.assert_not_called()\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nrunning build_ext\nrunning build_src\nbuild_src\nbuilding extension \"Orange.classification._simple_tree\" sources\nbuilding extension \"Orange.classification._tree_scorers\" sources\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._contingency\" sources\nbuilding extension \"Orange.data._io\" sources\nbuilding extension \"Orange.data._variable\" sources\nbuilding extension \"Orange.distance._distance\" sources\nbuilding extension \"Orange.preprocess._discretize\" sources\nbuilding extension \"Orange.preprocess._relieff\" sources\nbuilding extension \"Orange.projection._som\" sources\nbuilding extension \"Orange.widgets.utils._grid_density\" sources\nbuild_src: building npy-pkg config files\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpzjztv8q1/tmp\ncreating /tmp/tmpzjztv8q1/tmp/tmpzjztv8q1\ncompile options: '-MMD -MF /tmp/tmpzjztv8q1/file.c.d -c'\ncc: /tmp/tmpzjztv8q1/file.c\ncustomize UnixCCompiler using build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpem0gkfe3/tmp\ncreating /tmp/tmpem0gkfe3/tmp/tmpem0gkfe3\ncompile options: '-MMD -MF /tmp/tmpem0gkfe3/file.c.d -c'\ncc: /tmp/tmpem0gkfe3/file.c\ncustomize UnixCCompiler using build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/classification/_simple_tree.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/classification/_tree_scorers.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_valuecount.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_contingency.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_io.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_variable.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/distance/_distance.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/preprocess/_discretize.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncompiling C++ sources\nC compiler: c++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\nc++: Orange/preprocess/_relieff.cpp\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/projection/_som.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncompiling C++ sources\nC compiler: c++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\nc++: Orange/widgets/utils/_grid_density.cpp\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\nrunning develop\nrunning build_scripts\nrunning egg_info\nrunning build_src\nbuild_src\nbuilding extension \"Orange.classification._simple_tree\" sources\nbuilding extension \"Orange.classification._tree_scorers\" sources\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._contingency\" sources\nbuilding extension \"Orange.data._io\" sources\nbuilding extension \"Orange.data._variable\" sources\nbuilding extension \"Orange.distance._distance\" sources\nbuilding extension \"Orange.preprocess._discretize\" sources\nbuilding extension \"Orange.preprocess._relieff\" sources\nbuilding extension \"Orange.projection._som\" sources\nbuilding extension \"Orange.widgets.utils._grid_density\" sources\nbuild_src: building npy-pkg config files\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching 'COPYING'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmp7ut9he5l/tmp\ncreating /tmp/tmp7ut9he5l/tmp/tmp7ut9he5l\ncompile options: '-MMD -MF /tmp/tmp7ut9he5l/file.c.d -c'\ncc: /tmp/tmp7ut9he5l/file.c\ncustomize UnixCCompiler using build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpth33mnvl/tmp\ncreating /tmp/tmpth33mnvl/tmp/tmpth33mnvl\ncompile options: '-MMD -MF /tmp/tmpth33mnvl/file.c.d -c'\ncc: /tmp/tmpth33mnvl/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.24.0.dev0+89aa265 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656\nProcessing dependencies for Orange3==3.24.0.dev0+89aa265\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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for setuptools==59.8.0\nBest match: setuptools 59.8.0\nAdding setuptools 59.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for scipy==1.7.3\nBest match: scipy 1.7.3\nAdding scipy 1.7.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for scikit-learn==1.0.2\nBest match: scikit-learn 1.0.2\nAdding scikit-learn 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for requests==2.31.0\nBest match: requests 2.31.0\nAdding requests 2.31.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for PyYAML==6.0.1\nBest match: PyYAML 6.0.1\nAdding PyYAML 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for pyqtgraph==0.12.4\nBest match: pyqtgraph 0.12.4\nAdding pyqtgraph 0.12.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for pip==24.0\nBest match: pip 24.0\nAdding pip 24.0 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for pandas==1.3.5\nBest match: pandas 1.3.5\nAdding pandas 1.3.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for numpy==1.17.5\nBest match: numpy 1.17.5\nAdding numpy 1.17.5 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for networkx==2.6.3\nBest match: networkx 2.6.3\nAdding networkx 2.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for matplotlib==3.5.3\nBest match: matplotlib 3.5.3\nAdding matplotlib 3.5.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for keyrings.alt==4.2.0\nBest match: keyrings.alt 4.2.0\nAdding keyrings.alt 4.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for keyring==24.1.1\nBest match: keyring 24.1.1\nAdding keyring 24.1.1 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for joblib==1.3.2\nBest match: joblib 1.3.2\nAdding joblib 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for threadpoolctl==3.1.0\nBest match: threadpoolctl 3.1.0\nAdding threadpoolctl 3.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for urllib3==2.0.7\nBest match: urllib3 2.0.7\nAdding urllib3 2.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for typing-extensions==4.7.1\nBest match: typing-extensions 4.7.1\nAdding typing-extensions 4.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for packaging==24.0\nBest match: packaging 24.0\nAdding packaging 24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for qasync==0.25.0\nBest match: qasync 0.25.0\nAdding qasync 0.25.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for importlib-metadata==6.7.0\nBest match: importlib-metadata 6.7.0\nAdding importlib-metadata 6.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for requests-cache==1.1.1\nBest match: requests-cache 1.1.1\nAdding requests-cache 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for Pillow==9.5.0\nBest match: Pillow 9.5.0\nAdding Pillow 9.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for cycler==0.11.0\nBest match: cycler 0.11.0\nAdding cycler 0.11.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for fonttools==4.38.0\nBest match: fonttools 4.38.0\nAdding fonttools 4.38.0 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for kiwisolver==1.4.5\nBest match: kiwisolver 1.4.5\nAdding kiwisolver 1.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for jaraco.classes==3.2.3\nBest match: jaraco.classes 3.2.3\nAdding jaraco.classes 3.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for importlib-resources==5.12.0\nBest match: importlib-resources 5.12.0\nAdding importlib-resources 5.12.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for zipp==3.15.0\nBest match: zipp 3.15.0\nAdding zipp 3.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for cattrs==23.1.2\nBest match: cattrs 23.1.2\nAdding cattrs 23.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for platformdirs==4.0.0\nBest match: platformdirs 4.0.0\nAdding platformdirs 4.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for attrs==24.2.0\nBest match: attrs 24.2.0\nAdding attrs 24.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for more-itertools==9.1.0\nBest match: more-itertools 9.1.0\nAdding more-itertools 9.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for cffi==1.15.1\nBest match: cffi 1.15.1\nAdding cffi 1.15.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nSearching for pycparser==2.21\nBest match: pycparser 2.21\nAdding pycparser 2.21 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.24.0.dev0+89aa265\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.7\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 44ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 146ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\nResolved 38 packages in 360ms\nInstalled 33 packages in 829ms\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.15.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + cryptography==44.0.0\n + idna==3.10\n + importlib-resources==5.12.0\n + jaraco-classes==3.2.3\n + jeepney==0.8.0\n + joblib==1.3.2\n + keyring==24.1.1\n + keyrings-alt==4.2.0\n + more-itertools==9.1.0\n + networkx==2.6.3\n + opentsne==1.0.1\n + pandas==1.3.5\n + pip==24.0\n + pycparser==2.21\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.1\n + requests==2.31.0\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 34 packages in 143ms\nInstalled 19 packages in 278ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + cattrs==23.1.2\n + commonmark==0.9.1\n + cycler==0.11.0\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + fonttools==4.38.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + pillow==9.5.0\n + platformdirs==4.0.0\n + pyparsing==3.1.4\n + pyqtgraph==0.12.4\n + qasync==0.25.0\n + requests-cache==1.1.1\n + url-normalize==1.4.3\nResolved 2 packages in 3ms\nInstalled 2 packages in 38ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nAudited 1 package in 15ms\nAudited 2 packages in 22ms\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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3115:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3115 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4619:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4619 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5396:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5396 |           for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5517:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5517 |           __pyx_t_27 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5523:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5523 |           __pyx_t_27 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7051:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7051 |         for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7741:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7741 |         for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7801:143: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7801 |               __pyx_t_17 = (((*((__pyx_t_5numpy_int32_t *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_18 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                               ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:3881:24: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3881 |           __pyx_v_decs = (__pyx_v_decs / 10.0);\n      |           ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:4070:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4070 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 221, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:480:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  480 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1301:7: note: ‘__pyx_v_attr_index’ was declared here\n 1301 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:2935:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2935 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:581:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3655:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3655 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3654:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3654 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4547:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4547 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_38, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_39, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4114:7: note: ‘__pyx_v_win_y’ was declared here\n 4114 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4536:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4536 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_36, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_37, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4113:7: note: ‘__pyx_v_win_x’ was declared here\n 4113 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4746:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4746 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4745:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4745 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3141:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3141 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:2964:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2964 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2665:7: note: ‘__pyx_v_win_y’ was declared here\n 2665 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:2953:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2953 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_25, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2664:7: note: ‘__pyx_v_win_x’ was declared here\n 2664 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5515:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5515 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5514:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5514 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.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_89aa26514f0a1dbbf8becc1eea1b54906727b656/.venv/lib/python3.7/site-packages/setuptools/command/egg_info.py:628: SetuptoolsDeprecationWarning: Custom 'build_py' does not implement 'get_data_files_without_manifest'.\nPlease extend command classes from setuptools instead of distutils.\n  SetuptoolsDeprecationWarning\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656\ncollected 1 item\n\nr2e_tests/test_1.py .                                                    [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::SelectBestFeaturesTest::test_no_nice_features\n============================== 1 passed in 1.32s ===============================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_89aa26514f0a1dbbf8becc1eea1b54906727b656\ncollected 1 item\n\nr2e_tests/test_1.py F                                                    [100%]\n\n=================================== FAILURES ===================================\n_________________ SelectBestFeaturesTest.test_no_nice_features _________________\n\nself = <r2e_tests.test_1.SelectBestFeaturesTest testMethod=test_no_nice_features>\n\n    def test_no_nice_features(self):\n        method = Mock()\n        method.feature_type = DiscreteVariable\n        selector = fss.SelectBestFeatures(method, 5)\n    \n        domain = Domain([])\n        data = Table.from_numpy(domain, np.zeros((100, 0)))\n>       selection = selector.score_only_nice_features(data, method)\n\nr2e_tests/test_1.py:18: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = SelectBestFeatures(method=<Mock id='127841476104720'>, k=5)\ndata = [[],\n [],\n [],\n [],\n [],\n ...\n], method = <Mock id='127841476104720'>\n\n    def score_only_nice_features(self, data, method):\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes])\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n>       all_scores[mask] = scores\nE       IndexError: arrays used as indices must be of integer (or boolean) type\n\nOrange/preprocess/fss.py:101: IndexError\n=========================== short test summary info ============================\nFAILED r2e_tests/test_1.py::SelectBestFeaturesTest::test_no_nice_features - I...\n============================== 1 failed in 1.27s ===============================\n",
    "old_commit_res_stderr": ""
}
Orange/preprocess/fss.py Orange/preprocess/tests/test_fss.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Crash in SelectBestFeatures When No Relevant Features Are Present

**Description:**
When attempting to select relevant features using `SelectBestFeatures`, the process crashes if there are no features that match the specified feature type. This occurs even when the dataset has no attributes.

**Example Code:**
```python
from unittest.mock import Mock
from Orange.data import Domain, Table, DiscreteVariable
from Orange.preprocess import SelectBestFeatures

# Mock feature selection method
method = Mock()
method.feature_type = DiscreteVariable

# Initialize the selector with the mock method
selector = SelectBestFeatures(method, k=5)

# Create a dataset with no attributes
domain = Domain([])
data = Table.from_numpy(domain, np.zeros((100, 0)))

# Attempt to select relevant features
selection = selector.score_only_nice_features(data, method)
```

**Expected Behavior:**
The feature selection process should handle cases with no relevant features gracefully without crashing. It should return an empty selection or an appropriate default value indicating that no features were selected.

**Actual Behavior:**
An `IndexError` is raised with the message:
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
This error occurs when the selector tries to assign scores to an empty array of attributes.

[/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/preprocess/fss.py
import random
from itertools import takewhile
from operator import itemgetter

import numpy as np


import Orange
from Orange.util import Reprable
from Orange.preprocess.score import ANOVA, GainRatio, \
    UnivariateLinearRegression

__all__ = ["SelectBestFeatures", "SelectRandomFeatures"]


class SelectBestFeatures(Reprable):
    """
    A feature selector that builds a new dataset consisting of either the top
    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`
    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features
    are scored using the provided feature scoring `method`. By default it is
    assumed that feature importance decreases with decreasing scores.

    If both `k` and `threshold` are set, only features satisfying both
    conditions will be selected.

    If `method` is not set, it is automatically selected when presented with
    the dataset. Datasets with both continuous and discrete features are
    scored using a method suitable for the majority of features.

    Parameters
    ----------
    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer
        Univariate feature scoring method.

    k : int or float
        The number or propotion of top features to select.

    threshold : float
        A threshold that a feature should meet according to the provided method.

    decreasing : boolean
        The order of feature importance when sorted from the most to the least
        important feature.
    """

    def __init__(self, method=None, k=None, threshold=None, decreasing=True):
        self.method = method
        self.k = k
        self.threshold = threshold
        self.decreasing = decreasing

    def __call__(self, data):
        n_attrs = len(data.domain.attributes)
        if isinstance(self.k, float):
            effective_k = np.round(self.k * n_attrs).astype(int) or 1
        else:
            effective_k = self.k

        method = self.method
        # select default method according to the provided data
        if method is None:
            autoMethod = True
            discr_ratio = (sum(a.is_discrete
                               for a in data.domain.attributes)
                           / len(data.domain.attributes))
            if data.domain.has_discrete_class:
                if discr_ratio >= 0.5:
                    method = GainRatio()
                else:
                    method = ANOVA()
            else:
                method = UnivariateLinearRegression()

        features = data.domain.attributes
        try:
            scores = method(data)
        except ValueError:
            scores = self.score_only_nice_features(data, method)
        best = sorted(zip(scores, features), key=itemgetter(0),
                      reverse=self.decreasing)
        if self.k:
            best = best[:effective_k]
        if self.threshold:
            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else
                    (lambda x: x[0] <= self.threshold))
            best = takewhile(pred, best)

        domain = Orange.data.Domain([f for s, f in best],
                                    data.domain.class_vars, data.domain.metas)
        return data.transform(domain)

    def score_only_nice_features(self, data, method):
        mask = np.array([isinstance(a, method.feature_type)
                         for a in data.domain.attributes])
        features = [f for f in data.domain.attributes
                    if isinstance(f, method.feature_type)]
        scores = [method(data, f) for f in features]
        bad = float('-inf') if self.decreasing else float('inf')
        all_scores = np.array([bad] * len(data.domain.attributes))
        all_scores[mask] = scores
        return all_scores


class SelectRandomFeatures(Reprable):
    """
    A feature selector that selects random `k` features from an input
    dataset and returns a dataset with selected features. Parameter
    `k` is either an integer (number of feature) or float (from 0.0 to
    1.0, proportion of retained features).

    Parameters
    ----------

    k : int or float (default = 0.1)
        The number or proportion of features to retain.
    """

    def __init__(self, k=0.1):
        self.k = k

    def __call__(self, data):
        if isinstance(self.k, float):
            effective_k = int(len(data.domain.attributes) * self.k)
        else:
            effective_k = self.k

        domain = Orange.data.Domain(
            random.sample(data.domain.attributes,
                          min(effective_k, len(data.domain.attributes))),
            data.domain.class_vars, data.domain.metas)
        return data.transform(domain)

```
--- 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:** Crash in SelectBestFeatures When No Relevant Features Are Present

**Description:**
When attempting to select relevant features using `SelectBestFeatures`, the process crashes if there are no features that match the specified feature type. This occurs even when the dataset has no attributes.

**Example Code:**
```python
from unittest.mock import Mock
from Orange.data import Domain, Table, DiscreteVariable
from Orange.preprocess import SelectBestFeatures

# Mock feature selection method
method = Mock()
method.feature_type = DiscreteVariable

# Initialize the selector with the mock method
selector = SelectBestFeatures(method, k=5)

# Create a dataset with no attributes
domain = Domain([])
data = Table.from_numpy(domain, np.zeros((100, 0)))

# Attempt to select relevant features
selection = selector.score_only_nice_features(data, method)
```

**Expected Behavior:**
The feature selection process should handle cases with no relevant features gracefully without crashing. It should return an empty selection or an appropriate default value indicating that no features were selected.

**Actual Behavior:**
An `IndexError` is raised with the message:
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
This error occurs when the selector tries to assign scores to an empty array of attributes.

[/ISSUE]
{
    "SelectBestFeaturesTest.test_no_nice_features": "PASSED"
}
diff --git a/Orange/preprocess/fss.py b/Orange/preprocess/fss.py
index e6e3620cf..b892508bd 100644
--- a/Orange/preprocess/fss.py
+++ b/Orange/preprocess/fss.py
@@ -91,8 +91,9 @@ class SelectBestFeatures(Reprable):
         return data.transform(domain)
 
     def score_only_nice_features(self, data, method):
+        # dtype must be defined because array can be empty
         mask = np.array([isinstance(a, method.feature_type)
-                         for a in data.domain.attributes])
+                         for a in data.domain.attributes], dtype=np.bool)
         features = [f for f in data.domain.attributes
                     if isinstance(f, method.feature_type)]
         scores = [method(data, f) for f in features]

{"Orange/preprocess/fss.py": "import random\nfrom itertools import takewhile\nfrom operator import itemgetter\n\nimport numpy as np\n\n\nimport Orange\nfrom Orange.util import Reprable\nfrom Orange.preprocess.score import ANOVA, GainRatio, \\\n    UnivariateLinearRegression\n\n__all__ = [\"SelectBestFeatures\", \"SelectRandomFeatures\"]\n\n\nclass SelectBestFeatures(Reprable):\n    \"\"\"\n    A feature selector that builds a new dataset consisting of either the top\n    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`\n    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features\n    are scored using the provided feature scoring `method`. By default it is\n    assumed that feature importance decreases with decreasing scores.\n\n    If both `k` and `threshold` are set, only features satisfying both\n    conditions will be selected.\n\n    If `method` is not set, it is automatically selected when presented with\n    the dataset. Datasets with both continuous and discrete features are\n    scored using a method suitable for the majority of features.\n\n    Parameters\n    ----------\n    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer\n        Univariate feature scoring method.\n\n    k : int or float\n        The number or propotion of top features to select.\n\n    threshold : float\n        A threshold that a feature should meet according to the provided method.\n\n    decreasing : boolean\n        The order of feature importance when sorted from the most to the least\n        important feature.\n    \"\"\"\n\n    def __init__(self, method=None, k=None, threshold=None, decreasing=True):\n        self.method = method\n        self.k = k\n        self.threshold = threshold\n        self.decreasing = decreasing\n\n    def __call__(self, data):\n        n_attrs = len(data.domain.attributes)\n        if isinstance(self.k, float):\n            effective_k = np.round(self.k * n_attrs).astype(int) or 1\n        else:\n            effective_k = self.k\n\n        method = self.method\n        # select default method according to the provided data\n        if method is None:\n            autoMethod = True\n            discr_ratio = (sum(a.is_discrete\n                               for a in data.domain.attributes)\n                           / len(data.domain.attributes))\n            if data.domain.has_discrete_class:\n                if discr_ratio >= 0.5:\n                    method = GainRatio()\n                else:\n                    method = ANOVA()\n            else:\n                method = UnivariateLinearRegression()\n\n        features = data.domain.attributes\n        try:\n            scores = method(data)\n        except ValueError:\n            scores = self.score_only_nice_features(data, method)\n        best = sorted(zip(scores, features), key=itemgetter(0),\n                      reverse=self.decreasing)\n        if self.k:\n            best = best[:effective_k]\n        if self.threshold:\n            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else\n                    (lambda x: x[0] <= self.threshold))\n            best = takewhile(pred, best)\n\n        domain = Orange.data.Domain([f for s, f in best],\n                                    data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def score_only_nice_features(self, data, method):\n        mask = np.array([isinstance(a, method.feature_type)\n                         for a in data.domain.attributes])\n        features = [f for f in data.domain.attributes\n                    if isinstance(f, method.feature_type)]\n        scores = [method(data, f) for f in features]\n        bad = float('-inf') if self.decreasing else float('inf')\n        all_scores = np.array([bad] * len(data.domain.attributes))\n        all_scores[mask] = scores\n        return all_scores\n\n\nclass SelectRandomFeatures(Reprable):\n    \"\"\"\n    A feature selector that selects random `k` features from an input\n    dataset and returns a dataset with selected features. Parameter\n    `k` is either an integer (number of feature) or float (from 0.0 to\n    1.0, proportion of retained features).\n\n    Parameters\n    ----------\n\n    k : int or float (default = 0.1)\n        The number or proportion of features to retain.\n    \"\"\"\n\n    def __init__(self, k=0.1):\n        self.k = k\n\n    def __call__(self, data):\n        if isinstance(self.k, float):\n            effective_k = int(len(data.domain.attributes) * self.k)\n        else:\n            effective_k = self.k\n\n        domain = Orange.data.Domain(\n            random.sample(data.domain.attributes,\n                          min(effective_k, len(data.domain.attributes))),\n            data.domain.class_vars, data.domain.metas)\n        return data.transform(domain)\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:** Crash in SelectBestFeatures When No Relevant Features Are Present

**Description:**
When attempting to select relevant features using `SelectBestFeatures`, the process crashes if there are no features that match the specified feature type. This occurs even when the dataset has no attributes.

**Example Code:**
```python
from unittest.mock import Mock
from Orange.data import Domain, Table, DiscreteVariable
from Orange.preprocess import SelectBestFeatures

# Mock feature selection method
method = Mock()
method.feature_type = DiscreteVariable

# Initialize the selector with the mock method
selector = SelectBestFeatures(method, k=5)

# Create a dataset with no attributes
domain = Domain([])
data = Table.from_numpy(domain, np.zeros((100, 0)))

# Attempt to select relevant features
selection = selector.score_only_nice_features(data, method)
```

**Expected Behavior:**
The feature selection process should handle cases with no relevant features gracefully without crashing. It should return an empty selection or an appropriate default value indicating that no features were selected.

**Actual Behavior:**
An `IndexError` is raised with the message:
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
This error occurs when the selector tries to assign scores to an empty array of attributes.

[/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/preprocess/fss.py
import random
from itertools import takewhile
from operator import itemgetter

import numpy as np


import Orange
from Orange.util import Reprable
from Orange.preprocess.score import ANOVA, GainRatio, \
    UnivariateLinearRegression

__all__ = ["SelectBestFeatures", "SelectRandomFeatures"]


class SelectBestFeatures(Reprable):
    """
    A feature selector that builds a new dataset consisting of either the top
    `k` features (if `k` is an `int`) or a proportion (if `k` is a `float`
    between 0.0 and 1.0), or all those that exceed a given `threshold`. Features
    are scored using the provided feature scoring `method`. By default it is
    assumed that feature importance decreases with decreasing scores.

    If both `k` and `threshold` are set, only features satisfying both
    conditions will be selected.

    If `method` is not set, it is automatically selected when presented with
    the dataset. Datasets with both continuous and discrete features are
    scored using a method suitable for the majority of features.

    Parameters
    ----------
    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer
        Univariate feature scoring method.

    k : int or float
        The number or propotion of top features to select.

    threshold : float
        A threshold that a feature should meet according to the provided method.

    decreasing : boolean
        The order of feature importance when sorted from the most to the least
        important feature.
    """

    def __init__(self, method=None, k=None, threshold=None, decreasing=True):
        self.method = method
        self.k = k
        self.threshold = threshold
        self.decreasing = decreasing

    def __call__(self, data):
        n_attrs = len(data.domain.attributes)
        if isinstance(self.k, float):
            effective_k = np.round(self.k * n_attrs).astype(int) or 1
        else:
            effective_k = self.k

        method = self.method
        # select default method according to the provided data
        if method is None:
            autoMethod = True
            discr_ratio = (sum(a.is_discrete
                               for a in data.domain.attributes)
                           / len(data.domain.attributes))
            if data.domain.has_discrete_class:
                if discr_ratio >= 0.5:
                    method = GainRatio()
                else:
                    method = ANOVA()
            else:
                method = UnivariateLinearRegression()

        features = data.domain.attributes
        try:
            scores = method(data)
        except ValueError:
            scores = self.score_only_nice_features(data, method)
        best = sorted(zip(scores, features), key=itemgetter(0),
                      reverse=self.decreasing)
        if self.k:
            best = best[:effective_k]
        if self.threshold:
            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else
                    (lambda x: x[0] <= self.threshold))
            best = takewhile(pred, best)

        domain = Orange.data.Domain([f for s, f in best],
                                    data.domain.class_vars, data.domain.metas)
        return data.transform(domain)

    def score_only_nice_features(self, data, method):
        mask = np.array([isinstance(a, method.feature_type)
                         for a in data.domain.attributes])
        features = [f for f in data.domain.attributes
                    if isinstance(f, method.feature_type)]
        scores = [method(data, f) for f in features]
        bad = float('-inf') if self.decreasing else float('inf')
        all_scores = np.array([bad] * len(data.domain.attributes))
        all_scores[mask] = scores
        return all_scores


class SelectRandomFeatures(Reprable):
    """
    A feature selector that selects random `k` features from an input
    dataset and returns a dataset with selected features. Parameter
    `k` is either an integer (number of feature) or float (from 0.0 to
    1.0, proportion of retained features).

    Parameters
    ----------

    k : int or float (default = 0.1)
        The number or proportion of features to retain.
    """

    def __init__(self, k=0.1):
        self.k = k

    def __call__(self, data):
        if isinstance(self.k, float):
            effective_k = int(len(data.domain.attributes) * self.k)
        else:
            effective_k = self.k

        domain = Orange.data.Domain(
            random.sample(data.domain.attributes,
                          min(effective_k, len(data.domain.attributes))),
            data.domain.class_vars, data.domain.metas)
        return data.transform(domain)

```
--- 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