{
    "file_diffs": [
        {
            "old_file_content": "\"\"\"Python port for Louvain clustering, available at\nhttps://github.com/taynaud/python-louvain\n\nOriginal C++ implementation available at\nhttps://sites.google.com/site/findcommunities/\n\n\"\"\"\n\nimport numpy as np\nimport networkx as nx\n# NOTE: The ``community`` package might be renamed in the near future, see\n# GH issue https://github.com/taynaud/python-louvain/issues/23\nfrom community import best_partition\nfrom sklearn.neighbors import NearestNeighbors\n\nimport Orange\nfrom Orange.data import Table\n\n\ndef jaccard(x, y):\n    # type: (set, set) -> float\n    \"\"\"Compute the Jaccard similarity between two sets.\"\"\"\n    return len(x & y) / len(x | y)\n\n\ndef table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):\n    \"\"\"Convert tabular data to a graph using a nearest neighbors approach with\n    the Jaccard similarity as the edge weights.\n\n    Parameters\n    ----------\n    data : Table\n    k_neighbors : int\n    metric : str\n        A distance metric supported by sklearn.\n    progress_callback : Callable[[float], None]\n\n    Returns\n    -------\n    nx.Graph\n\n    \"\"\"\n    # We do k + 1 because each point is closest to itself, which is not useful\n    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)\n    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)\n    # Convert to list of sets so jaccard can be computed efficiently\n    nearest_neighbors = list(map(set, nearest_neighbors))\n    num_nodes = len(nearest_neighbors)\n\n    # Create an empty graph and add all the data ids as nodes for easy mapping\n    graph = nx.Graph()\n    graph.add_nodes_from(range(len(data)))\n\n    for idx, node in enumerate(graph.nodes):\n        if progress_callback:\n            progress_callback(idx / num_nodes)\n\n        for neighbor in nearest_neighbors[node]:\n            graph.add_edge(\n                node,\n                neighbor,\n                weight=jaccard(nearest_neighbors[node], nearest_neighbors[neighbor]),\n            )\n\n    return graph\n\n\nclass Louvain:\n    preprocessors = [Orange.preprocess.Continuize(), Orange.preprocess.SklImpute()]\n\n    def __init__(\n            self,\n            k_neighbors=30,\n            metric=\"l2\",\n            resolution=1.0,\n            random_state=None,\n            preprocessors=None,\n    ):\n        \"\"\"Louvain clustering for community detection in graphs.\n\n        Louvain clustering is a community detection algorithm for detecting\n        clusters of \"communities\" in graphs. As such, tabular data must first\n        be converted into graph form. This is typically done by computing the\n        KNN graph on the input data.\n\n        Parameters\n        ----------\n        k_neighbors : Optional[int]\n            The number of nearest neighbors to use for the KNN graph if\n            tabular data is passed.\n\n        metric : Optional[str]\n            The metric to use to compute the nearest neighbors.\n\n        resolution : Optional[float]\n            The resolution is a parameter of the Louvain method that affects\n            the size of the recovered clusters.\n\n        random_state: Union[int, RandomState]\n            The random state parameter follows the convention used in scikit-learn.\n            If the value is an int, random_state is the seed used by the random\n            number generator. If the value is a RandomState instance, then it will\n            be used as the random number generator. If the value is None, the random\n            number generator is the RandomState instance used by `np.random`.\n\n        \"\"\"\n        if preprocessors is None:\n            preprocessors = type(self).preprocessors\n        self.preprocessors = tuple(preprocessors)\n\n        self.k_neighbors = k_neighbors\n        self.metric = metric\n        self.resolution = resolution\n        self.random_state = random_state\n\n        self.labels = None\n\n    def __call__(self, data):\n        data = self.preprocess(data)\n        return self.fit_predict(data.X, data.Y)\n\n    def preprocess(self, data):\n        for pp in self.preprocessors:\n            data = pp(data)\n        return data\n\n    def fit(self, X, y=None):\n        # If we are given a table, we have to convert it to a graph first\n        if isinstance(X, Table):\n            graph = table_to_knn_graph(\n                X.X, metric=self.metric, k_neighbors=self.k_neighbors\n            )\n        # Same goes for a matrix\n        elif isinstance(X, np.ndarray):\n            graph = table_to_knn_graph(\n                X, metric=self.metric, k_neighbors=self.k_neighbors\n            )\n        elif isinstance(X, nx.Graph):\n            graph = X\n\n        partition = best_partition(\n            graph, resolution=self.resolution, random_state=self.random_state\n        )\n        partition = np.fromiter(list(zip(*sorted(partition.items())))[1], dtype=int)\n\n        self.labels = partition\n\n    def fit_predict(self, X, y=None):\n        self.fit(X, y)\n        return self.labels\n",
            "new_file_content": "\"\"\"Python port for Louvain clustering, available at\nhttps://github.com/taynaud/python-louvain\n\nOriginal C++ implementation available at\nhttps://sites.google.com/site/findcommunities/\n\n\"\"\"\n\nimport numpy as np\nimport networkx as nx\n# NOTE: The ``community`` package might be renamed in the near future, see\n# GH issue https://github.com/taynaud/python-louvain/issues/23\nfrom community import best_partition\nfrom sklearn.neighbors import NearestNeighbors\n\nimport Orange\nfrom Orange.data import Table\n\n\ndef jaccard(x, y):\n    # type: (set, set) -> float\n    \"\"\"Compute the Jaccard similarity between two sets.\"\"\"\n    return len(x & y) / len(x | y)\n\n\ndef table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):\n    \"\"\"Convert tabular data to a graph using a nearest neighbors approach with\n    the Jaccard similarity as the edge weights.\n\n    Parameters\n    ----------\n    data : Table\n    k_neighbors : int\n    metric : str\n        A distance metric supported by sklearn.\n    progress_callback : Callable[[float], None]\n\n    Returns\n    -------\n    nx.Graph\n\n    \"\"\"\n    # We do k + 1 because each point is closest to itself, which is not useful\n    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data)\n    nearest_neighbors = knn.kneighbors(data, return_distance=False)\n    # Convert to list of sets so jaccard can be computed efficiently\n    nearest_neighbors = list(map(set, nearest_neighbors))\n    num_nodes = len(nearest_neighbors)\n\n    # Create an empty graph and add all the data ids as nodes for easy mapping\n    graph = nx.Graph()\n    graph.add_nodes_from(range(len(data)))\n\n    for idx, node in enumerate(graph.nodes):\n        if progress_callback:\n            progress_callback(idx / num_nodes)\n\n        for neighbor in nearest_neighbors[node]:\n            graph.add_edge(\n                node,\n                neighbor,\n                weight=jaccard(nearest_neighbors[node], nearest_neighbors[neighbor]),\n            )\n\n    return graph\n\n\nclass Louvain:\n    preprocessors = [Orange.preprocess.Continuize(), Orange.preprocess.SklImpute()]\n\n    def __init__(\n            self,\n            k_neighbors=30,\n            metric=\"l2\",\n            resolution=1.0,\n            random_state=None,\n            preprocessors=None,\n    ):\n        \"\"\"Louvain clustering for community detection in graphs.\n\n        Louvain clustering is a community detection algorithm for detecting\n        clusters of \"communities\" in graphs. As such, tabular data must first\n        be converted into graph form. This is typically done by computing the\n        KNN graph on the input data.\n\n        Parameters\n        ----------\n        k_neighbors : Optional[int]\n            The number of nearest neighbors to use for the KNN graph if\n            tabular data is passed.\n\n        metric : Optional[str]\n            The metric to use to compute the nearest neighbors.\n\n        resolution : Optional[float]\n            The resolution is a parameter of the Louvain method that affects\n            the size of the recovered clusters.\n\n        random_state: Union[int, RandomState]\n            The random state parameter follows the convention used in scikit-learn.\n            If the value is an int, random_state is the seed used by the random\n            number generator. If the value is a RandomState instance, then it will\n            be used as the random number generator. If the value is None, the random\n            number generator is the RandomState instance used by `np.random`.\n\n        \"\"\"\n        if preprocessors is None:\n            preprocessors = type(self).preprocessors\n        self.preprocessors = tuple(preprocessors)\n\n        self.k_neighbors = k_neighbors\n        self.metric = metric\n        self.resolution = resolution\n        self.random_state = random_state\n\n        self.labels = None\n\n    def __call__(self, data):\n        data = self.preprocess(data)\n        return self.fit_predict(data.X, data.Y)\n\n    def preprocess(self, data):\n        for pp in self.preprocessors:\n            data = pp(data)\n        return data\n\n    def fit(self, X, y=None):\n        # If we are given a table, we have to convert it to a graph first\n        if isinstance(X, Table):\n            graph = table_to_knn_graph(\n                X.X, metric=self.metric, k_neighbors=self.k_neighbors\n            )\n        # Same goes for a matrix\n        elif isinstance(X, np.ndarray):\n            graph = table_to_knn_graph(\n                X, metric=self.metric, k_neighbors=self.k_neighbors\n            )\n        elif isinstance(X, nx.Graph):\n            graph = X\n\n        partition = best_partition(\n            graph, resolution=self.resolution, random_state=self.random_state\n        )\n        partition = np.fromiter(list(zip(*sorted(partition.items())))[1], dtype=int)\n\n        self.labels = partition\n\n    def fit_predict(self, X, y=None):\n        self.fit(X, y)\n        return self.labels\n\n\nif __name__ == \"__main__\":\n    # clustering run on iris data - orange table\n    data = Table(\"iris\")\n    louvain = Louvain(2)\n    louvain.fit(data)\n",
            "header": {
                "file": {
                    "path": "Orange/clustering/louvain.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "bb3011132",
                "new_commit_hash": "a4072341b",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/clustering/louvain.py"
            },
            "plus_file": {
                "path": "b/Orange/clustering/louvain.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 41,
                            "length": 8
                        },
                        "new_range": {
                            "start": 41,
                            "length": 8
                        },
                        "section": "def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    \"\"\"",
                                "type": "context"
                            },
                            {
                                "content": "    # We do k + 1 because each point is closest to itself, which is not useful",
                                "type": "context"
                            },
                            {
                                "content": "    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)",
                                "type": "deleted"
                            },
                            {
                                "content": "    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)",
                                "type": "deleted"
                            },
                            {
                                "content": "    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data)",
                                "type": "added"
                            },
                            {
                                "content": "    nearest_neighbors = knn.kneighbors(data, return_distance=False)",
                                "type": "added"
                            },
                            {
                                "content": "    # Convert to list of sets so jaccard can be computed efficiently",
                                "type": "context"
                            },
                            {
                                "content": "    nearest_neighbors = list(map(set, nearest_neighbors))",
                                "type": "context"
                            },
                            {
                                "content": "    num_nodes = len(nearest_neighbors)",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/clustering/louvain.py",
                            "type": "function",
                            "name": "table_to_knn_graph",
                            "content": "def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):\n    \"\"\"Convert tabular data to a graph using a nearest neighbors approach with\n    the Jaccard similarity as the edge weights.\n\n    Parameters\n    ----------\n    data : Table\n    k_neighbors : int\n    metric : str\n        A distance metric supported by sklearn.\n    progress_callback : Callable[[float], None]\n\n    Returns\n    -------\n    nx.Graph\n\n    \"\"\"\n    # We do k + 1 because each point is closest to itself, which is not useful\n    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)\n    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)\n    # Convert to list of sets so jaccard can be computed efficiently\n    nearest_neighbors = list(map(set, nearest_neighbors))\n    num_nodes = len(nearest_neighbors)\n\n    # Create an empty graph and add all the data ids as nodes for easy mapping\n    graph = nx.Graph()\n    graph.add_nodes_from(range(len(data)))\n\n    for idx, node in enumerate(graph.nodes):\n        if progress_callback:\n            progress_callback(idx / num_nodes)\n\n        for neighbor in nearest_neighbors[node]:\n            graph.add_edge(\n                node,\n                neighbor,\n                weight=jaccard(nearest_neighbors[node], nearest_neighbors[neighbor]),\n            )\n\n    return graph",
                            "ast_type_str": "FunctionDef",
                            "start_lineno": 26,
                            "end_lineno": 65,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 148,
                            "length": 3
                        },
                        "new_range": {
                            "start": 148,
                            "length": 10
                        },
                        "section": "class Louvain:"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "    def fit_predict(self, X, y=None):",
                                "type": "context"
                            },
                            {
                                "content": "        self.fit(X, y)",
                                "type": "context"
                            },
                            {
                                "content": "        return self.labels",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "added"
                            },
                            {
                                "content": "    # clustering run on iris data - orange table",
                                "type": "added"
                            },
                            {
                                "content": "    data = Table(\"iris\")",
                                "type": "added"
                            },
                            {
                                "content": "    louvain = Louvain(2)",
                                "type": "added"
                            },
                            {
                                "content": "    louvain.fit(data)",
                                "type": "added"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/clustering/louvain.py",
                            "type": "statement",
                            "name": "",
                            "content": "if __name__ == \"__main__\":\n    # clustering run on iris data - orange table\n    data = Table(\"iris\")\n    louvain = Louvain(2)\n    louvain.fit(data)",
                            "ast_type_str": "If",
                            "start_lineno": 153,
                            "end_lineno": 157,
                            "parent": null
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\nimport numpy as np\n\nfrom Orange.data import Table\nfrom Orange.clustering.louvain import Louvain\n\n\nclass TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])\n",
            "header": {
                "file": {
                    "path": "Orange/tests/test_louvain.py"
                },
                "misc_line": "new file mode 100644"
            },
            "index_line": {
                "old_commit_hash": "000000000",
                "new_commit_hash": "2a0128894",
                "mode": ""
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "/dev/null"
            },
            "plus_file": {
                "path": "b/Orange/tests/test_louvain.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 0,
                            "length": 0
                        },
                        "new_range": {
                            "start": 1,
                            "length": 26
                        },
                        "section": ""
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "# Test methods with long descriptive names can omit docstrings",
                                "type": "added"
                            },
                            {
                                "content": "# pylint: disable=missing-docstring",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "import unittest",
                                "type": "added"
                            },
                            {
                                "content": "import numpy as np",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.data import Table",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.clustering.louvain import Louvain",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "class TestSVMLearner(unittest.TestCase):",
                                "type": "added"
                            },
                            {
                                "content": "    @classmethod",
                                "type": "added"
                            },
                            {
                                "content": "    def setUpClass(cls):",
                                "type": "added"
                            },
                            {
                                "content": "        cls.data = Table('iris')",
                                "type": "added"
                            },
                            {
                                "content": "        cls.louvain = Louvain()",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    def test_orange_table(self):",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsNone(self.louvain.fit(self.data))",
                                "type": "added"
                            },
                            {
                                "content": "        clusters = self.louvain.fit_predict(self.data)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIn(type(clusters), [list, np.ndarray])",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "    def test_np_array(self):",
                                "type": "added"
                            },
                            {
                                "content": "        data_np = self.data.X",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIsNone(self.louvain.fit(data_np))",
                                "type": "added"
                            },
                            {
                                "content": "        clusters = self.louvain.fit_predict(data_np)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertIn(type(clusters), [list, np.ndarray])",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.data import Table",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 7,
                            "end_lineno": 7,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.clustering.louvain import Louvain",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 8,
                            "end_lineno": 8,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "class",
                            "name": "TestSVMLearner",
                            "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 11,
                            "end_lineno": 26,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "import",
                            "name": "unittest",
                            "content": "import unittest",
                            "ast_type_str": "Import",
                            "start_lineno": 4,
                            "end_lineno": 4,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "method",
                            "name": "TestSVMLearner.setUpClass",
                            "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 13,
                            "end_lineno": 15,
                            "parent": {
                                "file_name": "Orange/tests/test_louvain.py",
                                "type": "class",
                                "name": "TestSVMLearner",
                                "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 11,
                                "end_lineno": 26,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "method",
                            "name": "TestSVMLearner.test_np_array",
                            "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 22,
                            "end_lineno": 26,
                            "parent": {
                                "file_name": "Orange/tests/test_louvain.py",
                                "type": "class",
                                "name": "TestSVMLearner",
                                "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 11,
                                "end_lineno": 26,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "import",
                            "name": "numpy",
                            "content": "import numpy as np",
                            "ast_type_str": "Import",
                            "start_lineno": 5,
                            "end_lineno": 5,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/tests/test_louvain.py",
                            "type": "method",
                            "name": "TestSVMLearner.test_orange_table",
                            "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 17,
                            "end_lineno": 20,
                            "parent": {
                                "file_name": "Orange/tests/test_louvain.py",
                                "type": "class",
                                "name": "TestSVMLearner",
                                "content": "class TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 11,
                                "end_lineno": 26,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06^",
    "new_commit_hash": "d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06",
    "commit_message": "Fix: Louvain clustering to accept Table or ndarray.",
    "commit_date": "2019-02-20T20:58:01+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\nimport numpy as np\n\nfrom Orange.data import Table\nfrom Orange.clustering.louvain import Louvain\n\n\nclass TestSVMLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table('iris')\n        cls.louvain = Louvain()\n\n    def test_orange_table(self):\n        self.assertIsNone(self.louvain.fit(self.data))\n        clusters = self.louvain.fit_predict(self.data)\n        self.assertIn(type(clusters), [list, np.ndarray])\n\n    def test_np_array(self):\n        data_np = self.data.X\n        self.assertIsNone(self.louvain.fit(data_np))\n        clusters = self.louvain.fit_predict(data_np)\n        self.assertIn(type(clusters), [list, np.ndarray])\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/tmptjp6ubcd/tmp\ncreating /tmp/tmptjp6ubcd/tmp/tmptjp6ubcd\ncompile options: '-MMD -MF /tmp/tmptjp6ubcd/file.c.d -c'\ncc: /tmp/tmptjp6ubcd/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/tmpd23dnolm/tmp\ncreating /tmp/tmpd23dnolm/tmp/tmpd23dnolm\ncompile options: '-MMD -MF /tmp/tmpd23dnolm/file.c.d -c'\ncc: /tmp/tmpd23dnolm/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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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/tmp5wwsgsml/tmp\ncreating /tmp/tmp5wwsgsml/tmp/tmp5wwsgsml\ncompile options: '-MMD -MF /tmp/tmp5wwsgsml/file.c.d -c'\ncc: /tmp/tmp5wwsgsml/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/tmpvrgo6afu/tmp\ncreating /tmp/tmpvrgo6afu/tmp/tmpvrgo6afu\ncompile options: '-MMD -MF /tmp/tmpvrgo6afu/file.c.d -c'\ncc: /tmp/tmpvrgo6afu/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.21.0.dev0+d526718 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06\nProcessing dependencies for Orange3==3.21.0.dev0+d526718\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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.21.0.dev0+d526718\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 176ms\nPrepared 18 packages in 45ms\nInstalled 18 packages in 753ms\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 138ms\nInstalled 28 packages in 620ms\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 41ms\nInstalled 11 packages in 21ms\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 5ms\nInstalled 2 packages in 7ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 5 packages in 26ms\nInstalled 2 packages in 167ms\n + pandas==1.3.5\n + pytz==2024.2\nAudited 2 packages in 28ms\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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06/.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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06\ncollected 2 items\n\nr2e_tests/test_1.py ..                                                   [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestSVMLearner::test_np_array\nPASSED r2e_tests/test_1.py::TestSVMLearner::test_orange_table\n============================== 2 passed in 2.21s ===============================\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_d526718b8504fd59a2ec1ce8ab1d7c3a3636bf06\ncollected 2 items\n\nr2e_tests/test_1.py FF                                                   [100%]\n\n=================================== FAILURES ===================================\n_________________________ TestSVMLearner.test_np_array _________________________\n\nself = <r2e_tests.test_1.TestSVMLearner testMethod=test_np_array>\n\n    def test_np_array(self):\n        data_np = self.data.X\n>       self.assertIsNone(self.louvain.fit(data_np))\n\nr2e_tests/test_1.py:24: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/clustering/louvain.py:136: in fit\n    X, metric=self.metric, k_neighbors=self.k_neighbors\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ndata = array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n  ...],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]])\nk_neighbors = 30, metric = 'l2', progress_callback = None\n\n    def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):\n        \"\"\"Convert tabular data to a graph using a nearest neighbors approach with\n        the Jaccard similarity as the edge weights.\n    \n        Parameters\n        ----------\n        data : Table\n        k_neighbors : int\n        metric : str\n            A distance metric supported by sklearn.\n        progress_callback : Callable[[float], None]\n    \n        Returns\n        -------\n        nx.Graph\n    \n        \"\"\"\n        # We do k + 1 because each point is closest to itself, which is not useful\n>       knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)\nE       AttributeError: 'numpy.ndarray' object has no attribute 'X'\n\nOrange/clustering/louvain.py:44: AttributeError\n_______________________ TestSVMLearner.test_orange_table _______________________\n\nself = <r2e_tests.test_1.TestSVMLearner testMethod=test_orange_table>\n\n    def test_orange_table(self):\n>       self.assertIsNone(self.louvain.fit(self.data))\n\nr2e_tests/test_1.py:18: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/clustering/louvain.py:131: in fit\n    X.X, metric=self.metric, k_neighbors=self.k_neighbors\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ndata = array([[5.1, 3.5, 1.4, 0.2],\n       [4.9, 3. , 1.4, 0.2],\n       [4.7, 3.2, 1.3, 0.2],\n       [4.6, 3.1, 1.5, 0.2],\n  ...],\n       [6.3, 2.5, 5. , 1.9],\n       [6.5, 3. , 5.2, 2. ],\n       [6.2, 3.4, 5.4, 2.3],\n       [5.9, 3. , 5.1, 1.8]])\nk_neighbors = 30, metric = 'l2', progress_callback = None\n\n    def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):\n        \"\"\"Convert tabular data to a graph using a nearest neighbors approach with\n        the Jaccard similarity as the edge weights.\n    \n        Parameters\n        ----------\n        data : Table\n        k_neighbors : int\n        metric : str\n            A distance metric supported by sklearn.\n        progress_callback : Callable[[float], None]\n    \n        Returns\n        -------\n        nx.Graph\n    \n        \"\"\"\n        # We do k + 1 because each point is closest to itself, which is not useful\n>       knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)\nE       AttributeError: 'numpy.ndarray' object has no attribute 'X'\n\nOrange/clustering/louvain.py:44: AttributeError\n=========================== short test summary info ============================\nFAILED r2e_tests/test_1.py::TestSVMLearner::test_np_array - AttributeError: '...\nFAILED r2e_tests/test_1.py::TestSVMLearner::test_orange_table - AttributeErro...\n============================== 2 failed in 1.25s ===============================\n",
    "old_commit_res_stderr": ""
}
Orange/clustering/louvain.py Orange/tests/test_louvain.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Louvain Clustering Fails with AttributeError When Using NumPy ndarray

**Description:**
When attempting to use the Louvain clustering algorithm with a NumPy `ndarray`, an `AttributeError` is raised. The algorithm currently expects the input data to have an `.X` attribute, which is present in `Table` objects but not in NumPy arrays.

**Example Buggy Code:**
```python
from Orange.data import Table
from Orange.clustering.louvain import Louvain

# Using a NumPy ndarray as input
import numpy as np
data_np = np.array([[5.1, 3.5, 1.4, 0.2],
                    [4.9, 3.0, 1.4, 0.2],
                    [4.7, 3.2, 1.3, 0.2]])

louvain = Louvain()
louvain.fit(data_np)  # This line raises an AttributeError
```

**Expected Behavior:**
The Louvain clustering algorithm should accept both `Table` objects and NumPy `ndarray` as input data without raising errors. When provided with a NumPy array, it should process the data directly without attempting to access a non-existent `.X` attribute.

**Actual Behavior:**
An `AttributeError` is raised because the algorithm tries to access the `.X` attribute of the input data, which does not exist for NumPy arrays.

**Error Message:**
```
AttributeError: 'numpy.ndarray' object has no attribute 'X'
```
[/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/louvain.py
"""Python port for Louvain clustering, available at
https://github.com/taynaud/python-louvain

Original C++ implementation available at
https://sites.google.com/site/findcommunities/

"""

import numpy as np
import networkx as nx
# NOTE: The ``community`` package might be renamed in the near future, see
# GH issue https://github.com/taynaud/python-louvain/issues/23
from community import best_partition
from sklearn.neighbors import NearestNeighbors

import Orange
from Orange.data import Table


def jaccard(x, y):
    # type: (set, set) -> float
    """Compute the Jaccard similarity between two sets."""
    return len(x & y) / len(x | y)


def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):
    """Convert tabular data to a graph using a nearest neighbors approach with
    the Jaccard similarity as the edge weights.

    Parameters
    ----------
    data : Table
    k_neighbors : int
    metric : str
        A distance metric supported by sklearn.
    progress_callback : Callable[[float], None]

    Returns
    -------
    nx.Graph

    """
    # We do k + 1 because each point is closest to itself, which is not useful
    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)
    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)
    # Convert to list of sets so jaccard can be computed efficiently
    nearest_neighbors = list(map(set, nearest_neighbors))
    num_nodes = len(nearest_neighbors)

    # Create an empty graph and add all the data ids as nodes for easy mapping
    graph = nx.Graph()
    graph.add_nodes_from(range(len(data)))

    for idx, node in enumerate(graph.nodes):
        if progress_callback:
            progress_callback(idx / num_nodes)

        for neighbor in nearest_neighbors[node]:
            graph.add_edge(
                node,
                neighbor,
                weight=jaccard(nearest_neighbors[node], nearest_neighbors[neighbor]),
            )

    return graph


class Louvain:
    preprocessors = [Orange.preprocess.Continuize(), Orange.preprocess.SklImpute()]

    def __init__(
            self,
            k_neighbors=30,
            metric="l2",
            resolution=1.0,
            random_state=None,
            preprocessors=None,
    ):
        """Louvain clustering for community detection in graphs.

        Louvain clustering is a community detection algorithm for detecting
        clusters of "communities" in graphs. As such, tabular data must first
        be converted into graph form. This is typically done by computing the
        KNN graph on the input data.

        Parameters
        ----------
        k_neighbors : Optional[int]
            The number of nearest neighbors to use for the KNN graph if
            tabular data is passed.

        metric : Optional[str]
            The metric to use to compute the nearest neighbors.

        resolution : Optional[float]
            The resolution is a parameter of the Louvain method that affects
            the size of the recovered clusters.

        random_state: Union[int, RandomState]
            The random state parameter follows the convention used in scikit-learn.
            If the value is an int, random_state is the seed used by the random
            number generator. If the value is a RandomState instance, then it will
            be used as the random number generator. If the value is None, the random
            number generator is the RandomState instance used by `np.random`.

        """
        if preprocessors is None:
            preprocessors = type(self).preprocessors
        self.preprocessors = tuple(preprocessors)

        self.k_neighbors = k_neighbors
        self.metric = metric
        self.resolution = resolution
        self.random_state = random_state

        self.labels = None

    def __call__(self, data):
        data = self.preprocess(data)
        return self.fit_predict(data.X, data.Y)

    def preprocess(self, data):
        for pp in self.preprocessors:
            data = pp(data)
        return data

    def fit(self, X, y=None):
        # If we are given a table, we have to convert it to a graph first
        if isinstance(X, Table):
            graph = table_to_knn_graph(
                X.X, metric=self.metric, k_neighbors=self.k_neighbors
            )
        # Same goes for a matrix
        elif isinstance(X, np.ndarray):
            graph = table_to_knn_graph(
                X, metric=self.metric, k_neighbors=self.k_neighbors
            )
        elif isinstance(X, nx.Graph):
            graph = X

        partition = best_partition(
            graph, resolution=self.resolution, random_state=self.random_state
        )
        partition = np.fromiter(list(zip(*sorted(partition.items())))[1], dtype=int)

        self.labels = partition

    def fit_predict(self, X, y=None):
        self.fit(X, y)
        return self.labels

```
--- 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:** Louvain Clustering Fails with AttributeError When Using NumPy ndarray

**Description:**
When attempting to use the Louvain clustering algorithm with a NumPy `ndarray`, an `AttributeError` is raised. The algorithm currently expects the input data to have an `.X` attribute, which is present in `Table` objects but not in NumPy arrays.

**Example Buggy Code:**
```python
from Orange.data import Table
from Orange.clustering.louvain import Louvain

# Using a NumPy ndarray as input
import numpy as np
data_np = np.array([[5.1, 3.5, 1.4, 0.2],
                    [4.9, 3.0, 1.4, 0.2],
                    [4.7, 3.2, 1.3, 0.2]])

louvain = Louvain()
louvain.fit(data_np)  # This line raises an AttributeError
```

**Expected Behavior:**
The Louvain clustering algorithm should accept both `Table` objects and NumPy `ndarray` as input data without raising errors. When provided with a NumPy array, it should process the data directly without attempting to access a non-existent `.X` attribute.

**Actual Behavior:**
An `AttributeError` is raised because the algorithm tries to access the `.X` attribute of the input data, which does not exist for NumPy arrays.

**Error Message:**
```
AttributeError: 'numpy.ndarray' object has no attribute 'X'
```
[/ISSUE]
{
    "TestSVMLearner.test_np_array": "PASSED",
    "TestSVMLearner.test_orange_table": "PASSED"
}
diff --git a/Orange/clustering/louvain.py b/Orange/clustering/louvain.py
index bb3011132..a4072341b 100644
--- a/Orange/clustering/louvain.py
+++ b/Orange/clustering/louvain.py
@@ -41,8 +41,8 @@ def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):
 
     """
     # We do k + 1 because each point is closest to itself, which is not useful
-    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)
-    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)
+    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data)
+    nearest_neighbors = knn.kneighbors(data, return_distance=False)
     # Convert to list of sets so jaccard can be computed efficiently
     nearest_neighbors = list(map(set, nearest_neighbors))
     num_nodes = len(nearest_neighbors)
@@ -148,3 +148,10 @@ class Louvain:
     def fit_predict(self, X, y=None):
         self.fit(X, y)
         return self.labels
+
+
+if __name__ == "__main__":
+    # clustering run on iris data - orange table
+    data = Table("iris")
+    louvain = Louvain(2)
+    louvain.fit(data)

{"Orange/clustering/louvain.py": "\"\"\"Python port for Louvain clustering, available at\nhttps://github.com/taynaud/python-louvain\n\nOriginal C++ implementation available at\nhttps://sites.google.com/site/findcommunities/\n\n\"\"\"\n\nimport numpy as np\nimport networkx as nx\n# NOTE: The ``community`` package might be renamed in the near future, see\n# GH issue https://github.com/taynaud/python-louvain/issues/23\nfrom community import best_partition\nfrom sklearn.neighbors import NearestNeighbors\n\nimport Orange\nfrom Orange.data import Table\n\n\ndef jaccard(x, y):\n    # type: (set, set) -> float\n    \"\"\"Compute the Jaccard similarity between two sets.\"\"\"\n    return len(x & y) / len(x | y)\n\n\ndef table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):\n    \"\"\"Convert tabular data to a graph using a nearest neighbors approach with\n    the Jaccard similarity as the edge weights.\n\n    Parameters\n    ----------\n    data : Table\n    k_neighbors : int\n    metric : str\n        A distance metric supported by sklearn.\n    progress_callback : Callable[[float], None]\n\n    Returns\n    -------\n    nx.Graph\n\n    \"\"\"\n    # We do k + 1 because each point is closest to itself, which is not useful\n    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)\n    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)\n    # Convert to list of sets so jaccard can be computed efficiently\n    nearest_neighbors = list(map(set, nearest_neighbors))\n    num_nodes = len(nearest_neighbors)\n\n    # Create an empty graph and add all the data ids as nodes for easy mapping\n    graph = nx.Graph()\n    graph.add_nodes_from(range(len(data)))\n\n    for idx, node in enumerate(graph.nodes):\n        if progress_callback:\n            progress_callback(idx / num_nodes)\n\n        for neighbor in nearest_neighbors[node]:\n            graph.add_edge(\n                node,\n                neighbor,\n                weight=jaccard(nearest_neighbors[node], nearest_neighbors[neighbor]),\n            )\n\n    return graph\n\n\nclass Louvain:\n    preprocessors = [Orange.preprocess.Continuize(), Orange.preprocess.SklImpute()]\n\n    def __init__(\n            self,\n            k_neighbors=30,\n            metric=\"l2\",\n            resolution=1.0,\n            random_state=None,\n            preprocessors=None,\n    ):\n        \"\"\"Louvain clustering for community detection in graphs.\n\n        Louvain clustering is a community detection algorithm for detecting\n        clusters of \"communities\" in graphs. As such, tabular data must first\n        be converted into graph form. This is typically done by computing the\n        KNN graph on the input data.\n\n        Parameters\n        ----------\n        k_neighbors : Optional[int]\n            The number of nearest neighbors to use for the KNN graph if\n            tabular data is passed.\n\n        metric : Optional[str]\n            The metric to use to compute the nearest neighbors.\n\n        resolution : Optional[float]\n            The resolution is a parameter of the Louvain method that affects\n            the size of the recovered clusters.\n\n        random_state: Union[int, RandomState]\n            The random state parameter follows the convention used in scikit-learn.\n            If the value is an int, random_state is the seed used by the random\n            number generator. If the value is a RandomState instance, then it will\n            be used as the random number generator. If the value is None, the random\n            number generator is the RandomState instance used by `np.random`.\n\n        \"\"\"\n        if preprocessors is None:\n            preprocessors = type(self).preprocessors\n        self.preprocessors = tuple(preprocessors)\n\n        self.k_neighbors = k_neighbors\n        self.metric = metric\n        self.resolution = resolution\n        self.random_state = random_state\n\n        self.labels = None\n\n    def __call__(self, data):\n        data = self.preprocess(data)\n        return self.fit_predict(data.X, data.Y)\n\n    def preprocess(self, data):\n        for pp in self.preprocessors:\n            data = pp(data)\n        return data\n\n    def fit(self, X, y=None):\n        # If we are given a table, we have to convert it to a graph first\n        if isinstance(X, Table):\n            graph = table_to_knn_graph(\n                X.X, metric=self.metric, k_neighbors=self.k_neighbors\n            )\n        # Same goes for a matrix\n        elif isinstance(X, np.ndarray):\n            graph = table_to_knn_graph(\n                X, metric=self.metric, k_neighbors=self.k_neighbors\n            )\n        elif isinstance(X, nx.Graph):\n            graph = X\n\n        partition = best_partition(\n            graph, resolution=self.resolution, random_state=self.random_state\n        )\n        partition = np.fromiter(list(zip(*sorted(partition.items())))[1], dtype=int)\n\n        self.labels = partition\n\n    def fit_predict(self, X, y=None):\n        self.fit(X, y)\n        return self.labels\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:** Louvain Clustering Fails with AttributeError When Using NumPy ndarray

**Description:**
When attempting to use the Louvain clustering algorithm with a NumPy `ndarray`, an `AttributeError` is raised. The algorithm currently expects the input data to have an `.X` attribute, which is present in `Table` objects but not in NumPy arrays.

**Example Buggy Code:**
```python
from Orange.data import Table
from Orange.clustering.louvain import Louvain

# Using a NumPy ndarray as input
import numpy as np
data_np = np.array([[5.1, 3.5, 1.4, 0.2],
                    [4.9, 3.0, 1.4, 0.2],
                    [4.7, 3.2, 1.3, 0.2]])

louvain = Louvain()
louvain.fit(data_np)  # This line raises an AttributeError
```

**Expected Behavior:**
The Louvain clustering algorithm should accept both `Table` objects and NumPy `ndarray` as input data without raising errors. When provided with a NumPy array, it should process the data directly without attempting to access a non-existent `.X` attribute.

**Actual Behavior:**
An `AttributeError` is raised because the algorithm tries to access the `.X` attribute of the input data, which does not exist for NumPy arrays.

**Error Message:**
```
AttributeError: 'numpy.ndarray' object has no attribute 'X'
```
[/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/louvain.py
"""Python port for Louvain clustering, available at
https://github.com/taynaud/python-louvain

Original C++ implementation available at
https://sites.google.com/site/findcommunities/

"""

import numpy as np
import networkx as nx
# NOTE: The ``community`` package might be renamed in the near future, see
# GH issue https://github.com/taynaud/python-louvain/issues/23
from community import best_partition
from sklearn.neighbors import NearestNeighbors

import Orange
from Orange.data import Table


def jaccard(x, y):
    # type: (set, set) -> float
    """Compute the Jaccard similarity between two sets."""
    return len(x & y) / len(x | y)


def table_to_knn_graph(data, k_neighbors, metric, progress_callback=None):
    """Convert tabular data to a graph using a nearest neighbors approach with
    the Jaccard similarity as the edge weights.

    Parameters
    ----------
    data : Table
    k_neighbors : int
    metric : str
        A distance metric supported by sklearn.
    progress_callback : Callable[[float], None]

    Returns
    -------
    nx.Graph

    """
    # We do k + 1 because each point is closest to itself, which is not useful
    knn = NearestNeighbors(n_neighbors=k_neighbors, metric=metric).fit(data.X)
    nearest_neighbors = knn.kneighbors(data.X, return_distance=False)
    # Convert to list of sets so jaccard can be computed efficiently
    nearest_neighbors = list(map(set, nearest_neighbors))
    num_nodes = len(nearest_neighbors)

    # Create an empty graph and add all the data ids as nodes for easy mapping
    graph = nx.Graph()
    graph.add_nodes_from(range(len(data)))

    for idx, node in enumerate(graph.nodes):
        if progress_callback:
            progress_callback(idx / num_nodes)

        for neighbor in nearest_neighbors[node]:
            graph.add_edge(
                node,
                neighbor,
                weight=jaccard(nearest_neighbors[node], nearest_neighbors[neighbor]),
            )

    return graph


class Louvain:
    preprocessors = [Orange.preprocess.Continuize(), Orange.preprocess.SklImpute()]

    def __init__(
            self,
            k_neighbors=30,
            metric="l2",
            resolution=1.0,
            random_state=None,
            preprocessors=None,
    ):
        """Louvain clustering for community detection in graphs.

        Louvain clustering is a community detection algorithm for detecting
        clusters of "communities" in graphs. As such, tabular data must first
        be converted into graph form. This is typically done by computing the
        KNN graph on the input data.

        Parameters
        ----------
        k_neighbors : Optional[int]
            The number of nearest neighbors to use for the KNN graph if
            tabular data is passed.

        metric : Optional[str]
            The metric to use to compute the nearest neighbors.

        resolution : Optional[float]
            The resolution is a parameter of the Louvain method that affects
            the size of the recovered clusters.

        random_state: Union[int, RandomState]
            The random state parameter follows the convention used in scikit-learn.
            If the value is an int, random_state is the seed used by the random
            number generator. If the value is a RandomState instance, then it will
            be used as the random number generator. If the value is None, the random
            number generator is the RandomState instance used by `np.random`.

        """
        if preprocessors is None:
            preprocessors = type(self).preprocessors
        self.preprocessors = tuple(preprocessors)

        self.k_neighbors = k_neighbors
        self.metric = metric
        self.resolution = resolution
        self.random_state = random_state

        self.labels = None

    def __call__(self, data):
        data = self.preprocess(data)
        return self.fit_predict(data.X, data.Y)

    def preprocess(self, data):
        for pp in self.preprocessors:
            data = pp(data)
        return data

    def fit(self, X, y=None):
        # If we are given a table, we have to convert it to a graph first
        if isinstance(X, Table):
            graph = table_to_knn_graph(
                X.X, metric=self.metric, k_neighbors=self.k_neighbors
            )
        # Same goes for a matrix
        elif isinstance(X, np.ndarray):
            graph = table_to_knn_graph(
                X, metric=self.metric, k_neighbors=self.k_neighbors
            )
        elif isinstance(X, nx.Graph):
            graph = X

        partition = best_partition(
            graph, resolution=self.resolution, random_state=self.random_state
        )
        partition = np.fromiter(list(zip(*sorted(partition.items())))[1], dtype=int)

        self.labels = partition

    def fit_predict(self, X, y=None):
        self.fit(X, y)
        return self.labels

```
--- 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