{
    "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",
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                    "line_group": {
                        "all_lines": [
                            {
                                "content": "import sklearn.cluster as skl_cluster",
                                "type": "context"
                            },
                            {
                                "content": "from numpy import ndarray",
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                            },
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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",
                                "type": "context"
                            },
                            {
                                "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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                                "type": "context"
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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": "            domain = Domain([c])",
                                "type": "context"
                            },
                            {
                                "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"
                            },
                            {
                                "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))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertSetEqual({\"0\", \"1\"}, set(p.domain[0].values))",
                                "type": "added"
                            },
                            {
                                "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"
                            },
                            {
                                "content": "        p = c(table)",
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                                "content": "",
                                "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": []
                }
            ]
        }
    ],
    "old_commit_hash": "fce05b8949392f32b852ef73e343be79b53e82d0^",
    "new_commit_hash": "fce05b8949392f32b852ef73e343be79b53e82d0",
    "commit_message": "Merge pull request #3833 from PrimozGodec/fix-dbscan\n\n[FIX] DBSCAN: Fix predicted labels",
    "commit_date": "2019-05-31T09:49:59+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "fce05b8949392f32b852ef73e343be79b53e82d0",
    "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/tmpytu7n16r/tmp\ncreating /tmp/tmpytu7n16r/tmp/tmpytu7n16r\ncompile options: '-MMD -MF /tmp/tmpytu7n16r/file.c.d -c'\ncc: /tmp/tmpytu7n16r/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/tmpymv01ofo/tmp\ncreating /tmp/tmpymv01ofo/tmp/tmpymv01ofo\ncompile options: '-MMD -MF /tmp/tmpymv01ofo/file.c.d -c'\ncc: /tmp/tmpymv01ofo/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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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/tmpzc_odu27/tmp\ncreating /tmp/tmpzc_odu27/tmp/tmpzc_odu27\ncompile options: '-MMD -MF /tmp/tmpzc_odu27/file.c.d -c'\ncc: /tmp/tmpzc_odu27/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/tmpkf_j9rgh/tmp\ncreating /tmp/tmpkf_j9rgh/tmp/tmpkf_j9rgh\ncompile options: '-MMD -MF /tmp/tmpkf_j9rgh/file.c.d -c'\ncc: /tmp/tmpkf_j9rgh/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.22.0.dev0+fce05b8 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0\nProcessing dependencies for Orange3==3.22.0.dev0+fce05b8\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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.22.0.dev0+fce05b8\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 107ms\nPrepared 18 packages in 9ms\nInstalled 18 packages in 70ms\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 141ms\nInstalled 28 packages in 212ms\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 66ms\nInstalled 11 packages in 414ms\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 2ms\nInstalled 2 packages in 6ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 41ms\nInstalled 2 packages in 515ms\n + pandas==1.3.5\n + pytz==2024.2\nAudited 2 packages in 42ms\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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0/.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_fce05b8949392f32b852ef73e343be79b53e82d0\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 1.36s ===============================\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_fce05b8949392f32b852ef73e343be79b53e82d0\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 1.11s ==========================\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 Model Returns Incorrect Number of Predicted Labels

**Description:**
When using the DBSCAN clustering algorithm with `eps=1` on the Iris dataset, the predicted labels contain an unexpected number of unique values. Instead of clustering the data into the expected number of groups, each data point is assigned a unique label.

**Example Code:**
```python
from Orange.clustering import DBSCAN
from Orange.data import Table

iris = Table('iris')
dbscan = DBSCAN(eps=1)
cluster_model = dbscan(iris)
predictions = cluster_model(iris)

# Expected number of unique labels: 2
# Actual number of unique labels: 150
```

**Expected Behavior:**
The DBSCAN model should assign data points to a small number of clusters based on the `eps` parameter. For instance, with `eps=1`, it is expected to identify 2 distinct clusters in the Iris dataset.

**Actual Behavior:**
Each data point is assigned a unique label, resulting in 150 unique labels instead of the expected 2. This indicates that the clustering is not functioning as intended, and the model fails to group similar data points together.

[/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 Model Returns Incorrect Number of Predicted Labels

**Description:**
When using the DBSCAN clustering algorithm with `eps=1` on the Iris dataset, the predicted labels contain an unexpected number of unique values. Instead of clustering the data into the expected number of groups, each data point is assigned a unique label.

**Example Code:**
```python
from Orange.clustering import DBSCAN
from Orange.data import Table

iris = Table('iris')
dbscan = DBSCAN(eps=1)
cluster_model = dbscan(iris)
predictions = cluster_model(iris)

# Expected number of unique labels: 2
# Actual number of unique labels: 150
```

**Expected Behavior:**
The DBSCAN model should assign data points to a small number of clusters based on the `eps` parameter. For instance, with `eps=1`, it is expected to identify 2 distinct clusters in the Iris dataset.

**Actual Behavior:**
Each data point is assigned a unique label, resulting in 150 unique labels instead of the expected 2. This indicates that the clustering is not functioning as intended, and the model fails to group similar data points together.

[/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 Model Returns Incorrect Number of Predicted Labels

**Description:**
When using the DBSCAN clustering algorithm with `eps=1` on the Iris dataset, the predicted labels contain an unexpected number of unique values. Instead of clustering the data into the expected number of groups, each data point is assigned a unique label.

**Example Code:**
```python
from Orange.clustering import DBSCAN
from Orange.data import Table

iris = Table('iris')
dbscan = DBSCAN(eps=1)
cluster_model = dbscan(iris)
predictions = cluster_model(iris)

# Expected number of unique labels: 2
# Actual number of unique labels: 150
```

**Expected Behavior:**
The DBSCAN model should assign data points to a small number of clusters based on the `eps` parameter. For instance, with `eps=1`, it is expected to identify 2 distinct clusters in the Iris dataset.

**Actual Behavior:**
Each data point is assigned a unique label, resulting in 150 unique labels instead of the expected 2. This indicates that the clustering is not functioning as intended, and the model fails to group similar data points together.

[/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