{
    "file_diffs": [
        {
            "old_file_content": "import numpy as np\nimport bottleneck as bn\nfrom Orange import data as Orange_data\nfrom ..data.value import Value\nimport scipy\n\nclass Fitter:\n    supports_multiclass = False\n\n    def fit(self, X, Y, W):\n        raise TypeError(\"Descendants of Fitter must overload method fit\")\n\n    def __call__(self, data):\n        X, Y, W = data.X, data.Y, data.W if data.has_weights else None\n        if np.shape(Y)[1] > 1 and not self.supports_multiclass:\n            raise TypeError(\"fitter doesn't support multiple class variables\")\n        self.domain = data.domain\n        clf = self.fit(X, Y, W)\n        clf.domain = data.domain\n        clf.Y = Y\n        clf.supports_multiclass = self.supports_multiclass\n        return clf\n\n\nclass Model:\n    supports_multiclass = False\n    Y = None\n    Value = 0\n    Probs = 1\n    ValueProbs = 2\n\n    def __init__(self, domain=None):\n        if isinstance(self, Fitter):\n            domain = None\n        elif not domain:\n            raise ValueError(\"unspecified domain\")\n        self.domain = domain\n\n    def predict(self, table):\n        raise TypeError(\"Descendants of Model must overload method predict\")\n\n    def __call__(self, data, ret=Value):\n        if not 0 <= ret <= 2:\n            raise ValueError(\"invalid value of argument 'ret'\")\n        if (ret > 0\n            and any(isinstance(v, Orange_data.ContinuousVariable)\n                    for v in self.domain.class_vars)):\n            raise ValueError(\"cannot predict continuous distributions\")\n\n        # Call the predictor\n        if isinstance(data, np.ndarray):\n            prediction = self.predict(np.atleast_2d(data))\n        elif isinstance(data, scipy.sparse.csr.csr_matrix):\n            prediction = self.predict(data)\n        elif isinstance(data, Orange_data.Instance):\n            if data.domain != self.domain:\n                data = Orange_data.Instance(self.domain, data)\n            prediction = self.predict(np.atleast_2d(data._values))\n        elif isinstance(data, Orange_data.Table):\n            if data.domain != self.domain:\n                data = Orange_data.Table.from_table(self.domain, data)\n            prediction = self.predict(data.X)\n        else:\n            raise TypeError(\"Unrecognized argument (instance of '%s')\",\n                            type(data).__name__)\n\n        # Parse the result into value and probs\n        multitarget = len(self.domain.class_vars) > 1\n        if isinstance(prediction, tuple):\n            value, probs = prediction\n        elif prediction.ndim == 1 + multitarget:\n            value, probs = prediction, None\n        elif prediction.ndim == 2 + multitarget:\n            value, probs = None, prediction\n        else:\n            raise TypeError(\"model returned a %i-dimensional array\",\n                            prediction.ndim)\n\n        # Ensure that we have what we need to return\n        if ret != Model.Probs and value is None:\n            value = np.argmax(probs, axis=-1)\n        if ret != Model.Value and probs is None:\n            if multitarget:\n                max_card = max(len(c.values) for c in self.domain.class_vars)\n                probs = np.zeros(value.shape + (max_card,), float)\n                for i, cvar in enumerate(self.domain.class_vars):\n                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),\n                                           max_card)\n            else:\n                probs = bn.bincount(np.atleast_2d(value),\n                                    len(self.domain.class_var.values))\n            return probs\n\n        # Expand probability predictions for class values which are not present\n        if ret != self.Value:\n            n_class = len(self.domain.class_vars)\n            used_vals = [np.unique(y) for y in self.Y.T]\n            max_values = max(len(cv.values) for cv in self.domain.class_vars)\n            if max_values != probs.shape[-1]:\n                if not self.supports_multiclass:\n                    probs = probs[:, np.newaxis, :]\n                probs_ext = np.zeros((len(probs), n_class, max_values))\n                for c in range(n_class):\n                    i = 0\n                    class_values = len(self.domain.class_vars[c].values)\n                    for cv in range(class_values):\n                        if i < len(used_vals[c]) and cv == used_vals[c][i]:\n                            probs_ext[:, c, cv] = probs[:, c, i]\n                            i += 1\n                if self.supports_multiclass:\n                    probs = probs_ext\n                else:\n                    probs = probs_ext[:, 0, :]\n\n        # Return what we need to\n        if ret == Model.Probs:\n            return probs\n        if isinstance(data, Orange_data.Instance) and not multitarget:\n            value = Value(self.domain.class_var, value[0])\n        if ret == Model.Value:\n            return value\n        else:  # ret == Model.ValueProbs\n            return value, probs\n",
            "new_file_content": "import numpy as np\nimport bottleneck as bn\nfrom Orange import data as Orange_data\nfrom ..data.value import Value\nimport scipy\n\nclass Fitter:\n    supports_multiclass = False\n\n    def fit(self, X, Y, W):\n        raise TypeError(\"Descendants of Fitter must overload method fit\")\n\n    def __call__(self, data):\n        X, Y, W = data.X, data.Y, data.W if data.has_weights else None\n        if np.shape(Y)[1] > 1 and not self.supports_multiclass:\n            raise TypeError(\"fitter doesn't support multiple class variables\")\n        self.domain = data.domain\n        clf = self.fit(X, Y, W)\n        clf.domain = data.domain\n        clf.Y = Y\n        clf.supports_multiclass = self.supports_multiclass\n        return clf\n\n\nclass Model:\n    supports_multiclass = False\n    Y = None\n    Value = 0\n    Probs = 1\n    ValueProbs = 2\n\n    def __init__(self, domain=None):\n        if isinstance(self, Fitter):\n            domain = None\n        elif not domain:\n            raise ValueError(\"unspecified domain\")\n        self.domain = domain\n\n    def predict(self, table):\n        raise TypeError(\"Descendants of Model must overload method predict\")\n\n    def __call__(self, data, ret=Value):\n        if not 0 <= ret <= 2:\n            raise ValueError(\"invalid value of argument 'ret'\")\n        if (ret > 0\n            and any(isinstance(v, Orange_data.ContinuousVariable)\n                    for v in self.domain.class_vars)):\n            raise ValueError(\"cannot predict continuous distributions\")\n\n        # Call the predictor\n        if isinstance(data, np.ndarray):\n            prediction = self.predict(np.atleast_2d(data))\n        elif isinstance(data, scipy.sparse.csr.csr_matrix):\n            prediction = self.predict(data)\n        elif isinstance(data, Orange_data.Instance):\n            if data.domain != self.domain:\n                data = Orange_data.Instance(self.domain, data)\n            prediction = self.predict(np.atleast_2d(data.x))\n        elif isinstance(data, Orange_data.Table):\n            if data.domain != self.domain:\n                data = Orange_data.Table.from_table(self.domain, data)\n            prediction = self.predict(data.X)\n        else:\n            raise TypeError(\"Unrecognized argument (instance of '%s')\",\n                            type(data).__name__)\n\n        # Parse the result into value and probs\n        multitarget = len(self.domain.class_vars) > 1\n        if isinstance(prediction, tuple):\n            value, probs = prediction\n        elif prediction.ndim == 1 + multitarget:\n            value, probs = prediction, None\n        elif prediction.ndim == 2 + multitarget:\n            value, probs = None, prediction\n        else:\n            raise TypeError(\"model returned a %i-dimensional array\",\n                            prediction.ndim)\n\n        # Ensure that we have what we need to return\n        if ret != Model.Probs and value is None:\n            value = np.argmax(probs, axis=-1)\n        if ret != Model.Value and probs is None:\n            if multitarget:\n                max_card = max(len(c.values) for c in self.domain.class_vars)\n                probs = np.zeros(value.shape + (max_card,), float)\n                for i, cvar in enumerate(self.domain.class_vars):\n                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),\n                                           max_card)\n            else:\n                probs = bn.bincount(np.atleast_2d(value),\n                                    len(self.domain.class_var.values))\n            return probs\n\n        # Expand probability predictions for class values which are not present\n        if ret != self.Value:\n            n_class = len(self.domain.class_vars)\n            used_vals = [np.unique(y) for y in self.Y.T]\n            max_values = max(len(cv.values) for cv in self.domain.class_vars)\n            if max_values != probs.shape[-1]:\n                if not self.supports_multiclass:\n                    probs = probs[:, np.newaxis, :]\n                probs_ext = np.zeros((len(probs), n_class, max_values))\n                for c in range(n_class):\n                    i = 0\n                    class_values = len(self.domain.class_vars[c].values)\n                    for cv in range(class_values):\n                        if i < len(used_vals[c]) and cv == used_vals[c][i]:\n                            probs_ext[:, c, cv] = probs[:, c, i]\n                            i += 1\n                if self.supports_multiclass:\n                    probs = probs_ext\n                else:\n                    probs = probs_ext[:, 0, :]\n\n        # Return what we need to\n        if ret == Model.Probs:\n            return probs\n        if isinstance(data, Orange_data.Instance) and not multitarget:\n            value = Value(self.domain.class_var, value[0])\n        if ret == Model.Value:\n            return value\n        else:  # ret == Model.ValueProbs\n            return value, probs\n",
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                "file": {
                    "path": "Orange/classification/__init__.py"
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                            "start": 55,
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                        "section": "class Model:"
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                    "line_group": {
                        "all_lines": [
                            {
                                "content": "        elif isinstance(data, Orange_data.Instance):",
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                            },
                            {
                                "content": "            if data.domain != self.domain:",
                                "type": "context"
                            },
                            {
                                "content": "                data = Orange_data.Instance(self.domain, data)",
                                "type": "context"
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                            {
                                "content": "            prediction = self.predict(np.atleast_2d(data._values))",
                                "type": "deleted"
                            },
                            {
                                "content": "            prediction = self.predict(np.atleast_2d(data.x))",
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                                "type": "context"
                            },
                            {
                                "content": "            if data.domain != self.domain:",
                                "type": "context"
                            },
                            {
                                "content": "                data = Orange_data.Table.from_table(self.domain, data)",
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                    "modified_entities": [
                        {
                            "file_name": "Orange/classification/__init__.py",
                            "type": "class",
                            "name": "Model",
                            "content": "class Model:\n    supports_multiclass = False\n    Y = None\n    Value = 0\n    Probs = 1\n    ValueProbs = 2\n\n    def __init__(self, domain=None):\n        if isinstance(self, Fitter):\n            domain = None\n        elif not domain:\n            raise ValueError(\"unspecified domain\")\n        self.domain = domain\n\n    def predict(self, table):\n        raise TypeError(\"Descendants of Model must overload method predict\")\n\n    def __call__(self, data, ret=Value):\n        if not 0 <= ret <= 2:\n            raise ValueError(\"invalid value of argument 'ret'\")\n        if (ret > 0\n            and any(isinstance(v, Orange_data.ContinuousVariable)\n                    for v in self.domain.class_vars)):\n            raise ValueError(\"cannot predict continuous distributions\")\n\n        # Call the predictor\n        if isinstance(data, np.ndarray):\n            prediction = self.predict(np.atleast_2d(data))\n        elif isinstance(data, scipy.sparse.csr.csr_matrix):\n            prediction = self.predict(data)\n        elif isinstance(data, Orange_data.Instance):\n            if data.domain != self.domain:\n                data = Orange_data.Instance(self.domain, data)\n            prediction = self.predict(np.atleast_2d(data._values))\n        elif isinstance(data, Orange_data.Table):\n            if data.domain != self.domain:\n                data = Orange_data.Table.from_table(self.domain, data)\n            prediction = self.predict(data.X)\n        else:\n            raise TypeError(\"Unrecognized argument (instance of '%s')\",\n                            type(data).__name__)\n\n        # Parse the result into value and probs\n        multitarget = len(self.domain.class_vars) > 1\n        if isinstance(prediction, tuple):\n            value, probs = prediction\n        elif prediction.ndim == 1 + multitarget:\n            value, probs = prediction, None\n        elif prediction.ndim == 2 + multitarget:\n            value, probs = None, prediction\n        else:\n            raise TypeError(\"model returned a %i-dimensional array\",\n                            prediction.ndim)\n\n        # Ensure that we have what we need to return\n        if ret != Model.Probs and value is None:\n            value = np.argmax(probs, axis=-1)\n        if ret != Model.Value and probs is None:\n            if multitarget:\n                max_card = max(len(c.values) for c in self.domain.class_vars)\n                probs = np.zeros(value.shape + (max_card,), float)\n                for i, cvar in enumerate(self.domain.class_vars):\n                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),\n                                           max_card)\n            else:\n                probs = bn.bincount(np.atleast_2d(value),\n                                    len(self.domain.class_var.values))\n            return probs\n\n        # Expand probability predictions for class values which are not present\n        if ret != self.Value:\n            n_class = len(self.domain.class_vars)\n            used_vals = [np.unique(y) for y in self.Y.T]\n            max_values = max(len(cv.values) for cv in self.domain.class_vars)\n            if max_values != probs.shape[-1]:\n                if not self.supports_multiclass:\n                    probs = probs[:, np.newaxis, :]\n                probs_ext = np.zeros((len(probs), n_class, max_values))\n                for c in range(n_class):\n                    i = 0\n                    class_values = len(self.domain.class_vars[c].values)\n                    for cv in range(class_values):\n                        if i < len(used_vals[c]) and cv == used_vals[c][i]:\n                            probs_ext[:, c, cv] = probs[:, c, i]\n                            i += 1\n                if self.supports_multiclass:\n                    probs = probs_ext\n                else:\n                    probs = probs_ext[:, 0, :]\n\n        # Return what we need to\n        if ret == Model.Probs:\n            return probs\n        if isinstance(data, Orange_data.Instance) and not multitarget:\n            value = Value(self.domain.class_var, value[0])\n        if ret == Model.Value:\n            return value\n        else:  # ret == Model.ValueProbs\n            return value, probs",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 25,
                            "end_lineno": 123,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/classification/__init__.py",
                            "type": "method",
                            "name": "Model.__call__",
                            "content": "class Model:\n    supports_multiclass = False\n    Y = None\n    Value = 0\n    Probs = 1\n    ValueProbs = 2\n\n    def __init__(self, domain=None):\n        if isinstance(self, Fitter):\n            domain = None\n        elif not domain:\n            raise ValueError(\"unspecified domain\")\n        self.domain = domain\n\n    def predict(self, table):\n        raise TypeError(\"Descendants of Model must overload method predict\")\n\n    def __call__(self, data, ret=Value):\n        if not 0 <= ret <= 2:\n            raise ValueError(\"invalid value of argument 'ret'\")\n        if (ret > 0\n            and any(isinstance(v, Orange_data.ContinuousVariable)\n                    for v in self.domain.class_vars)):\n            raise ValueError(\"cannot predict continuous distributions\")\n\n        # Call the predictor\n        if isinstance(data, np.ndarray):\n            prediction = self.predict(np.atleast_2d(data))\n        elif isinstance(data, scipy.sparse.csr.csr_matrix):\n            prediction = self.predict(data)\n        elif isinstance(data, Orange_data.Instance):\n            if data.domain != self.domain:\n                data = Orange_data.Instance(self.domain, data)\n            prediction = self.predict(np.atleast_2d(data._values))\n        elif isinstance(data, Orange_data.Table):\n            if data.domain != self.domain:\n                data = Orange_data.Table.from_table(self.domain, data)\n            prediction = self.predict(data.X)\n        else:\n            raise TypeError(\"Unrecognized argument (instance of '%s')\",\n                            type(data).__name__)\n\n        # Parse the result into value and probs\n        multitarget = len(self.domain.class_vars) > 1\n        if isinstance(prediction, tuple):\n            value, probs = prediction\n        elif prediction.ndim == 1 + multitarget:\n            value, probs = prediction, None\n        elif prediction.ndim == 2 + multitarget:\n            value, probs = None, prediction\n        else:\n            raise TypeError(\"model returned a %i-dimensional array\",\n                            prediction.ndim)\n\n        # Ensure that we have what we need to return\n        if ret != Model.Probs and value is None:\n            value = np.argmax(probs, axis=-1)\n        if ret != Model.Value and probs is None:\n            if multitarget:\n                max_card = max(len(c.values) for c in self.domain.class_vars)\n                probs = np.zeros(value.shape + (max_card,), float)\n                for i, cvar in enumerate(self.domain.class_vars):\n                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),\n                                           max_card)\n            else:\n                probs = bn.bincount(np.atleast_2d(value),\n                                    len(self.domain.class_var.values))\n            return probs\n\n        # Expand probability predictions for class values which are not present\n        if ret != self.Value:\n            n_class = len(self.domain.class_vars)\n            used_vals = [np.unique(y) for y in self.Y.T]\n            max_values = max(len(cv.values) for cv in self.domain.class_vars)\n            if max_values != probs.shape[-1]:\n                if not self.supports_multiclass:\n                    probs = probs[:, np.newaxis, :]\n                probs_ext = np.zeros((len(probs), n_class, max_values))\n                for c in range(n_class):\n                    i = 0\n                    class_values = len(self.domain.class_vars[c].values)\n                    for cv in range(class_values):\n                        if i < len(used_vals[c]) and cv == used_vals[c][i]:\n                            probs_ext[:, c, cv] = probs[:, c, i]\n                            i += 1\n                if self.supports_multiclass:\n                    probs = probs_ext\n                else:\n                    probs = probs_ext[:, 0, :]\n\n        # Return what we need to\n        if ret == Model.Probs:\n            return probs\n        if isinstance(data, Orange_data.Instance) and not multitarget:\n            value = Value(self.domain.class_var, value[0])\n        if ret == Model.Value:\n            return value\n        else:  # ret == Model.ValueProbs\n            return value, probs",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 42,
                            "end_lineno": 123,
                            "parent": {
                                "file_name": "Orange/classification/__init__.py",
                                "type": "class",
                                "name": "Model",
                                "content": "class Model:\n    supports_multiclass = False\n    Y = None\n    Value = 0\n    Probs = 1\n    ValueProbs = 2\n\n    def __init__(self, domain=None):\n        if isinstance(self, Fitter):\n            domain = None\n        elif not domain:\n            raise ValueError(\"unspecified domain\")\n        self.domain = domain\n\n    def predict(self, table):\n        raise TypeError(\"Descendants of Model must overload method predict\")\n\n    def __call__(self, data, ret=Value):\n        if not 0 <= ret <= 2:\n            raise ValueError(\"invalid value of argument 'ret'\")\n        if (ret > 0\n            and any(isinstance(v, Orange_data.ContinuousVariable)\n                    for v in self.domain.class_vars)):\n            raise ValueError(\"cannot predict continuous distributions\")\n\n        # Call the predictor\n        if isinstance(data, np.ndarray):\n            prediction = self.predict(np.atleast_2d(data))\n        elif isinstance(data, scipy.sparse.csr.csr_matrix):\n            prediction = self.predict(data)\n        elif isinstance(data, Orange_data.Instance):\n            if data.domain != self.domain:\n                data = Orange_data.Instance(self.domain, data)\n            prediction = self.predict(np.atleast_2d(data._values))\n        elif isinstance(data, Orange_data.Table):\n            if data.domain != self.domain:\n                data = Orange_data.Table.from_table(self.domain, data)\n            prediction = self.predict(data.X)\n        else:\n            raise TypeError(\"Unrecognized argument (instance of '%s')\",\n                            type(data).__name__)\n\n        # Parse the result into value and probs\n        multitarget = len(self.domain.class_vars) > 1\n        if isinstance(prediction, tuple):\n            value, probs = prediction\n        elif prediction.ndim == 1 + multitarget:\n            value, probs = prediction, None\n        elif prediction.ndim == 2 + multitarget:\n            value, probs = None, prediction\n        else:\n            raise TypeError(\"model returned a %i-dimensional array\",\n                            prediction.ndim)\n\n        # Ensure that we have what we need to return\n        if ret != Model.Probs and value is None:\n            value = np.argmax(probs, axis=-1)\n        if ret != Model.Value and probs is None:\n            if multitarget:\n                max_card = max(len(c.values) for c in self.domain.class_vars)\n                probs = np.zeros(value.shape + (max_card,), float)\n                for i, cvar in enumerate(self.domain.class_vars):\n                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),\n                                           max_card)\n            else:\n                probs = bn.bincount(np.atleast_2d(value),\n                                    len(self.domain.class_var.values))\n            return probs\n\n        # Expand probability predictions for class values which are not present\n        if ret != self.Value:\n            n_class = len(self.domain.class_vars)\n            used_vals = [np.unique(y) for y in self.Y.T]\n            max_values = max(len(cv.values) for cv in self.domain.class_vars)\n            if max_values != probs.shape[-1]:\n                if not self.supports_multiclass:\n                    probs = probs[:, np.newaxis, :]\n                probs_ext = np.zeros((len(probs), n_class, max_values))\n                for c in range(n_class):\n                    i = 0\n                    class_values = len(self.domain.class_vars[c].values)\n                    for cv in range(class_values):\n                        if i < len(used_vals[c]) and cv == used_vals[c][i]:\n                            probs_ext[:, c, cv] = probs[:, c, i]\n                            i += 1\n                if self.supports_multiclass:\n                    probs = probs_ext\n                else:\n                    probs = probs_ext[:, 0, :]\n\n        # Return what we need to\n        if ret == Model.Probs:\n            return probs\n        if isinstance(data, Orange_data.Instance) and not multitarget:\n            value = Value(self.domain.class_var, value[0])\n        if ret == Model.Value:\n            return value\n        else:  # ret == Model.ValueProbs\n            return value, probs",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 25,
                                "end_lineno": 123,
                                "parent": null
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        },
        {
            "old_file_content": "import unittest\nimport numpy as np\n\nfrom Orange import data\nimport Orange.classification\nimport Orange.classification.dummies as dummies\nimport Orange.classification.majority as maj\nfrom Orange.data.io import BasketReader\n\nclass MultiClassTest(unittest.TestCase):\n    def test_unsupported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n\n        # multiple class variables\n        y = np.random.random_integers(10, 11, (nrows, 2))\n        t = data.Table(x, y)\n        learn = dummies.DummyLearner()\n        with self.assertRaises(TypeError):\n            clf = learn(t)\n\n        # single class variable\n        y = np.random.random_integers(10, 11, (nrows, 1))\n        t = data.Table(x, y)\n        learn = dummies.DummyLearner()\n        clf = learn(t)\n        z = clf(x)\n        self.assertEqual(z.ndim, 1)\n\n    def test_supported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n        y = np.random.random_integers(10, 11, (nrows, 2))\n        t = data.Table(x, y)\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(t)\n        z = clf(x)\n        self.assertEqual(z.shape, y.shape)\n\n\nclass ModelTest(unittest.TestCase):\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n\n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertTrue((y2 == 1).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2 == 1).all())\n\n        # multitarget\n        y = np.random.random_integers(1, 5, (nrows, 2))\n        y[:, 0] = y[:, 0] // 3        # majority = 1\n        y[:, 1] = (y[:, 1] + 4) // 3    # majority = 2\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertEqual(y2.shape, y.shape)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n\n    def test_probs_from_value(self):\n        pass\n\n\nclass ExpandProbabilitiesTest(unittest.TestCase):\n    def prepareTable(self, rows, attr, vars, class_var_domain):\n        attributes = [\"Feature %i\" % i for i in range(attr)]\n        classes = [\"Class %i\" % i for i in range(vars)]\n        attr_vars = [data.DiscreteVariable(name=a) for a in attributes]\n        class_vars = [data.DiscreteVariable(name=c,\n                                            values=range(class_var_domain))\n                      for c in classes]\n        meta_vars = []\n        self.domain = data.Domain(attr_vars, class_vars, meta_vars)\n        self.x = np.random.random_integers(0, 1, (rows, attr))\n\n    def test_single_class(self):\n        rows = 10\n        attr = 3\n        vars = 1\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n        t = data.Table(self.domain, self.x, y)\n        learn = dummies.DummyLearner()\n        clf = learn(t)\n        z, p = clf(self.x, ret=Orange.classification.Model.ValueProbs)\n        self.assertEqual(p.shape, (rows, class_var_domain))\n        self.assertTrue(np.all(z == np.argmax(p, axis=-1)))\n\n    def test_multi_class(self):\n        rows = 10\n        attr = 3\n        vars = 5\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n        t = data.Table(self.domain, self.x, y)\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(t)\n        z, p = clf(self.x, ret=Orange.classification.Model.ValueProbs)\n        self.assertEqual(p.shape, (rows, vars, class_var_domain))\n        self.assertTrue(np.all(z == np.argmax(p, axis=-1)))\n\n\nclass SparseTest(unittest.TestCase):\n    def test_sparse_basket(self):\n        table = BasketReader().read_file(\"iris.basket\")\n        test = Orange.data.Table.from_table_rows(table,range(0,len(table),2))\n        train = Orange.data.Table.from_table_rows(table,range(1,len(table),2))\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(train)\n        p = clf(test)\n        self.assertEqual(p.shape, test.Y.shape)\n        p = clf(test.X)\n        self.assertEqual(p.shape, test.Y.shape)\n",
            "new_file_content": "import unittest\nimport numpy as np\n\nfrom Orange import data\nimport Orange.classification\nimport Orange.classification.dummies as dummies\nimport Orange.classification.majority as maj\nimport Orange.classification.naive_bayes as nb\nfrom Orange.data.io import BasketReader\n\nclass MultiClassTest(unittest.TestCase):\n    def test_unsupported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n\n        # multiple class variables\n        y = np.random.random_integers(10, 11, (nrows, 2))\n        t = data.Table(x, y)\n        learn = dummies.DummyLearner()\n        with self.assertRaises(TypeError):\n            clf = learn(t)\n\n        # single class variable\n        y = np.random.random_integers(10, 11, (nrows, 1))\n        t = data.Table(x, y)\n        learn = dummies.DummyLearner()\n        clf = learn(t)\n        z = clf(x)\n        self.assertEqual(z.ndim, 1)\n\n    def test_supported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n        y = np.random.random_integers(10, 11, (nrows, 2))\n        t = data.Table(x, y)\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(t)\n        z = clf(x)\n        self.assertEqual(z.shape, y.shape)\n\n\nclass ModelTest(unittest.TestCase):\n\n    def test_predict_single_instance(self):\n        table = data.Table(\"iris\")\n        learn = nb.BayesLearner()\n        clf = learn(table)\n        pred = []\n        for row in table:\n            pred.append(clf(row))\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n\n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertTrue((y2 == 1).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2 == 1).all())\n\n        # multitarget\n        y = np.random.random_integers(1, 5, (nrows, 2))\n        y[:, 0] = y[:, 0] // 3        # majority = 1\n        y[:, 1] = (y[:, 1] + 4) // 3    # majority = 2\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertEqual(y2.shape, y.shape)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n\n    def test_probs_from_value(self):\n        pass\n\n\nclass ExpandProbabilitiesTest(unittest.TestCase):\n    def prepareTable(self, rows, attr, vars, class_var_domain):\n        attributes = [\"Feature %i\" % i for i in range(attr)]\n        classes = [\"Class %i\" % i for i in range(vars)]\n        attr_vars = [data.DiscreteVariable(name=a) for a in attributes]\n        class_vars = [data.DiscreteVariable(name=c,\n                                            values=range(class_var_domain))\n                      for c in classes]\n        meta_vars = []\n        self.domain = data.Domain(attr_vars, class_vars, meta_vars)\n        self.x = np.random.random_integers(0, 1, (rows, attr))\n\n    def test_single_class(self):\n        rows = 10\n        attr = 3\n        vars = 1\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n        t = data.Table(self.domain, self.x, y)\n        learn = dummies.DummyLearner()\n        clf = learn(t)\n        z, p = clf(self.x, ret=Orange.classification.Model.ValueProbs)\n        self.assertEqual(p.shape, (rows, class_var_domain))\n        self.assertTrue(np.all(z == np.argmax(p, axis=-1)))\n\n    def test_multi_class(self):\n        rows = 10\n        attr = 3\n        vars = 5\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n        t = data.Table(self.domain, self.x, y)\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(t)\n        z, p = clf(self.x, ret=Orange.classification.Model.ValueProbs)\n        self.assertEqual(p.shape, (rows, vars, class_var_domain))\n        self.assertTrue(np.all(z == np.argmax(p, axis=-1)))\n\n\nclass SparseTest(unittest.TestCase):\n    def test_sparse_basket(self):\n        table = BasketReader().read_file(\"iris.basket\")\n        test = Orange.data.Table.from_table_rows(table,range(0,len(table),2))\n        train = Orange.data.Table.from_table_rows(table,range(1,len(table),2))\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(train)\n        p = clf(test)\n        self.assertEqual(p.shape, test.Y.shape)\n        p = clf(test.X)\n        self.assertEqual(p.shape, test.Y.shape)\n",
            "header": {
                "file": {
                    "path": "Orange/tests/test_classification.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "da7ac21f7",
                "new_commit_hash": "cfa39fc6b",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
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                        "old_range": {
                            "start": 5,
                            "length": 6
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                            "start": 5,
                            "length": 7
                        },
                        "section": "from Orange import data"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "import Orange.classification\r",
                                "type": "context"
                            },
                            {
                                "content": "import Orange.classification.dummies as dummies\r",
                                "type": "context"
                            },
                            {
                                "content": "import Orange.classification.majority as maj\r",
                                "type": "context"
                            },
                            {
                                "content": "import Orange.classification.naive_bayes as nb\r",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.data.io import BasketReader\r",
                                "type": "context"
                            },
                            {
                                "content": "\r",
                                "type": "context"
                            },
                            {
                                "content": "class MultiClassTest(unittest.TestCase):\r",
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                        {
                            "file_name": "Orange/tests/test_classification.py",
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                            "ast_type_str": "Import",
                            "start_lineno": 8,
                            "end_lineno": 8,
                            "parent": null
                        }
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                        "section": "class MultiClassTest(unittest.TestCase):"
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                                "content": "\r",
                                "type": "context"
                            },
                            {
                                "content": "\r",
                                "type": "context"
                            },
                            {
                                "content": "class ModelTest(unittest.TestCase):\r",
                                "type": "context"
                            },
                            {
                                "content": "\r",
                                "type": "added"
                            },
                            {
                                "content": "    def test_predict_single_instance(self):\r",
                                "type": "added"
                            },
                            {
                                "content": "        table = data.Table(\"iris\")\r",
                                "type": "added"
                            },
                            {
                                "content": "        learn = nb.BayesLearner()\r",
                                "type": "added"
                            },
                            {
                                "content": "        clf = learn(table)\r",
                                "type": "added"
                            },
                            {
                                "content": "        pred = []\r",
                                "type": "added"
                            },
                            {
                                "content": "        for row in table:\r",
                                "type": "added"
                            },
                            {
                                "content": "            pred.append(clf(row))\r",
                                "type": "added"
                            },
                            {
                                "content": "\r",
                                "type": "added"
                            },
                            {
                                "content": "    def test_value_from_probs(self):\r",
                                "type": "context"
                            },
                            {
                                "content": "        nrows = 100\r",
                                "type": "context"
                            },
                            {
                                "content": "        ncols = 5\r",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/tests/test_classification.py",
                            "type": "class",
                            "name": "ModelTest",
                            "content": "class ModelTest(unittest.TestCase):\n\n    def test_predict_single_instance(self):\n        table = data.Table(\"iris\")\n        learn = nb.BayesLearner()\n        clf = learn(table)\n        pred = []\n        for row in table:\n            pred.append(clf(row))\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n\n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertTrue((y2 == 1).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2 == 1).all())\n\n        # multitarget\n        y = np.random.random_integers(1, 5, (nrows, 2))\n        y[:, 0] = y[:, 0] // 3        # majority = 1\n        y[:, 1] = (y[:, 1] + 4) // 3    # majority = 2\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertEqual(y2.shape, y.shape)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n\n    def test_probs_from_value(self):\n        pass",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 44,
                            "end_lineno": 85,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/tests/test_classification.py",
                            "type": "method",
                            "name": "ModelTest.test_predict_single_instance",
                            "content": "class ModelTest(unittest.TestCase):\n\n    def test_predict_single_instance(self):\n        table = data.Table(\"iris\")\n        learn = nb.BayesLearner()\n        clf = learn(table)\n        pred = []\n        for row in table:\n            pred.append(clf(row))\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n\n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertTrue((y2 == 1).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2 == 1).all())\n\n        # multitarget\n        y = np.random.random_integers(1, 5, (nrows, 2))\n        y[:, 0] = y[:, 0] // 3        # majority = 1\n        y[:, 1] = (y[:, 1] + 4) // 3    # majority = 2\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertEqual(y2.shape, y.shape)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n\n    def test_probs_from_value(self):\n        pass",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 46,
                            "end_lineno": 52,
                            "parent": {
                                "file_name": "Orange/tests/test_classification.py",
                                "type": "class",
                                "name": "ModelTest",
                                "content": "class ModelTest(unittest.TestCase):\n\n    def test_predict_single_instance(self):\n        table = data.Table(\"iris\")\n        learn = nb.BayesLearner()\n        clf = learn(table)\n        pred = []\n        for row in table:\n            pred.append(clf(row))\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n\n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertTrue((y2 == 1).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2 == 1).all())\n\n        # multitarget\n        y = np.random.random_integers(1, 5, (nrows, 2))\n        y[:, 0] = y[:, 0] // 3        # majority = 1\n        y[:, 1] = (y[:, 1] + 4) // 3    # majority = 2\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertEqual(y2.shape, y.shape)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n\n    def test_probs_from_value(self):\n        pass",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 44,
                                "end_lineno": 85,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "0adb9b4915306e9bf7520ce1b542e1d0cf960335^",
    "new_commit_hash": "0adb9b4915306e9bf7520ce1b542e1d0cf960335",
    "commit_message": "Fixed classification of a single instance.",
    "commit_date": "2013-06-04T09:28:49+02:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "0adb9b4915306e9bf7520ce1b542e1d0cf960335",
    "test_file_codes": [
        "import unittest\nimport numpy as np\n\nfrom Orange import data\nimport Orange.classification\nimport Orange.classification.dummies as dummies\nimport Orange.classification.majority as maj\nimport Orange.classification.naive_bayes as nb\nfrom Orange.data.io import BasketReader\n\nclass MultiClassTest(unittest.TestCase):\n    def test_unsupported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n\n        # multiple class variables\n        y = np.random.random_integers(10, 11, (nrows, 2))\n        t = data.Table(x, y)\n        learn = dummies.DummyLearner()\n        with self.assertRaises(TypeError):\n            clf = learn(t)\n\n        # single class variable\n        y = np.random.random_integers(10, 11, (nrows, 1))\n        t = data.Table(x, y)\n        learn = dummies.DummyLearner()\n        clf = learn(t)\n        z = clf(x)\n        self.assertEqual(z.ndim, 1)\n\n    def test_supported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n        y = np.random.random_integers(10, 11, (nrows, 2))\n        t = data.Table(x, y)\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(t)\n        z = clf(x)\n        self.assertEqual(z.shape, y.shape)\n\n\nclass ModelTest(unittest.TestCase):\n\n    def test_predict_single_instance(self):\n        table = data.Table(\"iris\")\n        learn = nb.BayesLearner()\n        clf = learn(table)\n        pred = []\n        for row in table:\n            pred.append(clf(row))\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n\n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertTrue((y2 == 1).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2 == 1).all())\n\n        # multitarget\n        y = np.random.random_integers(1, 5, (nrows, 2))\n        y[:, 0] = y[:, 0] // 3        # majority = 1\n        y[:, 1] = (y[:, 1] + 4) // 3    # majority = 2\n        t = data.Table(x, y)\n        learn = maj.MajorityLearner()\n        clf = learn(t)\n        clf.ret = Orange.classification.Model.Probs\n        y2 = clf(x, ret=Orange.classification.Model.Value)\n        self.assertEqual(y2.shape, y.shape)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n        y2, probs = clf(x, ret=Orange.classification.Model.ValueProbs)\n        self.assertTrue((y2[:, 0] == 1).all() and (y2[:, 1] == 2).all())\n\n    def test_probs_from_value(self):\n        pass\n\n\nclass ExpandProbabilitiesTest(unittest.TestCase):\n    def prepareTable(self, rows, attr, vars, class_var_domain):\n        attributes = [\"Feature %i\" % i for i in range(attr)]\n        classes = [\"Class %i\" % i for i in range(vars)]\n        attr_vars = [data.DiscreteVariable(name=a) for a in attributes]\n        class_vars = [data.DiscreteVariable(name=c,\n                                            values=range(class_var_domain))\n                      for c in classes]\n        meta_vars = []\n        self.domain = data.Domain(attr_vars, class_vars, meta_vars)\n        self.x = np.random.random_integers(0, 1, (rows, attr))\n\n    def test_single_class(self):\n        rows = 10\n        attr = 3\n        vars = 1\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n        t = data.Table(self.domain, self.x, y)\n        learn = dummies.DummyLearner()\n        clf = learn(t)\n        z, p = clf(self.x, ret=Orange.classification.Model.ValueProbs)\n        self.assertEqual(p.shape, (rows, class_var_domain))\n        self.assertTrue(np.all(z == np.argmax(p, axis=-1)))\n\n    def test_multi_class(self):\n        rows = 10\n        attr = 3\n        vars = 5\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n        t = data.Table(self.domain, self.x, y)\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(t)\n        z, p = clf(self.x, ret=Orange.classification.Model.ValueProbs)\n        self.assertEqual(p.shape, (rows, vars, class_var_domain))\n        self.assertTrue(np.all(z == np.argmax(p, axis=-1)))\n\n\nclass SparseTest(unittest.TestCase):\n    def test_sparse_basket(self):\n        table = BasketReader().read_file(\"iris.basket\")\n        test = Orange.data.Table.from_table_rows(table,range(0,len(table),2))\n        train = Orange.data.Table.from_table_rows(table,range(1,len(table),2))\n        learn = dummies.DummyMulticlassLearner()\n        clf = learn(train)\n        p = clf(test)\n        self.assertEqual(p.shape, test.Y.shape)\n        p = clf(test.X)\n        self.assertEqual(p.shape, test.Y.shape)\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\nrunning build_ext\nrunning build_src\nbuild_src\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._io\" sources\nbuild_src: building npy-pkg config files\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpgw4upa5l/tmp\ncreating /tmp/tmpgw4upa5l/tmp/tmpgw4upa5l\ncompile options: '-MMD -MF /tmp/tmpgw4upa5l/file.c.d -c'\ncc: /tmp/tmpgw4upa5l/file.c\ncustomize UnixCCompiler using build_ext\nbuilding 'Orange.data._valuecount' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_valuecount.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_io.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\nWarning: 'keywords' should be a list, got type 'tuple'\nWarning: 'classifiers' should be a list, got type 'tuple'\nrunning develop\nrunning build_scripts\nrunning egg_info\nrunning build_src\nbuild_src\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._io\" sources\nbuild_src: building npy-pkg config files\ncreating Orange.egg-info\nwriting Orange.egg-info/PKG-INFO\nwriting dependency_links to Orange.egg-info/dependency_links.txt\nwriting requirements to Orange.egg-info/requires.txt\nwriting top-level names to Orange.egg-info/top_level.txt\nwriting manifest file 'Orange.egg-info/SOURCES.txt'\nreading manifest file 'Orange.egg-info/SOURCES.txt'\nadding license file 'LICENSES'\nadding license file 'COPYING'\nwriting manifest file 'Orange.egg-info/SOURCES.txt'\nrunning build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmp9po0wgu9/tmp\ncreating /tmp/tmp9po0wgu9/tmp/tmp9po0wgu9\ncompile options: '-MMD -MF /tmp/tmp9po0wgu9/file.c.d -c'\ncc: /tmp/tmp9po0wgu9/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/Orange.egg-link (link to .)\nAdding Orange 3.0 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335\nProcessing dependencies for Orange==3.0\nSearching for bottleneck\nReading https://pypi.org/simple/bottleneck/\nDownloading https://files.pythonhosted.org/packages/2e/61/9fb34409d58f04e1929da41666a055c36f9495903ff669b80c893bdee65f/bottleneck-1.4.2.tar.gz#sha256=fa8e8e1799dea5483ce6669462660f9d9a95649f6f98a80d315b84ec89f449f4\nBest match: bottleneck 1.4.2\nProcessing bottleneck-1.4.2.tar.gz\nWriting /tmp/easy_install-gqgj6r6s/bottleneck-1.4.2/setup.cfg\nRunning bottleneck-1.4.2/setup.py -q bdist_egg --dist-dir /tmp/easy_install-gqgj6r6s/bottleneck-1.4.2/egg-dist-tmp-d29yspek\nUPDATING build/lib.linux-x86_64-3.7/bottleneck/_version.py\nset build/lib.linux-x86_64-3.7/bottleneck/_version.py to '1.4.2'\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/Bottleneck-1.4.2-py3.7-linux-x86_64.egg\nExtracting Bottleneck-1.4.2-py3.7-linux-x86_64.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages\nAdding Bottleneck 1.4.2 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/Bottleneck-1.4.2-py3.7-linux-x86_64.egg\nSearching for scipy==1.7.3\nBest match: scipy 1.7.3\nAdding scipy 1.7.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages\nSearching for numpy==1.17.5\nBest match: numpy 1.17.5\nAdding numpy 1.17.5 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages\nSearching for setuptools==59.8.0\nBest match: setuptools 59.8.0\nAdding setuptools 59.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange==3.0\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.7\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 129ms\nPrepared 18 packages in 15ms\nInstalled 18 packages in 79ms\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\nerror: File not found: `requirements-core.txt`\nerror: File not found: `requirements-gui.txt`\nerror: File not found: `requirements-sql.txt`\nResolved 5 packages in 25ms\nInstalled 4 packages in 88ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + threadpoolctl==3.1.0\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.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/_valuecount.c:2187:13: warning: ‘__pyx_pw_5numpy_7ndarray_3__releasebuffer__’ defined but not used [-Wunused-function]\n 2187 | static void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info) {\n      |             ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_valuecount.c:1371:12: warning: ‘__pyx_pw_5numpy_7ndarray_1__getbuffer__’ defined but not used [-Wunused-function]\n 1371 | static int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {\n      |            ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:21,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:253:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: ‘_import_array’ defined but not used [-Wunused-function]\n 1463 | _import_array(void)\n      | ^~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_io.c:314:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.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_2sparse_read_float’:\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_BEGIN_LINE’ not handled in switch [-Wswitch]\n 5028 |       switch (__pyx_v_state) {\n      |       ^~~~~~\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_READ_START_ATOM’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_READ’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_QUOTED’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_END_QUOTED’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_END_ATOM’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_ESCAPE’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_EQUALS’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_END_LINE’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_SET_VALUE’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_WAIT_VALUE’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_READ_VALUE’ not handled in switch [-Wswitch]\nOrange/data/_io.c:5028:7: warning: enumeration value ‘__pyx_e_6Orange_4data_3_io_READ_DECS’ not handled in switch [-Wswitch]\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:21,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_io.c:314:\nAt top level:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1463:1: warning: ‘_import_array’ defined but not used [-Wunused-function]\n 1463 | _import_array(void)\n      | ^~~~~~~~~~~~~\nOrange/data/_io.c: In function ‘__pyx_f_6Orange_4data_3_io_sparse_prescan_fast.constprop’:\nOrange/data/_io.c:1588:41: warning: ‘__pyx_v_output_count’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 1588 |       (__pyx_v_output_count[__pyx_t_6]) = ((__pyx_v_output_count[__pyx_t_6]) + 1);\n      |       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_2sparse_read_float.constprop’:\nOrange/data/_io.c:4759:9: warning: ‘__pyx_v_col_kind’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4759 |         switch (__pyx_v_col_kind) {\n      |         ^~~~~~\nOrange/data/_io.c:4641:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4641 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4488:43: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4488 |           __pyx_v_value = ((__pyx_v_value * 10.0) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~\nOrange/data/_io.c:4978:159: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4978 |           *__Pyx_BufPtrStrided1d(int *, __pyx_pybuffernd_metas_indices.rcbuffer->pybuffer.buf, __pyx_t_43, __pyx_pybuffernd_metas_indices.diminfo[0].strides) = __pyx_v_attr_index;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3952:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3952 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nsetup.py:3: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n  import imp\n/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib/python3.7/distutils/dist.py:274: UserWarning: Unknown distribution option: 'install_requires'\n  warnings.warn(msg)\n/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib/python3.7/distutils/dist.py:274: UserWarning: Unknown distribution option: 'zip_safe'\n  warnings.warn(msg)\nsetup.py:3: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n  import imp\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.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_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.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_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/setuptools/command/egg_info.py:628: SetuptoolsDeprecationWarning: Custom 'build_py' does not implement 'get_data_files_without_manifest'.\nPlease extend command classes from setuptools instead of distutils.\n  SetuptoolsDeprecationWarning\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.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_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.0-32bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.0-64bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.1-32bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.1-64bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.2-32bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.2-64bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.3-32bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning: 0.4.3-64bitOS is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nwarning: no files found matching 'tox.ini'\nwarning: no previously-included files found matching 'bottleneck/src/reduce.c'\nwarning: no previously-included files found matching 'bottleneck/src/move.c'\nwarning: no previously-included files found matching 'bottleneck/src/nonreduce.c'\nwarning: no previously-included files found matching 'bottleneck/src/nonreduce_axis.c'\nwarning: no previously-included files found matching 'bottleneck/src/bn_config.h'\nwarning: no previously-included files matching '*' found under directory 'doc/build'\nwarning: no previously-included files matching '__pycache__' found anywhere in distribution\nwarning: no previously-included files matching '*.pyc' found anywhere in distribution\nwarning: no previously-included files matching '*~' found anywhere in distribution\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335/.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",
    "new_commit_res_code": 1,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335\ncollected 8 items\n\nr2e_tests/test_1.py FF..FFFF                                             [100%]\n\n=================================== FAILURES ===================================\n________________________ MultiClassTest.test_supported _________________________\n\nself = <r2e_tests.test_1.MultiClassTest testMethod=test_supported>\n\n    def test_supported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n        y = np.random.random_integers(10, 11, (nrows, 2))\n>       t = data.Table(x, y)\n\nr2e_tests/test_1.py:37: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>, domain = None\nX = array([[1, 2, 1, 2, 1, 2, 2, 2, 1, 2],\n       [2, 2, 1, 3, 2, 3, 1, 1, 3, 1],\n       [3, 2, 2, 2, 3, 1, 1, 3, 1, 3],\n ...,\n       [3, 2, 3, 1, 2, 1, 2, 3, 1, 3],\n       [2, 1, 1, 3, 2, 3, 3, 1, 2, 1],\n       [2, 3, 3, 1, 1, 3, 3, 1, 1, 1]])\nY = array([[11, 10],\n       [10, 10],\n       [10, 10],\n       [10, 10],\n       [10, 11],\n       [11, 11],\n       [10, 11],...       [11, 10],\n       [10, 10],\n       [11, 10],\n       [10, 10],\n       [11, 11],\n       [11, 10],\n       [11, 11]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n_______________________ MultiClassTest.test_unsupported ________________________\n\nself = <r2e_tests.test_1.MultiClassTest testMethod=test_unsupported>\n\n    def test_unsupported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n    \n        # multiple class variables\n        y = np.random.random_integers(10, 11, (nrows, 2))\n>       t = data.Table(x, y)\n\nr2e_tests/test_1.py:19: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>, domain = None\nX = array([[1, 3, 1, 1, 2, 1, 1, 2, 1, 1],\n       [2, 1, 2, 2, 2, 3, 1, 1, 2, 2],\n       [1, 3, 1, 1, 3, 1, 3, 3, 1, 2],\n ...,\n       [2, 2, 2, 1, 2, 2, 1, 3, 1, 2],\n       [1, 1, 2, 3, 2, 3, 3, 2, 1, 1],\n       [3, 3, 3, 2, 2, 1, 2, 3, 3, 1]])\nY = array([[10, 11],\n       [11, 10],\n       [11, 11],\n       [10, 11],\n       [10, 10],\n       [10, 10],\n       [11, 11],...       [11, 10],\n       [10, 10],\n       [11, 11],\n       [11, 10],\n       [11, 11],\n       [11, 11],\n       [10, 10]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n_______________________ ModelTest.test_value_from_probs ________________________\n\nself = <r2e_tests.test_1.ModelTest testMethod=test_value_from_probs>\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n    \n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n>       t = data.Table(x, y)\n\nr2e_tests/test_1.py:61: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>, domain = None\nX = array([[1, 0, 0, 1, 1],\n       [0, 1, 0, 1, 0],\n       [1, 0, 0, 1, 1],\n       [1, 0, 0, 0, 0],\n       [0, 1, 1, 1, 0]...      [0, 1, 1, 0, 0],\n       [0, 0, 0, 0, 0],\n       [1, 0, 0, 1, 0],\n       [0, 1, 1, 0, 0],\n       [1, 0, 1, 1, 1]])\nY = array([[0, 1],\n       [0, 1],\n       [0, 1],\n       [1, 1],\n       [0, 1],\n       [1, 1],\n       [0, 0],\n       [1, 1]...      [0, 0],\n       [1, 1],\n       [0, 1],\n       [1, 1],\n       [1, 1],\n       [0, 1],\n       [0, 1],\n       [1, 1]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n___________________ ExpandProbabilitiesTest.test_multi_class ___________________\n\nself = <r2e_tests.test_1.ExpandProbabilitiesTest testMethod=test_multi_class>\n\n    def test_multi_class(self):\n        rows = 10\n        attr = 3\n        vars = 5\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n>       t = data.Table(self.domain, self.x, y)\n\nr2e_tests/test_1.py:121: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>\ndomain = <Orange.data.domain.Domain object at 0x75d1ac9e5610>\nX = array([[1, 0, 1],\n       [0, 1, 0],\n       [0, 1, 0],\n       [1, 0, 0],\n       [0, 0, 1],\n       [1, 1, 1],\n       [1, 0, 0],\n       [1, 1, 1],\n       [0, 1, 0],\n       [1, 1, 0]])\nY = array([[ 6,  6,  6,  8,  8],\n       [ 6,  8, 10, 10,  6],\n       [ 6,  6,  8, 10,  4],\n       [ 6,  4,  4, 10,  6],\n  ...],\n       [ 4,  6, 10, 10,  4],\n       [ 4, 10,  8,  6, 10],\n       [ 8,  4,  4,  4,  4],\n       [ 4,  6,  8,  8,  4]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n__________________ ExpandProbabilitiesTest.test_single_class ___________________\n\nself = <r2e_tests.test_1.ExpandProbabilitiesTest testMethod=test_single_class>\n\n    def test_single_class(self):\n        rows = 10\n        attr = 3\n        vars = 1\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n>       t = data.Table(self.domain, self.x, y)\n\nr2e_tests/test_1.py:107: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>\ndomain = <Orange.data.domain.Domain object at 0x75d1af2a0390>\nX = array([[1, 1, 1],\n       [0, 1, 1],\n       [0, 1, 1],\n       [1, 0, 1],\n       [1, 1, 0],\n       [0, 1, 0],\n       [1, 0, 0],\n       [0, 1, 0],\n       [0, 0, 1],\n       [0, 1, 1]])\nY = array([[ 4],\n       [ 6],\n       [10],\n       [ 6],\n       [ 4],\n       [10],\n       [ 6],\n       [ 6],\n       [ 8],\n       [ 4]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n________________________ SparseTest.test_sparse_basket _________________________\n\nself = <r2e_tests.test_1.SparseTest testMethod=test_sparse_basket>\n\n    def test_sparse_basket(self):\n>       table = BasketReader().read_file(\"iris.basket\")\n\nr2e_tests/test_1.py:131: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/io.py:204: in read_file\n    _io.sparse_read_float(filename.encode(sys.getdefaultencoding()))\n_io.pyx:94: in Orange.data._io.sparse_read_float (Orange/data/_io.c:2326)\n    ???\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\n>   ???\nE   OSError: File 'b'iris.basket'' cannot be opened\n\n_io.pyx:31: OSError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::ModelTest::test_predict_single_instance\nPASSED r2e_tests/test_1.py::ModelTest::test_probs_from_value\nFAILED r2e_tests/test_1.py::MultiClassTest::test_supported - AttributeError: ...\nFAILED r2e_tests/test_1.py::MultiClassTest::test_unsupported - AttributeError...\nFAILED r2e_tests/test_1.py::ModelTest::test_value_from_probs - AttributeError...\nFAILED r2e_tests/test_1.py::ExpandProbabilitiesTest::test_multi_class - Attri...\nFAILED r2e_tests/test_1.py::ExpandProbabilitiesTest::test_single_class - Attr...\nFAILED r2e_tests/test_1.py::SparseTest::test_sparse_basket - OSError: File 'b...\n========================= 6 failed, 2 passed in 1.81s ==========================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_0adb9b4915306e9bf7520ce1b542e1d0cf960335\ncollected 8 items\n\nr2e_tests/test_1.py FFF.FFFF                                             [100%]\n\n=================================== FAILURES ===================================\n________________________ MultiClassTest.test_supported _________________________\n\nself = <r2e_tests.test_1.MultiClassTest testMethod=test_supported>\n\n    def test_supported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n        y = np.random.random_integers(10, 11, (nrows, 2))\n>       t = data.Table(x, y)\n\nr2e_tests/test_1.py:37: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>, domain = None\nX = array([[3, 2, 3, 3, 2, 1, 1, 1, 3, 1],\n       [1, 1, 1, 1, 3, 3, 2, 2, 1, 2],\n       [1, 2, 2, 1, 2, 3, 1, 3, 3, 2],\n ...,\n       [2, 2, 3, 1, 1, 1, 3, 1, 3, 2],\n       [1, 1, 2, 2, 1, 1, 2, 1, 3, 3],\n       [2, 1, 2, 1, 1, 2, 3, 2, 3, 3]])\nY = array([[11, 11],\n       [10, 10],\n       [10, 10],\n       [11, 10],\n       [10, 11],\n       [11, 10],\n       [10, 10],...       [11, 10],\n       [10, 11],\n       [10, 11],\n       [10, 11],\n       [10, 10],\n       [10, 11],\n       [11, 11]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n_______________________ MultiClassTest.test_unsupported ________________________\n\nself = <r2e_tests.test_1.MultiClassTest testMethod=test_unsupported>\n\n    def test_unsupported(self):\n        nrows = 20\n        ncols = 10\n        x = np.random.random_integers(1, 3, (nrows, ncols))\n    \n        # multiple class variables\n        y = np.random.random_integers(10, 11, (nrows, 2))\n>       t = data.Table(x, y)\n\nr2e_tests/test_1.py:19: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>, domain = None\nX = array([[1, 3, 1, 3, 3, 3, 2, 1, 3, 3],\n       [1, 1, 1, 2, 3, 1, 2, 3, 2, 1],\n       [1, 3, 2, 2, 1, 3, 1, 2, 3, 2],\n ...,\n       [3, 3, 2, 2, 1, 2, 2, 2, 3, 1],\n       [3, 2, 2, 2, 1, 3, 2, 2, 3, 1],\n       [3, 3, 1, 3, 2, 1, 3, 3, 2, 1]])\nY = array([[11, 11],\n       [10, 10],\n       [11, 10],\n       [11, 11],\n       [11, 11],\n       [10, 10],\n       [11, 10],...       [10, 11],\n       [10, 10],\n       [10, 10],\n       [10, 10],\n       [11, 10],\n       [11, 11],\n       [11, 11]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n____________________ ModelTest.test_predict_single_instance ____________________\n\nself = <r2e_tests.test_1.ModelTest testMethod=test_predict_single_instance>\n\n    def test_predict_single_instance(self):\n        table = data.Table(\"iris\")\n        learn = nb.BayesLearner()\n        clf = learn(table)\n        pred = []\n        for row in table:\n>           pred.append(clf(row))\n\nr2e_tests/test_1.py:52: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/classification/__init__.py:58: in __call__\n    prediction = self.predict(np.atleast_2d(data._values))\nOrange/classification/naive_bayes.py:16: in predict\n    value = self.clf.predict(X)\n.venv/lib/python3.7/site-packages/sklearn/naive_bayes.py:82: in predict\n    X = self._check_X(X)\n.venv/lib/python3.7/site-packages/sklearn/naive_bayes.py:251: in _check_X\n    return self._validate_data(X, reset=False)\n.venv/lib/python3.7/site-packages/sklearn/base.py:585: in _validate_data\n    self._check_n_features(X, reset=reset)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself = GaussianNB(), X = array([[5.1, 3.5, 1.4, 0.2, 0. ]]), reset = False\n\n    def _check_n_features(self, X, reset):\n        \"\"\"Set the `n_features_in_` attribute, or check against it.\n    \n        Parameters\n        ----------\n        X : {ndarray, sparse matrix} of shape (n_samples, n_features)\n            The input samples.\n        reset : bool\n            If True, the `n_features_in_` attribute is set to `X.shape[1]`.\n            If False and the attribute exists, then check that it is equal to\n            `X.shape[1]`. If False and the attribute does *not* exist, then\n            the check is skipped.\n            .. note::\n               It is recommended to call reset=True in `fit` and in the first\n               call to `partial_fit`. All other methods that validate `X`\n               should set `reset=False`.\n        \"\"\"\n        try:\n            n_features = _num_features(X)\n        except TypeError as e:\n            if not reset and hasattr(self, \"n_features_in_\"):\n                raise ValueError(\n                    \"X does not contain any features, but \"\n                    f\"{self.__class__.__name__} is expecting \"\n                    f\"{self.n_features_in_} features\"\n                ) from e\n            # If the number of features is not defined and reset=True,\n            # then we skip this check\n            return\n    \n        if reset:\n            self.n_features_in_ = n_features\n            return\n    \n        if not hasattr(self, \"n_features_in_\"):\n            # Skip this check if the expected number of expected input features\n            # was not recorded by calling fit first. This is typically the case\n            # for stateless transformers.\n            return\n    \n        if n_features != self.n_features_in_:\n            raise ValueError(\n>               f\"X has {n_features} features, but {self.__class__.__name__} \"\n                f\"is expecting {self.n_features_in_} features as input.\"\n            )\nE           ValueError: X has 5 features, but GaussianNB is expecting 4 features as input.\n\n.venv/lib/python3.7/site-packages/sklearn/base.py:401: ValueError\n_______________________ ModelTest.test_value_from_probs ________________________\n\nself = <r2e_tests.test_1.ModelTest testMethod=test_value_from_probs>\n\n    def test_value_from_probs(self):\n        nrows = 100\n        ncols = 5\n        x = np.random.random_integers(0, 1, (nrows, ncols))\n    \n        # single class variable\n        y = np.random.random_integers(1, 3, (nrows, 2)) // 2    # majority = 1\n>       t = data.Table(x, y)\n\nr2e_tests/test_1.py:61: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>, domain = None\nX = array([[1, 1, 0, 1, 1],\n       [1, 1, 1, 1, 1],\n       [1, 0, 0, 1, 1],\n       [1, 0, 0, 1, 0],\n       [1, 0, 1, 0, 0]...      [0, 0, 1, 0, 0],\n       [0, 0, 0, 1, 0],\n       [0, 1, 1, 1, 1],\n       [0, 0, 0, 0, 1],\n       [1, 0, 1, 0, 0]])\nY = array([[1, 1],\n       [0, 0],\n       [1, 1],\n       [0, 1],\n       [1, 1],\n       [1, 1],\n       [0, 0],\n       [0, 1]...      [1, 0],\n       [1, 0],\n       [0, 1],\n       [1, 0],\n       [1, 1],\n       [1, 1],\n       [1, 1],\n       [0, 0]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n___________________ ExpandProbabilitiesTest.test_multi_class ___________________\n\nself = <r2e_tests.test_1.ExpandProbabilitiesTest testMethod=test_multi_class>\n\n    def test_multi_class(self):\n        rows = 10\n        attr = 3\n        vars = 5\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n>       t = data.Table(self.domain, self.x, y)\n\nr2e_tests/test_1.py:121: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>\ndomain = <Orange.data.domain.Domain object at 0x7378989915d0>\nX = array([[1, 1, 1],\n       [0, 0, 1],\n       [0, 1, 0],\n       [1, 1, 0],\n       [0, 1, 0],\n       [1, 1, 1],\n       [1, 0, 1],\n       [1, 0, 1],\n       [1, 1, 0],\n       [0, 1, 1]])\nY = array([[ 4,  8,  4,  6,  6],\n       [ 6,  8,  6, 10,  6],\n       [ 4,  6,  6, 10,  4],\n       [ 4, 10, 10, 10,  6],\n  ...],\n       [ 4,  4,  4, 10,  8],\n       [ 6,  6, 10,  6,  6],\n       [10,  4,  6,  8,  6],\n       [10, 10,  6,  4, 10]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n__________________ ExpandProbabilitiesTest.test_single_class ___________________\n\nself = <r2e_tests.test_1.ExpandProbabilitiesTest testMethod=test_single_class>\n\n    def test_single_class(self):\n        rows = 10\n        attr = 3\n        vars = 1\n        class_var_domain = 20\n        self.prepareTable(rows, attr, vars, class_var_domain)\n        y = np.random.random_integers(2, 5, (rows, vars)) * 2\n>       t = data.Table(self.domain, self.x, y)\n\nr2e_tests/test_1.py:107: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/table.py:168: in __new__\n    return cls.from_numpy(domain, *args, **kwargs)\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncls = <class 'Orange.data.table.Table'>\ndomain = <Orange.data.domain.Domain object at 0x7378989c3dd0>\nX = array([[0, 0, 1],\n       [0, 0, 1],\n       [0, 0, 1],\n       [1, 0, 1],\n       [1, 0, 0],\n       [0, 0, 0],\n       [0, 0, 1],\n       [1, 1, 1],\n       [1, 1, 1],\n       [1, 1, 1]])\nY = array([[ 6],\n       [ 6],\n       [ 8],\n       [ 4],\n       [ 4],\n       [ 6],\n       [ 6],\n       [ 6],\n       [ 8],\n       [10]])\nmetas = None, W = None\n\n    @classmethod\n    def from_numpy(cls, domain, X, Y=None, metas=None, W=None):\n        \"\"\"\n        Construct a table from numpy arrays with the given domain. The number\n        of variables in the domain must match the number of columns in the\n        corresponding arrays. All arrays must have the same number of rows.\n        Arrays may be of different numpy types, and may be dense or sparse.\n    \n        :param domain: the domain for the new table\n        :type domain: Orange.data.Domain\n        :param X: array with attribute values\n        :type X: np.array\n        :param Y: array with class values\n        :type Y: np.array\n        :param metas: array with meta attributes\n        :type metas: np.array\n        :param W: array with weights\n        :type W: np.array\n        :return:\n        \"\"\"\n>       X, Y, metas, W = validation.check_arrays(X, Y, metas, W)\nE       AttributeError: module 'sklearn.utils.validation' has no attribute 'check_arrays'\n\nOrange/data/table.py:319: AttributeError\n________________________ SparseTest.test_sparse_basket _________________________\n\nself = <r2e_tests.test_1.SparseTest testMethod=test_sparse_basket>\n\n    def test_sparse_basket(self):\n>       table = BasketReader().read_file(\"iris.basket\")\n\nr2e_tests/test_1.py:131: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nOrange/data/io.py:204: in read_file\n    _io.sparse_read_float(filename.encode(sys.getdefaultencoding()))\n_io.pyx:94: in Orange.data._io.sparse_read_float (Orange/data/_io.c:2326)\n    ???\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\n>   ???\nE   OSError: File 'b'iris.basket'' cannot be opened\n\n_io.pyx:31: OSError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::ModelTest::test_probs_from_value\nFAILED r2e_tests/test_1.py::MultiClassTest::test_supported - AttributeError: ...\nFAILED r2e_tests/test_1.py::MultiClassTest::test_unsupported - AttributeError...\nFAILED r2e_tests/test_1.py::ModelTest::test_predict_single_instance - ValueEr...\nFAILED r2e_tests/test_1.py::ModelTest::test_value_from_probs - AttributeError...\nFAILED r2e_tests/test_1.py::ExpandProbabilitiesTest::test_multi_class - Attri...\nFAILED r2e_tests/test_1.py::ExpandProbabilitiesTest::test_single_class - Attr...\nFAILED r2e_tests/test_1.py::SparseTest::test_sparse_basket - OSError: File 'b...\n========================= 7 failed, 1 passed in 1.69s ==========================\n",
    "old_commit_res_stderr": ""
}
Orange/classification/__init__.py Orange/tests/test_classification.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** Error When Classifying a Single Instance Due to Feature Dimension Mismatch

**Description:**
When attempting to classify individual instances using the classifier, a `ValueError` is raised because of mismatched feature dimensions. This issue occurs specifically when passing a single data instance to the `predict` method.

**Example Code:**
```python
import Orange.data
import Orange.classification.naive_bayes as nb

def test_predict_single_instance():
    table = Orange.data.Table("iris")
    learner = nb.BayesLearner()
    classifier = learner(table)
    predictions = []
    for instance in table:
        predictions.append(classifier(instance))
```

**Expected Behavior:**
The classifier should successfully predict the class of each single instance without encountering any errors related to feature dimensions.

**Actual Behavior:**
A `ValueError` is raised with the message:
```
ValueError: X has 5 features, but GaussianNB is expecting 4 features as input.
```
This error indicates that the input instance has an unexpected number of features, causing the prediction to fail.

[/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/__init__.py
import numpy as np
import bottleneck as bn
from Orange import data as Orange_data
from ..data.value import Value
import scipy

class Fitter:
    supports_multiclass = False

    def fit(self, X, Y, W):
        raise TypeError("Descendants of Fitter must overload method fit")

    def __call__(self, data):
        X, Y, W = data.X, data.Y, data.W if data.has_weights else None
        if np.shape(Y)[1] > 1 and not self.supports_multiclass:
            raise TypeError("fitter doesn't support multiple class variables")
        self.domain = data.domain
        clf = self.fit(X, Y, W)
        clf.domain = data.domain
        clf.Y = Y
        clf.supports_multiclass = self.supports_multiclass
        return clf


class Model:
    supports_multiclass = False
    Y = None
    Value = 0
    Probs = 1
    ValueProbs = 2

    def __init__(self, domain=None):
        if isinstance(self, Fitter):
            domain = None
        elif not domain:
            raise ValueError("unspecified domain")
        self.domain = domain

    def predict(self, table):
        raise TypeError("Descendants of Model must overload method predict")

    def __call__(self, data, ret=Value):
        if not 0 <= ret <= 2:
            raise ValueError("invalid value of argument 'ret'")
        if (ret > 0
            and any(isinstance(v, Orange_data.ContinuousVariable)
                    for v in self.domain.class_vars)):
            raise ValueError("cannot predict continuous distributions")

        # Call the predictor
        if isinstance(data, np.ndarray):
            prediction = self.predict(np.atleast_2d(data))
        elif isinstance(data, scipy.sparse.csr.csr_matrix):
            prediction = self.predict(data)
        elif isinstance(data, Orange_data.Instance):
            if data.domain != self.domain:
                data = Orange_data.Instance(self.domain, data)
            prediction = self.predict(np.atleast_2d(data._values))
        elif isinstance(data, Orange_data.Table):
            if data.domain != self.domain:
                data = Orange_data.Table.from_table(self.domain, data)
            prediction = self.predict(data.X)
        else:
            raise TypeError("Unrecognized argument (instance of '%s')",
                            type(data).__name__)

        # Parse the result into value and probs
        multitarget = len(self.domain.class_vars) > 1
        if isinstance(prediction, tuple):
            value, probs = prediction
        elif prediction.ndim == 1 + multitarget:
            value, probs = prediction, None
        elif prediction.ndim == 2 + multitarget:
            value, probs = None, prediction
        else:
            raise TypeError("model returned a %i-dimensional array",
                            prediction.ndim)

        # Ensure that we have what we need to return
        if ret != Model.Probs and value is None:
            value = np.argmax(probs, axis=-1)
        if ret != Model.Value and probs is None:
            if multitarget:
                max_card = max(len(c.values) for c in self.domain.class_vars)
                probs = np.zeros(value.shape + (max_card,), float)
                for i, cvar in enumerate(self.domain.class_vars):
                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),
                                           max_card)
            else:
                probs = bn.bincount(np.atleast_2d(value),
                                    len(self.domain.class_var.values))
            return probs

        # Expand probability predictions for class values which are not present
        if ret != self.Value:
            n_class = len(self.domain.class_vars)
            used_vals = [np.unique(y) for y in self.Y.T]
            max_values = max(len(cv.values) for cv in self.domain.class_vars)
            if max_values != probs.shape[-1]:
                if not self.supports_multiclass:
                    probs = probs[:, np.newaxis, :]
                probs_ext = np.zeros((len(probs), n_class, max_values))
                for c in range(n_class):
                    i = 0
                    class_values = len(self.domain.class_vars[c].values)
                    for cv in range(class_values):
                        if i < len(used_vals[c]) and cv == used_vals[c][i]:
                            probs_ext[:, c, cv] = probs[:, c, i]
                            i += 1
                if self.supports_multiclass:
                    probs = probs_ext
                else:
                    probs = probs_ext[:, 0, :]

        # Return what we need to
        if ret == Model.Probs:
            return probs
        if isinstance(data, Orange_data.Instance) and not multitarget:
            value = Value(self.domain.class_var, value[0])
        if ret == Model.Value:
            return value
        else:  # ret == Model.ValueProbs
            return value, probs

```
--- 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:** Error When Classifying a Single Instance Due to Feature Dimension Mismatch

**Description:**
When attempting to classify individual instances using the classifier, a `ValueError` is raised because of mismatched feature dimensions. This issue occurs specifically when passing a single data instance to the `predict` method.

**Example Code:**
```python
import Orange.data
import Orange.classification.naive_bayes as nb

def test_predict_single_instance():
    table = Orange.data.Table("iris")
    learner = nb.BayesLearner()
    classifier = learner(table)
    predictions = []
    for instance in table:
        predictions.append(classifier(instance))
```

**Expected Behavior:**
The classifier should successfully predict the class of each single instance without encountering any errors related to feature dimensions.

**Actual Behavior:**
A `ValueError` is raised with the message:
```
ValueError: X has 5 features, but GaussianNB is expecting 4 features as input.
```
This error indicates that the input instance has an unexpected number of features, causing the prediction to fail.

[/ISSUE]
{
    "ModelTest.test_predict_single_instance": "PASSED",
    "ModelTest.test_probs_from_value": "PASSED",
    "MultiClassTest.test_supported": "FAILED",
    "MultiClassTest.test_unsupported": "FAILED",
    "ModelTest.test_value_from_probs": "FAILED",
    "ExpandProbabilitiesTest.test_multi_class": "FAILED",
    "ExpandProbabilitiesTest.test_single_class": "FAILED",
    "SparseTest.test_sparse_basket": "FAILED"
}
diff --git a/Orange/classification/__init__.py b/Orange/classification/__init__.py
index d367f9c81..fa57ebf00 100644
--- a/Orange/classification/__init__.py
+++ b/Orange/classification/__init__.py
@@ -55,7 +55,7 @@ class Model:
         elif isinstance(data, Orange_data.Instance):
             if data.domain != self.domain:
                 data = Orange_data.Instance(self.domain, data)
-            prediction = self.predict(np.atleast_2d(data._values))
+            prediction = self.predict(np.atleast_2d(data.x))
         elif isinstance(data, Orange_data.Table):
             if data.domain != self.domain:
                 data = Orange_data.Table.from_table(self.domain, data)

{"Orange/classification/__init__.py": "import numpy as np\nimport bottleneck as bn\nfrom Orange import data as Orange_data\nfrom ..data.value import Value\nimport scipy\n\nclass Fitter:\n    supports_multiclass = False\n\n    def fit(self, X, Y, W):\n        raise TypeError(\"Descendants of Fitter must overload method fit\")\n\n    def __call__(self, data):\n        X, Y, W = data.X, data.Y, data.W if data.has_weights else None\n        if np.shape(Y)[1] > 1 and not self.supports_multiclass:\n            raise TypeError(\"fitter doesn't support multiple class variables\")\n        self.domain = data.domain\n        clf = self.fit(X, Y, W)\n        clf.domain = data.domain\n        clf.Y = Y\n        clf.supports_multiclass = self.supports_multiclass\n        return clf\n\n\nclass Model:\n    supports_multiclass = False\n    Y = None\n    Value = 0\n    Probs = 1\n    ValueProbs = 2\n\n    def __init__(self, domain=None):\n        if isinstance(self, Fitter):\n            domain = None\n        elif not domain:\n            raise ValueError(\"unspecified domain\")\n        self.domain = domain\n\n    def predict(self, table):\n        raise TypeError(\"Descendants of Model must overload method predict\")\n\n    def __call__(self, data, ret=Value):\n        if not 0 <= ret <= 2:\n            raise ValueError(\"invalid value of argument 'ret'\")\n        if (ret > 0\n            and any(isinstance(v, Orange_data.ContinuousVariable)\n                    for v in self.domain.class_vars)):\n            raise ValueError(\"cannot predict continuous distributions\")\n\n        # Call the predictor\n        if isinstance(data, np.ndarray):\n            prediction = self.predict(np.atleast_2d(data))\n        elif isinstance(data, scipy.sparse.csr.csr_matrix):\n            prediction = self.predict(data)\n        elif isinstance(data, Orange_data.Instance):\n            if data.domain != self.domain:\n                data = Orange_data.Instance(self.domain, data)\n            prediction = self.predict(np.atleast_2d(data._values))\n        elif isinstance(data, Orange_data.Table):\n            if data.domain != self.domain:\n                data = Orange_data.Table.from_table(self.domain, data)\n            prediction = self.predict(data.X)\n        else:\n            raise TypeError(\"Unrecognized argument (instance of '%s')\",\n                            type(data).__name__)\n\n        # Parse the result into value and probs\n        multitarget = len(self.domain.class_vars) > 1\n        if isinstance(prediction, tuple):\n            value, probs = prediction\n        elif prediction.ndim == 1 + multitarget:\n            value, probs = prediction, None\n        elif prediction.ndim == 2 + multitarget:\n            value, probs = None, prediction\n        else:\n            raise TypeError(\"model returned a %i-dimensional array\",\n                            prediction.ndim)\n\n        # Ensure that we have what we need to return\n        if ret != Model.Probs and value is None:\n            value = np.argmax(probs, axis=-1)\n        if ret != Model.Value and probs is None:\n            if multitarget:\n                max_card = max(len(c.values) for c in self.domain.class_vars)\n                probs = np.zeros(value.shape + (max_card,), float)\n                for i, cvar in enumerate(self.domain.class_vars):\n                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),\n                                           max_card)\n            else:\n                probs = bn.bincount(np.atleast_2d(value),\n                                    len(self.domain.class_var.values))\n            return probs\n\n        # Expand probability predictions for class values which are not present\n        if ret != self.Value:\n            n_class = len(self.domain.class_vars)\n            used_vals = [np.unique(y) for y in self.Y.T]\n            max_values = max(len(cv.values) for cv in self.domain.class_vars)\n            if max_values != probs.shape[-1]:\n                if not self.supports_multiclass:\n                    probs = probs[:, np.newaxis, :]\n                probs_ext = np.zeros((len(probs), n_class, max_values))\n                for c in range(n_class):\n                    i = 0\n                    class_values = len(self.domain.class_vars[c].values)\n                    for cv in range(class_values):\n                        if i < len(used_vals[c]) and cv == used_vals[c][i]:\n                            probs_ext[:, c, cv] = probs[:, c, i]\n                            i += 1\n                if self.supports_multiclass:\n                    probs = probs_ext\n                else:\n                    probs = probs_ext[:, 0, :]\n\n        # Return what we need to\n        if ret == Model.Probs:\n            return probs\n        if isinstance(data, Orange_data.Instance) and not multitarget:\n            value = Value(self.domain.class_var, value[0])\n        if ret == Model.Value:\n            return value\n        else:  # ret == Model.ValueProbs\n            return value, probs\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:** Error When Classifying a Single Instance Due to Feature Dimension Mismatch

**Description:**
When attempting to classify individual instances using the classifier, a `ValueError` is raised because of mismatched feature dimensions. This issue occurs specifically when passing a single data instance to the `predict` method.

**Example Code:**
```python
import Orange.data
import Orange.classification.naive_bayes as nb

def test_predict_single_instance():
    table = Orange.data.Table("iris")
    learner = nb.BayesLearner()
    classifier = learner(table)
    predictions = []
    for instance in table:
        predictions.append(classifier(instance))
```

**Expected Behavior:**
The classifier should successfully predict the class of each single instance without encountering any errors related to feature dimensions.

**Actual Behavior:**
A `ValueError` is raised with the message:
```
ValueError: X has 5 features, but GaussianNB is expecting 4 features as input.
```
This error indicates that the input instance has an unexpected number of features, causing the prediction to fail.

[/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/__init__.py
import numpy as np
import bottleneck as bn
from Orange import data as Orange_data
from ..data.value import Value
import scipy

class Fitter:
    supports_multiclass = False

    def fit(self, X, Y, W):
        raise TypeError("Descendants of Fitter must overload method fit")

    def __call__(self, data):
        X, Y, W = data.X, data.Y, data.W if data.has_weights else None
        if np.shape(Y)[1] > 1 and not self.supports_multiclass:
            raise TypeError("fitter doesn't support multiple class variables")
        self.domain = data.domain
        clf = self.fit(X, Y, W)
        clf.domain = data.domain
        clf.Y = Y
        clf.supports_multiclass = self.supports_multiclass
        return clf


class Model:
    supports_multiclass = False
    Y = None
    Value = 0
    Probs = 1
    ValueProbs = 2

    def __init__(self, domain=None):
        if isinstance(self, Fitter):
            domain = None
        elif not domain:
            raise ValueError("unspecified domain")
        self.domain = domain

    def predict(self, table):
        raise TypeError("Descendants of Model must overload method predict")

    def __call__(self, data, ret=Value):
        if not 0 <= ret <= 2:
            raise ValueError("invalid value of argument 'ret'")
        if (ret > 0
            and any(isinstance(v, Orange_data.ContinuousVariable)
                    for v in self.domain.class_vars)):
            raise ValueError("cannot predict continuous distributions")

        # Call the predictor
        if isinstance(data, np.ndarray):
            prediction = self.predict(np.atleast_2d(data))
        elif isinstance(data, scipy.sparse.csr.csr_matrix):
            prediction = self.predict(data)
        elif isinstance(data, Orange_data.Instance):
            if data.domain != self.domain:
                data = Orange_data.Instance(self.domain, data)
            prediction = self.predict(np.atleast_2d(data._values))
        elif isinstance(data, Orange_data.Table):
            if data.domain != self.domain:
                data = Orange_data.Table.from_table(self.domain, data)
            prediction = self.predict(data.X)
        else:
            raise TypeError("Unrecognized argument (instance of '%s')",
                            type(data).__name__)

        # Parse the result into value and probs
        multitarget = len(self.domain.class_vars) > 1
        if isinstance(prediction, tuple):
            value, probs = prediction
        elif prediction.ndim == 1 + multitarget:
            value, probs = prediction, None
        elif prediction.ndim == 2 + multitarget:
            value, probs = None, prediction
        else:
            raise TypeError("model returned a %i-dimensional array",
                            prediction.ndim)

        # Ensure that we have what we need to return
        if ret != Model.Probs and value is None:
            value = np.argmax(probs, axis=-1)
        if ret != Model.Value and probs is None:
            if multitarget:
                max_card = max(len(c.values) for c in self.domain.class_vars)
                probs = np.zeros(value.shape + (max_card,), float)
                for i, cvar in enumerate(self.domain.class_vars):
                    probs[i] = bn.bincount(np.atleast_2d(value[:, i]),
                                           max_card)
            else:
                probs = bn.bincount(np.atleast_2d(value),
                                    len(self.domain.class_var.values))
            return probs

        # Expand probability predictions for class values which are not present
        if ret != self.Value:
            n_class = len(self.domain.class_vars)
            used_vals = [np.unique(y) for y in self.Y.T]
            max_values = max(len(cv.values) for cv in self.domain.class_vars)
            if max_values != probs.shape[-1]:
                if not self.supports_multiclass:
                    probs = probs[:, np.newaxis, :]
                probs_ext = np.zeros((len(probs), n_class, max_values))
                for c in range(n_class):
                    i = 0
                    class_values = len(self.domain.class_vars[c].values)
                    for cv in range(class_values):
                        if i < len(used_vals[c]) and cv == used_vals[c][i]:
                            probs_ext[:, c, cv] = probs[:, c, i]
                            i += 1
                if self.supports_multiclass:
                    probs = probs_ext
                else:
                    probs = probs_ext[:, 0, :]

        # Return what we need to
        if ret == Model.Probs:
            return probs
        if isinstance(data, Orange_data.Instance) and not multitarget:
            value = Value(self.domain.class_var, value[0])
        if ret == Model.Value:
            return value
        else:  # ret == Model.ValueProbs
            return value, probs

```
--- 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