{
    "file_diffs": [
        {
            "old_file_content": "import sklearn.cluster as skl_cluster\nfrom numpy import ndarray\n\nfrom Orange.data import Table, DiscreteVariable, Domain, Instance\nfrom Orange.projection import SklProjector, Projection\n\n\n__all__ = [\"DBSCAN\"]\n\nclass DBSCAN(SklProjector):\n    __wraps__ = skl_cluster.DBSCAN\n\n    def __init__(self, eps=0.5, min_samples=5, metric='euclidean',\n                 algorithm='auto', leaf_size=30, p=None,\n                 preprocessors=None):\n        super().__init__(preprocessors=preprocessors)\n        self.params = vars()\n\n    def fit(self, X, Y=None):\n        proj = skl_cluster.DBSCAN(**self.params)\n        self.X = X\n        if isinstance(X, Table):\n            proj = proj.fit(X.X,)\n        else:\n            proj = proj.fit(X, )\n        return DBSCANModel(proj)\n\n\nclass DBSCANModel(Projection):\n    def __init__(self, proj):\n        super().__init__(proj=proj)\n\n    def __call__(self, data):\n        if isinstance(data, ndarray):\n            return self.proj.fit_predict(data).reshape((len(data), 1))\n\n        if isinstance(data, Table):\n            if data.domain is not self.pre_domain:\n                data = data.transform(self.pre_domain)\n            y = self.proj.fit_predict(data.X)\n            vals = [-1] + list(self.proj.core_sample_indices_)\n            c = DiscreteVariable(name='Core sample index',\n                                 values=[str(v) for v in vals])\n            domain = Domain([c])\n            return Table(domain, y.reshape(len(y), 1))\n\n        elif isinstance(data, Instance):\n            if data.domain is not self.pre_domain:\n                data = Instance(self.pre_domain, data)\n            # Instances-by-Instance classification is not defined;\n            raise Exception(\"Core sample assignment is not supported \"\n                            \"for single instances.\")\n",
            "new_file_content": "import sklearn.cluster as skl_cluster\nfrom numpy import ndarray, unique\n\nfrom Orange.data import Table, DiscreteVariable, Domain, Instance\nfrom Orange.projection import SklProjector, Projection\n\n\n__all__ = [\"DBSCAN\"]\n\nclass DBSCAN(SklProjector):\n    __wraps__ = skl_cluster.DBSCAN\n\n    def __init__(self, eps=0.5, min_samples=5, metric='euclidean',\n                 algorithm='auto', leaf_size=30, p=None,\n                 preprocessors=None):\n        super().__init__(preprocessors=preprocessors)\n        self.params = vars()\n\n    def fit(self, X, Y=None):\n        proj = skl_cluster.DBSCAN(**self.params)\n        self.X = X\n        if isinstance(X, Table):\n            proj = proj.fit(X.X,)\n        else:\n            proj = proj.fit(X, )\n        return DBSCANModel(proj)\n\n\nclass DBSCANModel(Projection):\n    def __init__(self, proj):\n        super().__init__(proj=proj)\n\n    def __call__(self, data):\n        if isinstance(data, ndarray):\n            return self.proj.fit_predict(data).reshape((len(data), 1))\n\n        if isinstance(data, Table):\n            if data.domain is not self.pre_domain:\n                data = data.transform(self.pre_domain)\n            y = self.proj.fit_predict(data.X)\n            vals, indices = unique(y, return_inverse=True)\n            c = DiscreteVariable(name='Core sample index',\n                                 values=[str(v) for v in vals])\n            domain = Domain([c])\n            return Table(domain, indices.reshape(len(y), 1))\n\n        elif isinstance(data, Instance):\n            if data.domain is not self.pre_domain:\n                data = Instance(self.pre_domain, data)\n            # Instances-by-Instance classification is not defined;\n            raise Exception(\"Core sample assignment is not supported \"\n                            \"for single instances.\")\n",
            "header": {
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                    "line_group": {
                        "all_lines": [
                            {
                                "content": "import sklearn.cluster as skl_cluster",
                                "type": "context"
                            },
                            {
                                "content": "from numpy import ndarray",
                                "type": "deleted"
                            },
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                                "content": "from numpy import ndarray, unique",
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                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data import Table, DiscreteVariable, Domain, Instance",
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                            },
                            {
                                "content": "from Orange.projection import SklProjector, Projection",
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                            "file_name": "Orange/clustering/dbscan.py",
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                                "content": "            if data.domain is not self.pre_domain:",
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                                "content": "            y = self.proj.fit_predict(data.X)",
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                                "content": "            vals = [-1] + list(self.proj.core_sample_indices_)",
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                                "content": "            vals, indices = unique(y, return_inverse=True)",
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                                "content": "            c = DiscreteVariable(name='Core sample index',",
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                                "content": "            domain = Domain([c])",
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                            },
                            {
                                "content": "            return Table(domain, y.reshape(len(y), 1))",
                                "type": "deleted"
                            },
                            {
                                "content": "            return Table(domain, indices.reshape(len(y), 1))",
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                                "content": "",
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                        {
                            "file_name": "Orange/clustering/dbscan.py",
                            "type": "method",
                            "name": "DBSCANModel.__call__",
                            "content": "class DBSCANModel(Projection):\n    def __init__(self, proj):\n        super().__init__(proj=proj)\n\n    def __call__(self, data):\n        if isinstance(data, ndarray):\n            return self.proj.fit_predict(data).reshape((len(data), 1))\n\n        if isinstance(data, Table):\n            if data.domain is not self.pre_domain:\n                data = data.transform(self.pre_domain)\n            y = self.proj.fit_predict(data.X)\n            vals = [-1] + list(self.proj.core_sample_indices_)\n            c = DiscreteVariable(name='Core sample index',\n                                 values=[str(v) for v in vals])\n            domain = Domain([c])\n            return Table(domain, y.reshape(len(y), 1))\n\n        elif isinstance(data, Instance):\n            if data.domain is not self.pre_domain:\n                data = Instance(self.pre_domain, data)\n            # Instances-by-Instance classification is not defined;\n            raise Exception(\"Core sample assignment is not supported \"\n                            \"for single instances.\")",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 33,
                            "end_lineno": 52,
                            "parent": {
                                "file_name": "Orange/clustering/dbscan.py",
                                "type": "class",
                                "name": "DBSCANModel",
                                "content": "class DBSCANModel(Projection):\n    def __init__(self, proj):\n        super().__init__(proj=proj)\n\n    def __call__(self, data):\n        if isinstance(data, ndarray):\n            return self.proj.fit_predict(data).reshape((len(data), 1))\n\n        if isinstance(data, Table):\n            if data.domain is not self.pre_domain:\n                data = data.transform(self.pre_domain)\n            y = self.proj.fit_predict(data.X)\n            vals = [-1] + list(self.proj.core_sample_indices_)\n            c = DiscreteVariable(name='Core sample index',\n                                 values=[str(v) for v in vals])\n            domain = Domain([c])\n            return Table(domain, y.reshape(len(y), 1))\n\n        elif isinstance(data, Instance):\n            if data.domain is not self.pre_domain:\n                data = Instance(self.pre_domain, data)\n            # Instances-by-Instance classification is not defined;\n            raise Exception(\"Core sample assignment is not supported \"\n                            \"for single instances.\")",
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                                "end_lineno": 52,
                                "parent": null
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                            "file_name": "Orange/clustering/dbscan.py",
                            "type": "class",
                            "name": "DBSCANModel",
                            "content": "class DBSCANModel(Projection):\n    def __init__(self, proj):\n        super().__init__(proj=proj)\n\n    def __call__(self, data):\n        if isinstance(data, ndarray):\n            return self.proj.fit_predict(data).reshape((len(data), 1))\n\n        if isinstance(data, Table):\n            if data.domain is not self.pre_domain:\n                data = data.transform(self.pre_domain)\n            y = self.proj.fit_predict(data.X)\n            vals = [-1] + list(self.proj.core_sample_indices_)\n            c = DiscreteVariable(name='Core sample index',\n                                 values=[str(v) for v in vals])\n            domain = Domain([c])\n            return Table(domain, y.reshape(len(y), 1))\n\n        elif isinstance(data, Instance):\n            if data.domain is not self.pre_domain:\n                data = Instance(self.pre_domain, data)\n            # Instances-by-Instance classification is not defined;\n            raise Exception(\"Core sample assignment is not supported \"\n                            \"for single instances.\")",
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                            "start_lineno": 29,
                            "end_lineno": 52,
                            "parent": null
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            ]
        },
        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\n\nimport Orange\nfrom Orange.clustering.dbscan import DBSCAN\n\n\nclass TestDBSCAN(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.iris = Orange.data.Table('iris')\n\n    def test_dbscan_parameters(self):\n        dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',\n                        algorithm='auto', leaf_size=12, p=None)\n        c = dbscan(self.iris)\n\n    def test_predict_table(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        table = self.iris[:20]\n        p = c(table)\n\n    def test_predict_numpy(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        X = self.iris.X[::20]\n        p = c(X)\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\n\nimport Orange\nfrom Orange.clustering.dbscan import DBSCAN\n\n\nclass TestDBSCAN(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.iris = Orange.data.Table('iris')\n\n    def test_dbscan_parameters(self):\n        dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',\n                        algorithm='auto', leaf_size=12, p=None)\n        c = dbscan(self.iris)\n\n    def test_predict_table(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        table = self.iris[:20]\n        p = c(table)\n\n    def test_predict_numpy(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        X = self.iris.X[::20]\n        p = c(X)\n\n    def test_values(self):\n        dbscan = DBSCAN(eps=1)  # it clusters data in two classes\n        c = dbscan(self.iris)\n        table = self.iris\n        p = c(table)\n\n        self.assertEqual(2, len(p.domain[0].values))\n        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))\n\n        table.X[0] = [100, 100, 100, 100]  # we add a big outlier\n\n        p = c(table)\n\n        self.assertEqual(3, len(p.domain[0].values))\n        self.assertSetEqual({\"-1\", \"0\", \"1\"}, set(p.domain[0].values))\n",
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                        "section": "class TestDBSCAN(unittest.TestCase):"
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                            {
                                "content": "        c = dbscan(self.iris)",
                                "type": "context"
                            },
                            {
                                "content": "        X = self.iris.X[::20]",
                                "type": "context"
                            },
                            {
                                "content": "        p = c(X)",
                                "type": "context"
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                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    def test_values(self):",
                                "type": "added"
                            },
                            {
                                "content": "        dbscan = DBSCAN(eps=1)  # it clusters data in two classes",
                                "type": "added"
                            },
                            {
                                "content": "        c = dbscan(self.iris)",
                                "type": "added"
                            },
                            {
                                "content": "        table = self.iris",
                                "type": "added"
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                            {
                                "content": "        p = c(table)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(2, len(p.domain[0].values))",
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                            },
                            {
                                "content": "        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))",
                                "type": "added"
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                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        table.X[0] = [100, 100, 100, 100]  # we add a big outlier",
                                "type": "added"
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                            {
                                "content": "",
                                "type": "added"
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                            {
                                "content": "        p = c(table)",
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                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(3, len(p.domain[0].values))",
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                            {
                                "content": "        self.assertSetEqual({\"-1\", \"0\", \"1\"}, set(p.domain[0].values))",
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                        {
                            "file_name": "Orange/tests/test_clustering_dbscan.py",
                            "type": "class",
                            "name": "TestDBSCAN",
                            "content": "class TestDBSCAN(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.iris = Orange.data.Table('iris')\n\n    def test_dbscan_parameters(self):\n        dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',\n                        algorithm='auto', leaf_size=12, p=None)\n        c = dbscan(self.iris)\n\n    def test_predict_table(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        table = self.iris[:20]\n        p = c(table)\n\n    def test_predict_numpy(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        X = self.iris.X[::20]\n        p = c(X)\n\n    def test_values(self):\n        dbscan = DBSCAN(eps=1)  # it clusters data in two classes\n        c = dbscan(self.iris)\n        table = self.iris\n        p = c(table)\n\n        self.assertEqual(2, len(p.domain[0].values))\n        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))\n\n        table.X[0] = [100, 100, 100, 100]  # we add a big outlier\n\n        p = c(table)\n\n        self.assertEqual(3, len(p.domain[0].values))\n        self.assertSetEqual({\"-1\", \"0\", \"1\"}, set(p.domain[0].values))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 10,
                            "end_lineno": 47,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/tests/test_clustering_dbscan.py",
                            "type": "method",
                            "name": "TestDBSCAN.test_values",
                            "content": "class TestDBSCAN(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.iris = Orange.data.Table('iris')\n\n    def test_dbscan_parameters(self):\n        dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',\n                        algorithm='auto', leaf_size=12, p=None)\n        c = dbscan(self.iris)\n\n    def test_predict_table(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        table = self.iris[:20]\n        p = c(table)\n\n    def test_predict_numpy(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        X = self.iris.X[::20]\n        p = c(X)\n\n    def test_values(self):\n        dbscan = DBSCAN(eps=1)  # it clusters data in two classes\n        c = dbscan(self.iris)\n        table = self.iris\n        p = c(table)\n\n        self.assertEqual(2, len(p.domain[0].values))\n        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))\n\n        table.X[0] = [100, 100, 100, 100]  # we add a big outlier\n\n        p = c(table)\n\n        self.assertEqual(3, len(p.domain[0].values))\n        self.assertSetEqual({\"-1\", \"0\", \"1\"}, set(p.domain[0].values))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 33,
                            "end_lineno": 47,
                            "parent": {
                                "file_name": "Orange/tests/test_clustering_dbscan.py",
                                "type": "class",
                                "name": "TestDBSCAN",
                                "content": "class TestDBSCAN(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.iris = Orange.data.Table('iris')\n\n    def test_dbscan_parameters(self):\n        dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',\n                        algorithm='auto', leaf_size=12, p=None)\n        c = dbscan(self.iris)\n\n    def test_predict_table(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        table = self.iris[:20]\n        p = c(table)\n\n    def test_predict_numpy(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        X = self.iris.X[::20]\n        p = c(X)\n\n    def test_values(self):\n        dbscan = DBSCAN(eps=1)  # it clusters data in two classes\n        c = dbscan(self.iris)\n        table = self.iris\n        p = c(table)\n\n        self.assertEqual(2, len(p.domain[0].values))\n        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))\n\n        table.X[0] = [100, 100, 100, 100]  # we add a big outlier\n\n        p = c(table)\n\n        self.assertEqual(3, len(p.domain[0].values))\n        self.assertSetEqual({\"-1\", \"0\", \"1\"}, set(p.domain[0].values))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 10,
                                "end_lineno": 47,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
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    ],
    "old_commit_hash": "40396e392f184aa9e70348039f43cfb852d701f6^",
    "new_commit_hash": "40396e392f184aa9e70348039f43cfb852d701f6",
    "commit_message": "DBSCAN: fix indices and tests",
    "commit_date": "2019-05-30T13:10:25+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "40396e392f184aa9e70348039f43cfb852d701f6",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\n\nimport Orange\nfrom Orange.clustering.dbscan import DBSCAN\n\n\nclass TestDBSCAN(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        cls.iris = Orange.data.Table('iris')\n\n    def test_dbscan_parameters(self):\n        dbscan = DBSCAN(eps=0.1, min_samples=7, metric='euclidean',\n                        algorithm='auto', leaf_size=12, p=None)\n        c = dbscan(self.iris)\n\n    def test_predict_table(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        table = self.iris[:20]\n        p = c(table)\n\n    def test_predict_numpy(self):\n        dbscan = DBSCAN()\n        c = dbscan(self.iris)\n        X = self.iris.X[::20]\n        p = c(X)\n\n    def test_values(self):\n        dbscan = DBSCAN(eps=1)  # it clusters data in two classes\n        c = dbscan(self.iris)\n        table = self.iris\n        p = c(table)\n\n        self.assertEqual(2, len(p.domain[0].values))\n        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))\n\n        table.X[0] = [100, 100, 100, 100]  # we add a big outlier\n\n        p = c(table)\n\n        self.assertEqual(3, len(p.domain[0].values))\n        self.assertSetEqual({\"-1\", \"0\", \"1\"}, set(p.domain[0].values))\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\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.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/tmpyckyn_0b/tmp\ncreating /tmp/tmpyckyn_0b/tmp/tmpyckyn_0b\ncompile options: '-MMD -MF /tmp/tmpyckyn_0b/file.c.d -c'\ncc: /tmp/tmpyckyn_0b/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/tmpkwjuri6b/tmp\ncreating /tmp/tmpkwjuri6b/tmp/tmpkwjuri6b\ncompile options: '-MMD -MF /tmp/tmpkwjuri6b/file.c.d -c'\ncc: /tmp/tmpkwjuri6b/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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\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.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 '*.html' under directory 'Orange/canvas'\nwarning: no files found matching '*.css' 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/tmpi8x_qrbg/tmp\ncreating /tmp/tmpi8x_qrbg/tmp/tmpi8x_qrbg\ncompile options: '-MMD -MF /tmp/tmpi8x_qrbg/file.c.d -c'\ncc: /tmp/tmpi8x_qrbg/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/tmpwk9a6las/tmp\ncreating /tmp/tmpwk9a6las/tmp/tmpwk9a6las\ncompile options: '-MMD -MF /tmp/tmpwk9a6las/file.c.d -c'\ncc: /tmp/tmpwk9a6las/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.22.0.dev0+40396e3 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6\nProcessing dependencies for Orange3==3.22.0.dev0+40396e3\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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.22.0.dev0+40396e3\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 47ms\nPrepared 18 packages in 32ms\nInstalled 18 packages in 539ms\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 33 packages in 152ms\nInstalled 28 packages in 448ms\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 + pip==24.0\n + pycparser==2.21\n + python-louvain==0.16\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 + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 14 packages in 52ms\nInstalled 11 packages in 32ms\n + anyqt==0.2.0\n + cycler==0.11.0\n + docutils==0.20.1\n + fonttools==4.38.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + pillow==9.5.0\n + pyparsing==3.1.4\n + pyqtgraph==0.12.4\n + python-dateutil==2.9.0.post0\n + six==1.17.0\nResolved 2 packages in 3ms\nInstalled 2 packages in 24ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 18ms\nInstalled 2 packages in 121ms\n + pandas==1.3.5\n + pytz==2024.2\nAudited 2 packages in 1ms\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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      |  ^~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6/.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_40396e392f184aa9e70348039f43cfb852d701f6\ncollected 4 items\n\nr2e_tests/test_1.py ....                                                 [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_dbscan_parameters\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_predict_numpy\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_predict_table\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_values\n============================== 4 passed in 1.49s ===============================\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_40396e392f184aa9e70348039f43cfb852d701f6\ncollected 4 items\n\nr2e_tests/test_1.py ...F                                                 [100%]\n\n=================================== FAILURES ===================================\n____________________________ TestDBSCAN.test_values ____________________________\n\nself = <r2e_tests.test_1.TestDBSCAN testMethod=test_values>\n\n    def test_values(self):\n        dbscan = DBSCAN(eps=1)  # it clusters data in two classes\n        c = dbscan(self.iris)\n        table = self.iris\n        p = c(table)\n    \n>       self.assertEqual(2, len(p.domain[0].values))\nE       AssertionError: 2 != 150\n\nr2e_tests/test_1.py:39: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_dbscan_parameters\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_predict_numpy\nPASSED r2e_tests/test_1.py::TestDBSCAN::test_predict_table\nFAILED r2e_tests/test_1.py::TestDBSCAN::test_values - AssertionError: 2 != 150\n========================= 1 failed, 3 passed in 1.13s ==========================\n",
    "old_commit_res_stderr": ""
}
Orange/clustering/dbscan.py Orange/tests/test_clustering_dbscan.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** DBSCAN Assigns Excessive Cluster Labels with eps=1

**Description:**
When using DBSCAN with an epsilon value of 1, the clustering process incorrectly assigns a unique cluster label to each data point instead of grouping them into the expected number of clusters.

**Example:**
```python
def test_dbscan_values():
    dbscan = DBSCAN(eps=1)  # Expected to form two clusters
    model = dbscan(iris_data)
    clustered_table = model(iris_data)
    # Expected number of cluster labels: 2
    # Actual number of cluster labels: 150
```

**Expected Behavior:**
DBSCAN should group the data points into two clusters based on the epsilon parameter, resulting in only two unique cluster labels.

**Actual Behavior:**
Each data point receives a distinct cluster label, resulting in 150 unique clusters instead of the expected two.

[/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/clustering/dbscan.py
import sklearn.cluster as skl_cluster
from numpy import ndarray

from Orange.data import Table, DiscreteVariable, Domain, Instance
from Orange.projection import SklProjector, Projection


__all__ = ["DBSCAN"]

class DBSCAN(SklProjector):
    __wraps__ = skl_cluster.DBSCAN

    def __init__(self, eps=0.5, min_samples=5, metric='euclidean',
                 algorithm='auto', leaf_size=30, p=None,
                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        proj = skl_cluster.DBSCAN(**self.params)
        self.X = X
        if isinstance(X, Table):
            proj = proj.fit(X.X,)
        else:
            proj = proj.fit(X, )
        return DBSCANModel(proj)


class DBSCANModel(Projection):
    def __init__(self, proj):
        super().__init__(proj=proj)

    def __call__(self, data):
        if isinstance(data, ndarray):
            return self.proj.fit_predict(data).reshape((len(data), 1))

        if isinstance(data, Table):
            if data.domain is not self.pre_domain:
                data = data.transform(self.pre_domain)
            y = self.proj.fit_predict(data.X)
            vals = [-1] + list(self.proj.core_sample_indices_)
            c = DiscreteVariable(name='Core sample index',
                                 values=[str(v) for v in vals])
            domain = Domain([c])
            return Table(domain, y.reshape(len(y), 1))

        elif isinstance(data, Instance):
            if data.domain is not self.pre_domain:
                data = Instance(self.pre_domain, data)
            # Instances-by-Instance classification is not defined;
            raise Exception("Core sample assignment is not supported "
                            "for single instances.")

```
--- 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:** DBSCAN Assigns Excessive Cluster Labels with eps=1

**Description:**
When using DBSCAN with an epsilon value of 1, the clustering process incorrectly assigns a unique cluster label to each data point instead of grouping them into the expected number of clusters.

**Example:**
```python
def test_dbscan_values():
    dbscan = DBSCAN(eps=1)  # Expected to form two clusters
    model = dbscan(iris_data)
    clustered_table = model(iris_data)
    # Expected number of cluster labels: 2
    # Actual number of cluster labels: 150
```

**Expected Behavior:**
DBSCAN should group the data points into two clusters based on the epsilon parameter, resulting in only two unique cluster labels.

**Actual Behavior:**
Each data point receives a distinct cluster label, resulting in 150 unique clusters instead of the expected two.

[/ISSUE]
{
    "TestDBSCAN.test_dbscan_parameters": "PASSED",
    "TestDBSCAN.test_predict_numpy": "PASSED",
    "TestDBSCAN.test_predict_table": "PASSED",
    "TestDBSCAN.test_values": "PASSED"
}
diff --git a/Orange/clustering/dbscan.py b/Orange/clustering/dbscan.py
index d69bb019d..7481f1c34 100644
--- a/Orange/clustering/dbscan.py
+++ b/Orange/clustering/dbscan.py
@@ -1,5 +1,5 @@
 import sklearn.cluster as skl_cluster
-from numpy import ndarray
+from numpy import ndarray, unique
 
 from Orange.data import Table, DiscreteVariable, Domain, Instance
 from Orange.projection import SklProjector, Projection
@@ -38,11 +38,11 @@ class DBSCANModel(Projection):
             if data.domain is not self.pre_domain:
                 data = data.transform(self.pre_domain)
             y = self.proj.fit_predict(data.X)
-            vals = [-1] + list(self.proj.core_sample_indices_)
+            vals, indices = unique(y, return_inverse=True)
             c = DiscreteVariable(name='Core sample index',
                                  values=[str(v) for v in vals])
             domain = Domain([c])
-            return Table(domain, y.reshape(len(y), 1))
+            return Table(domain, indices.reshape(len(y), 1))
 
         elif isinstance(data, Instance):
             if data.domain is not self.pre_domain:

{"Orange/clustering/dbscan.py": "import sklearn.cluster as skl_cluster\nfrom numpy import ndarray\n\nfrom Orange.data import Table, DiscreteVariable, Domain, Instance\nfrom Orange.projection import SklProjector, Projection\n\n\n__all__ = [\"DBSCAN\"]\n\nclass DBSCAN(SklProjector):\n    __wraps__ = skl_cluster.DBSCAN\n\n    def __init__(self, eps=0.5, min_samples=5, metric='euclidean',\n                 algorithm='auto', leaf_size=30, p=None,\n                 preprocessors=None):\n        super().__init__(preprocessors=preprocessors)\n        self.params = vars()\n\n    def fit(self, X, Y=None):\n        proj = skl_cluster.DBSCAN(**self.params)\n        self.X = X\n        if isinstance(X, Table):\n            proj = proj.fit(X.X,)\n        else:\n            proj = proj.fit(X, )\n        return DBSCANModel(proj)\n\n\nclass DBSCANModel(Projection):\n    def __init__(self, proj):\n        super().__init__(proj=proj)\n\n    def __call__(self, data):\n        if isinstance(data, ndarray):\n            return self.proj.fit_predict(data).reshape((len(data), 1))\n\n        if isinstance(data, Table):\n            if data.domain is not self.pre_domain:\n                data = data.transform(self.pre_domain)\n            y = self.proj.fit_predict(data.X)\n            vals = [-1] + list(self.proj.core_sample_indices_)\n            c = DiscreteVariable(name='Core sample index',\n                                 values=[str(v) for v in vals])\n            domain = Domain([c])\n            return Table(domain, y.reshape(len(y), 1))\n\n        elif isinstance(data, Instance):\n            if data.domain is not self.pre_domain:\n                data = Instance(self.pre_domain, data)\n            # Instances-by-Instance classification is not defined;\n            raise Exception(\"Core sample assignment is not supported \"\n                            \"for single instances.\")\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:** DBSCAN Assigns Excessive Cluster Labels with eps=1

**Description:**
When using DBSCAN with an epsilon value of 1, the clustering process incorrectly assigns a unique cluster label to each data point instead of grouping them into the expected number of clusters.

**Example:**
```python
def test_dbscan_values():
    dbscan = DBSCAN(eps=1)  # Expected to form two clusters
    model = dbscan(iris_data)
    clustered_table = model(iris_data)
    # Expected number of cluster labels: 2
    # Actual number of cluster labels: 150
```

**Expected Behavior:**
DBSCAN should group the data points into two clusters based on the epsilon parameter, resulting in only two unique cluster labels.

**Actual Behavior:**
Each data point receives a distinct cluster label, resulting in 150 unique clusters instead of the expected two.

[/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/clustering/dbscan.py
import sklearn.cluster as skl_cluster
from numpy import ndarray

from Orange.data import Table, DiscreteVariable, Domain, Instance
from Orange.projection import SklProjector, Projection


__all__ = ["DBSCAN"]

class DBSCAN(SklProjector):
    __wraps__ = skl_cluster.DBSCAN

    def __init__(self, eps=0.5, min_samples=5, metric='euclidean',
                 algorithm='auto', leaf_size=30, p=None,
                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        proj = skl_cluster.DBSCAN(**self.params)
        self.X = X
        if isinstance(X, Table):
            proj = proj.fit(X.X,)
        else:
            proj = proj.fit(X, )
        return DBSCANModel(proj)


class DBSCANModel(Projection):
    def __init__(self, proj):
        super().__init__(proj=proj)

    def __call__(self, data):
        if isinstance(data, ndarray):
            return self.proj.fit_predict(data).reshape((len(data), 1))

        if isinstance(data, Table):
            if data.domain is not self.pre_domain:
                data = data.transform(self.pre_domain)
            y = self.proj.fit_predict(data.X)
            vals = [-1] + list(self.proj.core_sample_indices_)
            c = DiscreteVariable(name='Core sample index',
                                 values=[str(v) for v in vals])
            domain = Domain([c])
            return Table(domain, y.reshape(len(y), 1))

        elif isinstance(data, Instance):
            if data.domain is not self.pre_domain:
                data = Instance(self.pre_domain, data)
            # Instances-by-Instance classification is not defined;
            raise Exception("Core sample assignment is not supported "
                            "for single instances.")

```
--- 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