{
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        {
            "old_file_content": "import numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.classification import Learner, Model\nfrom Orange.data import Instance, Storage, Table\nfrom Orange.statistics import contingency\nfrom Orange.preprocess import Discretize, RemoveNaNColumns\n\n__all__ = [\"NaiveBayesLearner\"]\n\n\nclass NaiveBayesLearner(Learner):\n    \"\"\"\n    Naive Bayes classifier. Works only with discrete attributes. By default,\n    continuous attributes are discretized.\n\n    Parameters\n    ----------\n    preprocessors : list, optional (default=\"[Orange.preprocess.Discretize]\")\n        An ordered list of preprocessors applied to data before training\n        or testing.\n    \"\"\"\n    preprocessors = [RemoveNaNColumns(), Discretize()]\n    name = 'naive bayes'\n\n    def fit_storage(self, table):\n        if not isinstance(table, Storage):\n            raise TypeError(\"Data is not a subclass of Orange.data.Storage.\")\n        if not all(var.is_discrete\n                   for var in table.domain.variables):\n            raise NotImplementedError(\"Only categorical variables are \"\n                                      \"supported.\")\n\n        cont = contingency.get_contingencies(table)\n        class_freq = np.array(np.diag(\n            contingency.get_contingency(table, table.domain.class_var)))\n        nclss = (class_freq != 0).sum()\n        if not nclss:\n            raise ValueError(\"Data has no defined target values.\")\n\n        # Laplacian smoothing considers only classes that appear in the data,\n        # in part to avoid cases where the probabilities are affected by empty\n        # (or completely spurious) classes that appear because of Orange's reuse\n        # of variables. See GH-2943.\n        # The corresponding elements of class_probs are set to zero only after\n        # mock non-zero values are used in computation of log_cont_prob to\n        # prevent division by zero.\n        class_prob = (class_freq + 1) / (np.sum(class_freq) + nclss)\n        log_cont_prob = [np.log(\n            (np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] + nclss)\n            / class_prob[:, None])\n                         for c in cont]\n        class_prob[class_freq == 0] = 0\n        return NaiveBayesModel(log_cont_prob, class_prob, table.domain)\n\n\nclass NaiveBayesModel(Model):\n    def __init__(self, log_cont_prob, class_prob, domain):\n        super().__init__(domain)\n        self.log_cont_prob = log_cont_prob\n        self.class_prob = class_prob\n\n    def predict_storage(self, data):\n        if isinstance(data, Instance):\n            data = Table.from_numpy(None, np.atleast_2d(data.x))\n        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck\n            return self.predict(data.X)\n\n        if not len(data) or not len(data[0]):\n            probs = np.tile(self.class_prob, (len(data), 1))\n        else:\n            isnan = np.isnan\n            zeros = np.zeros_like(self.class_prob)\n            probs = self.class_prob * np.exp(np.array([\n                zeros if isnan(ins.x).all() else\n                sum(attr_prob[:, int(attr_val)]\n                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)\n                    if not isnan(attr_val))\n                for ins in data]))\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def predict(self, X):\n        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))\n        if self.log_cont_prob is not None:\n            if sp.issparse(X):\n                self._sparse_probs(X, probs)\n            else:\n                self._dense_probs(X, probs)\n        np.exp(probs, probs)\n        probs *= self.class_prob\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def _dense_probs(self, data, probs):\n        zeros = np.zeros((1, probs.shape[1]))\n        for col, attr_prob in zip(data.T, self.log_cont_prob):\n            col = col.copy()\n            col[np.isnan(col)] = attr_prob.shape[1] - 1\n            col = col.astype(int)\n            probs0 = np.vstack((attr_prob.T, zeros))\n            probs += probs0[col]\n        return probs\n\n    def _sparse_probs(self, data, probs):\n        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1\n        log_prob = np.zeros((len(self.log_cont_prob),\n                             n_vals,\n                             self.log_cont_prob[0].shape[0]))\n        for i, p in enumerate(self.log_cont_prob):\n            p0 = p.T[0].copy()\n            probs[:] += p0\n            log_prob[i, :p.shape[1]] = p.T - p0\n\n        dat = data.data.copy()\n        dat[np.isnan(dat)] = n_vals - 1\n        dat = dat.astype(int)\n\n        if sp.isspmatrix_csr(data):\n            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):\n                row += log_prob[data.indices[start:end],\n                                dat[start:end]].sum(axis=0)\n        else:\n            csc = data.tocsc()\n            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],\n                                             log_prob):\n                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]\n\n        return probs\n\n\nNaiveBayesLearner.__returns__ = NaiveBayesModel\n",
            "new_file_content": "import numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.classification import Learner, Model\nfrom Orange.data import Instance, Storage, Table\nfrom Orange.statistics import contingency\nfrom Orange.preprocess import Discretize, RemoveNaNColumns\n\n__all__ = [\"NaiveBayesLearner\"]\n\n\nclass NaiveBayesLearner(Learner):\n    \"\"\"\n    Naive Bayes classifier. Works only with discrete attributes. By default,\n    continuous attributes are discretized.\n\n    Parameters\n    ----------\n    preprocessors : list, optional (default=\"[Orange.preprocess.Discretize]\")\n        An ordered list of preprocessors applied to data before training\n        or testing.\n    \"\"\"\n    preprocessors = [RemoveNaNColumns(), Discretize()]\n    name = 'naive bayes'\n\n    def fit_storage(self, table):\n        if not isinstance(table, Storage):\n            raise TypeError(\"Data is not a subclass of Orange.data.Storage.\")\n        if not all(var.is_discrete\n                   for var in table.domain.variables):\n            raise NotImplementedError(\"Only categorical variables are \"\n                                      \"supported.\")\n\n        cont = contingency.get_contingencies(table)\n        class_freq = np.array(np.diag(\n            contingency.get_contingency(table, table.domain.class_var)))\n        nclss = (class_freq != 0).sum()\n        if not nclss:\n            raise ValueError(\"Data has no defined target values.\")\n\n        # Laplacian smoothing considers only classes that appear in the data,\n        # in part to avoid cases where the probabilities are affected by empty\n        # (or completely spurious) classes that appear because of Orange's reuse\n        # of variables. See GH-2943.\n        # The corresponding elements of class_probs are set to zero only after\n        # mock non-zero values are used in computation of log_cont_prob to\n        # prevent division by zero.\n        class_prob = (class_freq + 1) / (np.sum(class_freq) + nclss)\n        log_cont_prob = [np.log(\n            (np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] + nclss)\n            / class_prob[:, None])\n                         for c in cont]\n        class_prob[class_freq == 0] = 0\n        return NaiveBayesModel(log_cont_prob, class_prob, table.domain)\n\n\nclass NaiveBayesModel(Model):\n    def __init__(self, log_cont_prob, class_prob, domain):\n        super().__init__(domain)\n        self.log_cont_prob = log_cont_prob\n        self.class_prob = class_prob\n\n    def predict_storage(self, data):\n        if isinstance(data, Instance):\n            data = Table.from_numpy(None, np.atleast_2d(data.x))\n        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck\n            return self.predict(data.X)\n\n        if not len(data) or not len(data[0]):\n            probs = np.tile(self.class_prob, (len(data), 1))\n        else:\n            isnan = np.isnan\n            zeros = np.zeros_like(self.class_prob)\n            probs = self.class_prob * np.exp(np.array([\n                zeros if isnan(ins.x).all() else\n                sum(attr_prob[:, int(attr_val)]\n                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)\n                    if not isnan(attr_val))\n                for ins in data]))\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def predict(self, X):\n        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))\n        if self.log_cont_prob is not None:\n            if sp.issparse(X):\n                self._sparse_probs(X, probs)\n            else:\n                self._dense_probs(X, probs)\n        np.exp(probs, probs)\n        probs *= self.class_prob\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def _dense_probs(self, data, probs):\n        zeros = np.zeros((1, probs.shape[1]))\n        for col, attr_prob in zip(data.T, self.log_cont_prob):\n            col = col.copy()\n            col[np.isnan(col)] = attr_prob.shape[1]\n            col = col.astype(int)\n            probs0 = np.vstack((attr_prob.T, zeros))\n            probs += probs0[col]\n        return probs\n\n    def _sparse_probs(self, data, probs):\n        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1\n        log_prob = np.zeros((len(self.log_cont_prob),\n                             n_vals,\n                             self.log_cont_prob[0].shape[0]))\n        for i, p in enumerate(self.log_cont_prob):\n            p0 = p.T[0].copy()\n            probs[:] += p0\n            log_prob[i, :p.shape[1]] = p.T - p0\n\n        dat = data.data.copy()\n        dat[np.isnan(dat)] = n_vals - 1\n        dat = dat.astype(int)\n\n        if sp.isspmatrix_csr(data):\n            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):\n                row += log_prob[data.indices[start:end],\n                                dat[start:end]].sum(axis=0)\n        else:\n            csc = data.tocsc()\n            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],\n                                             log_prob):\n                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]\n\n        return probs\n\n\nNaiveBayesLearner.__returns__ = NaiveBayesModel\n",
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                        "section": "class NaiveBayesModel(Model):"
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                            {
                                "content": "        zeros = np.zeros((1, probs.shape[1]))",
                                "type": "context"
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                            {
                                "content": "        for col, attr_prob in zip(data.T, self.log_cont_prob):",
                                "type": "context"
                            },
                            {
                                "content": "            col = col.copy()",
                                "type": "context"
                            },
                            {
                                "content": "            col[np.isnan(col)] = attr_prob.shape[1] - 1",
                                "type": "deleted"
                            },
                            {
                                "content": "            col[np.isnan(col)] = attr_prob.shape[1]",
                                "type": "added"
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                            {
                                "content": "            col = col.astype(int)",
                                "type": "context"
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                            {
                                "content": "            probs0 = np.vstack((attr_prob.T, zeros))",
                                "type": "context"
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                        {
                            "file_name": "Orange/classification/naive_bayes.py",
                            "type": "method",
                            "name": "NaiveBayesModel._dense_probs",
                            "content": "class NaiveBayesModel(Model):\n    def __init__(self, log_cont_prob, class_prob, domain):\n        super().__init__(domain)\n        self.log_cont_prob = log_cont_prob\n        self.class_prob = class_prob\n\n    def predict_storage(self, data):\n        if isinstance(data, Instance):\n            data = Table.from_numpy(None, np.atleast_2d(data.x))\n        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck\n            return self.predict(data.X)\n\n        if not len(data) or not len(data[0]):\n            probs = np.tile(self.class_prob, (len(data), 1))\n        else:\n            isnan = np.isnan\n            zeros = np.zeros_like(self.class_prob)\n            probs = self.class_prob * np.exp(np.array([\n                zeros if isnan(ins.x).all() else\n                sum(attr_prob[:, int(attr_val)]\n                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)\n                    if not isnan(attr_val))\n                for ins in data]))\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def predict(self, X):\n        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))\n        if self.log_cont_prob is not None:\n            if sp.issparse(X):\n                self._sparse_probs(X, probs)\n            else:\n                self._dense_probs(X, probs)\n        np.exp(probs, probs)\n        probs *= self.class_prob\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def _dense_probs(self, data, probs):\n        zeros = np.zeros((1, probs.shape[1]))\n        for col, attr_prob in zip(data.T, self.log_cont_prob):\n            col = col.copy()\n            col[np.isnan(col)] = attr_prob.shape[1] - 1\n            col = col.astype(int)\n            probs0 = np.vstack((attr_prob.T, zeros))\n            probs += probs0[col]\n        return probs\n\n    def _sparse_probs(self, data, probs):\n        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1\n        log_prob = np.zeros((len(self.log_cont_prob),\n                             n_vals,\n                             self.log_cont_prob[0].shape[0]))\n        for i, p in enumerate(self.log_cont_prob):\n            p0 = p.T[0].copy()\n            probs[:] += p0\n            log_prob[i, :p.shape[1]] = p.T - p0\n\n        dat = data.data.copy()\n        dat[np.isnan(dat)] = n_vals - 1\n        dat = dat.astype(int)\n\n        if sp.isspmatrix_csr(data):\n            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):\n                row += log_prob[data.indices[start:end],\n                                dat[start:end]].sum(axis=0)\n        else:\n            csc = data.tocsc()\n            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],\n                                             log_prob):\n                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]\n\n        return probs",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 97,
                            "end_lineno": 105,
                            "parent": {
                                "file_name": "Orange/classification/naive_bayes.py",
                                "type": "class",
                                "name": "NaiveBayesModel",
                                "content": "class NaiveBayesModel(Model):\n    def __init__(self, log_cont_prob, class_prob, domain):\n        super().__init__(domain)\n        self.log_cont_prob = log_cont_prob\n        self.class_prob = class_prob\n\n    def predict_storage(self, data):\n        if isinstance(data, Instance):\n            data = Table.from_numpy(None, np.atleast_2d(data.x))\n        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck\n            return self.predict(data.X)\n\n        if not len(data) or not len(data[0]):\n            probs = np.tile(self.class_prob, (len(data), 1))\n        else:\n            isnan = np.isnan\n            zeros = np.zeros_like(self.class_prob)\n            probs = self.class_prob * np.exp(np.array([\n                zeros if isnan(ins.x).all() else\n                sum(attr_prob[:, int(attr_val)]\n                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)\n                    if not isnan(attr_val))\n                for ins in data]))\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def predict(self, X):\n        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))\n        if self.log_cont_prob is not None:\n            if sp.issparse(X):\n                self._sparse_probs(X, probs)\n            else:\n                self._dense_probs(X, probs)\n        np.exp(probs, probs)\n        probs *= self.class_prob\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def _dense_probs(self, data, probs):\n        zeros = np.zeros((1, probs.shape[1]))\n        for col, attr_prob in zip(data.T, self.log_cont_prob):\n            col = col.copy()\n            col[np.isnan(col)] = attr_prob.shape[1] - 1\n            col = col.astype(int)\n            probs0 = np.vstack((attr_prob.T, zeros))\n            probs += probs0[col]\n        return probs\n\n    def _sparse_probs(self, data, probs):\n        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1\n        log_prob = np.zeros((len(self.log_cont_prob),\n                             n_vals,\n                             self.log_cont_prob[0].shape[0]))\n        for i, p in enumerate(self.log_cont_prob):\n            p0 = p.T[0].copy()\n            probs[:] += p0\n            log_prob[i, :p.shape[1]] = p.T - p0\n\n        dat = data.data.copy()\n        dat[np.isnan(dat)] = n_vals - 1\n        dat = dat.astype(int)\n\n        if sp.isspmatrix_csr(data):\n            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):\n                row += log_prob[data.indices[start:end],\n                                dat[start:end]].sum(axis=0)\n        else:\n            csc = data.tocsc()\n            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],\n                                             log_prob):\n                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]\n\n        return probs",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 57,
                                "end_lineno": 131,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/classification/naive_bayes.py",
                            "type": "class",
                            "name": "NaiveBayesModel",
                            "content": "class NaiveBayesModel(Model):\n    def __init__(self, log_cont_prob, class_prob, domain):\n        super().__init__(domain)\n        self.log_cont_prob = log_cont_prob\n        self.class_prob = class_prob\n\n    def predict_storage(self, data):\n        if isinstance(data, Instance):\n            data = Table.from_numpy(None, np.atleast_2d(data.x))\n        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck\n            return self.predict(data.X)\n\n        if not len(data) or not len(data[0]):\n            probs = np.tile(self.class_prob, (len(data), 1))\n        else:\n            isnan = np.isnan\n            zeros = np.zeros_like(self.class_prob)\n            probs = self.class_prob * np.exp(np.array([\n                zeros if isnan(ins.x).all() else\n                sum(attr_prob[:, int(attr_val)]\n                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)\n                    if not isnan(attr_val))\n                for ins in data]))\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def predict(self, X):\n        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))\n        if self.log_cont_prob is not None:\n            if sp.issparse(X):\n                self._sparse_probs(X, probs)\n            else:\n                self._dense_probs(X, probs)\n        np.exp(probs, probs)\n        probs *= self.class_prob\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def _dense_probs(self, data, probs):\n        zeros = np.zeros((1, probs.shape[1]))\n        for col, attr_prob in zip(data.T, self.log_cont_prob):\n            col = col.copy()\n            col[np.isnan(col)] = attr_prob.shape[1] - 1\n            col = col.astype(int)\n            probs0 = np.vstack((attr_prob.T, zeros))\n            probs += probs0[col]\n        return probs\n\n    def _sparse_probs(self, data, probs):\n        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1\n        log_prob = np.zeros((len(self.log_cont_prob),\n                             n_vals,\n                             self.log_cont_prob[0].shape[0]))\n        for i, p in enumerate(self.log_cont_prob):\n            p0 = p.T[0].copy()\n            probs[:] += p0\n            log_prob[i, :p.shape[1]] = p.T - p0\n\n        dat = data.data.copy()\n        dat[np.isnan(dat)] = n_vals - 1\n        dat = dat.astype(int)\n\n        if sp.isspmatrix_csr(data):\n            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):\n                row += log_prob[data.indices[start:end],\n                                dat[start:end]].sum(axis=0)\n        else:\n            csc = data.tocsc()\n            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],\n                                             log_prob):\n                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]\n\n        return probs",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 57,
                            "end_lineno": 131,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.classification import NaiveBayesLearner\nfrom Orange.data import Table, Domain, DiscreteVariable, ContinuousVariable\nfrom Orange.evaluation import CrossValidation, CA\n\n\n# This class is used to force predict_storage to fall back to the slower\n# procedure instead of calling `predict`\nfrom Orange.tests import test_filename\n\n\nclass NotATable(Table):  # pylint: disable=too-many-ancestors,abstract-method\n    @classmethod\n    def from_file(cls, *args, **kwargs):\n        table = super().from_file(*args, **kwargs)\n        return cls(table)\n\n\nclass TestNaiveBayesLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = data = Table('titanic')\n        cls.learner = NaiveBayesLearner()\n        cls.table = data[::20]\n\n    def setUp(self):\n        self.model = self.learner(self.data)\n\n    def test_NaiveBayes(self):\n        cv = CrossValidation(k=10)\n        results = cv(self.table, [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n        self.assertLess(ca, 0.9)\n\n        cv = CrossValidation(k=10)\n        results = cv(Table(\"iris\"), [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n\n    def test_degenerate(self):\n        d = Domain((ContinuousVariable(name=\"A\"),\n                    ContinuousVariable(name=\"B\"),\n                    ContinuousVariable(name=\"C\")),\n                   DiscreteVariable(name=\"CLASS\", values=(\"M\", \"F\")))\n        t = Table.from_list(d, [[0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1]])\n        nb = NaiveBayesLearner()\n        model = nb(t)\n        self.assertEqual(model.domain.attributes, ())\n        self.assertEqual(model(t[0]), 1)\n        self.assertTrue(all(model(t) == 1))\n\n    def test_allnan_cv(self):\n        # GH 2740\n        data = Table(test_filename('datasets/lenses.tab'))\n        cv = CrossValidation(stratified=False)\n        results = cv(data, [self.learner])\n        self.assertFalse(any(results.failed))\n\n    def test_prediction_routing(self):\n        data = self.data\n        predict = self.model.predict = Mock(return_value=(data.Y, None))\n\n        self.model(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model(data.X)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data[0])\n        predict.assert_called()\n\n    def test_compare_results_of_predict_and_predict_storage(self):\n        data2 = NotATable(\"titanic\")\n\n        self.model = self.learner(self.data[:50])\n        predict = self.model.predict = Mock(side_effect=self.model.predict)\n        values, probs = self.model.predict_storage(self.data[50:])\n        predict.assert_called()\n        predict.reset_mock()\n        values2, probs2 = self.model.predict_storage(data2[50:])\n        predict.assert_not_called()\n\n        np.testing.assert_equal(values, values2)\n        np.testing.assert_equal(probs, probs2)\n\n    def test_predictions(self):\n        self._test_predictions(sparse=None)\n        self._test_predictions_with_absent_class(sparse=None)\n\n    def test_predictions_csr_matrix(self):\n        self._test_predictions(sparse=sp.csr_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csr_matrix)\n\n    def test_predictions_csc_matrix(self):\n        self._test_predictions(sparse=sp.csc_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csc_matrix)\n\n    def _test_predictions(self, sparse):\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def _test_predictions_with_absent_class(self, sparse):\n        \"\"\"Empty classes should not affect predictions\"\"\"\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 2, 2, 2, 3, 3])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abcd\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 0, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [0, 0], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [0, 0, 0], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [0], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def test_no_attributes(self):\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain([], DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, np.zeros((len(y), 0)), y.T)\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.predict_storage(np.zeros((5, 0)))[1],\n            [[4/11, 4/11, 3/11]] * 5\n        )\n\n    def test_no_targets(self):\n        x = np.array([[0], [1], [2]])\n        y = np.full(3, np.nan)\n        domain = Domain([DiscreteVariable(\"x\", values=\"abc\")],\n                        DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n        self.assertRaises(ValueError, self.learner, data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.classification import NaiveBayesLearner\nfrom Orange.data import Table, Domain, DiscreteVariable, ContinuousVariable\nfrom Orange.evaluation import CrossValidation, CA\n\n\n# This class is used to force predict_storage to fall back to the slower\n# procedure instead of calling `predict`\nfrom Orange.tests import test_filename\n\n\nclass NotATable(Table):  # pylint: disable=too-many-ancestors,abstract-method\n    @classmethod\n    def from_file(cls, *args, **kwargs):\n        table = super().from_file(*args, **kwargs)\n        return cls(table)\n\n\nclass TestNaiveBayesLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = data = Table('titanic')\n        cls.learner = NaiveBayesLearner()\n        cls.table = data[::20]\n\n    def setUp(self):\n        self.model = self.learner(self.data)\n\n    def test_NaiveBayes(self):\n        cv = CrossValidation(k=10)\n        results = cv(self.table, [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n        self.assertLess(ca, 0.9)\n\n        cv = CrossValidation(k=10)\n        results = cv(Table(\"iris\"), [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n\n    def test_degenerate(self):\n        d = Domain((ContinuousVariable(name=\"A\"),\n                    ContinuousVariable(name=\"B\"),\n                    ContinuousVariable(name=\"C\")),\n                   DiscreteVariable(name=\"CLASS\", values=(\"M\", \"F\")))\n        t = Table.from_list(d, [[0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1]])\n        nb = NaiveBayesLearner()\n        model = nb(t)\n        self.assertEqual(model.domain.attributes, ())\n        self.assertEqual(model(t[0]), 1)\n        self.assertTrue(all(model(t) == 1))\n\n    def test_allnan_cv(self):\n        # GH 2740\n        data = Table(test_filename('datasets/lenses.tab'))\n        cv = CrossValidation(stratified=False)\n        results = cv(data, [self.learner])\n        self.assertFalse(any(results.failed))\n\n    def test_prediction_routing(self):\n        data = self.data\n        predict = self.model.predict = Mock(return_value=(data.Y, None))\n\n        self.model(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model(data.X)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data[0])\n        predict.assert_called()\n\n    def test_compare_results_of_predict_and_predict_storage(self):\n        data2 = NotATable(\"titanic\")\n\n        self.model = self.learner(self.data[:50])\n        predict = self.model.predict = Mock(side_effect=self.model.predict)\n        values, probs = self.model.predict_storage(self.data[50:])\n        predict.assert_called()\n        predict.reset_mock()\n        values2, probs2 = self.model.predict_storage(data2[50:])\n        predict.assert_not_called()\n\n        np.testing.assert_equal(values, values2)\n        np.testing.assert_equal(probs, probs2)\n\n    def test_predictions(self):\n        self._test_predictions(sparse=None)\n        self._test_predictions_with_absent_class(sparse=None)\n\n    def test_predictions_csr_matrix(self):\n        self._test_predictions(sparse=sp.csr_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csr_matrix)\n\n    def test_predictions_csc_matrix(self):\n        self._test_predictions(sparse=sp.csc_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csc_matrix)\n\n    def _test_predictions(self, sparse):\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def _test_predictions_with_absent_class(self, sparse):\n        \"\"\"Empty classes should not affect predictions\"\"\"\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 2, 2, 2, 3, 3])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abcd\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 0, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [0, 0], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [0, 0, 0], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [0], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def test_predict_missing_attributes(self):\n        x = np.array([\n            [1, 0, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [0, 1, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [1, 2, np.nan]])\n        y = np.array([1,0,0,0,1,1,1])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"AB\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        test_x = np.array([[np.nan, np.nan, np.nan],\n                           [np.nan, 0, np.nan],\n                           [0, np.nan, np.nan]])\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(probs, [[(3+1)/(7+2), (4+1)/(7+2)],\n                                               [(1+1)/(2+2), (1+1)/(2+2)],\n                                               [(3+1)/(3+2), (0+1)/(3+2)]])\n\n    def test_no_attributes(self):\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain([], DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, np.zeros((len(y), 0)), y.T)\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.predict_storage(np.zeros((5, 0)))[1],\n            [[4/11, 4/11, 3/11]] * 5\n        )\n\n    def test_no_targets(self):\n        x = np.array([[0], [1], [2]])\n        y = np.full(3, np.nan)\n        domain = Domain([DiscreteVariable(\"x\", values=\"abc\")],\n                        DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n        self.assertRaises(ValueError, self.learner, data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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                        "section": "class TestNaiveBayesLearner(unittest.TestCase):"
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                                "content": "    def test_predict_missing_attributes(self):",
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                                "content": "        domain = Domain(",
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                                "content": "            DiscreteVariable(\"y\", values=\"AB\"))",
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                                "type": "added"
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                            "file_name": "Orange/tests/test_naive_bayes.py",
                            "type": "class",
                            "name": "TestNaiveBayesLearner",
                            "content": "class TestNaiveBayesLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = data = Table('titanic')\n        cls.learner = NaiveBayesLearner()\n        cls.table = data[::20]\n\n    def setUp(self):\n        self.model = self.learner(self.data)\n\n    def test_NaiveBayes(self):\n        cv = CrossValidation(k=10)\n        results = cv(self.table, [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n        self.assertLess(ca, 0.9)\n\n        cv = CrossValidation(k=10)\n        results = cv(Table(\"iris\"), [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n\n    def test_degenerate(self):\n        d = Domain((ContinuousVariable(name=\"A\"),\n                    ContinuousVariable(name=\"B\"),\n                    ContinuousVariable(name=\"C\")),\n                   DiscreteVariable(name=\"CLASS\", values=(\"M\", \"F\")))\n        t = Table.from_list(d, [[0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1]])\n        nb = NaiveBayesLearner()\n        model = nb(t)\n        self.assertEqual(model.domain.attributes, ())\n        self.assertEqual(model(t[0]), 1)\n        self.assertTrue(all(model(t) == 1))\n\n    def test_allnan_cv(self):\n        # GH 2740\n        data = Table(test_filename('datasets/lenses.tab'))\n        cv = CrossValidation(stratified=False)\n        results = cv(data, [self.learner])\n        self.assertFalse(any(results.failed))\n\n    def test_prediction_routing(self):\n        data = self.data\n        predict = self.model.predict = Mock(return_value=(data.Y, None))\n\n        self.model(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model(data.X)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data[0])\n        predict.assert_called()\n\n    def test_compare_results_of_predict_and_predict_storage(self):\n        data2 = NotATable(\"titanic\")\n\n        self.model = self.learner(self.data[:50])\n        predict = self.model.predict = Mock(side_effect=self.model.predict)\n        values, probs = self.model.predict_storage(self.data[50:])\n        predict.assert_called()\n        predict.reset_mock()\n        values2, probs2 = self.model.predict_storage(data2[50:])\n        predict.assert_not_called()\n\n        np.testing.assert_equal(values, values2)\n        np.testing.assert_equal(probs, probs2)\n\n    def test_predictions(self):\n        self._test_predictions(sparse=None)\n        self._test_predictions_with_absent_class(sparse=None)\n\n    def test_predictions_csr_matrix(self):\n        self._test_predictions(sparse=sp.csr_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csr_matrix)\n\n    def test_predictions_csc_matrix(self):\n        self._test_predictions(sparse=sp.csc_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csc_matrix)\n\n    def _test_predictions(self, sparse):\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def _test_predictions_with_absent_class(self, sparse):\n        \"\"\"Empty classes should not affect predictions\"\"\"\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 2, 2, 2, 3, 3])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abcd\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 0, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [0, 0], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [0, 0, 0], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [0], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def test_predict_missing_attributes(self):\n        x = np.array([\n            [1, 0, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [0, 1, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [1, 2, np.nan]])\n        y = np.array([1,0,0,0,1,1,1])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"AB\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        test_x = np.array([[np.nan, np.nan, np.nan],\n                           [np.nan, 0, np.nan],\n                           [0, np.nan, np.nan]])\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(probs, [[(3+1)/(7+2), (4+1)/(7+2)],\n                                               [(1+1)/(2+2), (1+1)/(2+2)],\n                                               [(3+1)/(3+2), (0+1)/(3+2)]])\n\n    def test_no_attributes(self):\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain([], DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, np.zeros((len(y), 0)), y.T)\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.predict_storage(np.zeros((5, 0)))[1],\n            [[4/11, 4/11, 3/11]] * 5\n        )\n\n    def test_no_targets(self):\n        x = np.array([[0], [1], [2]])\n        y = np.full(3, np.nan)\n        domain = Domain([DiscreteVariable(\"x\", values=\"abc\")],\n                        DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n        self.assertRaises(ValueError, self.learner, data)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 27,
                            "end_lineno": 356,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/tests/test_naive_bayes.py",
                            "type": "method",
                            "name": "TestNaiveBayesLearner.test_predict_missing_attributes",
                            "content": "class TestNaiveBayesLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = data = Table('titanic')\n        cls.learner = NaiveBayesLearner()\n        cls.table = data[::20]\n\n    def setUp(self):\n        self.model = self.learner(self.data)\n\n    def test_NaiveBayes(self):\n        cv = CrossValidation(k=10)\n        results = cv(self.table, [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n        self.assertLess(ca, 0.9)\n\n        cv = CrossValidation(k=10)\n        results = cv(Table(\"iris\"), [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n\n    def test_degenerate(self):\n        d = Domain((ContinuousVariable(name=\"A\"),\n                    ContinuousVariable(name=\"B\"),\n                    ContinuousVariable(name=\"C\")),\n                   DiscreteVariable(name=\"CLASS\", values=(\"M\", \"F\")))\n        t = Table.from_list(d, [[0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1]])\n        nb = NaiveBayesLearner()\n        model = nb(t)\n        self.assertEqual(model.domain.attributes, ())\n        self.assertEqual(model(t[0]), 1)\n        self.assertTrue(all(model(t) == 1))\n\n    def test_allnan_cv(self):\n        # GH 2740\n        data = Table(test_filename('datasets/lenses.tab'))\n        cv = CrossValidation(stratified=False)\n        results = cv(data, [self.learner])\n        self.assertFalse(any(results.failed))\n\n    def test_prediction_routing(self):\n        data = self.data\n        predict = self.model.predict = Mock(return_value=(data.Y, None))\n\n        self.model(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model(data.X)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data[0])\n        predict.assert_called()\n\n    def test_compare_results_of_predict_and_predict_storage(self):\n        data2 = NotATable(\"titanic\")\n\n        self.model = self.learner(self.data[:50])\n        predict = self.model.predict = Mock(side_effect=self.model.predict)\n        values, probs = self.model.predict_storage(self.data[50:])\n        predict.assert_called()\n        predict.reset_mock()\n        values2, probs2 = self.model.predict_storage(data2[50:])\n        predict.assert_not_called()\n\n        np.testing.assert_equal(values, values2)\n        np.testing.assert_equal(probs, probs2)\n\n    def test_predictions(self):\n        self._test_predictions(sparse=None)\n        self._test_predictions_with_absent_class(sparse=None)\n\n    def test_predictions_csr_matrix(self):\n        self._test_predictions(sparse=sp.csr_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csr_matrix)\n\n    def test_predictions_csc_matrix(self):\n        self._test_predictions(sparse=sp.csc_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csc_matrix)\n\n    def _test_predictions(self, sparse):\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def _test_predictions_with_absent_class(self, sparse):\n        \"\"\"Empty classes should not affect predictions\"\"\"\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 2, 2, 2, 3, 3])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abcd\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 0, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [0, 0], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [0, 0, 0], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [0], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def test_predict_missing_attributes(self):\n        x = np.array([\n            [1, 0, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [0, 1, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [1, 2, np.nan]])\n        y = np.array([1,0,0,0,1,1,1])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"AB\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        test_x = np.array([[np.nan, np.nan, np.nan],\n                           [np.nan, 0, np.nan],\n                           [0, np.nan, np.nan]])\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(probs, [[(3+1)/(7+2), (4+1)/(7+2)],\n                                               [(1+1)/(2+2), (1+1)/(2+2)],\n                                               [(3+1)/(3+2), (0+1)/(3+2)]])\n\n    def test_no_attributes(self):\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain([], DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, np.zeros((len(y), 0)), y.T)\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.predict_storage(np.zeros((5, 0)))[1],\n            [[4/11, 4/11, 3/11]] * 5\n        )\n\n    def test_no_targets(self):\n        x = np.array([[0], [1], [2]])\n        y = np.full(3, np.nan)\n        domain = Domain([DiscreteVariable(\"x\", values=\"abc\")],\n                        DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n        self.assertRaises(ValueError, self.learner, data)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 314,
                            "end_lineno": 338,
                            "parent": {
                                "file_name": "Orange/tests/test_naive_bayes.py",
                                "type": "class",
                                "name": "TestNaiveBayesLearner",
                                "content": "class TestNaiveBayesLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = data = Table('titanic')\n        cls.learner = NaiveBayesLearner()\n        cls.table = data[::20]\n\n    def setUp(self):\n        self.model = self.learner(self.data)\n\n    def test_NaiveBayes(self):\n        cv = CrossValidation(k=10)\n        results = cv(self.table, [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n        self.assertLess(ca, 0.9)\n\n        cv = CrossValidation(k=10)\n        results = cv(Table(\"iris\"), [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n\n    def test_degenerate(self):\n        d = Domain((ContinuousVariable(name=\"A\"),\n                    ContinuousVariable(name=\"B\"),\n                    ContinuousVariable(name=\"C\")),\n                   DiscreteVariable(name=\"CLASS\", values=(\"M\", \"F\")))\n        t = Table.from_list(d, [[0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1]])\n        nb = NaiveBayesLearner()\n        model = nb(t)\n        self.assertEqual(model.domain.attributes, ())\n        self.assertEqual(model(t[0]), 1)\n        self.assertTrue(all(model(t) == 1))\n\n    def test_allnan_cv(self):\n        # GH 2740\n        data = Table(test_filename('datasets/lenses.tab'))\n        cv = CrossValidation(stratified=False)\n        results = cv(data, [self.learner])\n        self.assertFalse(any(results.failed))\n\n    def test_prediction_routing(self):\n        data = self.data\n        predict = self.model.predict = Mock(return_value=(data.Y, None))\n\n        self.model(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model(data.X)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data[0])\n        predict.assert_called()\n\n    def test_compare_results_of_predict_and_predict_storage(self):\n        data2 = NotATable(\"titanic\")\n\n        self.model = self.learner(self.data[:50])\n        predict = self.model.predict = Mock(side_effect=self.model.predict)\n        values, probs = self.model.predict_storage(self.data[50:])\n        predict.assert_called()\n        predict.reset_mock()\n        values2, probs2 = self.model.predict_storage(data2[50:])\n        predict.assert_not_called()\n\n        np.testing.assert_equal(values, values2)\n        np.testing.assert_equal(probs, probs2)\n\n    def test_predictions(self):\n        self._test_predictions(sparse=None)\n        self._test_predictions_with_absent_class(sparse=None)\n\n    def test_predictions_csr_matrix(self):\n        self._test_predictions(sparse=sp.csr_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csr_matrix)\n\n    def test_predictions_csc_matrix(self):\n        self._test_predictions(sparse=sp.csc_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csc_matrix)\n\n    def _test_predictions(self, sparse):\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def _test_predictions_with_absent_class(self, sparse):\n        \"\"\"Empty classes should not affect predictions\"\"\"\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 2, 2, 2, 3, 3])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abcd\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 0, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [0, 0], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [0, 0, 0], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [0], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def test_predict_missing_attributes(self):\n        x = np.array([\n            [1, 0, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [0, 1, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [1, 2, np.nan]])\n        y = np.array([1,0,0,0,1,1,1])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"AB\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        test_x = np.array([[np.nan, np.nan, np.nan],\n                           [np.nan, 0, np.nan],\n                           [0, np.nan, np.nan]])\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(probs, [[(3+1)/(7+2), (4+1)/(7+2)],\n                                               [(1+1)/(2+2), (1+1)/(2+2)],\n                                               [(3+1)/(3+2), (0+1)/(3+2)]])\n\n    def test_no_attributes(self):\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain([], DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, np.zeros((len(y), 0)), y.T)\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.predict_storage(np.zeros((5, 0)))[1],\n            [[4/11, 4/11, 3/11]] * 5\n        )\n\n    def test_no_targets(self):\n        x = np.array([[0], [1], [2]])\n        y = np.full(3, np.nan)\n        domain = Domain([DiscreteVariable(\"x\", values=\"abc\")],\n                        DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n        self.assertRaises(ValueError, self.learner, data)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 27,
                                "end_lineno": 356,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "9b95c177f454e5e4477ae73037275c0a194624de^",
    "new_commit_hash": "9b95c177f454e5e4477ae73037275c0a194624de",
    "commit_message": "fix Naive Bayes predictions with unknown values",
    "commit_date": "2023-09-01T14:56:41+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "9b95c177f454e5e4477ae73037275c0a194624de",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\nfrom unittest.mock import Mock\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.classification import NaiveBayesLearner\nfrom Orange.data import Table, Domain, DiscreteVariable, ContinuousVariable\nfrom Orange.evaluation import CrossValidation, CA\n\n\n# This class is used to force predict_storage to fall back to the slower\n# procedure instead of calling `predict`\nfrom Orange.tests import test_filename\n\n\nclass NotATable(Table):  # pylint: disable=too-many-ancestors,abstract-method\n    @classmethod\n    def from_file(cls, *args, **kwargs):\n        table = super().from_file(*args, **kwargs)\n        return cls(table)\n\n\nclass TestNaiveBayesLearner(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.data = data = Table('titanic')\n        cls.learner = NaiveBayesLearner()\n        cls.table = data[::20]\n\n    def setUp(self):\n        self.model = self.learner(self.data)\n\n    def test_NaiveBayes(self):\n        cv = CrossValidation(k=10)\n        results = cv(self.table, [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n        self.assertLess(ca, 0.9)\n\n        cv = CrossValidation(k=10)\n        results = cv(Table(\"iris\"), [self.learner])\n        ca = CA(results)\n        self.assertGreater(ca, 0.7)\n\n    def test_degenerate(self):\n        d = Domain((ContinuousVariable(name=\"A\"),\n                    ContinuousVariable(name=\"B\"),\n                    ContinuousVariable(name=\"C\")),\n                   DiscreteVariable(name=\"CLASS\", values=(\"M\", \"F\")))\n        t = Table.from_list(d, [[0, 1, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1]])\n        nb = NaiveBayesLearner()\n        model = nb(t)\n        self.assertEqual(model.domain.attributes, ())\n        self.assertEqual(model(t[0]), 1)\n        self.assertTrue(all(model(t) == 1))\n\n    def test_allnan_cv(self):\n        # GH 2740\n        data = Table(test_filename('datasets/lenses.tab'))\n        cv = CrossValidation(stratified=False)\n        results = cv(data, [self.learner])\n        self.assertFalse(any(results.failed))\n\n    def test_prediction_routing(self):\n        data = self.data\n        predict = self.model.predict = Mock(return_value=(data.Y, None))\n\n        self.model(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model(data.X)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data)\n        predict.assert_called()\n        predict.reset_mock()\n\n        self.model.predict_storage(data[0])\n        predict.assert_called()\n\n    def test_compare_results_of_predict_and_predict_storage(self):\n        data2 = NotATable(\"titanic\")\n\n        self.model = self.learner(self.data[:50])\n        predict = self.model.predict = Mock(side_effect=self.model.predict)\n        values, probs = self.model.predict_storage(self.data[50:])\n        predict.assert_called()\n        predict.reset_mock()\n        values2, probs2 = self.model.predict_storage(data2[50:])\n        predict.assert_not_called()\n\n        np.testing.assert_equal(values, values2)\n        np.testing.assert_equal(probs, probs2)\n\n    def test_predictions(self):\n        self._test_predictions(sparse=None)\n        self._test_predictions_with_absent_class(sparse=None)\n\n    def test_predictions_csr_matrix(self):\n        self._test_predictions(sparse=sp.csr_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csr_matrix)\n\n    def test_predictions_csc_matrix(self):\n        self._test_predictions(sparse=sp.csc_matrix)\n        self._test_predictions_with_absent_class(sparse=sp.csc_matrix)\n\n    def _test_predictions(self, sparse):\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def _test_predictions_with_absent_class(self, sparse):\n        \"\"\"Empty classes should not affect predictions\"\"\"\n        x = np.array([\n            [1, 0, 0],\n            [0, np.nan, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [1, 2, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [0, 1, 0]])\n        if sparse is not None:\n            x = sparse(x)\n\n        y = np.array([0, 0, 0, 2, 2, 2, 3, 3])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"abcd\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.class_prob,\n            [4/11, 0, 4/11, 3/11]\n        )\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[0]) * model.class_prob[:, None],\n            [[3/7, 2/7], [0, 0], [2/7, 3/7], [2/7, 2/7]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[1]) * model.class_prob[:, None],\n            [[2/5, 1/3, 1/5], [0, 0, 0], [2/5, 1/3, 2/5], [1/5, 1/3, 2/5]])\n        np.testing.assert_almost_equal(\n            np.exp(model.log_cont_prob[2]) * model.class_prob[:, None],\n            [[4/11], [0], [4/11], [3/11]])\n\n        test_x = np.array([[a, b, 0] for a in [0, 1] for b in [0, 1, 2]])\n        # Classifiers reject csc matrices in the base class\n        # Naive bayesian classifier supports them if predict_storage is\n        # called directly, which we do below\n        if sparse is not None and sparse is not sp.csc_matrix:\n            test_x = sparse(test_x)\n        test_y = np.full((6, ), np.nan)\n        # The following was computed manually, too\n        exp_probs = np.array([\n            [0.47368421052632, 0, 0.31578947368421, 0.21052631578947],\n            [0.39130434782609, 0, 0.26086956521739, 0.34782608695652],\n            [0.24324324324324, 0, 0.32432432432432, 0.43243243243243],\n            [0.31578947368421, 0, 0.47368421052632, 0.21052631578947],\n            [0.26086956521739, 0, 0.39130434782609, 0.34782608695652],\n            [0.15000000000000, 0, 0.45000000000000, 0.40000000000000]\n        ])\n\n        # Test the faster algorithm for Table (numpy matrices)\n        test_data = Table.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test the slower algorithm for non-Table data (iteration in Python)\n        test_data = NotATable.from_numpy(domain, test_x, test_y)\n        probs = model(test_data, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_data)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_data, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction directly on numpy\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        values = model(test_x)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n        values, probs = model(test_x, ret=model.ValueProbs)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n        # Test prediction on instances\n        for inst, exp_prob in zip(test_data, exp_probs):\n            np.testing.assert_almost_equal(\n                model(inst, ret=model.Probs),\n                exp_prob)\n            self.assertEqual(model(inst), np.argmax(exp_prob))\n            value, prob = model(inst, ret=model.ValueProbs)\n            np.testing.assert_almost_equal(prob, exp_prob)\n            self.assertEqual(value, np.argmax(exp_prob))\n\n        # Test prediction by directly calling predict. This is needed to test\n        # csc_matrix, but doesn't hurt others\n        if sparse is sp.csc_matrix:\n            test_x = sparse(test_x)\n        values, probs = model.predict(test_x)\n        np.testing.assert_almost_equal(exp_probs, probs)\n        np.testing.assert_equal(values, np.argmax(exp_probs, axis=1))\n\n    def test_predict_missing_attributes(self):\n        x = np.array([\n            [1, 0, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [0, 1, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [1, 2, np.nan]])\n        y = np.array([1,0,0,0,1,1,1])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"AB\"))\n        data = Table.from_numpy(domain, x, y)\n\n        model = self.learner(data)\n        test_x = np.array([[np.nan, np.nan, np.nan],\n                           [np.nan, 0, np.nan],\n                           [0, np.nan, np.nan]])\n        probs = model(test_x, ret=model.Probs)\n        np.testing.assert_almost_equal(probs, [[(3+1)/(7+2), (4+1)/(7+2)],\n                                               [(1+1)/(2+2), (1+1)/(2+2)],\n                                               [(3+1)/(3+2), (0+1)/(3+2)]])\n\n    def test_no_attributes(self):\n        y = np.array([0, 0, 0, 1, 1, 1, 2, 2])\n        domain = Domain([], DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, np.zeros((len(y), 0)), y.T)\n        model = self.learner(data)\n        np.testing.assert_almost_equal(\n            model.predict_storage(np.zeros((5, 0)))[1],\n            [[4/11, 4/11, 3/11]] * 5\n        )\n\n    def test_no_targets(self):\n        x = np.array([[0], [1], [2]])\n        y = np.full(3, np.nan)\n        domain = Domain([DiscreteVariable(\"x\", values=\"abc\")],\n                        DiscreteVariable(\"y\", values=\"abc\"))\n        data = Table.from_numpy(domain, x, y)\n        self.assertRaises(ValueError, self.learner, data)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build\ncreating build/temp.linux-x86_64-3.7\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-3.7\ncreating build/lib.linux-x86_64-3.7/Orange\ncreating build/lib.linux-x86_64-3.7/Orange/classification\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 build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-3.7/Orange/widgets\ncreating build/lib.linux-x86_64-3.7/Orange/widgets/utils\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 build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o\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 build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_contingency.c -o build/temp.linux-x86_64-3.7/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-3.7/Orange/data\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 build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_io.c -o build/temp.linux-x86_64-3.7/Orange/data/_io.o\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 build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o\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 build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_variable.c -o build/temp.linux-x86_64-3.7/Orange/data/_variable.o\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 build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/distance/_distance.c -o build/temp.linux-x86_64-3.7/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-3.7/Orange/distance\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 build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-3.7/Orange/preprocess\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 build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o\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 build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/projection/_som.c -o build/temp.linux-x86_64-3.7/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-3.7/Orange/projection\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning egg_info\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'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.36.0.dev0+9b95c17 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de\nProcessing dependencies for Orange3==3.36.0.dev0+9b95c17\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/XlsxWriter-3.2.0-py3.7.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-7pdkcusm/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-7pdkcusm/serverfiles-0.3.1/egg-dist-tmp-geqzk5xw\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nExtracting serverfiles-0.3.1-py3.7.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nrunning build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nrunning develop\nrunning 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\ndependency 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the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.36.0.dev0+9b95c17 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de\nProcessing dependencies for Orange3==3.36.0.dev0+9b95c17\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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for setuptools==75.3.0\nBest match: setuptools 75.3.0\nAdding setuptools 75.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for scipy==1.10.1\nBest match: scipy 1.10.1\nAdding scipy 1.10.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.1.3\nBest match: scikit-learn 1.1.3\nAdding scikit-learn 1.1.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for requests==2.32.3\nBest match: requests 2.32.3\nAdding requests 2.32.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for qtconsole==5.6.1\nBest match: qtconsole 5.6.1\nAdding qtconsole 5.6.1 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for PyYAML==6.0.2\nBest match: PyYAML 6.0.2\nAdding PyYAML 6.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pyqtgraph==0.13.3\nBest match: pyqtgraph 0.13.3\nAdding pyqtgraph 0.13.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pygments==2.19.1\nBest match: pygments 2.19.1\nAdding pygments 2.19.1 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pip==24.3.1\nBest match: pip 24.3.1\nAdding pip 24.3.1 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pandas==2.0.3\nBest match: pandas 2.0.3\nAdding pandas 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for openpyxl==3.1.5\nBest match: openpyxl 3.1.5\nAdding openpyxl 3.1.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for numpy==1.24.4\nBest match: numpy 1.24.4\nAdding numpy 1.24.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for networkx==3.1\nBest match: networkx 3.1\nAdding networkx 3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for matplotlib==3.7.5\nBest match: matplotlib 3.7.5\nAdding matplotlib 3.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for keyrings.alt==5.0.2\nBest match: keyrings.alt 5.0.2\nAdding keyrings.alt 5.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for keyring==25.5.0\nBest match: keyring 25.5.0\nAdding keyring 25.5.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for joblib==1.4.2\nBest match: joblib 1.4.2\nAdding joblib 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for httpx==0.28.1\nBest match: httpx 0.28.1\nAdding httpx 0.28.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for threadpoolctl==3.5.0\nBest match: threadpoolctl 3.5.0\nAdding threadpoolctl 3.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for urllib3==2.2.3\nBest match: urllib3 2.2.3\nAdding urllib3 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for packaging==24.2\nBest match: packaging 24.2\nAdding packaging 24.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for ipykernel==6.29.5\nBest match: ipykernel 6.29.5\nAdding ipykernel 6.29.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for jupyter-client==8.6.3\nBest match: jupyter-client 8.6.3\nAdding jupyter-client 8.6.3 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for jupyter-core==5.7.2\nBest match: jupyter-core 5.7.2\nAdding jupyter-core 5.7.2 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for traitlets==5.14.3\nBest match: traitlets 5.14.3\nAdding traitlets 5.14.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for tzdata==2024.2\nBest match: tzdata 2024.2\nAdding tzdata 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\nAdding typing-extensions 4.12.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for requests-cache==1.2.1\nBest match: requests-cache 1.2.1\nAdding requests-cache 1.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for et-xmlfile==2.0.0\nBest match: et-xmlfile 2.0.0\nAdding et-xmlfile 2.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for importlib-resources==6.4.5\nBest match: importlib-resources 6.4.5\nAdding importlib-resources 6.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pillow==10.4.0\nBest match: pillow 10.4.0\nAdding pillow 10.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for kiwisolver==1.4.7\nBest match: kiwisolver 1.4.7\nAdding kiwisolver 1.4.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for fonttools==4.55.3\nBest match: fonttools 4.55.3\nAdding fonttools 4.55.3 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for cycler==0.12.1\nBest match: cycler 0.12.1\nAdding cycler 0.12.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for contourpy==1.1.1\nBest match: contourpy 1.1.1\nAdding contourpy 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for jaraco.context==6.0.1\nBest match: jaraco.context 6.0.1\nAdding jaraco.context 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for jaraco.classes==3.4.0\nBest match: jaraco.classes 3.4.0\nAdding jaraco.classes 3.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for jaraco.functools==4.1.0\nBest match: jaraco.functools 4.1.0\nAdding jaraco.functools 4.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for httpcore==1.0.7\nBest match: httpcore 1.0.7\nAdding httpcore 1.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for anyio==4.5.2\nBest match: anyio 4.5.2\nAdding anyio 4.5.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for tornado==6.4.2\nBest match: tornado 6.4.2\nAdding tornado 6.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for matplotlib-inline==0.1.7\nBest match: matplotlib-inline 0.1.7\nAdding matplotlib-inline 0.1.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for ipython==8.12.3\nBest match: ipython 8.12.3\nAdding ipython 8.12.3 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for debugpy==1.8.11\nBest match: debugpy 1.8.11\nAdding debugpy 1.8.11 to easy-install.pth file\nInstalling debugpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for comm==0.2.2\nBest match: comm 0.2.2\nAdding comm 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for platformdirs==4.3.6\nBest match: platformdirs 4.3.6\nAdding platformdirs 4.3.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for cattrs==24.1.2\nBest match: cattrs 24.1.2\nAdding cattrs 24.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for attrs==24.3.0\nBest match: attrs 24.3.0\nAdding attrs 24.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for zipp==3.20.2\nBest match: zipp 3.20.2\nAdding zipp 3.20.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for backports.tarfile==1.2.0\nBest match: backports.tarfile 1.2.0\nbackports.tarfile 1.2.0 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for more-itertools==10.5.0\nBest match: more-itertools 10.5.0\nAdding more-itertools 10.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for stack-data==0.6.3\nBest match: stack-data 0.6.3\nAdding stack-data 0.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for cffi==1.17.1\nBest match: cffi 1.17.1\nAdding cffi 1.17.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pure-eval==0.2.3\nBest match: pure-eval 0.2.3\nAdding pure-eval 0.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for asttokens==3.0.0\nBest match: asttokens 3.0.0\nAdding asttokens 3.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for executing==2.1.0\nBest match: executing 2.1.0\nAdding executing 2.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nSearching for pycparser==2.22\nBest match: pycparser 2.22\nAdding pycparser 2.22 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.36.0.dev0+9b95c17\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.8\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 59ms\nPrepared 18 packages in 19ms\nInstalled 18 packages in 110ms\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\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n      depend on Python>=3.8,<3.12, we can conclude that all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n       cannot be used.\n      And because only the following versions of scipy are available:\n          scipy<=1.9.0\n          scipy==1.9.1\n          scipy==1.9.2\n          scipy==1.9.3\n          scipy==1.10.0\n          scipy==1.10.1\n          scipy==1.11.0\n          scipy==1.11.1\n          scipy==1.11.2\n          scipy==1.11.3\n          scipy==1.11.4\n          scipy==1.12.0\n          scipy==1.13.0\n          scipy==1.13.1\n          scipy==1.14.0\n          scipy==1.14.1\n          scipy==1.15.0\n      we can conclude that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.9.2,<=1.9.3 depends on Python>=3.8,\n      we can conclude that scipy>=1.9.2,<=1.9.3 cannot be used.\n      And because we know from (1) that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used, we can conclude that scipy>=1.9.0,<1.11.0 cannot be used.\n      And because scipy==1.11.0 was yanked (reason: License Violation), we can conclude that scipy>=1.9.0,<1.11.1 cannot be\n      used. (2)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.1,<=1.11.3 depends on\n      Python>=3.9,<3.13, we can conclude that scipy>=1.11.1,<=1.11.3 cannot be used.\n      And because we know from (2) that scipy>=1.9.0,<1.11.1 cannot be used, we can conclude that scipy>=1.9.0,<1.11.4 cannot\n      be used. (3)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.4,<=1.13.1 depends on\n      Python>=3.9, we can conclude that scipy>=1.11.4,<=1.13.1 cannot be used.\n      And because we know from (3) that scipy>=1.9.0,<1.11.4 cannot be used, we can conclude that scipy>=1.9.0,<1.14.0 cannot\n      be used. (4)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.14.0 depends on Python>=3.10, we can\n      conclude that scipy>=1.14.0 cannot be used.\n      And because we know from (4) that scipy>=1.9.0,<1.14.0 cannot be used, we can conclude that scipy>=1.9.0 cannot be used.\n      And because you require scipy>=1.9, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scipy` in the requested range (e.g., 1.15.0rc2), but pre-releases weren't enabled\n      (try: `--prerelease=allow`)\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and pyqtgraph>=0.13.1,<=0.13.3 depends on\n      Python>=3.8, we can conclude that pyqtgraph>=0.13.1,<=0.13.3 cannot be used.\n      And because only the following versions of pyqtgraph are available:\n          pyqtgraph<=0.13.1\n          pyqtgraph==0.13.2\n          pyqtgraph==0.13.3\n          pyqtgraph==0.13.4\n          pyqtgraph==0.13.5\n          pyqtgraph==0.13.6\n          pyqtgraph==0.13.7\n      we can conclude that pyqtgraph>=0.13.1,<0.13.4 cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.9 and pyqtgraph>=0.13.4 depends on Python>=3.9, we\n      can conclude that pyqtgraph>=0.13.4 cannot be used.\n      And because we know from (1) that pyqtgraph>=0.13.1,<0.13.4 cannot be used, we can conclude that pyqtgraph>=0.13.1 cannot\n      be used.\n      And because you require pyqtgraph>=0.13.1, we can conclude that your requirements are unsatisfiable.\nResolved 2 packages in 4ms\nInstalled 2 packages in 31ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 120ms\nInstalled 16 packages in 2.42s\n + catboost==1.2.7\n + cycler==0.11.0\n + fonttools==4.38.0\n + graphviz==0.20.1\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + pandas==1.3.5\n + pillow==9.5.0\n + plotly==5.18.0\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + pytz==2024.2\n + scipy==1.7.3\n + six==1.17.0\n + tenacity==8.2.3\n + xgboost==1.6.2\nResolved 5 packages in 17ms\nInstalled 3 packages in 120ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + threadpoolctl==3.1.0\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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __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:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __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:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__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:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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_9b95c177f454e5e4477ae73037275c0a194624de/.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,\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 '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nerror: Couldn't find a setup script in /tmp/easy_install-hzx30sl_/scipy-1.15.0.tar.gz\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 112ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 40ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==1.24.4\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.15.0\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.3.0\n + tomli==2.2.1\n + wheel==0.45.1\nResolved 59 packages in 211ms\nInstalled 55 packages in 188ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.1.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\n + h11==0.14.0\n + httpcore==1.0.7\n + httpx==0.28.1\n + idna==3.10\n + importlib-metadata==8.5.0\n + importlib-resources==6.4.5\n + jaraco-classes==3.4.0\n + jaraco-context==6.0.1\n + jaraco-functools==4.1.0\n + jeepney==0.8.0\n + joblib==1.4.2\n + keyring==25.5.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + more-itertools==10.5.0\n + networkx==3.1\n + openpyxl==3.1.5\n + opentsne==1.0.1\n + pandas==2.0.3\n + pillow==10.4.0\n + pip==24.3.1\n + pycparser==2.22\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.2\n + requests==2.32.3\n + scikit-learn==1.1.3\n + scipy==1.10.1\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.2.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 64 packages in 85ms\nInstalled 41 packages in 131ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.12.3\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.3\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 3ms\nInstalled 2 packages in 4ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nResolved 23 packages in 16ms\nInstalled 6 packages in 689ms\n + catboost==1.2.7\n + graphviz==0.20.3\n + nvidia-nccl-cu12==2.24.3\n + plotly==5.24.1\n + tenacity==9.0.0\n + xgboost==2.1.3\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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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:3426:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3426 |   __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:4872:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4872 |   __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:5618:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5618 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5739:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5739 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5745:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5745 |           __pyx_t_20 = (((__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:7206:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7206 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7863:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7863 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7923:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7923 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/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_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\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 '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n",
    "new_commit_res_code": 1,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 12 items\n\nr2e_tests/test_1.py E...........                                         [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/Orange/tests/__init__.py, line 68\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/Orange/tests/__init__.py:68\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_NaiveBayes\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_allnan_cv\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_compare_results_of_predict_and_predict_storage\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_degenerate\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_no_attributes\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_no_targets\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predict_missing_attributes\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_prediction_routing\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predictions\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predictions_csc_matrix\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predictions_csr_matrix\nERROR r2e_tests/test_1.py::test_filename\n========================= 11 passed, 1 error in 3.51s ==========================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 12 items\n\nr2e_tests/test_1.py E......F....                                         [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/Orange/tests/__init__.py, line 68\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_9b95c177f454e5e4477ae73037275c0a194624de/Orange/tests/__init__.py:68\n=================================== FAILURES ===================================\n____________ TestNaiveBayesLearner.test_predict_missing_attributes _____________\n\nself = <r2e_tests.test_1.TestNaiveBayesLearner testMethod=test_predict_missing_attributes>\n\n    def test_predict_missing_attributes(self):\n        x = np.array([\n            [1, 0, 0],\n            [0, 1, 0],\n            [0, 0, 0],\n            [0, 1, 0],\n            [1, 1, 0],\n            [1, 2, 0],\n            [1, 2, np.nan]])\n        y = np.array([1,0,0,0,1,1,1])\n        domain = Domain(\n            [DiscreteVariable(\"a\", values=\"ab\"),\n             DiscreteVariable(\"b\", values=\"abc\"),\n             DiscreteVariable(\"c\", values=\"a\")],\n            DiscreteVariable(\"y\", values=\"AB\"))\n        data = Table.from_numpy(domain, x, y)\n    \n        model = self.learner(data)\n        test_x = np.array([[np.nan, np.nan, np.nan],\n                           [np.nan, 0, np.nan],\n                           [0, np.nan, np.nan]])\n        probs = model(test_x, ret=model.Probs)\n>       np.testing.assert_almost_equal(probs, [[(3+1)/(7+2), (4+1)/(7+2)],\n                                               [(1+1)/(2+2), (1+1)/(2+2)],\n                                               [(3+1)/(3+2), (0+1)/(3+2)]])\n\nr2e_tests/test_1.py:336: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n../../../../.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib/python3.8/contextlib.py:75: in inner\n    return func(*args, **kwds)\n../../../../.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib/python3.8/contextlib.py:75: in inner\n    return func(*args, **kwds)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nargs = (<function assert_array_almost_equal.<locals>.compare at 0x7bb1f69649d0>, array([[0.09433962, 0.90566038],\n       [0.2...24, 0.76190476],\n       [0.67567568, 0.32432432]]), [[0.4444444444444444, 0.5555555555555556], [0.5, 0.5], [0.8, 0.2]])\nkwds = {'err_msg': '', 'header': 'Arrays are not almost equal to 7 decimals', 'precision': 7, 'verbose': True}\n\n    @wraps(func)\n    def inner(*args, **kwds):\n        with self._recreate_cm():\n>           return func(*args, **kwds)\nE           AssertionError: \nE           Arrays are not almost equal to 7 decimals\nE           \nE           Mismatched elements: 6 / 6 (100%)\nE           Max absolute difference: 0.35010482\nE           Max relative difference: 0.78773585\nE            x: array([[0.0943396, 0.9056604],\nE                  [0.2380952, 0.7619048],\nE                  [0.6756757, 0.3243243]])\nE            y: array([[0.4444444, 0.5555556],\nE                  [0.5      , 0.5      ],\nE                  [0.8      , 0.2      ]])\n\n../../../../.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib/python3.8/contextlib.py:75: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_NaiveBayes\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_allnan_cv\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_compare_results_of_predict_and_predict_storage\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_degenerate\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_no_attributes\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_no_targets\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_prediction_routing\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predictions\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predictions_csc_matrix\nPASSED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predictions_csr_matrix\nERROR r2e_tests/test_1.py::test_filename\nFAILED r2e_tests/test_1.py::TestNaiveBayesLearner::test_predict_missing_attributes\n==================== 1 failed, 10 passed, 1 error in 2.63s =====================\n",
    "old_commit_res_stderr": ""
}
Orange/classification/naive_bayes.py Orange/tests/test_naive_bayes.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** Naive Bayes Model Returns Incorrect Probabilities for Inputs with Missing Attributes

**Description:**

When using the Naive Bayes classifier to make predictions on data that includes missing attribute values (`NaN`), the model returns incorrect probability distributions. This causes discrepancies between the expected and actual prediction probabilities, leading to failed validations.

**Example Code:**

```python
import numpy as np
from Orange.classification import NaiveBayesLearner
from Orange.data import Domain, DiscreteVariable, Table

# Create dataset with missing values
x = np.array([
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 0],
    [0, 1, 0],
    [1, 1, 0],
    [1, 2, 0],
    [1, 2, np.nan]
])
y = np.array([1, 0, 0, 0, 1, 1, 1])
domain = Domain(
    [DiscreteVariable("a", values="ab"),
     DiscreteVariable("b", values="abc"),
     DiscreteVariable("c", values="a")],
    DiscreteVariable("y", values="AB")
)
data = Table.from_numpy(domain, x, y)

# Train the model
learner = NaiveBayesLearner()
model = learner(data)

# Make predictions on data with missing attributes
test_x = np.array([
    [np.nan, np.nan, np.nan],
    [np.nan, 0, np.nan],
    [0, np.nan, np.nan]
])
probs = model(test_x, ret=model.Probs)
print(probs)
```

**Expected Behavior:**

The Naive Bayes model should correctly handle missing attribute values by appropriately adjusting the probability calculations. The output probabilities for each class should align closely with the expected distributions based on the training data.

**Actual Behavior:**

The model returns probability distributions that significantly differ from the expected values when predicting on inputs with missing attributes. This results in incorrect class assignments and failed validations.

[/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/classification/naive_bayes.py
import numpy as np
import scipy.sparse as sp

from Orange.classification import Learner, Model
from Orange.data import Instance, Storage, Table
from Orange.statistics import contingency
from Orange.preprocess import Discretize, RemoveNaNColumns

__all__ = ["NaiveBayesLearner"]


class NaiveBayesLearner(Learner):
    """
    Naive Bayes classifier. Works only with discrete attributes. By default,
    continuous attributes are discretized.

    Parameters
    ----------
    preprocessors : list, optional (default="[Orange.preprocess.Discretize]")
        An ordered list of preprocessors applied to data before training
        or testing.
    """
    preprocessors = [RemoveNaNColumns(), Discretize()]
    name = 'naive bayes'

    def fit_storage(self, table):
        if not isinstance(table, Storage):
            raise TypeError("Data is not a subclass of Orange.data.Storage.")
        if not all(var.is_discrete
                   for var in table.domain.variables):
            raise NotImplementedError("Only categorical variables are "
                                      "supported.")

        cont = contingency.get_contingencies(table)
        class_freq = np.array(np.diag(
            contingency.get_contingency(table, table.domain.class_var)))
        nclss = (class_freq != 0).sum()
        if not nclss:
            raise ValueError("Data has no defined target values.")

        # Laplacian smoothing considers only classes that appear in the data,
        # in part to avoid cases where the probabilities are affected by empty
        # (or completely spurious) classes that appear because of Orange's reuse
        # of variables. See GH-2943.
        # The corresponding elements of class_probs are set to zero only after
        # mock non-zero values are used in computation of log_cont_prob to
        # prevent division by zero.
        class_prob = (class_freq + 1) / (np.sum(class_freq) + nclss)
        log_cont_prob = [np.log(
            (np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] + nclss)
            / class_prob[:, None])
                         for c in cont]
        class_prob[class_freq == 0] = 0
        return NaiveBayesModel(log_cont_prob, class_prob, table.domain)


class NaiveBayesModel(Model):
    def __init__(self, log_cont_prob, class_prob, domain):
        super().__init__(domain)
        self.log_cont_prob = log_cont_prob
        self.class_prob = class_prob

    def predict_storage(self, data):
        if isinstance(data, Instance):
            data = Table.from_numpy(None, np.atleast_2d(data.x))
        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck
            return self.predict(data.X)

        if not len(data) or not len(data[0]):
            probs = np.tile(self.class_prob, (len(data), 1))
        else:
            isnan = np.isnan
            zeros = np.zeros_like(self.class_prob)
            probs = self.class_prob * np.exp(np.array([
                zeros if isnan(ins.x).all() else
                sum(attr_prob[:, int(attr_val)]
                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)
                    if not isnan(attr_val))
                for ins in data]))
        probs /= probs.sum(axis=1)[:, None]
        values = probs.argmax(axis=1)
        return values, probs

    def predict(self, X):
        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))
        if self.log_cont_prob is not None:
            if sp.issparse(X):
                self._sparse_probs(X, probs)
            else:
                self._dense_probs(X, probs)
        np.exp(probs, probs)
        probs *= self.class_prob
        probs /= probs.sum(axis=1)[:, None]
        values = probs.argmax(axis=1)
        return values, probs

    def _dense_probs(self, data, probs):
        zeros = np.zeros((1, probs.shape[1]))
        for col, attr_prob in zip(data.T, self.log_cont_prob):
            col = col.copy()
            col[np.isnan(col)] = attr_prob.shape[1] - 1
            col = col.astype(int)
            probs0 = np.vstack((attr_prob.T, zeros))
            probs += probs0[col]
        return probs

    def _sparse_probs(self, data, probs):
        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1
        log_prob = np.zeros((len(self.log_cont_prob),
                             n_vals,
                             self.log_cont_prob[0].shape[0]))
        for i, p in enumerate(self.log_cont_prob):
            p0 = p.T[0].copy()
            probs[:] += p0
            log_prob[i, :p.shape[1]] = p.T - p0

        dat = data.data.copy()
        dat[np.isnan(dat)] = n_vals - 1
        dat = dat.astype(int)

        if sp.isspmatrix_csr(data):
            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):
                row += log_prob[data.indices[start:end],
                                dat[start:end]].sum(axis=0)
        else:
            csc = data.tocsc()
            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],
                                             log_prob):
                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]

        return probs


NaiveBayesLearner.__returns__ = NaiveBayesModel

```
--- 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:** Naive Bayes Model Returns Incorrect Probabilities for Inputs with Missing Attributes

**Description:**

When using the Naive Bayes classifier to make predictions on data that includes missing attribute values (`NaN`), the model returns incorrect probability distributions. This causes discrepancies between the expected and actual prediction probabilities, leading to failed validations.

**Example Code:**

```python
import numpy as np
from Orange.classification import NaiveBayesLearner
from Orange.data import Domain, DiscreteVariable, Table

# Create dataset with missing values
x = np.array([
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 0],
    [0, 1, 0],
    [1, 1, 0],
    [1, 2, 0],
    [1, 2, np.nan]
])
y = np.array([1, 0, 0, 0, 1, 1, 1])
domain = Domain(
    [DiscreteVariable("a", values="ab"),
     DiscreteVariable("b", values="abc"),
     DiscreteVariable("c", values="a")],
    DiscreteVariable("y", values="AB")
)
data = Table.from_numpy(domain, x, y)

# Train the model
learner = NaiveBayesLearner()
model = learner(data)

# Make predictions on data with missing attributes
test_x = np.array([
    [np.nan, np.nan, np.nan],
    [np.nan, 0, np.nan],
    [0, np.nan, np.nan]
])
probs = model(test_x, ret=model.Probs)
print(probs)
```

**Expected Behavior:**

The Naive Bayes model should correctly handle missing attribute values by appropriately adjusting the probability calculations. The output probabilities for each class should align closely with the expected distributions based on the training data.

**Actual Behavior:**

The model returns probability distributions that significantly differ from the expected values when predicting on inputs with missing attributes. This results in incorrect class assignments and failed validations.

[/ISSUE]
{
    "TestNaiveBayesLearner.test_NaiveBayes": "PASSED",
    "TestNaiveBayesLearner.test_allnan_cv": "PASSED",
    "TestNaiveBayesLearner.test_compare_results_of_predict_and_predict_storage": "PASSED",
    "TestNaiveBayesLearner.test_degenerate": "PASSED",
    "TestNaiveBayesLearner.test_no_attributes": "PASSED",
    "TestNaiveBayesLearner.test_no_targets": "PASSED",
    "TestNaiveBayesLearner.test_predict_missing_attributes": "PASSED",
    "TestNaiveBayesLearner.test_prediction_routing": "PASSED",
    "TestNaiveBayesLearner.test_predictions": "PASSED",
    "TestNaiveBayesLearner.test_predictions_csc_matrix": "PASSED",
    "TestNaiveBayesLearner.test_predictions_csr_matrix": "PASSED",
    "test_filename": "ERROR"
}
diff --git a/Orange/classification/naive_bayes.py b/Orange/classification/naive_bayes.py
index 2db384f91..83054b202 100644
--- a/Orange/classification/naive_bayes.py
+++ b/Orange/classification/naive_bayes.py
@@ -98,7 +98,7 @@ class NaiveBayesModel(Model):
         zeros = np.zeros((1, probs.shape[1]))
         for col, attr_prob in zip(data.T, self.log_cont_prob):
             col = col.copy()
-            col[np.isnan(col)] = attr_prob.shape[1] - 1
+            col[np.isnan(col)] = attr_prob.shape[1]
             col = col.astype(int)
             probs0 = np.vstack((attr_prob.T, zeros))
             probs += probs0[col]

{"Orange/classification/naive_bayes.py": "import numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.classification import Learner, Model\nfrom Orange.data import Instance, Storage, Table\nfrom Orange.statistics import contingency\nfrom Orange.preprocess import Discretize, RemoveNaNColumns\n\n__all__ = [\"NaiveBayesLearner\"]\n\n\nclass NaiveBayesLearner(Learner):\n    \"\"\"\n    Naive Bayes classifier. Works only with discrete attributes. By default,\n    continuous attributes are discretized.\n\n    Parameters\n    ----------\n    preprocessors : list, optional (default=\"[Orange.preprocess.Discretize]\")\n        An ordered list of preprocessors applied to data before training\n        or testing.\n    \"\"\"\n    preprocessors = [RemoveNaNColumns(), Discretize()]\n    name = 'naive bayes'\n\n    def fit_storage(self, table):\n        if not isinstance(table, Storage):\n            raise TypeError(\"Data is not a subclass of Orange.data.Storage.\")\n        if not all(var.is_discrete\n                   for var in table.domain.variables):\n            raise NotImplementedError(\"Only categorical variables are \"\n                                      \"supported.\")\n\n        cont = contingency.get_contingencies(table)\n        class_freq = np.array(np.diag(\n            contingency.get_contingency(table, table.domain.class_var)))\n        nclss = (class_freq != 0).sum()\n        if not nclss:\n            raise ValueError(\"Data has no defined target values.\")\n\n        # Laplacian smoothing considers only classes that appear in the data,\n        # in part to avoid cases where the probabilities are affected by empty\n        # (or completely spurious) classes that appear because of Orange's reuse\n        # of variables. See GH-2943.\n        # The corresponding elements of class_probs are set to zero only after\n        # mock non-zero values are used in computation of log_cont_prob to\n        # prevent division by zero.\n        class_prob = (class_freq + 1) / (np.sum(class_freq) + nclss)\n        log_cont_prob = [np.log(\n            (np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] + nclss)\n            / class_prob[:, None])\n                         for c in cont]\n        class_prob[class_freq == 0] = 0\n        return NaiveBayesModel(log_cont_prob, class_prob, table.domain)\n\n\nclass NaiveBayesModel(Model):\n    def __init__(self, log_cont_prob, class_prob, domain):\n        super().__init__(domain)\n        self.log_cont_prob = log_cont_prob\n        self.class_prob = class_prob\n\n    def predict_storage(self, data):\n        if isinstance(data, Instance):\n            data = Table.from_numpy(None, np.atleast_2d(data.x))\n        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck\n            return self.predict(data.X)\n\n        if not len(data) or not len(data[0]):\n            probs = np.tile(self.class_prob, (len(data), 1))\n        else:\n            isnan = np.isnan\n            zeros = np.zeros_like(self.class_prob)\n            probs = self.class_prob * np.exp(np.array([\n                zeros if isnan(ins.x).all() else\n                sum(attr_prob[:, int(attr_val)]\n                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)\n                    if not isnan(attr_val))\n                for ins in data]))\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def predict(self, X):\n        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))\n        if self.log_cont_prob is not None:\n            if sp.issparse(X):\n                self._sparse_probs(X, probs)\n            else:\n                self._dense_probs(X, probs)\n        np.exp(probs, probs)\n        probs *= self.class_prob\n        probs /= probs.sum(axis=1)[:, None]\n        values = probs.argmax(axis=1)\n        return values, probs\n\n    def _dense_probs(self, data, probs):\n        zeros = np.zeros((1, probs.shape[1]))\n        for col, attr_prob in zip(data.T, self.log_cont_prob):\n            col = col.copy()\n            col[np.isnan(col)] = attr_prob.shape[1] - 1\n            col = col.astype(int)\n            probs0 = np.vstack((attr_prob.T, zeros))\n            probs += probs0[col]\n        return probs\n\n    def _sparse_probs(self, data, probs):\n        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1\n        log_prob = np.zeros((len(self.log_cont_prob),\n                             n_vals,\n                             self.log_cont_prob[0].shape[0]))\n        for i, p in enumerate(self.log_cont_prob):\n            p0 = p.T[0].copy()\n            probs[:] += p0\n            log_prob[i, :p.shape[1]] = p.T - p0\n\n        dat = data.data.copy()\n        dat[np.isnan(dat)] = n_vals - 1\n        dat = dat.astype(int)\n\n        if sp.isspmatrix_csr(data):\n            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):\n                row += log_prob[data.indices[start:end],\n                                dat[start:end]].sum(axis=0)\n        else:\n            csc = data.tocsc()\n            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],\n                                             log_prob):\n                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]\n\n        return probs\n\n\nNaiveBayesLearner.__returns__ = NaiveBayesModel\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:** Naive Bayes Model Returns Incorrect Probabilities for Inputs with Missing Attributes

**Description:**

When using the Naive Bayes classifier to make predictions on data that includes missing attribute values (`NaN`), the model returns incorrect probability distributions. This causes discrepancies between the expected and actual prediction probabilities, leading to failed validations.

**Example Code:**

```python
import numpy as np
from Orange.classification import NaiveBayesLearner
from Orange.data import Domain, DiscreteVariable, Table

# Create dataset with missing values
x = np.array([
    [1, 0, 0],
    [0, 1, 0],
    [0, 0, 0],
    [0, 1, 0],
    [1, 1, 0],
    [1, 2, 0],
    [1, 2, np.nan]
])
y = np.array([1, 0, 0, 0, 1, 1, 1])
domain = Domain(
    [DiscreteVariable("a", values="ab"),
     DiscreteVariable("b", values="abc"),
     DiscreteVariable("c", values="a")],
    DiscreteVariable("y", values="AB")
)
data = Table.from_numpy(domain, x, y)

# Train the model
learner = NaiveBayesLearner()
model = learner(data)

# Make predictions on data with missing attributes
test_x = np.array([
    [np.nan, np.nan, np.nan],
    [np.nan, 0, np.nan],
    [0, np.nan, np.nan]
])
probs = model(test_x, ret=model.Probs)
print(probs)
```

**Expected Behavior:**

The Naive Bayes model should correctly handle missing attribute values by appropriately adjusting the probability calculations. The output probabilities for each class should align closely with the expected distributions based on the training data.

**Actual Behavior:**

The model returns probability distributions that significantly differ from the expected values when predicting on inputs with missing attributes. This results in incorrect class assignments and failed validations.

[/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/classification/naive_bayes.py
import numpy as np
import scipy.sparse as sp

from Orange.classification import Learner, Model
from Orange.data import Instance, Storage, Table
from Orange.statistics import contingency
from Orange.preprocess import Discretize, RemoveNaNColumns

__all__ = ["NaiveBayesLearner"]


class NaiveBayesLearner(Learner):
    """
    Naive Bayes classifier. Works only with discrete attributes. By default,
    continuous attributes are discretized.

    Parameters
    ----------
    preprocessors : list, optional (default="[Orange.preprocess.Discretize]")
        An ordered list of preprocessors applied to data before training
        or testing.
    """
    preprocessors = [RemoveNaNColumns(), Discretize()]
    name = 'naive bayes'

    def fit_storage(self, table):
        if not isinstance(table, Storage):
            raise TypeError("Data is not a subclass of Orange.data.Storage.")
        if not all(var.is_discrete
                   for var in table.domain.variables):
            raise NotImplementedError("Only categorical variables are "
                                      "supported.")

        cont = contingency.get_contingencies(table)
        class_freq = np.array(np.diag(
            contingency.get_contingency(table, table.domain.class_var)))
        nclss = (class_freq != 0).sum()
        if not nclss:
            raise ValueError("Data has no defined target values.")

        # Laplacian smoothing considers only classes that appear in the data,
        # in part to avoid cases where the probabilities are affected by empty
        # (or completely spurious) classes that appear because of Orange's reuse
        # of variables. See GH-2943.
        # The corresponding elements of class_probs are set to zero only after
        # mock non-zero values are used in computation of log_cont_prob to
        # prevent division by zero.
        class_prob = (class_freq + 1) / (np.sum(class_freq) + nclss)
        log_cont_prob = [np.log(
            (np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] + nclss)
            / class_prob[:, None])
                         for c in cont]
        class_prob[class_freq == 0] = 0
        return NaiveBayesModel(log_cont_prob, class_prob, table.domain)


class NaiveBayesModel(Model):
    def __init__(self, log_cont_prob, class_prob, domain):
        super().__init__(domain)
        self.log_cont_prob = log_cont_prob
        self.class_prob = class_prob

    def predict_storage(self, data):
        if isinstance(data, Instance):
            data = Table.from_numpy(None, np.atleast_2d(data.x))
        if type(data) is Table:  # pylint: disable=unidiomatic-typecheck
            return self.predict(data.X)

        if not len(data) or not len(data[0]):
            probs = np.tile(self.class_prob, (len(data), 1))
        else:
            isnan = np.isnan
            zeros = np.zeros_like(self.class_prob)
            probs = self.class_prob * np.exp(np.array([
                zeros if isnan(ins.x).all() else
                sum(attr_prob[:, int(attr_val)]
                    for attr_val, attr_prob in zip(ins, self.log_cont_prob)
                    if not isnan(attr_val))
                for ins in data]))
        probs /= probs.sum(axis=1)[:, None]
        values = probs.argmax(axis=1)
        return values, probs

    def predict(self, X):
        probs = np.zeros((X.shape[0], self.class_prob.shape[0]))
        if self.log_cont_prob is not None:
            if sp.issparse(X):
                self._sparse_probs(X, probs)
            else:
                self._dense_probs(X, probs)
        np.exp(probs, probs)
        probs *= self.class_prob
        probs /= probs.sum(axis=1)[:, None]
        values = probs.argmax(axis=1)
        return values, probs

    def _dense_probs(self, data, probs):
        zeros = np.zeros((1, probs.shape[1]))
        for col, attr_prob in zip(data.T, self.log_cont_prob):
            col = col.copy()
            col[np.isnan(col)] = attr_prob.shape[1] - 1
            col = col.astype(int)
            probs0 = np.vstack((attr_prob.T, zeros))
            probs += probs0[col]
        return probs

    def _sparse_probs(self, data, probs):
        n_vals = max(p.shape[1] for p in self.log_cont_prob) + 1
        log_prob = np.zeros((len(self.log_cont_prob),
                             n_vals,
                             self.log_cont_prob[0].shape[0]))
        for i, p in enumerate(self.log_cont_prob):
            p0 = p.T[0].copy()
            probs[:] += p0
            log_prob[i, :p.shape[1]] = p.T - p0

        dat = data.data.copy()
        dat[np.isnan(dat)] = n_vals - 1
        dat = dat.astype(int)

        if sp.isspmatrix_csr(data):
            for row, start, end in zip(probs, data.indptr, data.indptr[1:]):
                row += log_prob[data.indices[start:end],
                                dat[start:end]].sum(axis=0)
        else:
            csc = data.tocsc()
            for start, end, attr_prob in zip(csc.indptr, csc.indptr[1:],
                                             log_prob):
                probs[csc.indices[start:end]] += attr_prob[dat[start:end]]

        return probs


NaiveBayesLearner.__returns__ = NaiveBayesModel

```
--- 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