{
    "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": "7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0^",
    "new_commit_hash": "7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0",
    "commit_message": "fss.SelectBestFeatures.score_only_nice_features: Fix case when no features",
    "commit_date": "2019-11-21T22:04:20+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0",
    "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/tmpydlsvxa8/tmp\ncreating /tmp/tmpydlsvxa8/tmp/tmpydlsvxa8\ncompile options: '-MMD -MF /tmp/tmpydlsvxa8/file.c.d -c'\ncc: /tmp/tmpydlsvxa8/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/tmp2jcoonuc/tmp\ncreating /tmp/tmp2jcoonuc/tmp/tmp2jcoonuc\ncompile options: '-MMD -MF /tmp/tmp2jcoonuc/file.c.d -c'\ncc: /tmp/tmp2jcoonuc/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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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/tmpiaa36y8t/tmp\ncreating /tmp/tmpiaa36y8t/tmp/tmpiaa36y8t\ncompile options: '-MMD -MF /tmp/tmpiaa36y8t/file.c.d -c'\ncc: /tmp/tmpiaa36y8t/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/tmpo5mcnbzp/tmp\ncreating /tmp/tmpo5mcnbzp/tmp/tmpo5mcnbzp\ncompile options: '-MMD -MF /tmp/tmpo5mcnbzp/file.c.d -c'\ncc: /tmp/tmpo5mcnbzp/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.24.0.dev0+7f5c9da to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0\nProcessing dependencies for Orange3==3.24.0.dev0+7f5c9da\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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.24.0.dev0+7f5c9da\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 134ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 108ms\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 272ms\nInstalled 33 packages in 925ms\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 144ms\nInstalled 19 packages in 226ms\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 56ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nAudited 1 package in 26ms\nAudited 2 packages in 2ms\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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0/.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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0\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 1.50s ===============================\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_7f5c9dace89a846e6fb7aed86e4ff7eb05e01dc0\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='136118144389904'>, k=5)\ndata = [[],\n [],\n [],\n [],\n [],\n ...\n], method = <Mock id='136118144389904'>\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 1.12s ===============================\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:** `score_only_nice_features` Raises IndexError When No Features Are Present

**Description:**
When the `score_only_nice_features` method is invoked with data that contains no attributes of the specified feature type, it results in an `IndexError`. This occurs because the mask array used for indexing does not have a boolean data type, which is required for proper indexing.

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

# Setup mock method and selector
method = Mock()
method.feature_type = DiscreteVariable
selector = SelectBestFeatures(method, k=5)

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

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

**Expected Behavior:**
When no attributes of the required feature type are present in the data, the `score_only_nice_features` method should return an empty selection without raising any errors.

**Actual Behavior:**
An `IndexError` is raised with the message:
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
This error prevents the method from handling datasets without the necessary feature types gracefully.

[/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:** `score_only_nice_features` Raises IndexError When No Features Are Present

**Description:**
When the `score_only_nice_features` method is invoked with data that contains no attributes of the specified feature type, it results in an `IndexError`. This occurs because the mask array used for indexing does not have a boolean data type, which is required for proper indexing.

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

# Setup mock method and selector
method = Mock()
method.feature_type = DiscreteVariable
selector = SelectBestFeatures(method, k=5)

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

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

**Expected Behavior:**
When no attributes of the required feature type are present in the data, the `score_only_nice_features` method should return an empty selection without raising any errors.

**Actual Behavior:**
An `IndexError` is raised with the message:
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
This error prevents the method from handling datasets without the necessary feature types gracefully.

[/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:** `score_only_nice_features` Raises IndexError When No Features Are Present

**Description:**
When the `score_only_nice_features` method is invoked with data that contains no attributes of the specified feature type, it results in an `IndexError`. This occurs because the mask array used for indexing does not have a boolean data type, which is required for proper indexing.

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

# Setup mock method and selector
method = Mock()
method.feature_type = DiscreteVariable
selector = SelectBestFeatures(method, k=5)

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

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

**Expected Behavior:**
When no attributes of the required feature type are present in the data, the `score_only_nice_features` method should return an empty selection without raising any errors.

**Actual Behavior:**
An `IndexError` is raised with the message:
```
IndexError: arrays used as indices must be of integer (or boolean) type
```
This error prevents the method from handling datasets without the necessary feature types gracefully.

[/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