{
    "file_diffs": [
        {
            "old_file_content": "import numpy as np\nimport scipy.sparse as sp\n\nimport Orange.data\nfrom Orange.statistics import distribution, basic_stats\nfrom Orange.util import Reprable\nfrom .transformation import Transformation, Lookup\n\n__all__ = [\"ReplaceUnknowns\", \"Average\", \"DoNotImpute\", \"DropInstances\",\n           \"Model\", \"AsValue\", \"Random\", \"Default\", \"FixedValueByType\"]\n\n\nclass ReplaceUnknowns(Transformation):\n    \"\"\"\n    A column transformation which replaces unknown values with a fixed `value`.\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for imputation.\n    value : int or float\n        The value with which to replace the unknown values\n    \"\"\"\n    def __init__(self, variable, value=0):\n        super().__init__(variable)\n        self.value = value\n\n    def transform(self, c):\n        if sp.issparse(c):\n            c.data = np.where(np.isnan(c.data), self.value, c.data)\n            return c\n        else:\n            return np.where(np.isnan(c), self.value, c)\n\n    def __eq__(self, other):\n        return super().__eq__(other) and self.value == other.value\n\n    def __hash__(self):\n        return hash((type(self), self.variable, float(self.value)))\n\n\nclass BaseImputeMethod(Reprable):\n    name = \"\"\n    short_name = \"\"\n    description = \"\"\n    format = \"{var.name} -> {self.short_name}\"\n    columns_only = False\n\n    def __call__(self, data, variable):\n        \"\"\" Imputes table along variable column.\n\n        Args:\n            data (Table): A table to impute.\n            variable (Variable): Variable for completing missing values.\n\n        Returns:\n            A new Variable instance with completed missing values or\n            a array mask of rows to drop out.\n        \"\"\"\n        raise NotImplementedError\n\n    def format_variable(self, var):\n        return self.format.format(var=var, self=self)\n\n    def __str__(self):\n        return self.name\n\n    def copy(self):\n        return self\n\n    @classmethod\n    def supports_variable(cls, variable):\n        return True\n\n\nclass DoNotImpute(BaseImputeMethod):\n    name = \"Don't impute\"\n    short_name = \"leave\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        return variable\n\n\nclass DropInstances(BaseImputeMethod):\n    name = \"Remove instances with unknown values\"\n    short_name = \"drop\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        col = data.get_column(variable)\n        return np.isnan(col)\n\n\nclass Average(BaseImputeMethod):\n    name = \"Average/Most frequent\"\n    short_name = \"average\"\n    description = \"Replace with average/mode of the column\"\n\n    def __call__(self, data, variable, value=None):\n        variable = data.domain[variable]\n        if value is None:\n            if variable.is_continuous:\n                stats = basic_stats.BasicStats(data, variable)\n                value = stats.mean\n            elif variable.is_discrete:\n                dist = distribution.get_distribution(data, variable)\n                value = dist.modus()\n            else:\n                raise TypeError(\"Variable must be numeric or categorical.\")\n\n        a = variable.copy(compute_value=ReplaceUnknowns(variable, value))\n        a.to_sql = ImputeSql(variable, value)\n        return a\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n\n\nclass ImputeSql(Reprable):\n    def __init__(self, var, default):\n        self.var = var\n        self.default = default\n\n    def __call__(self):\n        return 'coalesce(%s, %s)' % (self.var.to_sql(), str(self.default))\n\n\nclass Default(BaseImputeMethod):\n    name = \"Fixed value\"\n    short_name = \"value\"\n    description = \"\"\n    columns_only = True\n    format = '{var} -> {self.default}'\n\n    def __init__(self, default=0):\n        self.default = default\n\n    def __call__(self, data, variable, *, default=None):\n        variable = data.domain[variable]\n        default = default if default is not None else self.default\n        return variable.copy(compute_value=ReplaceUnknowns(variable, default))\n\n    def copy(self):\n        return Default(self.default)\n\n\nclass FixedValueByType(BaseImputeMethod):\n    name = \"Fixed value\"\n    short_name = \"Fixed Value\"\n    format = \"{var.name}\"\n\n    def __init__(self,\n                 default_discrete=np.nan, default_continuous=np.nan,\n                 default_string=None, default_time=np.nan):\n        # If you change the order of args or in dict, also fix method copy\n        self.defaults = {\n            Orange.data.DiscreteVariable: default_discrete,\n            Orange.data.ContinuousVariable: default_continuous,\n            Orange.data.StringVariable: default_string,\n            Orange.data.TimeVariable: default_time\n        }\n\n    def __call__(self, data, variable, *, default=None):\n        variable = data.domain[variable]\n        if default is None:\n            default = self.defaults[type(variable)]\n        return variable.copy(compute_value=ReplaceUnknowns(variable, default))\n\n    def copy(self):\n        return FixedValueByType(*self.defaults.values())\n\n\nclass ReplaceUnknownsModel(Reprable):\n    \"\"\"\n    Replace unknown values with predicted values using a `Orange.base.Model`\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for the imputation.\n    model : Orange.base.Model\n        A fitted model predicting `variable`.\n    \"\"\"\n    def __init__(self, variable, model):\n        assert model.domain.class_var == variable\n        self.variable = variable\n        self.model = model\n\n    def __call__(self, data):\n        if isinstance(data, Orange.data.Instance):\n            data = Orange.data.Table.from_list(data.domain, [data])\n        domain = data.domain\n        column = data.get_column(self.variable, copy=True)\n\n        mask = np.isnan(column)\n        if not np.any(mask):\n            return column\n\n        if domain.class_vars:\n            # cannot have class var in domain (due to backmappers in model)\n            data = data.transform(\n                Orange.data.Domain(domain.attributes, None, domain.metas)\n            )\n        predicted = self.model(data[mask])\n        column[mask] = predicted\n        return column\n\n    def __eq__(self, other):\n        return type(self) is type(other) \\\n               and self.variable == other.variable \\\n               and self.model == other.model\n\n    def __hash__(self):\n        return hash((super().__hash__(), hash(self.variable), hash(self.model)))\n\n\nclass Model(BaseImputeMethod):\n    _name = \"Model-based imputer\"\n    short_name = \"model\"\n    description = \"\"\n    format = BaseImputeMethod.format + \" ({self.learner.name})\"\n    @property\n    def name(self):\n        return \"{} ({})\".format(self._name, getattr(self.learner, 'name', ''))\n\n    def __init__(self, learner):\n        self.learner = learner\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        domain = domain_with_class_var(data.domain, variable)\n\n        incompatibility_reason = self.learner.incompatibility_reason(domain)\n        if incompatibility_reason is None:\n            data = data.transform(domain)\n            model = self.learner(data)\n            assert model.domain.class_var == variable\n            return variable.copy(\n                compute_value=ReplaceUnknownsModel(variable, model))\n        else:\n            raise ValueError(\"`{}` doesn't support domain type\"\n                             .format(self.learner.name))\n\n    def copy(self):\n        return Model(self.learner)\n\n    def supports_variable(self, variable):\n        domain = Orange.data.Domain([], class_vars=variable)\n        return self.learner.incompatibility_reason(domain) is None\n\n\ndef domain_with_class_var(domain, class_var):\n    \"\"\"\n    Return a domain with class_var as output domain.class_var.\n\n    If class_var is in the input domain's attributes it is removed from the\n    output's domain.attributes.\n    \"\"\"\n    if domain.class_var is class_var:\n        return domain\n    elif class_var in domain.attributes:\n        attrs = [var for var in domain.attributes\n                 if var is not class_var]\n    else:\n        attrs = domain.attributes\n    return Orange.data.Domain(attrs, class_var)\n\n\nclass IsDefined(Transformation):\n    def transform(self, c):\n        if sp.issparse(c):\n            c = c.toarray()\n        return ~np.isnan(c)\n\n\nclass AsValue(BaseImputeMethod):\n    name = \"As a distinct value\"\n    short_name = \"new value\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        if variable.is_discrete:\n            fmt = \"{var.name}\"\n            value = \"N/A\"\n            var = Orange.data.DiscreteVariable(\n                fmt.format(var=variable),\n                values=variable.values + (value, ),\n                compute_value=Lookup(\n                    variable,\n                    np.arange(len(variable.values), dtype=int),\n                    unknown=len(variable.values)),\n                sparse=variable.sparse,\n                )\n            return var\n\n        elif variable.is_continuous:\n            fmt = \"{var.name}_def\"\n            indicator_var = Orange.data.DiscreteVariable(\n                fmt.format(var=variable),\n                values=(\"undef\", \"def\"),\n                compute_value=IsDefined(variable),\n                sparse=variable.sparse,\n            )\n            stats = basic_stats.BasicStats(data, variable)\n            return (variable.copy(compute_value=ReplaceUnknowns(variable,\n                                                                stats.mean)),\n                    indicator_var)\n        else:\n            raise TypeError(type(variable))\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n\nclass ReplaceUnknownsRandom(Transformation):\n    \"\"\"\n    A column transformation replacing unknowns with values drawn randomly from\n    an empirical distribution.\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for imputation.\n    distribution : Orange.statistics.distribution.Distribution\n        The corresponding sampling distribution\n    \"\"\"\n    def __init__(self, variable, distribution):\n        assert distribution.size > 0\n        assert distribution.variable == variable\n        super().__init__(variable)\n        self.distribution = distribution\n\n        if variable.is_discrete:\n            counts = np.array(distribution)\n        elif variable.is_continuous:\n            counts = np.array(distribution)[1, :]\n        else:\n            raise TypeError(\"Only categorical and numeric \"\n                            \"variables are supported.\")\n        csum = np.sum(counts)\n        if csum > 0:\n            self.sample_prob = counts / csum\n        else:\n            self.sample_prob = np.ones_like(counts) / len(counts)\n\n    def transform(self, c):\n        if not sp.issparse(c):\n            c = np.array(c, copy=True)\n        else:\n            c = c.toarray().ravel()\n        nanindices = np.flatnonzero(np.isnan(c))\n\n        if self.variable.is_discrete:\n            sample = np.random.choice(\n                len(self.variable.values), size=len(nanindices),\n                replace=True, p=self.sample_prob)\n        else:\n            sample = np.random.choice(\n                np.asarray(self.distribution)[0, :], size=len(nanindices),\n                replace=True, p=self.sample_prob)\n\n        c[nanindices] = sample\n        return c\n\n    def __eq__(self, other):\n        return super().__eq__(other) and self.distribution == other.distribution\n\n    def __hash__(self):\n        return hash((type(self), self.variable, self.distribution))\n\n\nclass Random(BaseImputeMethod):\n    name = \"Random values\"\n    short_name = \"random\"\n    description = \"Replace with a random value\"\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        dist = distribution.get_distribution(data, variable)\n        # A distribution is invalid if a continuous variable's column does not\n        # contain any known values or if a discrete variable's .values == []\n        isinvalid = dist.size == 0\n        if isinvalid and variable.is_discrete:\n            assert len(variable.values) == 0\n            raise ValueError(\"'{}' has no values\".format(variable))\n        elif isinvalid and variable.is_continuous:\n            raise ValueError(\"'{}' has an unknown distribution\"\n                             .format(variable))\n\n        if variable.is_discrete and np.sum(dist) == 0:\n            dist += 1 / len(dist)\n        elif variable.is_continuous and np.sum(dist[1, :]) == 0:\n            dist[1, :] += 1 / dist.shape[1]\n        return variable.copy(\n            compute_value=ReplaceUnknownsRandom(variable, dist))\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n",
            "new_file_content": "import numpy as np\nimport scipy.sparse as sp\n\nimport Orange.data\nfrom Orange.statistics import distribution, basic_stats\nfrom Orange.util import Reprable\nfrom .transformation import Transformation, Lookup\n\n__all__ = [\"ReplaceUnknowns\", \"Average\", \"DoNotImpute\", \"DropInstances\",\n           \"Model\", \"AsValue\", \"Random\", \"Default\", \"FixedValueByType\"]\n\n\nclass ReplaceUnknowns(Transformation):\n    \"\"\"\n    A column transformation which replaces unknown values with a fixed `value`.\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for imputation.\n    value : int or float\n        The value with which to replace the unknown values\n    \"\"\"\n    def __init__(self, variable, value=0):\n        super().__init__(variable)\n        self.value = value\n\n    def transform(self, c):\n        if sp.issparse(c):\n            c.data = np.where(np.isnan(c.data), self.value, c.data)\n            return c\n        else:\n            return np.where(np.isnan(c), self.value, c)\n\n    def __eq__(self, other):\n        return super().__eq__(other) and self.value == other.value\n\n    def __hash__(self):\n        return hash((type(self), self.variable, float(self.value)))\n\n\nclass BaseImputeMethod(Reprable):\n    name = \"\"\n    short_name = \"\"\n    description = \"\"\n    format = \"{var.name} -> {self.short_name}\"\n    columns_only = False\n\n    def __call__(self, data, variable):\n        \"\"\" Imputes table along variable column.\n\n        Args:\n            data (Table): A table to impute.\n            variable (Variable): Variable for completing missing values.\n\n        Returns:\n            A new Variable instance with completed missing values or\n            a array mask of rows to drop out.\n        \"\"\"\n        raise NotImplementedError\n\n    def format_variable(self, var):\n        return self.format.format(var=var, self=self)\n\n    def __str__(self):\n        return self.name\n\n    def copy(self):\n        return self\n\n    @classmethod\n    def supports_variable(cls, variable):\n        return True\n\n\nclass DoNotImpute(BaseImputeMethod):\n    name = \"Don't impute\"\n    short_name = \"leave\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        return variable\n\n\nclass DropInstances(BaseImputeMethod):\n    name = \"Remove instances with unknown values\"\n    short_name = \"drop\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        col = data.get_column(variable)\n        return np.isnan(col)\n\n\nclass Average(BaseImputeMethod):\n    name = \"Average/Most frequent\"\n    short_name = \"average\"\n    description = \"Replace with average/mode of the column\"\n\n    def __call__(self, data, variable, value=None):\n        variable = data.domain[variable]\n        if value is None:\n            if variable.is_continuous:\n                stats = basic_stats.BasicStats(data, variable)\n                value = stats.mean\n            elif variable.is_discrete:\n                dist = distribution.get_distribution(data, variable)\n                value = dist.modus()\n            else:\n                raise TypeError(\"Variable must be numeric or categorical.\")\n\n        a = variable.copy(compute_value=ReplaceUnknowns(variable, value))\n        a.to_sql = ImputeSql(variable, value)\n        return a\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n\n\nclass ImputeSql(Reprable):\n    def __init__(self, var, default):\n        self.var = var\n        self.default = default\n\n    def __call__(self):\n        return 'coalesce(%s, %s)' % (self.var.to_sql(), str(self.default))\n\n\nclass Default(BaseImputeMethod):\n    name = \"Fixed value\"\n    short_name = \"value\"\n    description = \"\"\n    columns_only = True\n    format = '{var} -> {self.default}'\n\n    def __init__(self, default=0):\n        self.default = default\n\n    def __call__(self, data, variable, *, default=None):\n        variable = data.domain[variable]\n        default = default if default is not None else self.default\n        return variable.copy(compute_value=ReplaceUnknowns(variable, default))\n\n    def copy(self):\n        return Default(self.default)\n\n\nclass FixedValueByType(BaseImputeMethod):\n    name = \"Fixed value\"\n    short_name = \"Fixed Value\"\n    format = \"{var.name}\"\n\n    def __init__(self,\n                 default_discrete=np.nan, default_continuous=np.nan,\n                 default_string=None, default_time=np.nan):\n        # If you change the order of args or in dict, also fix method copy\n        self.defaults = {\n            Orange.data.DiscreteVariable: default_discrete,\n            Orange.data.ContinuousVariable: default_continuous,\n            Orange.data.StringVariable: default_string,\n            Orange.data.TimeVariable: default_time\n        }\n\n    def __call__(self, data, variable, *, default=None):\n        variable = data.domain[variable]\n        if default is None:\n            default = self.defaults[type(variable)]\n        return variable.copy(compute_value=ReplaceUnknowns(variable, default))\n\n    def copy(self):\n        return FixedValueByType(*self.defaults.values())\n\n\nclass ReplaceUnknownsModel(Reprable):\n    \"\"\"\n    Replace unknown values with predicted values using a `Orange.base.Model`\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for the imputation.\n    model : Orange.base.Model\n        A fitted model predicting `variable`.\n    \"\"\"\n    def __init__(self, variable, model):\n        assert model.domain.class_var == variable\n        self.variable = variable\n        self.model = model\n\n    def __call__(self, data):\n        if isinstance(data, Orange.data.Instance):\n            data = Orange.data.Table.from_list(data.domain, [data])\n        domain = data.domain\n        column = data.get_column(self.variable, copy=True)\n\n        mask = np.isnan(column)\n        if not np.any(mask):\n            return column\n\n        if domain.class_vars:\n            # cannot have class var in domain (due to backmappers in model)\n            data = data.transform(\n                Orange.data.Domain(domain.attributes, None, domain.metas)\n            )\n        predicted = self.model(data[mask])\n        column[mask] = predicted\n        return column\n\n    def __eq__(self, other):\n        return type(self) is type(other) \\\n               and self.variable == other.variable \\\n               and self.model == other.model\n\n    def __hash__(self):\n        return hash((type(self), hash(self.variable), hash(self.model)))\n\n\nclass Model(BaseImputeMethod):\n    _name = \"Model-based imputer\"\n    short_name = \"model\"\n    description = \"\"\n    format = BaseImputeMethod.format + \" ({self.learner.name})\"\n    @property\n    def name(self):\n        return \"{} ({})\".format(self._name, getattr(self.learner, 'name', ''))\n\n    def __init__(self, learner):\n        self.learner = learner\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        domain = domain_with_class_var(data.domain, variable)\n\n        incompatibility_reason = self.learner.incompatibility_reason(domain)\n        if incompatibility_reason is None:\n            data = data.transform(domain)\n            model = self.learner(data)\n            assert model.domain.class_var == variable\n            return variable.copy(\n                compute_value=ReplaceUnknownsModel(variable, model))\n        else:\n            raise ValueError(\"`{}` doesn't support domain type\"\n                             .format(self.learner.name))\n\n    def copy(self):\n        return Model(self.learner)\n\n    def supports_variable(self, variable):\n        domain = Orange.data.Domain([], class_vars=variable)\n        return self.learner.incompatibility_reason(domain) is None\n\n\ndef domain_with_class_var(domain, class_var):\n    \"\"\"\n    Return a domain with class_var as output domain.class_var.\n\n    If class_var is in the input domain's attributes it is removed from the\n    output's domain.attributes.\n    \"\"\"\n    if domain.class_var is class_var:\n        return domain\n    elif class_var in domain.attributes:\n        attrs = [var for var in domain.attributes\n                 if var is not class_var]\n    else:\n        attrs = domain.attributes\n    return Orange.data.Domain(attrs, class_var)\n\n\nclass IsDefined(Transformation):\n    def transform(self, c):\n        if sp.issparse(c):\n            c = c.toarray()\n        return ~np.isnan(c)\n\n\nclass AsValue(BaseImputeMethod):\n    name = \"As a distinct value\"\n    short_name = \"new value\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        if variable.is_discrete:\n            fmt = \"{var.name}\"\n            value = \"N/A\"\n            var = Orange.data.DiscreteVariable(\n                fmt.format(var=variable),\n                values=variable.values + (value, ),\n                compute_value=Lookup(\n                    variable,\n                    np.arange(len(variable.values), dtype=int),\n                    unknown=len(variable.values)),\n                sparse=variable.sparse,\n                )\n            return var\n\n        elif variable.is_continuous:\n            fmt = \"{var.name}_def\"\n            indicator_var = Orange.data.DiscreteVariable(\n                fmt.format(var=variable),\n                values=(\"undef\", \"def\"),\n                compute_value=IsDefined(variable),\n                sparse=variable.sparse,\n            )\n            stats = basic_stats.BasicStats(data, variable)\n            return (variable.copy(compute_value=ReplaceUnknowns(variable,\n                                                                stats.mean)),\n                    indicator_var)\n        else:\n            raise TypeError(type(variable))\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n\nclass ReplaceUnknownsRandom(Transformation):\n    \"\"\"\n    A column transformation replacing unknowns with values drawn randomly from\n    an empirical distribution.\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for imputation.\n    distribution : Orange.statistics.distribution.Distribution\n        The corresponding sampling distribution\n    \"\"\"\n    def __init__(self, variable, distribution):\n        assert distribution.size > 0\n        assert distribution.variable == variable\n        super().__init__(variable)\n        self.distribution = distribution\n\n        if variable.is_discrete:\n            counts = np.array(distribution)\n        elif variable.is_continuous:\n            counts = np.array(distribution)[1, :]\n        else:\n            raise TypeError(\"Only categorical and numeric \"\n                            \"variables are supported.\")\n        csum = np.sum(counts)\n        if csum > 0:\n            self.sample_prob = counts / csum\n        else:\n            self.sample_prob = np.ones_like(counts) / len(counts)\n\n    def transform(self, c):\n        if not sp.issparse(c):\n            c = np.array(c, copy=True)\n        else:\n            c = c.toarray().ravel()\n        nanindices = np.flatnonzero(np.isnan(c))\n\n        if self.variable.is_discrete:\n            sample = np.random.choice(\n                len(self.variable.values), size=len(nanindices),\n                replace=True, p=self.sample_prob)\n        else:\n            sample = np.random.choice(\n                np.asarray(self.distribution)[0, :], size=len(nanindices),\n                replace=True, p=self.sample_prob)\n\n        c[nanindices] = sample\n        return c\n\n    def __eq__(self, other):\n        return super().__eq__(other) and self.distribution == other.distribution\n\n    def __hash__(self):\n        return hash((type(self), self.variable, self.distribution))\n\n\nclass Random(BaseImputeMethod):\n    name = \"Random values\"\n    short_name = \"random\"\n    description = \"Replace with a random value\"\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        dist = distribution.get_distribution(data, variable)\n        # A distribution is invalid if a continuous variable's column does not\n        # contain any known values or if a discrete variable's .values == []\n        isinvalid = dist.size == 0\n        if isinvalid and variable.is_discrete:\n            assert len(variable.values) == 0\n            raise ValueError(\"'{}' has no values\".format(variable))\n        elif isinvalid and variable.is_continuous:\n            raise ValueError(\"'{}' has an unknown distribution\"\n                             .format(variable))\n\n        if variable.is_discrete and np.sum(dist) == 0:\n            dist += 1 / len(dist)\n        elif variable.is_continuous and np.sum(dist[1, :]) == 0:\n            dist[1, :] += 1 / dist.shape[1]\n        return variable.copy(\n            compute_value=ReplaceUnknownsRandom(variable, dist))\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n",
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                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "class Model(BaseImputeMethod):",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/preprocess/impute.py",
                            "type": "method",
                            "name": "ReplaceUnknownsModel.__hash__",
                            "content": "class ReplaceUnknownsModel(Reprable):\n    \"\"\"\n    Replace unknown values with predicted values using a `Orange.base.Model`\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for the imputation.\n    model : Orange.base.Model\n        A fitted model predicting `variable`.\n    \"\"\"\n    def __init__(self, variable, model):\n        assert model.domain.class_var == variable\n        self.variable = variable\n        self.model = model\n\n    def __call__(self, data):\n        if isinstance(data, Orange.data.Instance):\n            data = Orange.data.Table.from_list(data.domain, [data])\n        domain = data.domain\n        column = data.get_column(self.variable, copy=True)\n\n        mask = np.isnan(column)\n        if not np.any(mask):\n            return column\n\n        if domain.class_vars:\n            # cannot have class var in domain (due to backmappers in model)\n            data = data.transform(\n                Orange.data.Domain(domain.attributes, None, domain.metas)\n            )\n        predicted = self.model(data[mask])\n        column[mask] = predicted\n        return column\n\n    def __eq__(self, other):\n        return type(self) is type(other) \\\n               and self.variable == other.variable \\\n               and self.model == other.model\n\n    def __hash__(self):\n        return hash((super().__hash__(), hash(self.variable), hash(self.model)))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 215,
                            "end_lineno": 216,
                            "parent": {
                                "file_name": "Orange/preprocess/impute.py",
                                "type": "class",
                                "name": "ReplaceUnknownsModel",
                                "content": "class ReplaceUnknownsModel(Reprable):\n    \"\"\"\n    Replace unknown values with predicted values using a `Orange.base.Model`\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for the imputation.\n    model : Orange.base.Model\n        A fitted model predicting `variable`.\n    \"\"\"\n    def __init__(self, variable, model):\n        assert model.domain.class_var == variable\n        self.variable = variable\n        self.model = model\n\n    def __call__(self, data):\n        if isinstance(data, Orange.data.Instance):\n            data = Orange.data.Table.from_list(data.domain, [data])\n        domain = data.domain\n        column = data.get_column(self.variable, copy=True)\n\n        mask = np.isnan(column)\n        if not np.any(mask):\n            return column\n\n        if domain.class_vars:\n            # cannot have class var in domain (due to backmappers in model)\n            data = data.transform(\n                Orange.data.Domain(domain.attributes, None, domain.metas)\n            )\n        predicted = self.model(data[mask])\n        column[mask] = predicted\n        return column\n\n    def __eq__(self, other):\n        return type(self) is type(other) \\\n               and self.variable == other.variable \\\n               and self.model == other.model\n\n    def __hash__(self):\n        return hash((super().__hash__(), hash(self.variable), hash(self.model)))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 175,
                                "end_lineno": 216,
                                "parent": null
                            }
                        },
                        {
                            "file_name": "Orange/preprocess/impute.py",
                            "type": "class",
                            "name": "ReplaceUnknownsModel",
                            "content": "class ReplaceUnknownsModel(Reprable):\n    \"\"\"\n    Replace unknown values with predicted values using a `Orange.base.Model`\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for the imputation.\n    model : Orange.base.Model\n        A fitted model predicting `variable`.\n    \"\"\"\n    def __init__(self, variable, model):\n        assert model.domain.class_var == variable\n        self.variable = variable\n        self.model = model\n\n    def __call__(self, data):\n        if isinstance(data, Orange.data.Instance):\n            data = Orange.data.Table.from_list(data.domain, [data])\n        domain = data.domain\n        column = data.get_column(self.variable, copy=True)\n\n        mask = np.isnan(column)\n        if not np.any(mask):\n            return column\n\n        if domain.class_vars:\n            # cannot have class var in domain (due to backmappers in model)\n            data = data.transform(\n                Orange.data.Domain(domain.attributes, None, domain.metas)\n            )\n        predicted = self.model(data[mask])\n        column[mask] = predicted\n        return column\n\n    def __eq__(self, other):\n        return type(self) is type(other) \\\n               and self.variable == other.variable \\\n               and self.model == other.model\n\n    def __hash__(self):\n        return hash((super().__hash__(), hash(self.variable), hash(self.model)))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 175,
                            "end_lineno": 216,
                            "parent": null
                        }
                    ],
                    "added_entities": [],
                    "deleted_entities": []
                }
            ]
        },
        {
            "old_file_content": "import unittest\n\nimport numpy as np\n\nfrom Orange.data import \\\n    Domain, Table, \\\n    DiscreteVariable, ContinuousVariable, TimeVariable, StringVariable\nfrom Orange.preprocess.impute import ReplaceUnknownsRandom, ReplaceUnknowns, \\\n    FixedValueByType\nfrom Orange.statistics.distribution import Discrete\n\n\nclass TestReplaceUnknowns(unittest.TestCase):\n    def test_equality(self):\n        v1 = ContinuousVariable(\"x\")\n        v2 = ContinuousVariable(\"x\")\n        v3 = ContinuousVariable(\"y\")\n\n        t1 = ReplaceUnknowns(v1, 0)\n        t1a = ReplaceUnknowns(v2, 0)\n        t2 = ReplaceUnknowns(v3, 0)\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n        t1 = ReplaceUnknowns(v1, 0)\n        t1a = ReplaceUnknowns(v1, 1)\n        self.assertNotEqual(t1, t1a)\n        self.assertNotEqual(hash(t1), hash(t1a))\n\n\nclass TestReplaceUnknownsRandom(unittest.TestCase):\n    def test_equality(self):\n        v1 = DiscreteVariable(\"x\", tuple(\"abc\"))\n        v2 = DiscreteVariable(\"x\", tuple(\"abc\"))\n        v3 = DiscreteVariable(\"y\", tuple(\"abc\"))\n\n        d1 = Discrete([1, 2, 3], v1)\n        d2 = Discrete([1, 2, 3], v2)\n        d3 = Discrete([1, 2, 3], v3)\n\n        t1 = ReplaceUnknownsRandom(v1, d1)\n        t1a = ReplaceUnknownsRandom(v2, d2)\n        t2 = ReplaceUnknownsRandom(v3, d3)\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n        d1[1] += 1\n        self.assertNotEqual(t1, t1a)\n        self.assertNotEqual(hash(t1), hash(t1a))\n\n\nclass TestFixedValuesByType(unittest.TestCase):\n    def setUp(self):\n        domain = Domain(\n            [DiscreteVariable(\"d\", values=tuple(\"abc\")),\n             ContinuousVariable(\"c\"),\n             TimeVariable(\"t\")],\n            [],\n            [StringVariable(\"s\")]\n        )\n        n = np.nan\n        self.data = Table(\n            domain,\n            np.array([[1, n, 15], [n, 42, n]]),\n            np.empty((2, 0)),\n            np.array([[\"foo\"], [\"\"]]))\n\n    def test_none_defined(self):\n        d, c, t = self.data.domain.attributes\n        s,  = self.data.domain.metas\n\n        imputer = FixedValueByType()\n        for var in (d, c, t):\n            imp = imputer(self.data, var)\n            self.assertIsInstance(imp.compute_value, ReplaceUnknowns)\n            self.assertTrue(np.isnan(imp.compute_value.value))\n        imp = imputer(self.data, s)\n        self.assertIsInstance(imp.compute_value, ReplaceUnknowns)\n        self.assertIsNone(imp.compute_value.value)\n\n    def test_all_defined(self):\n        d, c, t = self.data.domain.attributes\n        s, = self.data.domain.metas\n\n        imputer = FixedValueByType(\n            default_discrete=1, default_continuous=42,\n            default_string=\"foo\", default_time=3.14)\n\n        self.assertEqual(imputer(self.data, d).compute_value.value, 1)\n        self.assertEqual(imputer(self.data, c).compute_value.value, 42)\n        self.assertEqual(imputer(self.data, t).compute_value.value, 3.14)\n        self.assertEqual(imputer(self.data, s).compute_value.value, \"foo\")\n\n    def test_with_default(self):\n        s, = self.data.domain.metas\n\n        imputer = FixedValueByType(\n            default_discrete=1, default_continuous=42,\n            default_string=\"foo\", default_time=3.14)\n\n        self.assertEqual(\n            imputer(self.data, s, default=\"bar\").compute_value.value,\n            \"bar\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "new_file_content": "import unittest\n\nimport numpy as np\n\nfrom Orange.data import \\\n    Domain, Table, \\\n    DiscreteVariable, ContinuousVariable, TimeVariable, StringVariable\nfrom Orange.preprocess.impute import ReplaceUnknownsRandom, ReplaceUnknowns, \\\n    FixedValueByType, ReplaceUnknownsModel\nfrom Orange.regression import LinearRegressionLearner\nfrom Orange.statistics.distribution import Discrete\n\n\nclass TestReplaceUnknowns(unittest.TestCase):\n    def test_equality(self):\n        v1 = ContinuousVariable(\"x\")\n        v2 = ContinuousVariable(\"x\")\n        v3 = ContinuousVariable(\"y\")\n\n        t1 = ReplaceUnknowns(v1, 0)\n        t1a = ReplaceUnknowns(v2, 0)\n        t2 = ReplaceUnknowns(v3, 0)\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n        t1 = ReplaceUnknowns(v1, 0)\n        t1a = ReplaceUnknowns(v1, 1)\n        self.assertNotEqual(t1, t1a)\n        self.assertNotEqual(hash(t1), hash(t1a))\n\n\nclass TestReplaceUnknownsRandom(unittest.TestCase):\n    def test_equality(self):\n        v1 = DiscreteVariable(\"x\", tuple(\"abc\"))\n        v2 = DiscreteVariable(\"x\", tuple(\"abc\"))\n        v3 = DiscreteVariable(\"y\", tuple(\"abc\"))\n\n        d1 = Discrete([1, 2, 3], v1)\n        d2 = Discrete([1, 2, 3], v2)\n        d3 = Discrete([1, 2, 3], v3)\n\n        t1 = ReplaceUnknownsRandom(v1, d1)\n        t1a = ReplaceUnknownsRandom(v2, d2)\n        t2 = ReplaceUnknownsRandom(v3, d3)\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n        d1[1] += 1\n        self.assertNotEqual(t1, t1a)\n        self.assertNotEqual(hash(t1), hash(t1a))\n\n\nclass TestFixedValuesByType(unittest.TestCase):\n    def setUp(self):\n        domain = Domain(\n            [DiscreteVariable(\"d\", values=tuple(\"abc\")),\n             ContinuousVariable(\"c\"),\n             TimeVariable(\"t\")],\n            [],\n            [StringVariable(\"s\")]\n        )\n        n = np.nan\n        self.data = Table(\n            domain,\n            np.array([[1, n, 15], [n, 42, n]]),\n            np.empty((2, 0)),\n            np.array([[\"foo\"], [\"\"]]))\n\n    def test_none_defined(self):\n        d, c, t = self.data.domain.attributes\n        s,  = self.data.domain.metas\n\n        imputer = FixedValueByType()\n        for var in (d, c, t):\n            imp = imputer(self.data, var)\n            self.assertIsInstance(imp.compute_value, ReplaceUnknowns)\n            self.assertTrue(np.isnan(imp.compute_value.value))\n        imp = imputer(self.data, s)\n        self.assertIsInstance(imp.compute_value, ReplaceUnknowns)\n        self.assertIsNone(imp.compute_value.value)\n\n    def test_all_defined(self):\n        d, c, t = self.data.domain.attributes\n        s, = self.data.domain.metas\n\n        imputer = FixedValueByType(\n            default_discrete=1, default_continuous=42,\n            default_string=\"foo\", default_time=3.14)\n\n        self.assertEqual(imputer(self.data, d).compute_value.value, 1)\n        self.assertEqual(imputer(self.data, c).compute_value.value, 42)\n        self.assertEqual(imputer(self.data, t).compute_value.value, 3.14)\n        self.assertEqual(imputer(self.data, s).compute_value.value, \"foo\")\n\n    def test_with_default(self):\n        s, = self.data.domain.metas\n\n        imputer = FixedValueByType(\n            default_discrete=1, default_continuous=42,\n            default_string=\"foo\", default_time=3.14)\n\n        self.assertEqual(\n            imputer(self.data, s, default=\"bar\").compute_value.value,\n            \"bar\")\n\n\nclass TestReplaceUnknownsModel(unittest.TestCase):\n    def test_eq(self):\n        iris = Table(\"iris\")\n\n        v1 = iris.domain[0]\n        v2 = iris.domain[0]\n        v3 = iris.domain[1]\n\n        l = LinearRegressionLearner()\n        def new_target(t):\n            dom = Domain(iris.domain[2:], class_vars=[t])\n            return iris.transform(dom)\n\n        mod1 = l(new_target(v1))\n        t1 = ReplaceUnknownsModel(v1, mod1)\n        t1a = ReplaceUnknownsModel(v2, mod1)\n        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))\n\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        # the following should be equal, but will not be unless __eq__ for that\n        # particular model is defined\n        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))\n        self.assertNotEqual(t1, t1b)  # this is WRONG\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
            "header": {
                "file": {
                    "path": "Orange/preprocess/tests/test_impute.py"
                },
                "misc_line": null
            },
            "index_line": {
                "old_commit_hash": "2eeaf0e9c",
                "new_commit_hash": "b2320cc42",
                "mode": "100644"
            },
            "is_binary_file": false,
            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/preprocess/tests/test_impute.py"
            },
            "plus_file": {
                "path": "b/Orange/preprocess/tests/test_impute.py"
            },
            "hunks": [
                {
                    "descriptor": {
                        "old_range": {
                            "start": 6,
                            "length": 7
                        },
                        "new_range": {
                            "start": 6,
                            "length": 8
                        },
                        "section": "from Orange.data import \\"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "    Domain, Table, \\",
                                "type": "context"
                            },
                            {
                                "content": "    DiscreteVariable, ContinuousVariable, TimeVariable, StringVariable",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.preprocess.impute import ReplaceUnknownsRandom, ReplaceUnknowns, \\",
                                "type": "context"
                            },
                            {
                                "content": "    FixedValueByType",
                                "type": "deleted"
                            },
                            {
                                "content": "    FixedValueByType, ReplaceUnknownsModel",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.regression import LinearRegressionLearner",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.statistics.distribution import Discrete",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/preprocess/tests/test_impute.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.regression import LinearRegressionLearner",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 10,
                            "end_lineno": 10,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_impute.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.preprocess.impute import ReplaceUnknownsRandom, ReplaceUnknowns, \\\n    FixedValueByType, ReplaceUnknownsModel",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 8,
                            "end_lineno": 9,
                            "parent": null
                        }
                    ],
                    "deleted_entities": [
                        {
                            "file_name": "Orange/preprocess/tests/test_impute.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.preprocess.impute import ReplaceUnknownsRandom, ReplaceUnknowns, \\\n    FixedValueByType",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 8,
                            "end_lineno": 9,
                            "parent": null
                        }
                    ]
                },
                {
                    "descriptor": {
                        "old_range": {
                            "start": 111,
                            "length": 5
                        },
                        "new_range": {
                            "start": 112,
                            "length": 36
                        },
                        "section": "class TestFixedValuesByType(unittest.TestCase):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "            \"bar\")",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "class TestReplaceUnknownsModel(unittest.TestCase):",
                                "type": "added"
                            },
                            {
                                "content": "    def test_eq(self):",
                                "type": "added"
                            },
                            {
                                "content": "        iris = Table(\"iris\")",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        v1 = iris.domain[0]",
                                "type": "added"
                            },
                            {
                                "content": "        v2 = iris.domain[0]",
                                "type": "added"
                            },
                            {
                                "content": "        v3 = iris.domain[1]",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        l = LinearRegressionLearner()",
                                "type": "added"
                            },
                            {
                                "content": "        def new_target(t):",
                                "type": "added"
                            },
                            {
                                "content": "            dom = Domain(iris.domain[2:], class_vars=[t])",
                                "type": "added"
                            },
                            {
                                "content": "            return iris.transform(dom)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        mod1 = l(new_target(v1))",
                                "type": "added"
                            },
                            {
                                "content": "        t1 = ReplaceUnknownsModel(v1, mod1)",
                                "type": "added"
                            },
                            {
                                "content": "        t1a = ReplaceUnknownsModel(v2, mod1)",
                                "type": "added"
                            },
                            {
                                "content": "        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(t1, t1)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(t1, t1a)",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertNotEqual(t1, t2)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        # the following should be equal, but will not be unless __eq__ for that",
                                "type": "added"
                            },
                            {
                                "content": "        # particular model is defined",
                                "type": "added"
                            },
                            {
                                "content": "        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertNotEqual(t1, t1b)  # this is WRONG",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertEqual(hash(t1), hash(t1a))",
                                "type": "added"
                            },
                            {
                                "content": "        self.assertNotEqual(hash(t1), hash(t2))",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "added"
                            },
                            {
                                "content": "if __name__ == \"__main__\":",
                                "type": "context"
                            },
                            {
                                "content": "    unittest.main()",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/preprocess/tests/test_impute.py",
                            "type": "class",
                            "name": "TestReplaceUnknownsModel",
                            "content": "class TestReplaceUnknownsModel(unittest.TestCase):\n    def test_eq(self):\n        iris = Table(\"iris\")\n\n        v1 = iris.domain[0]\n        v2 = iris.domain[0]\n        v3 = iris.domain[1]\n\n        l = LinearRegressionLearner()\n        def new_target(t):\n            dom = Domain(iris.domain[2:], class_vars=[t])\n            return iris.transform(dom)\n\n        mod1 = l(new_target(v1))\n        t1 = ReplaceUnknownsModel(v1, mod1)\n        t1a = ReplaceUnknownsModel(v2, mod1)\n        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))\n\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        # the following should be equal, but will not be unless __eq__ for that\n        # particular model is defined\n        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))\n        self.assertNotEqual(t1, t1b)  # this is WRONG\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 115,
                            "end_lineno": 143,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/tests/test_impute.py",
                            "type": "method",
                            "name": "TestReplaceUnknownsModel.test_eq",
                            "content": "class TestReplaceUnknownsModel(unittest.TestCase):\n    def test_eq(self):\n        iris = Table(\"iris\")\n\n        v1 = iris.domain[0]\n        v2 = iris.domain[0]\n        v3 = iris.domain[1]\n\n        l = LinearRegressionLearner()\n        def new_target(t):\n            dom = Domain(iris.domain[2:], class_vars=[t])\n            return iris.transform(dom)\n\n        mod1 = l(new_target(v1))\n        t1 = ReplaceUnknownsModel(v1, mod1)\n        t1a = ReplaceUnknownsModel(v2, mod1)\n        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))\n\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        # the following should be equal, but will not be unless __eq__ for that\n        # particular model is defined\n        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))\n        self.assertNotEqual(t1, t1b)  # this is WRONG\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 116,
                            "end_lineno": 143,
                            "parent": {
                                "file_name": "Orange/preprocess/tests/test_impute.py",
                                "type": "class",
                                "name": "TestReplaceUnknownsModel",
                                "content": "class TestReplaceUnknownsModel(unittest.TestCase):\n    def test_eq(self):\n        iris = Table(\"iris\")\n\n        v1 = iris.domain[0]\n        v2 = iris.domain[0]\n        v3 = iris.domain[1]\n\n        l = LinearRegressionLearner()\n        def new_target(t):\n            dom = Domain(iris.domain[2:], class_vars=[t])\n            return iris.transform(dom)\n\n        mod1 = l(new_target(v1))\n        t1 = ReplaceUnknownsModel(v1, mod1)\n        t1a = ReplaceUnknownsModel(v2, mod1)\n        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))\n\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        # the following should be equal, but will not be unless __eq__ for that\n        # particular model is defined\n        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))\n        self.assertNotEqual(t1, t1b)  # this is WRONG\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 115,
                                "end_lineno": 143,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "a146cf9b2093f2ec2fd9eae122f0b67779dbcff5^",
    "new_commit_hash": "a146cf9b2093f2ec2fd9eae122f0b67779dbcff5",
    "commit_message": "Partially fix and test ReplaceUnknownsModel",
    "commit_date": "2022-11-22T15:45:55+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "a146cf9b2093f2ec2fd9eae122f0b67779dbcff5",
    "test_file_codes": [
        "import unittest\n\nimport numpy as np\n\nfrom Orange.data import \\\n    Domain, Table, \\\n    DiscreteVariable, ContinuousVariable, TimeVariable, StringVariable\nfrom Orange.preprocess.impute import ReplaceUnknownsRandom, ReplaceUnknowns, \\\n    FixedValueByType, ReplaceUnknownsModel\nfrom Orange.regression import LinearRegressionLearner\nfrom Orange.statistics.distribution import Discrete\n\n\nclass TestReplaceUnknowns(unittest.TestCase):\n    def test_equality(self):\n        v1 = ContinuousVariable(\"x\")\n        v2 = ContinuousVariable(\"x\")\n        v3 = ContinuousVariable(\"y\")\n\n        t1 = ReplaceUnknowns(v1, 0)\n        t1a = ReplaceUnknowns(v2, 0)\n        t2 = ReplaceUnknowns(v3, 0)\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n        t1 = ReplaceUnknowns(v1, 0)\n        t1a = ReplaceUnknowns(v1, 1)\n        self.assertNotEqual(t1, t1a)\n        self.assertNotEqual(hash(t1), hash(t1a))\n\n\nclass TestReplaceUnknownsRandom(unittest.TestCase):\n    def test_equality(self):\n        v1 = DiscreteVariable(\"x\", tuple(\"abc\"))\n        v2 = DiscreteVariable(\"x\", tuple(\"abc\"))\n        v3 = DiscreteVariable(\"y\", tuple(\"abc\"))\n\n        d1 = Discrete([1, 2, 3], v1)\n        d2 = Discrete([1, 2, 3], v2)\n        d3 = Discrete([1, 2, 3], v3)\n\n        t1 = ReplaceUnknownsRandom(v1, d1)\n        t1a = ReplaceUnknownsRandom(v2, d2)\n        t2 = ReplaceUnknownsRandom(v3, d3)\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n        d1[1] += 1\n        self.assertNotEqual(t1, t1a)\n        self.assertNotEqual(hash(t1), hash(t1a))\n\n\nclass TestFixedValuesByType(unittest.TestCase):\n    def setUp(self):\n        domain = Domain(\n            [DiscreteVariable(\"d\", values=tuple(\"abc\")),\n             ContinuousVariable(\"c\"),\n             TimeVariable(\"t\")],\n            [],\n            [StringVariable(\"s\")]\n        )\n        n = np.nan\n        self.data = Table(\n            domain,\n            np.array([[1, n, 15], [n, 42, n]]),\n            np.empty((2, 0)),\n            np.array([[\"foo\"], [\"\"]]))\n\n    def test_none_defined(self):\n        d, c, t = self.data.domain.attributes\n        s,  = self.data.domain.metas\n\n        imputer = FixedValueByType()\n        for var in (d, c, t):\n            imp = imputer(self.data, var)\n            self.assertIsInstance(imp.compute_value, ReplaceUnknowns)\n            self.assertTrue(np.isnan(imp.compute_value.value))\n        imp = imputer(self.data, s)\n        self.assertIsInstance(imp.compute_value, ReplaceUnknowns)\n        self.assertIsNone(imp.compute_value.value)\n\n    def test_all_defined(self):\n        d, c, t = self.data.domain.attributes\n        s, = self.data.domain.metas\n\n        imputer = FixedValueByType(\n            default_discrete=1, default_continuous=42,\n            default_string=\"foo\", default_time=3.14)\n\n        self.assertEqual(imputer(self.data, d).compute_value.value, 1)\n        self.assertEqual(imputer(self.data, c).compute_value.value, 42)\n        self.assertEqual(imputer(self.data, t).compute_value.value, 3.14)\n        self.assertEqual(imputer(self.data, s).compute_value.value, \"foo\")\n\n    def test_with_default(self):\n        s, = self.data.domain.metas\n\n        imputer = FixedValueByType(\n            default_discrete=1, default_continuous=42,\n            default_string=\"foo\", default_time=3.14)\n\n        self.assertEqual(\n            imputer(self.data, s, default=\"bar\").compute_value.value,\n            \"bar\")\n\n\nclass TestReplaceUnknownsModel(unittest.TestCase):\n    def test_eq(self):\n        iris = Table(\"iris\")\n\n        v1 = iris.domain[0]\n        v2 = iris.domain[0]\n        v3 = iris.domain[1]\n\n        l = LinearRegressionLearner()\n        def new_target(t):\n            dom = Domain(iris.domain[2:], class_vars=[t])\n            return iris.transform(dom)\n\n        mod1 = l(new_target(v1))\n        t1 = ReplaceUnknownsModel(v1, mod1)\n        t1a = ReplaceUnknownsModel(v2, mod1)\n        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))\n\n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n\n        # the following should be equal, but will not be unless __eq__ for that\n        # particular model is defined\n        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))\n        self.assertNotEqual(t1, t1b)  # this is WRONG\n\n        self.assertEqual(hash(t1), hash(t1a))\n        self.assertNotEqual(hash(t1), hash(t2))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
    ],
    "test_file_names": [
        "test_1.py"
    ],
    "setup_res_code": 0,
    "setup_res_stdout": "Starting Orange installation attempts...\nAttempting installation with Python 3.7...\nrunning build_ext\nCompiling Orange/classification/_tree_scorers.pyx because it changed.\nCompiling Orange/data/_contingency.pyx because it changed.\nCompiling Orange/data/_io.pyx because it changed.\nCompiling Orange/data/_valuecount.pyx because it changed.\nCompiling Orange/data/_variable.pyx because it changed.\nCompiling Orange/distance/_distance.pyx because it changed.\nCompiling Orange/preprocess/_discretize.pyx because it changed.\nCompiling Orange/preprocess/_relieff.pyx because it changed.\nCompiling Orange/projection/_som.pyx because it changed.\n[1/9] Cythonizing Orange/classification/_tree_scorers.pyx\n[2/9] Cythonizing Orange/data/_contingency.pyx\n[3/9] Cythonizing Orange/data/_io.pyx\n[4/9] Cythonizing Orange/data/_valuecount.pyx\n[5/9] Cythonizing Orange/data/_variable.pyx\n[6/9] Cythonizing Orange/distance/_distance.pyx\n[7/9] Cythonizing Orange/preprocess/_discretize.pyx\n[8/9] Cythonizing Orange/preprocess/_relieff.pyx\n[9/9] Cythonizing Orange/projection/_som.pyx\nbuilding 'Orange.classification._tree_scorers' extension\ncreating build\ncreating build/temp.linux-x86_64-3.7\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o\ncreating build/lib.linux-x86_64-3.7\ncreating build/lib.linux-x86_64-3.7/Orange\ncreating build/lib.linux-x86_64-3.7/Orange/classification\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_contingency.c -o build/temp.linux-x86_64-3.7/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-3.7/Orange/data\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_io.c -o build/temp.linux-x86_64-3.7/Orange/data/_io.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/data/_variable.c -o build/temp.linux-x86_64-3.7/Orange/data/_variable.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/distance/_distance.c -o build/temp.linux-x86_64-3.7/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-3.7/Orange/distance\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-3.7/Orange/preprocess\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include 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-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/projection/_som.c -o build/temp.linux-x86_64-3.7/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-3.7/Orange/projection\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._simple_tree' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-3.7/Orange/widgets\ncreating build/lib.linux-x86_64-3.7/Orange/widgets/utils\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\nrunning develop\nrunning egg_info\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_io.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-3.7/Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-3.7/Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-3.7/Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so -> Orange/widgets/utils\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.34.0.dev0+a146cf9 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5\nProcessing dependencies for Orange3==3.34.0.dev0+a146cf9\nSearching for xlsxwriter\nReading https://pypi.org/simple/xlsxwriter/\nDownloading https://files.pythonhosted.org/packages/a7/ea/53d1fe468e63e092cf16e2c18d16f50c29851242f9dd12d6a66e0d7f0d02/XlsxWriter-3.2.0-py3-none-any.whl#sha256=ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e\nBest match: XlsxWriter 3.2.0\nProcessing XlsxWriter-3.2.0-py3-none-any.whl\nInstalling XlsxWriter-3.2.0-py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages\nAdding XlsxWriter 3.2.0 to easy-install.pth file\nInstalling vba_extract.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/XlsxWriter-3.2.0-py3.7.egg\nSearching for xlrd>=1.2.0\nReading https://pypi.org/simple/xlrd/\nDownloading https://files.pythonhosted.org/packages/a6/0c/c2a72d51fe56e08a08acc85d13013558a2d793028ae7385448a6ccdfae64/xlrd-2.0.1-py2.py3-none-any.whl#sha256=6a33ee89877bd9abc1158129f6e94be74e2679636b8a205b43b85206c3f0bbdd\nBest match: xlrd 2.0.1\nProcessing xlrd-2.0.1-py2.py3-none-any.whl\nInstalling xlrd-2.0.1-py2.py3-none-any.whl to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages\nAdding xlrd 2.0.1 to easy-install.pth file\nInstalling runxlrd.py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/xlrd-2.0.1-py3.7.egg\nSearching for serverfiles\nReading https://pypi.org/simple/serverfiles/\nDownloading https://files.pythonhosted.org/packages/33/36/794fe1c4ed29b0f56a26d72e956baf0285d8fe9e29faf36797a28a8ad45b/serverfiles-0.3.1.tar.gz#sha256=5e10fc32e758791e3735bc08bce2ed4702a83b1e45ab96c5d59cc8aeecfbebe1\nBest match: serverfiles 0.3.1\nProcessing serverfiles-0.3.1.tar.gz\nWriting /tmp/easy_install-96kgleag/serverfiles-0.3.1/setup.cfg\nRunning serverfiles-0.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-96kgleag/serverfiles-0.3.1/egg-dist-tmp-w4x7l4b1\ncreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nExtracting serverfiles-0.3.1-py3.7.egg to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/serverfiles-0.3.1-py3.7.egg\nSearching for scipy>=1.9\nReading https://pypi.org/simple/scipy/\nDownloading https://files.pythonhosted.org/packages/d9/7b/2b8ac283cf32465ed08bc20a83d559fe7b174a484781702ba8accea001d6/scipy-1.15.0.tar.gz#sha256=300742e2cc94e36a2880ebe464a1c8b4352a7b0f3e36ec3d2ac006cdbe0219ac\nBest match: scipy 1.15.0\nProcessing scipy-1.15.0.tar.gz\nVerifying Orange installation...\n❌ Orange verification failed\nPython 3.7 installation failed, trying Python 3.8...\nAttempting installation with Python 3.8...\nrunning build_ext\nbuilding 'Orange.classification._tree_scorers' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_tree_scorers.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/classification\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/data\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_contingency.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/data\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_io.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_io.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_valuecount.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/data/_variable.c -o build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/distance/_distance.c -o build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/distance\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_discretize.c -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/preprocess\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/preprocess/_relieff.cpp -o build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/projection/_som.c -o build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/projection\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/projection/_som.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.classification._simple_tree' extension\ncc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/classification/_simple_tree.c -o build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o\ncc -pthread -shared -Wl,--exclude-libs,ALL build/temp.linux-x86_64-cpython-38/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncreating build/temp.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -fPIC -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/include/python3.8 -c Orange/widgets/utils/_grid_density.cpp -o build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o\ncreating build/lib.linux-x86_64-cpython-38/Orange/widgets/utils\nc++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -pthread -shared build/temp.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.8.20-linux-x86_64-gnu/lib -lm -o build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\nrunning develop\nrunning egg_info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\ndependency 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automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayscalars.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_math.h won't be automatically included in the manifest: the path must be relative\ndependency /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ufuncobject.h won't be automatically included in the manifest: the path must be relative\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_tree_scorers.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_contingency.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_io.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_valuecount.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/data/_variable.cpython-38-x86_64-linux-gnu.so -> Orange/data\ncopying build/lib.linux-x86_64-cpython-38/Orange/distance/_distance.cpython-38-x86_64-linux-gnu.so -> Orange/distance\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_discretize.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/preprocess/_relieff.cpython-38-x86_64-linux-gnu.so -> Orange/preprocess\ncopying build/lib.linux-x86_64-cpython-38/Orange/projection/_som.cpython-38-x86_64-linux-gnu.so -> Orange/projection\ncopying build/lib.linux-x86_64-cpython-38/Orange/classification/_simple_tree.cpython-38-x86_64-linux-gnu.so -> Orange/classification\ncopying build/lib.linux-x86_64-cpython-38/Orange/widgets/utils/_grid_density.cpython-38-x86_64-linux-gnu.so -> Orange/widgets/utils\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.34.0.dev0+a146cf9 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5\nProcessing dependencies for Orange3==3.34.0.dev0+a146cf9\nSearching for XlsxWriter==3.2.0\nBest match: XlsxWriter 3.2.0\nAdding XlsxWriter 3.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for setuptools==75.3.0\nBest match: setuptools 75.3.0\nAdding setuptools 75.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for scipy==1.10.1\nBest match: scipy 1.10.1\nAdding scipy 1.10.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for scikit-learn==1.3.2\nBest match: scikit-learn 1.3.2\nAdding scikit-learn 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for requests==2.32.3\nBest match: requests 2.32.3\nAdding requests 2.32.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for qtconsole==5.6.1\nBest match: qtconsole 5.6.1\nAdding qtconsole 5.6.1 to easy-install.pth file\nInstalling jupyter-qtconsole script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for PyYAML==6.0.2\nBest match: PyYAML 6.0.2\nAdding PyYAML 6.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pyqtgraph==0.13.3\nBest match: pyqtgraph 0.13.3\nAdding pyqtgraph 0.13.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pygments==2.19.1\nBest match: pygments 2.19.1\nAdding pygments 2.19.1 to easy-install.pth file\nInstalling pygmentize script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pip==24.3.1\nBest match: pip 24.3.1\nAdding pip 24.3.1 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pandas==2.0.3\nBest match: pandas 2.0.3\nAdding pandas 2.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for orange-widget-base==4.22.0\nBest match: orange-widget-base 4.22.0\nAdding orange-widget-base 4.22.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for orange-canvas-core==0.1.35\nBest match: orange-canvas-core 0.1.35\nAdding orange-canvas-core 0.1.35 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for openpyxl==3.1.5\nBest match: openpyxl 3.1.5\nAdding openpyxl 3.1.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for numpy==1.24.4\nBest match: numpy 1.24.4\nAdding numpy 1.24.4 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling f2py3.8 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for networkx==3.1\nBest match: networkx 3.1\nAdding networkx 3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for matplotlib==3.7.5\nBest match: matplotlib 3.7.5\nAdding matplotlib 3.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for keyrings.alt==5.0.2\nBest match: keyrings.alt 5.0.2\nAdding keyrings.alt 5.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for keyring==25.5.0\nBest match: keyring 25.5.0\nAdding keyring 25.5.0 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for joblib==1.4.2\nBest match: joblib 1.4.2\nAdding joblib 1.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for httpx==0.28.1\nBest match: httpx 0.28.1\nAdding httpx 0.28.1 to easy-install.pth file\nInstalling httpx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for baycomp==1.0.3\nBest match: baycomp 1.0.3\nAdding baycomp 1.0.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for PyQtWebEngine==5.15.7\nBest match: PyQtWebEngine 5.15.7\nAdding PyQtWebEngine 5.15.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for PyQt5==5.15.11\nBest match: PyQt5 5.15.11\nAdding PyQt5 5.15.11 to easy-install.pth file\nInstalling pylupdate5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling pyrcc5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling pyuic5 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for threadpoolctl==3.5.0\nBest match: threadpoolctl 3.5.0\nAdding threadpoolctl 3.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for urllib3==2.2.3\nBest match: urllib3 2.2.3\nAdding urllib3 2.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for packaging==24.2\nBest match: packaging 24.2\nAdding packaging 24.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for QtPy==2.4.2\nBest match: QtPy 2.4.2\nAdding QtPy 2.4.2 to easy-install.pth file\nInstalling qtpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for ipykernel==6.29.5\nBest match: ipykernel 6.29.5\nAdding ipykernel 6.29.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jupyter-client==8.6.3\nBest match: jupyter-client 8.6.3\nAdding jupyter-client 8.6.3 to easy-install.pth file\nInstalling jupyter-kernel script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling jupyter-kernelspec script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling jupyter-run script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jupyter-core==5.7.2\nBest match: jupyter-core 5.7.2\nAdding jupyter-core 5.7.2 to easy-install.pth file\nInstalling jupyter script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling jupyter-migrate script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling jupyter-troubleshoot script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for traitlets==5.14.3\nBest match: traitlets 5.14.3\nAdding traitlets 5.14.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for tzdata==2024.2\nBest match: tzdata 2024.2\nAdding tzdata 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pytz==2024.2\nBest match: pytz 2024.2\nAdding pytz 2024.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for typing-extensions==4.12.2\nBest match: typing-extensions 4.12.2\nAdding typing-extensions 4.12.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for qasync==0.27.1\nBest match: qasync 0.27.1\nAdding qasync 0.27.1 to easy-install.pth file\ndetected new path './setuptools/_vendor'\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for dictdiffer==0.9.0\nBest match: dictdiffer 0.9.0\nAdding dictdiffer 0.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for requests-cache==1.2.1\nBest match: requests-cache 1.2.1\nAdding requests-cache 1.2.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for commonmark==0.9.1\nBest match: commonmark 0.9.1\nAdding commonmark 0.9.1 to easy-install.pth file\nInstalling cmark script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for et-xmlfile==2.0.0\nBest match: et-xmlfile 2.0.0\nAdding et-xmlfile 2.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for importlib-resources==6.4.5\nBest match: importlib-resources 6.4.5\nAdding importlib-resources 6.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pillow==10.4.0\nBest match: pillow 10.4.0\nAdding pillow 10.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for kiwisolver==1.4.7\nBest match: kiwisolver 1.4.7\nAdding kiwisolver 1.4.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for fonttools==4.55.3\nBest match: fonttools 4.55.3\nAdding fonttools 4.55.3 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for cycler==0.12.1\nBest match: cycler 0.12.1\nAdding cycler 0.12.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for contourpy==1.1.1\nBest match: contourpy 1.1.1\nAdding contourpy 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jaraco.context==6.0.1\nBest match: jaraco.context 6.0.1\nAdding jaraco.context 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jaraco.classes==3.4.0\nBest match: jaraco.classes 3.4.0\nAdding jaraco.classes 3.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for importlib-metadata==8.5.0\nBest match: importlib-metadata 8.5.0\nAdding importlib-metadata 8.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jaraco.functools==4.1.0\nBest match: jaraco.functools 4.1.0\nAdding jaraco.functools 4.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for httpcore==1.0.7\nBest match: httpcore 1.0.7\nAdding httpcore 1.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for anyio==4.5.2\nBest match: anyio 4.5.2\nAdding anyio 4.5.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for PyQtWebEngine-Qt5==5.15.16\nBest match: PyQtWebEngine-Qt5 5.15.16\nAdding PyQtWebEngine-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for PyQt5-sip==12.15.0\nBest match: PyQt5-sip 12.15.0\nAdding PyQt5-sip 12.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for PyQt5-Qt5==5.15.16\nBest match: PyQt5-Qt5 5.15.16\nAdding PyQt5-Qt5 5.15.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for tornado==6.4.2\nBest match: tornado 6.4.2\nAdding tornado 6.4.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pyzmq==26.2.0\nBest match: pyzmq 26.2.0\nAdding pyzmq 26.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for psutil==6.1.1\nBest match: psutil 6.1.1\nAdding psutil 6.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for nest-asyncio==1.6.0\nBest match: nest-asyncio 1.6.0\nAdding nest-asyncio 1.6.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for matplotlib-inline==0.1.7\nBest match: matplotlib-inline 0.1.7\nAdding matplotlib-inline 0.1.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for ipython==8.12.3\nBest match: ipython 8.12.3\nAdding ipython 8.12.3 to easy-install.pth file\nInstalling ipython script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\nInstalling ipython3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for debugpy==1.8.11\nBest match: debugpy 1.8.11\nAdding debugpy 1.8.11 to easy-install.pth file\nInstalling debugpy script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for comm==0.2.2\nBest match: comm 0.2.2\nAdding comm 0.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for platformdirs==4.3.6\nBest match: platformdirs 4.3.6\nAdding platformdirs 4.3.6 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for url-normalize==1.4.3\nBest match: url-normalize 1.4.3\nAdding url-normalize 1.4.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for cattrs==24.1.2\nBest match: cattrs 24.1.2\nAdding cattrs 24.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for attrs==24.3.0\nBest match: attrs 24.3.0\nAdding attrs 24.3.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for zipp==3.20.2\nBest match: zipp 3.20.2\nAdding zipp 3.20.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for backports.tarfile==1.2.0\nBest match: backports.tarfile 1.2.0\nbackports.tarfile 1.2.0 is already the active version in easy-install.pth\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/setuptools/_vendor\nSearching for more-itertools==10.5.0\nBest match: more-itertools 10.5.0\nAdding more-itertools 10.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for h11==0.14.0\nBest match: h11 0.14.0\nAdding h11 0.14.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for exceptiongroup==1.2.2\nBest match: exceptiongroup 1.2.2\nAdding exceptiongroup 1.2.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for sniffio==1.3.1\nBest match: sniffio 1.3.1\nAdding sniffio 1.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pexpect==4.9.0\nBest match: pexpect 4.9.0\nAdding pexpect 4.9.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for stack-data==0.6.3\nBest match: stack-data 0.6.3\nAdding stack-data 0.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for prompt-toolkit==3.0.48\nBest match: prompt-toolkit 3.0.48\nAdding prompt-toolkit 3.0.48 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pickleshare==0.7.5\nBest match: pickleshare 0.7.5\nAdding pickleshare 0.7.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for jedi==0.19.2\nBest match: jedi 0.19.2\nAdding jedi 0.19.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for decorator==5.1.1\nBest match: decorator 5.1.1\nAdding decorator 5.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for backcall==0.2.0\nBest match: backcall 0.2.0\nAdding backcall 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for cffi==1.17.1\nBest match: cffi 1.17.1\nAdding cffi 1.17.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for ptyprocess==0.7.0\nBest match: ptyprocess 0.7.0\nAdding ptyprocess 0.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pure-eval==0.2.3\nBest match: pure-eval 0.2.3\nAdding pure-eval 0.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for asttokens==3.0.0\nBest match: asttokens 3.0.0\nAdding asttokens 3.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for executing==2.1.0\nBest match: executing 2.1.0\nAdding executing 2.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for wcwidth==0.2.13\nBest match: wcwidth 0.2.13\nAdding wcwidth 0.2.13 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for parso==0.8.4\nBest match: parso 0.8.4\nAdding parso 0.8.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nSearching for pycparser==2.22\nBest match: pycparser 2.22\nAdding pycparser 2.22 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages\nFinished processing dependencies for Orange3==3.34.0.dev0+a146cf9\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.8\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 110ms\nPrepared 18 packages in 19ms\nInstalled 18 packages in 123ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\n  × No solution found when resolving dependencies:\n  ╰─▶ Because the current Python version (3.7.9) does not satisfy Python>=3.8 and all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n      depend on Python>=3.8,<3.12, we can conclude that all of:\n          scipy>=1.9.0,<=1.9.1\n          scipy>=1.10.0,<=1.10.1\n       cannot be used.\n      And because only the following versions of scipy are available:\n          scipy<=1.9.0\n          scipy==1.9.1\n          scipy==1.9.2\n          scipy==1.9.3\n          scipy==1.10.0\n          scipy==1.10.1\n          scipy==1.11.0\n          scipy==1.11.1\n          scipy==1.11.2\n          scipy==1.11.3\n          scipy==1.11.4\n          scipy==1.12.0\n          scipy==1.13.0\n          scipy==1.13.1\n          scipy==1.14.0\n          scipy==1.14.1\n          scipy==1.15.0\n      we can conclude that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used. (1)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.9.2,<=1.9.3 depends on Python>=3.8,\n      we can conclude that scipy>=1.9.2,<=1.9.3 cannot be used.\n      And because we know from (1) that all of:\n          scipy>=1.9.0,<1.9.2\n          scipy>1.9.3,<1.11.0\n       cannot be used, we can conclude that scipy>=1.9.0,<1.11.0 cannot be used.\n      And because scipy==1.11.0 was yanked (reason: License Violation), we can conclude that scipy>=1.9.0,<1.11.1 cannot be\n      used. (2)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.1,<=1.11.3 depends on\n      Python>=3.9,<3.13, we can conclude that scipy>=1.11.1,<=1.11.3 cannot be used.\n      And because we know from (2) that scipy>=1.9.0,<1.11.1 cannot be used, we can conclude that scipy>=1.9.0,<1.11.4 cannot\n      be used. (3)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.11.4,<=1.13.1 depends on\n      Python>=3.9, we can conclude that scipy>=1.11.4,<=1.13.1 cannot be used.\n      And because we know from (3) that scipy>=1.9.0,<1.11.4 cannot be used, we can conclude that scipy>=1.9.0,<1.14.0 cannot\n      be used. (4)\n\n      Because the current Python version (3.7.9) does not satisfy Python>=3.8 and scipy>=1.14.0 depends on Python>=3.10, we can\n      conclude that scipy>=1.14.0 cannot be used.\n      And because we know from (4) that scipy>=1.9.0,<1.14.0 cannot be used, we can conclude that scipy>=1.9.0 cannot be used.\n      And because you require scipy>=1.9, we can conclude that your requirements are unsatisfiable.\n\n      hint: Pre-releases are available for `scipy` in the requested range (e.g., 1.15.0rc2), but pre-releases weren't enabled\n      (try: `--prerelease=allow`)\nResolved 59 packages in 735ms\nInstalled 52 packages in 741ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + backcall==0.2.0\n + cattrs==23.1.2\n + certifi==2024.12.14\n + charset-normalizer==3.4.1\n + commonmark==0.9.1\n + cycler==0.11.0\n + debugpy==1.7.0\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + entrypoints==0.4\n + fonttools==4.38.0\n + idna==3.10\n + ipykernel==6.16.2\n + ipython==7.34.0\n + ipython-genutils==0.2.0\n + jedi==0.19.2\n + jupyter-client==7.4.9\n + jupyter-core==4.12.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + matplotlib-inline==0.1.6\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + pillow==9.5.0\n + pip==24.0\n + platformdirs==4.0.0\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pygments==2.17.2\n + pyparsing==3.1.4\n + pyqtgraph==0.12.3\n + python-dateutil==2.9.0.post0\n + pyzmq==26.2.0\n + qasync==0.25.0\n + qtconsole==5.4.4\n + qtpy==2.4.2\n + requests==2.31.0\n + requests-cache==1.1.1\n + six==1.17.0\n + tornado==6.2\n + traitlets==5.9.0\n + url-normalize==1.4.3\n + urllib3==2.0.7\n + wcwidth==0.2.13\nResolved 2 packages in 9ms\nInstalled 2 packages in 70ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nResolved 19 packages in 172ms\nInstalled 8 packages in 1.64s\n + catboost==1.2.7\n + graphviz==0.20.1\n + pandas==1.3.5\n + plotly==5.18.0\n + pytz==2024.2\n + scipy==1.7.3\n + tenacity==8.2.3\n + xgboost==1.6.2\nResolved 5 packages in 55ms\nInstalled 3 packages in 37ms\n + joblib==1.3.2\n + scikit-learn==1.0.2\n + threadpoolctl==3.1.0\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3427:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3427 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4883:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4883 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5629:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5629 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5750:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5750 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5756:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5756 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7217:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7217 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7874:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7874 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7934:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7934 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.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_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n  setuptools.SetuptoolsDeprecationWarning,\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.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_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.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_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.7/site-packages/pkg_resources/__init__.py:119: PkgResourcesDeprecationWarning:  is an invalid version and will not be supported in a future release\n  PkgResourcesDeprecationWarning,\nerror: Couldn't find a setup script in /tmp/easy_install-sigtn5ze/scipy-1.15.0.tar.gz\nUsing CPython 3.8.20\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 15 packages in 59ms\nPrepared 15 packages in 1ms\nInstalled 15 packages in 37ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + iniconfig==2.0.0\n + numpy==1.24.4\n + packaging==24.2\n + pluggy==1.5.0\n + pyqt5==5.15.11\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.15.0\n + pyqtwebengine==5.15.7\n + pyqtwebengine-qt5==5.15.16\n + pytest==8.3.4\n + setuptools==75.3.0\n + tomli==2.2.1\n + wheel==0.45.1\nResolved 59 packages in 86ms\nInstalled 55 packages in 116ms\n + anyio==4.5.2\n + backports-tarfile==1.2.0\n + baycomp==1.0.3\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.17.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + contourpy==1.1.1\n + cryptography==44.0.0\n + cycler==0.12.1\n + et-xmlfile==2.0.0\n + fonttools==4.55.3\n + h11==0.14.0\n + httpcore==1.0.7\n + httpx==0.28.1\n + idna==3.10\n + importlib-metadata==8.5.0\n + importlib-resources==6.4.5\n + jaraco-classes==3.4.0\n + jaraco-context==6.0.1\n + jaraco-functools==4.1.0\n + jeepney==0.8.0\n + joblib==1.4.2\n + keyring==25.5.0\n + keyrings-alt==5.0.2\n + kiwisolver==1.4.7\n + matplotlib==3.7.5\n + more-itertools==10.5.0\n + networkx==3.1\n + openpyxl==3.1.5\n + opentsne==1.0.1\n + pandas==2.0.3\n + pillow==10.4.0\n + pip==24.3.1\n + pycparser==2.22\n + pyparsing==3.1.4\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.2\n + requests==2.32.3\n + scikit-learn==1.3.2\n + scipy==1.10.1\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + sniffio==1.3.1\n + threadpoolctl==3.5.0\n + typing-extensions==4.12.2\n + tzdata==2024.2\n + urllib3==2.2.3\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\n + zipp==3.20.2\nResolved 64 packages in 93ms\nInstalled 41 packages in 98ms\n + anyqt==0.2.0\n + asttokens==3.0.0\n + attrs==24.3.0\n + backcall==0.2.0\n + cattrs==24.1.2\n + comm==0.2.2\n + commonmark==0.9.1\n + debugpy==1.8.11\n + decorator==5.1.1\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + executing==2.1.0\n + ipykernel==6.29.5\n + ipython==8.12.3\n + jedi==0.19.2\n + jupyter-client==8.6.3\n + jupyter-core==5.7.2\n + matplotlib-inline==0.1.7\n + nest-asyncio==1.6.0\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + parso==0.8.4\n + pexpect==4.9.0\n + pickleshare==0.7.5\n + platformdirs==4.3.6\n + prompt-toolkit==3.0.48\n + psutil==6.1.1\n + ptyprocess==0.7.0\n + pure-eval==0.2.3\n + pygments==2.19.1\n + pyqtgraph==0.13.3\n + pyzmq==26.2.0\n + qasync==0.27.1\n + qtconsole==5.6.1\n + qtpy==2.4.2\n + requests-cache==1.2.1\n + stack-data==0.6.3\n + tornado==6.4.2\n + traitlets==5.14.3\n + url-normalize==1.4.3\n + wcwidth==0.2.13\nResolved 2 packages in 2ms\nInstalled 2 packages in 5ms\n + psycopg2==2.9.10\n + pymssql==2.3.2\nResolved 23 packages in 14ms\nInstalled 6 packages in 468ms\n + catboost==1.2.7\n + graphviz==0.20.3\n + nvidia-nccl-cu12==2.24.3\n + plotly==5.24.1\n + tenacity==9.0.0\n + xgboost==2.1.3\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/classification/_tree_scorers.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3427:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3427 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4883:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4883 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5629:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5629 |           for (__pyx_t_19 = 0; __pyx_t_19 < __pyx_t_18; __pyx_t_19+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5750:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5750 |           __pyx_t_20 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5756:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5756 |           __pyx_t_20 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7217:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7217 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7874:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7874 |         for (__pyx_t_12 = 0; __pyx_t_12 < __pyx_t_11; __pyx_t_12+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7934:150: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7934 |               __pyx_t_16 = (((*((__pyx_t_5numpy_int32_t const  *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_15 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                                      ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_contingency.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:4120:43: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4120 |           __pyx_v_value = ((__pyx_v_value * __pyx_v_decs) + (__pyx_v_c & 0xf));\n      |                            ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/data/_io.c:4318:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4318 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 223, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:683:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  683 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1590:7: note: ‘__pyx_v_attr_index’ was declared here\n 1590 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:3183:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3183 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/data/_valuecount.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/distance/_distance.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_discretize.c:774:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/preprocess/_relieff.cpp:790:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1940,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:5,\n                 from Orange/projection/_som.c:775:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_5update_hex’:\nOrange/projection/_som.c:3863:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3863 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3862:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3862 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_8update_sparse.constprop’:\nOrange/projection/_som.c:4934:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4934 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4933:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4933 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pf_6Orange_10projection_4_som_10update_sparse_hex.constprop’:\nOrange/projection/_som.c:5682:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5682 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5681:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5681 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:3184:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3184 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2887:7: note: ‘__pyx_v_win_y’ was declared here\n 2887 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3173:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3173 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2886:7: note: ‘__pyx_v_win_x’ was declared here\n 2886 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3357:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3357 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_7get_winners_sparse’:\nOrange/projection/_som.c:4732:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4732 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4307:7: note: ‘__pyx_v_win_y’ was declared here\n 4307 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4721:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4721 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4306:7: note: ‘__pyx_v_win_x’ was declared here\n 4306 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'test_suite'\n  warnings.warn(msg)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/setuptools/command/develop.py:41: EasyInstallDeprecationWarning: easy_install command is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` and ``easy_install``.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://github.com/pypa/setuptools/issues/917 for details.\n        ********************************************************************************\n\n!!\n  easy_install.initialize_options(self)\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5/.venv/lib/python3.8/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.\n!!\n\n        ********************************************************************************\n        Please avoid running ``setup.py`` directly.\n        Instead, use pypa/build, pypa/installer or other\n        standards-based tools.\n\n        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.\n        ********************************************************************************\n\n!!\n  self.initialize_options()\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.svg' under directory 'Orange/canvas'\nwarning: no files found matching '*.js' under directory 'Orange/widgets'\nwarning: no files found matching '*.css' under directory 'Orange/widgets'\nwarning: no files found matching '*.html' under directory 'Orange/widgets'\nwarning: no files found matching '*.svg' under directory 'distribute'\nwarning: no files found matching 'COPYING'\n",
    "new_commit_res_code": 0,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 6 items\n\nr2e_tests/test_1.py ......                                               [100%]\n\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestReplaceUnknowns::test_equality\nPASSED r2e_tests/test_1.py::TestReplaceUnknownsRandom::test_equality\nPASSED r2e_tests/test_1.py::TestFixedValuesByType::test_all_defined\nPASSED r2e_tests/test_1.py::TestFixedValuesByType::test_none_defined\nPASSED r2e_tests/test_1.py::TestFixedValuesByType::test_with_default\nPASSED r2e_tests/test_1.py::TestReplaceUnknownsModel::test_eq\n============================== 6 passed in 2.97s ===============================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.8.20, pytest-8.3.4, pluggy-1.5.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_a146cf9b2093f2ec2fd9eae122f0b67779dbcff5\nconfigfile: pyproject.toml\nplugins: anyio-4.5.2, typeguard-4.3.0\ncollected 6 items\n\nr2e_tests/test_1.py .....F                                               [100%]\n\n=================================== FAILURES ===================================\n_______________________ TestReplaceUnknownsModel.test_eq _______________________\n\nself = <r2e_tests.test_1.TestReplaceUnknownsModel testMethod=test_eq>\n\n    def test_eq(self):\n        iris = Table(\"iris\")\n    \n        v1 = iris.domain[0]\n        v2 = iris.domain[0]\n        v3 = iris.domain[1]\n    \n        l = LinearRegressionLearner()\n        def new_target(t):\n            dom = Domain(iris.domain[2:], class_vars=[t])\n            return iris.transform(dom)\n    \n        mod1 = l(new_target(v1))\n        t1 = ReplaceUnknownsModel(v1, mod1)\n        t1a = ReplaceUnknownsModel(v2, mod1)\n        t2 = ReplaceUnknownsModel(v3, l(new_target(v3)))\n    \n        self.assertEqual(t1, t1)\n        self.assertEqual(t1, t1a)\n        self.assertNotEqual(t1, t2)\n    \n        # the following should be equal, but will not be unless __eq__ for that\n        # particular model is defined\n        t1b = ReplaceUnknownsModel(v1, l(new_target(v1)))\n        self.assertNotEqual(t1, t1b)  # this is WRONG\n    \n>       self.assertEqual(hash(t1), hash(t1a))\nE       AssertionError: 8777740589145223895 != 256119653977424407\n\nr2e_tests/test_1.py:142: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestReplaceUnknowns::test_equality\nPASSED r2e_tests/test_1.py::TestReplaceUnknownsRandom::test_equality\nPASSED r2e_tests/test_1.py::TestFixedValuesByType::test_all_defined\nPASSED r2e_tests/test_1.py::TestFixedValuesByType::test_none_defined\nPASSED r2e_tests/test_1.py::TestFixedValuesByType::test_with_default\nFAILED r2e_tests/test_1.py::TestReplaceUnknownsModel::test_eq - AssertionErro...\n========================= 1 failed, 5 passed in 2.00s ==========================\n",
    "old_commit_res_stderr": ""
}
Orange/preprocess/impute.py Orange/preprocess/tests/test_impute.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]
**Title:** ReplaceUnknownsModel Instances with Identical Attributes Have Different Hash Values

**Description:**
When creating multiple `ReplaceUnknownsModel` instances with the same variable and model, their hash values differ even though they should be considered equal. This discrepancy causes equality tests that rely on hash comparisons to fail unexpectedly.

**Example Code:**
```python
from Orange.data import Table, Domain
from Orange.preprocess.impute import ReplaceUnknownsModel
from Orange.regression import LinearRegressionLearner

iris = Table("iris")
v1 = iris.domain[0]
v2 = iris.domain[0]

learner = LinearRegressionLearner()

def create_model(variable):
    domain = Domain(iris.domain[2:], class_vars=[variable])
    transformed = iris.transform(domain)
    return learner(transformed)

model1 = create_model(v1)
model2 = create_model(v2)

t1 = ReplaceUnknownsModel(v1, model1)
t1a = ReplaceUnknownsModel(v2, model1)

print(hash(t1))   # Outputs: 8777740589145223895
print(hash(t1a))  # Outputs: 256119653977424407
```

**Expected Behavior:**
Instances of `ReplaceUnknownsModel` with identical variables and models should produce the same hash value, ensuring they are treated as equal in hash-based collections and during equality checks.

**Actual Behavior:**
Hash values of `ReplaceUnknownsModel` instances with identical attributes differ, causing equality checks based on hashes to fail. For example, `hash(t1)` does not equal `hash(t1a)` despite `t1` and `t1a` having the same attributes.

[/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/preprocess/impute.py
import numpy as np
import scipy.sparse as sp

import Orange.data
from Orange.statistics import distribution, basic_stats
from Orange.util import Reprable
from .transformation import Transformation, Lookup

__all__ = ["ReplaceUnknowns", "Average", "DoNotImpute", "DropInstances",
           "Model", "AsValue", "Random", "Default", "FixedValueByType"]


class ReplaceUnknowns(Transformation):
    """
    A column transformation which replaces unknown values with a fixed `value`.

    Parameters
    ----------
    variable : Orange.data.Variable
        The target variable for imputation.
    value : int or float
        The value with which to replace the unknown values
    """
    def __init__(self, variable, value=0):
        super().__init__(variable)
        self.value = value

    def transform(self, c):
        if sp.issparse(c):
            c.data = np.where(np.isnan(c.data), self.value, c.data)
            return c
        else:
            return np.where(np.isnan(c), self.value, c)

    def __eq__(self, other):
        return super().__eq__(other) and self.value == other.value

    def __hash__(self):
        return hash((type(self), self.variable, float(self.value)))


class BaseImputeMethod(Reprable):
    name = ""
    short_name = ""
    description = ""
    format = "{var.name} -> {self.short_name}"
    columns_only = False

    def __call__(self, data, variable):
        """ Imputes table along variable column.

        Args:
            data (Table): A table to impute.
            variable (Variable): Variable for completing missing values.

        Returns:
            A new Variable instance with completed missing values or
            a array mask of rows to drop out.
        """
        raise NotImplementedError

    def format_variable(self, var):
        return self.format.format(var=var, self=self)

    def __str__(self):
        return self.name

    def copy(self):
        return self

    @classmethod
    def supports_variable(cls, variable):
        return True


class DoNotImpute(BaseImputeMethod):
    name = "Don't impute"
    short_name = "leave"
    description = ""

    def __call__(self, data, variable):
        return variable


class DropInstances(BaseImputeMethod):
    name = "Remove instances with unknown values"
    short_name = "drop"
    description = ""

    def __call__(self, data, variable):
        col = data.get_column(variable)
        return np.isnan(col)


class Average(BaseImputeMethod):
    name = "Average/Most frequent"
    short_name = "average"
    description = "Replace with average/mode of the column"

    def __call__(self, data, variable, value=None):
        variable = data.domain[variable]
        if value is None:
            if variable.is_continuous:
                stats = basic_stats.BasicStats(data, variable)
                value = stats.mean
            elif variable.is_discrete:
                dist = distribution.get_distribution(data, variable)
                value = dist.modus()
            else:
                raise TypeError("Variable must be numeric or categorical.")

        a = variable.copy(compute_value=ReplaceUnknowns(variable, value))
        a.to_sql = ImputeSql(variable, value)
        return a

    @staticmethod
    def supports_variable(variable):
        return variable.is_primitive()


class ImputeSql(Reprable):
    def __init__(self, var, default):
        self.var = var
        self.default = default

    def __call__(self):
        return 'coalesce(%s, %s)' % (self.var.to_sql(), str(self.default))


class Default(BaseImputeMethod):
    name = "Fixed value"
    short_name = "value"
    description = ""
    columns_only = True
    format = '{var} -> {self.default}'

    def __init__(self, default=0):
        self.default = default

    def __call__(self, data, variable, *, default=None):
        variable = data.domain[variable]
        default = default if default is not None else self.default
        return variable.copy(compute_value=ReplaceUnknowns(variable, default))

    def copy(self):
        return Default(self.default)


class FixedValueByType(BaseImputeMethod):
    name = "Fixed value"
    short_name = "Fixed Value"
    format = "{var.name}"

    def __init__(self,
                 default_discrete=np.nan, default_continuous=np.nan,
                 default_string=None, default_time=np.nan):
        # If you change the order of args or in dict, also fix method copy
        self.defaults = {
            Orange.data.DiscreteVariable: default_discrete,
            Orange.data.ContinuousVariable: default_continuous,
            Orange.data.StringVariable: default_string,
            Orange.data.TimeVariable: default_time
        }

    def __call__(self, data, variable, *, default=None):
        variable = data.domain[variable]
        if default is None:
            default = self.defaults[type(variable)]
        return variable.copy(compute_value=ReplaceUnknowns(variable, default))

    def copy(self):
        return FixedValueByType(*self.defaults.values())


class ReplaceUnknownsModel(Reprable):
    """
    Replace unknown values with predicted values using a `Orange.base.Model`

    Parameters
    ----------
    variable : Orange.data.Variable
        The target variable for the imputation.
    model : Orange.base.Model
        A fitted model predicting `variable`.
    """
    def __init__(self, variable, model):
        assert model.domain.class_var == variable
        self.variable = variable
        self.model = model

    def __call__(self, data):
        if isinstance(data, Orange.data.Instance):
            data = Orange.data.Table.from_list(data.domain, [data])
        domain = data.domain
        column = data.get_column(self.variable, copy=True)

        mask = np.isnan(column)
        if not np.any(mask):
            return column

        if domain.class_vars:
            # cannot have class var in domain (due to backmappers in model)
            data = data.transform(
                Orange.data.Domain(domain.attributes, None, domain.metas)
            )
        predicted = self.model(data[mask])
        column[mask] = predicted
        return column

    def __eq__(self, other):
        return type(self) is type(other) \
               and self.variable == other.variable \
               and self.model == other.model

    def __hash__(self):
        return hash((super().__hash__(), hash(self.variable), hash(self.model)))


class Model(BaseImputeMethod):
    _name = "Model-based imputer"
    short_name = "model"
    description = ""
    format = BaseImputeMethod.format + " ({self.learner.name})"
    @property
    def name(self):
        return "{} ({})".format(self._name, getattr(self.learner, 'name', ''))

    def __init__(self, learner):
        self.learner = learner

    def __call__(self, data, variable):
        variable = data.domain[variable]
        domain = domain_with_class_var(data.domain, variable)

        incompatibility_reason = self.learner.incompatibility_reason(domain)
        if incompatibility_reason is None:
            data = data.transform(domain)
            model = self.learner(data)
            assert model.domain.class_var == variable
            return variable.copy(
                compute_value=ReplaceUnknownsModel(variable, model))
        else:
            raise ValueError("`{}` doesn't support domain type"
                             .format(self.learner.name))

    def copy(self):
        return Model(self.learner)

    def supports_variable(self, variable):
        domain = Orange.data.Domain([], class_vars=variable)
        return self.learner.incompatibility_reason(domain) is None


def domain_with_class_var(domain, class_var):
    """
    Return a domain with class_var as output domain.class_var.

    If class_var is in the input domain's attributes it is removed from the
    output's domain.attributes.
    """
    if domain.class_var is class_var:
        return domain
    elif class_var in domain.attributes:
        attrs = [var for var in domain.attributes
                 if var is not class_var]
    else:
        attrs = domain.attributes
    return Orange.data.Domain(attrs, class_var)


class IsDefined(Transformation):
    def transform(self, c):
        if sp.issparse(c):
            c = c.toarray()
        return ~np.isnan(c)


class AsValue(BaseImputeMethod):
    name = "As a distinct value"
    short_name = "new value"
    description = ""

    def __call__(self, data, variable):
        variable = data.domain[variable]
        if variable.is_discrete:
            fmt = "{var.name}"
            value = "N/A"
            var = Orange.data.DiscreteVariable(
                fmt.format(var=variable),
                values=variable.values + (value, ),
                compute_value=Lookup(
                    variable,
                    np.arange(len(variable.values), dtype=int),
                    unknown=len(variable.values)),
                sparse=variable.sparse,
                )
            return var

        elif variable.is_continuous:
            fmt = "{var.name}_def"
            indicator_var = Orange.data.DiscreteVariable(
                fmt.format(var=variable),
                values=("undef", "def"),
                compute_value=IsDefined(variable),
                sparse=variable.sparse,
            )
            stats = basic_stats.BasicStats(data, variable)
            return (variable.copy(compute_value=ReplaceUnknowns(variable,
                                                                stats.mean)),
                    indicator_var)
        else:
            raise TypeError(type(variable))

    @staticmethod
    def supports_variable(variable):
        return variable.is_primitive()

class ReplaceUnknownsRandom(Transformation):
    """
    A column transformation replacing unknowns with values drawn randomly from
    an empirical distribution.

    Parameters
    ----------
    variable : Orange.data.Variable
        The target variable for imputation.
    distribution : Orange.statistics.distribution.Distribution
        The corresponding sampling distribution
    """
    def __init__(self, variable, distribution):
        assert distribution.size > 0
        assert distribution.variable == variable
        super().__init__(variable)
        self.distribution = distribution

        if variable.is_discrete:
            counts = np.array(distribution)
        elif variable.is_continuous:
            counts = np.array(distribution)[1, :]
        else:
            raise TypeError("Only categorical and numeric "
                            "variables are supported.")
        csum = np.sum(counts)
        if csum > 0:
            self.sample_prob = counts / csum
        else:
            self.sample_prob = np.ones_like(counts) / len(counts)

    def transform(self, c):
        if not sp.issparse(c):
            c = np.array(c, copy=True)
        else:
            c = c.toarray().ravel()
        nanindices = np.flatnonzero(np.isnan(c))

        if self.variable.is_discrete:
            sample = np.random.choice(
                len(self.variable.values), size=len(nanindices),
                replace=True, p=self.sample_prob)
        else:
            sample = np.random.choice(
                np.asarray(self.distribution)[0, :], size=len(nanindices),
                replace=True, p=self.sample_prob)

        c[nanindices] = sample
        return c

    def __eq__(self, other):
        return super().__eq__(other) and self.distribution == other.distribution

    def __hash__(self):
        return hash((type(self), self.variable, self.distribution))


class Random(BaseImputeMethod):
    name = "Random values"
    short_name = "random"
    description = "Replace with a random value"

    def __call__(self, data, variable):
        variable = data.domain[variable]
        dist = distribution.get_distribution(data, variable)
        # A distribution is invalid if a continuous variable's column does not
        # contain any known values or if a discrete variable's .values == []
        isinvalid = dist.size == 0
        if isinvalid and variable.is_discrete:
            assert len(variable.values) == 0
            raise ValueError("'{}' has no values".format(variable))
        elif isinvalid and variable.is_continuous:
            raise ValueError("'{}' has an unknown distribution"
                             .format(variable))

        if variable.is_discrete and np.sum(dist) == 0:
            dist += 1 / len(dist)
        elif variable.is_continuous and np.sum(dist[1, :]) == 0:
            dist[1, :] += 1 / dist.shape[1]
        return variable.copy(
            compute_value=ReplaceUnknownsRandom(variable, dist))

    @staticmethod
    def supports_variable(variable):
        return variable.is_primitive()

```
--- 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:** ReplaceUnknownsModel Instances with Identical Attributes Have Different Hash Values

**Description:**
When creating multiple `ReplaceUnknownsModel` instances with the same variable and model, their hash values differ even though they should be considered equal. This discrepancy causes equality tests that rely on hash comparisons to fail unexpectedly.

**Example Code:**
```python
from Orange.data import Table, Domain
from Orange.preprocess.impute import ReplaceUnknownsModel
from Orange.regression import LinearRegressionLearner

iris = Table("iris")
v1 = iris.domain[0]
v2 = iris.domain[0]

learner = LinearRegressionLearner()

def create_model(variable):
    domain = Domain(iris.domain[2:], class_vars=[variable])
    transformed = iris.transform(domain)
    return learner(transformed)

model1 = create_model(v1)
model2 = create_model(v2)

t1 = ReplaceUnknownsModel(v1, model1)
t1a = ReplaceUnknownsModel(v2, model1)

print(hash(t1))   # Outputs: 8777740589145223895
print(hash(t1a))  # Outputs: 256119653977424407
```

**Expected Behavior:**
Instances of `ReplaceUnknownsModel` with identical variables and models should produce the same hash value, ensuring they are treated as equal in hash-based collections and during equality checks.

**Actual Behavior:**
Hash values of `ReplaceUnknownsModel` instances with identical attributes differ, causing equality checks based on hashes to fail. For example, `hash(t1)` does not equal `hash(t1a)` despite `t1` and `t1a` having the same attributes.

[/ISSUE]
{
    "TestReplaceUnknowns.test_equality": "PASSED",
    "TestReplaceUnknownsRandom.test_equality": "PASSED",
    "TestFixedValuesByType.test_all_defined": "PASSED",
    "TestFixedValuesByType.test_none_defined": "PASSED",
    "TestFixedValuesByType.test_with_default": "PASSED",
    "TestReplaceUnknownsModel.test_eq": "PASSED"
}
diff --git a/Orange/preprocess/impute.py b/Orange/preprocess/impute.py
index 0affc62d4..c67c4a974 100644
--- a/Orange/preprocess/impute.py
+++ b/Orange/preprocess/impute.py
@@ -213,7 +213,7 @@ class ReplaceUnknownsModel(Reprable):
                and self.model == other.model
 
     def __hash__(self):
-        return hash((super().__hash__(), hash(self.variable), hash(self.model)))
+        return hash((type(self), hash(self.variable), hash(self.model)))
 
 
 class Model(BaseImputeMethod):

{"Orange/preprocess/impute.py": "import numpy as np\nimport scipy.sparse as sp\n\nimport Orange.data\nfrom Orange.statistics import distribution, basic_stats\nfrom Orange.util import Reprable\nfrom .transformation import Transformation, Lookup\n\n__all__ = [\"ReplaceUnknowns\", \"Average\", \"DoNotImpute\", \"DropInstances\",\n           \"Model\", \"AsValue\", \"Random\", \"Default\", \"FixedValueByType\"]\n\n\nclass ReplaceUnknowns(Transformation):\n    \"\"\"\n    A column transformation which replaces unknown values with a fixed `value`.\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for imputation.\n    value : int or float\n        The value with which to replace the unknown values\n    \"\"\"\n    def __init__(self, variable, value=0):\n        super().__init__(variable)\n        self.value = value\n\n    def transform(self, c):\n        if sp.issparse(c):\n            c.data = np.where(np.isnan(c.data), self.value, c.data)\n            return c\n        else:\n            return np.where(np.isnan(c), self.value, c)\n\n    def __eq__(self, other):\n        return super().__eq__(other) and self.value == other.value\n\n    def __hash__(self):\n        return hash((type(self), self.variable, float(self.value)))\n\n\nclass BaseImputeMethod(Reprable):\n    name = \"\"\n    short_name = \"\"\n    description = \"\"\n    format = \"{var.name} -> {self.short_name}\"\n    columns_only = False\n\n    def __call__(self, data, variable):\n        \"\"\" Imputes table along variable column.\n\n        Args:\n            data (Table): A table to impute.\n            variable (Variable): Variable for completing missing values.\n\n        Returns:\n            A new Variable instance with completed missing values or\n            a array mask of rows to drop out.\n        \"\"\"\n        raise NotImplementedError\n\n    def format_variable(self, var):\n        return self.format.format(var=var, self=self)\n\n    def __str__(self):\n        return self.name\n\n    def copy(self):\n        return self\n\n    @classmethod\n    def supports_variable(cls, variable):\n        return True\n\n\nclass DoNotImpute(BaseImputeMethod):\n    name = \"Don't impute\"\n    short_name = \"leave\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        return variable\n\n\nclass DropInstances(BaseImputeMethod):\n    name = \"Remove instances with unknown values\"\n    short_name = \"drop\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        col = data.get_column(variable)\n        return np.isnan(col)\n\n\nclass Average(BaseImputeMethod):\n    name = \"Average/Most frequent\"\n    short_name = \"average\"\n    description = \"Replace with average/mode of the column\"\n\n    def __call__(self, data, variable, value=None):\n        variable = data.domain[variable]\n        if value is None:\n            if variable.is_continuous:\n                stats = basic_stats.BasicStats(data, variable)\n                value = stats.mean\n            elif variable.is_discrete:\n                dist = distribution.get_distribution(data, variable)\n                value = dist.modus()\n            else:\n                raise TypeError(\"Variable must be numeric or categorical.\")\n\n        a = variable.copy(compute_value=ReplaceUnknowns(variable, value))\n        a.to_sql = ImputeSql(variable, value)\n        return a\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n\n\nclass ImputeSql(Reprable):\n    def __init__(self, var, default):\n        self.var = var\n        self.default = default\n\n    def __call__(self):\n        return 'coalesce(%s, %s)' % (self.var.to_sql(), str(self.default))\n\n\nclass Default(BaseImputeMethod):\n    name = \"Fixed value\"\n    short_name = \"value\"\n    description = \"\"\n    columns_only = True\n    format = '{var} -> {self.default}'\n\n    def __init__(self, default=0):\n        self.default = default\n\n    def __call__(self, data, variable, *, default=None):\n        variable = data.domain[variable]\n        default = default if default is not None else self.default\n        return variable.copy(compute_value=ReplaceUnknowns(variable, default))\n\n    def copy(self):\n        return Default(self.default)\n\n\nclass FixedValueByType(BaseImputeMethod):\n    name = \"Fixed value\"\n    short_name = \"Fixed Value\"\n    format = \"{var.name}\"\n\n    def __init__(self,\n                 default_discrete=np.nan, default_continuous=np.nan,\n                 default_string=None, default_time=np.nan):\n        # If you change the order of args or in dict, also fix method copy\n        self.defaults = {\n            Orange.data.DiscreteVariable: default_discrete,\n            Orange.data.ContinuousVariable: default_continuous,\n            Orange.data.StringVariable: default_string,\n            Orange.data.TimeVariable: default_time\n        }\n\n    def __call__(self, data, variable, *, default=None):\n        variable = data.domain[variable]\n        if default is None:\n            default = self.defaults[type(variable)]\n        return variable.copy(compute_value=ReplaceUnknowns(variable, default))\n\n    def copy(self):\n        return FixedValueByType(*self.defaults.values())\n\n\nclass ReplaceUnknownsModel(Reprable):\n    \"\"\"\n    Replace unknown values with predicted values using a `Orange.base.Model`\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for the imputation.\n    model : Orange.base.Model\n        A fitted model predicting `variable`.\n    \"\"\"\n    def __init__(self, variable, model):\n        assert model.domain.class_var == variable\n        self.variable = variable\n        self.model = model\n\n    def __call__(self, data):\n        if isinstance(data, Orange.data.Instance):\n            data = Orange.data.Table.from_list(data.domain, [data])\n        domain = data.domain\n        column = data.get_column(self.variable, copy=True)\n\n        mask = np.isnan(column)\n        if not np.any(mask):\n            return column\n\n        if domain.class_vars:\n            # cannot have class var in domain (due to backmappers in model)\n            data = data.transform(\n                Orange.data.Domain(domain.attributes, None, domain.metas)\n            )\n        predicted = self.model(data[mask])\n        column[mask] = predicted\n        return column\n\n    def __eq__(self, other):\n        return type(self) is type(other) \\\n               and self.variable == other.variable \\\n               and self.model == other.model\n\n    def __hash__(self):\n        return hash((super().__hash__(), hash(self.variable), hash(self.model)))\n\n\nclass Model(BaseImputeMethod):\n    _name = \"Model-based imputer\"\n    short_name = \"model\"\n    description = \"\"\n    format = BaseImputeMethod.format + \" ({self.learner.name})\"\n    @property\n    def name(self):\n        return \"{} ({})\".format(self._name, getattr(self.learner, 'name', ''))\n\n    def __init__(self, learner):\n        self.learner = learner\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        domain = domain_with_class_var(data.domain, variable)\n\n        incompatibility_reason = self.learner.incompatibility_reason(domain)\n        if incompatibility_reason is None:\n            data = data.transform(domain)\n            model = self.learner(data)\n            assert model.domain.class_var == variable\n            return variable.copy(\n                compute_value=ReplaceUnknownsModel(variable, model))\n        else:\n            raise ValueError(\"`{}` doesn't support domain type\"\n                             .format(self.learner.name))\n\n    def copy(self):\n        return Model(self.learner)\n\n    def supports_variable(self, variable):\n        domain = Orange.data.Domain([], class_vars=variable)\n        return self.learner.incompatibility_reason(domain) is None\n\n\ndef domain_with_class_var(domain, class_var):\n    \"\"\"\n    Return a domain with class_var as output domain.class_var.\n\n    If class_var is in the input domain's attributes it is removed from the\n    output's domain.attributes.\n    \"\"\"\n    if domain.class_var is class_var:\n        return domain\n    elif class_var in domain.attributes:\n        attrs = [var for var in domain.attributes\n                 if var is not class_var]\n    else:\n        attrs = domain.attributes\n    return Orange.data.Domain(attrs, class_var)\n\n\nclass IsDefined(Transformation):\n    def transform(self, c):\n        if sp.issparse(c):\n            c = c.toarray()\n        return ~np.isnan(c)\n\n\nclass AsValue(BaseImputeMethod):\n    name = \"As a distinct value\"\n    short_name = \"new value\"\n    description = \"\"\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        if variable.is_discrete:\n            fmt = \"{var.name}\"\n            value = \"N/A\"\n            var = Orange.data.DiscreteVariable(\n                fmt.format(var=variable),\n                values=variable.values + (value, ),\n                compute_value=Lookup(\n                    variable,\n                    np.arange(len(variable.values), dtype=int),\n                    unknown=len(variable.values)),\n                sparse=variable.sparse,\n                )\n            return var\n\n        elif variable.is_continuous:\n            fmt = \"{var.name}_def\"\n            indicator_var = Orange.data.DiscreteVariable(\n                fmt.format(var=variable),\n                values=(\"undef\", \"def\"),\n                compute_value=IsDefined(variable),\n                sparse=variable.sparse,\n            )\n            stats = basic_stats.BasicStats(data, variable)\n            return (variable.copy(compute_value=ReplaceUnknowns(variable,\n                                                                stats.mean)),\n                    indicator_var)\n        else:\n            raise TypeError(type(variable))\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\n\nclass ReplaceUnknownsRandom(Transformation):\n    \"\"\"\n    A column transformation replacing unknowns with values drawn randomly from\n    an empirical distribution.\n\n    Parameters\n    ----------\n    variable : Orange.data.Variable\n        The target variable for imputation.\n    distribution : Orange.statistics.distribution.Distribution\n        The corresponding sampling distribution\n    \"\"\"\n    def __init__(self, variable, distribution):\n        assert distribution.size > 0\n        assert distribution.variable == variable\n        super().__init__(variable)\n        self.distribution = distribution\n\n        if variable.is_discrete:\n            counts = np.array(distribution)\n        elif variable.is_continuous:\n            counts = np.array(distribution)[1, :]\n        else:\n            raise TypeError(\"Only categorical and numeric \"\n                            \"variables are supported.\")\n        csum = np.sum(counts)\n        if csum > 0:\n            self.sample_prob = counts / csum\n        else:\n            self.sample_prob = np.ones_like(counts) / len(counts)\n\n    def transform(self, c):\n        if not sp.issparse(c):\n            c = np.array(c, copy=True)\n        else:\n            c = c.toarray().ravel()\n        nanindices = np.flatnonzero(np.isnan(c))\n\n        if self.variable.is_discrete:\n            sample = np.random.choice(\n                len(self.variable.values), size=len(nanindices),\n                replace=True, p=self.sample_prob)\n        else:\n            sample = np.random.choice(\n                np.asarray(self.distribution)[0, :], size=len(nanindices),\n                replace=True, p=self.sample_prob)\n\n        c[nanindices] = sample\n        return c\n\n    def __eq__(self, other):\n        return super().__eq__(other) and self.distribution == other.distribution\n\n    def __hash__(self):\n        return hash((type(self), self.variable, self.distribution))\n\n\nclass Random(BaseImputeMethod):\n    name = \"Random values\"\n    short_name = \"random\"\n    description = \"Replace with a random value\"\n\n    def __call__(self, data, variable):\n        variable = data.domain[variable]\n        dist = distribution.get_distribution(data, variable)\n        # A distribution is invalid if a continuous variable's column does not\n        # contain any known values or if a discrete variable's .values == []\n        isinvalid = dist.size == 0\n        if isinvalid and variable.is_discrete:\n            assert len(variable.values) == 0\n            raise ValueError(\"'{}' has no values\".format(variable))\n        elif isinvalid and variable.is_continuous:\n            raise ValueError(\"'{}' has an unknown distribution\"\n                             .format(variable))\n\n        if variable.is_discrete and np.sum(dist) == 0:\n            dist += 1 / len(dist)\n        elif variable.is_continuous and np.sum(dist[1, :]) == 0:\n            dist[1, :] += 1 / dist.shape[1]\n        return variable.copy(\n            compute_value=ReplaceUnknownsRandom(variable, dist))\n\n    @staticmethod\n    def supports_variable(variable):\n        return variable.is_primitive()\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:** ReplaceUnknownsModel Instances with Identical Attributes Have Different Hash Values

**Description:**
When creating multiple `ReplaceUnknownsModel` instances with the same variable and model, their hash values differ even though they should be considered equal. This discrepancy causes equality tests that rely on hash comparisons to fail unexpectedly.

**Example Code:**
```python
from Orange.data import Table, Domain
from Orange.preprocess.impute import ReplaceUnknownsModel
from Orange.regression import LinearRegressionLearner

iris = Table("iris")
v1 = iris.domain[0]
v2 = iris.domain[0]

learner = LinearRegressionLearner()

def create_model(variable):
    domain = Domain(iris.domain[2:], class_vars=[variable])
    transformed = iris.transform(domain)
    return learner(transformed)

model1 = create_model(v1)
model2 = create_model(v2)

t1 = ReplaceUnknownsModel(v1, model1)
t1a = ReplaceUnknownsModel(v2, model1)

print(hash(t1))   # Outputs: 8777740589145223895
print(hash(t1a))  # Outputs: 256119653977424407
```

**Expected Behavior:**
Instances of `ReplaceUnknownsModel` with identical variables and models should produce the same hash value, ensuring they are treated as equal in hash-based collections and during equality checks.

**Actual Behavior:**
Hash values of `ReplaceUnknownsModel` instances with identical attributes differ, causing equality checks based on hashes to fail. For example, `hash(t1)` does not equal `hash(t1a)` despite `t1` and `t1a` having the same attributes.

[/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/preprocess/impute.py
import numpy as np
import scipy.sparse as sp

import Orange.data
from Orange.statistics import distribution, basic_stats
from Orange.util import Reprable
from .transformation import Transformation, Lookup

__all__ = ["ReplaceUnknowns", "Average", "DoNotImpute", "DropInstances",
           "Model", "AsValue", "Random", "Default", "FixedValueByType"]


class ReplaceUnknowns(Transformation):
    """
    A column transformation which replaces unknown values with a fixed `value`.

    Parameters
    ----------
    variable : Orange.data.Variable
        The target variable for imputation.
    value : int or float
        The value with which to replace the unknown values
    """
    def __init__(self, variable, value=0):
        super().__init__(variable)
        self.value = value

    def transform(self, c):
        if sp.issparse(c):
            c.data = np.where(np.isnan(c.data), self.value, c.data)
            return c
        else:
            return np.where(np.isnan(c), self.value, c)

    def __eq__(self, other):
        return super().__eq__(other) and self.value == other.value

    def __hash__(self):
        return hash((type(self), self.variable, float(self.value)))


class BaseImputeMethod(Reprable):
    name = ""
    short_name = ""
    description = ""
    format = "{var.name} -> {self.short_name}"
    columns_only = False

    def __call__(self, data, variable):
        """ Imputes table along variable column.

        Args:
            data (Table): A table to impute.
            variable (Variable): Variable for completing missing values.

        Returns:
            A new Variable instance with completed missing values or
            a array mask of rows to drop out.
        """
        raise NotImplementedError

    def format_variable(self, var):
        return self.format.format(var=var, self=self)

    def __str__(self):
        return self.name

    def copy(self):
        return self

    @classmethod
    def supports_variable(cls, variable):
        return True


class DoNotImpute(BaseImputeMethod):
    name = "Don't impute"
    short_name = "leave"
    description = ""

    def __call__(self, data, variable):
        return variable


class DropInstances(BaseImputeMethod):
    name = "Remove instances with unknown values"
    short_name = "drop"
    description = ""

    def __call__(self, data, variable):
        col = data.get_column(variable)
        return np.isnan(col)


class Average(BaseImputeMethod):
    name = "Average/Most frequent"
    short_name = "average"
    description = "Replace with average/mode of the column"

    def __call__(self, data, variable, value=None):
        variable = data.domain[variable]
        if value is None:
            if variable.is_continuous:
                stats = basic_stats.BasicStats(data, variable)
                value = stats.mean
            elif variable.is_discrete:
                dist = distribution.get_distribution(data, variable)
                value = dist.modus()
            else:
                raise TypeError("Variable must be numeric or categorical.")

        a = variable.copy(compute_value=ReplaceUnknowns(variable, value))
        a.to_sql = ImputeSql(variable, value)
        return a

    @staticmethod
    def supports_variable(variable):
        return variable.is_primitive()


class ImputeSql(Reprable):
    def __init__(self, var, default):
        self.var = var
        self.default = default

    def __call__(self):
        return 'coalesce(%s, %s)' % (self.var.to_sql(), str(self.default))


class Default(BaseImputeMethod):
    name = "Fixed value"
    short_name = "value"
    description = ""
    columns_only = True
    format = '{var} -> {self.default}'

    def __init__(self, default=0):
        self.default = default

    def __call__(self, data, variable, *, default=None):
        variable = data.domain[variable]
        default = default if default is not None else self.default
        return variable.copy(compute_value=ReplaceUnknowns(variable, default))

    def copy(self):
        return Default(self.default)


class FixedValueByType(BaseImputeMethod):
    name = "Fixed value"
    short_name = "Fixed Value"
    format = "{var.name}"

    def __init__(self,
                 default_discrete=np.nan, default_continuous=np.nan,
                 default_string=None, default_time=np.nan):
        # If you change the order of args or in dict, also fix method copy
        self.defaults = {
            Orange.data.DiscreteVariable: default_discrete,
            Orange.data.ContinuousVariable: default_continuous,
            Orange.data.StringVariable: default_string,
            Orange.data.TimeVariable: default_time
        }

    def __call__(self, data, variable, *, default=None):
        variable = data.domain[variable]
        if default is None:
            default = self.defaults[type(variable)]
        return variable.copy(compute_value=ReplaceUnknowns(variable, default))

    def copy(self):
        return FixedValueByType(*self.defaults.values())


class ReplaceUnknownsModel(Reprable):
    """
    Replace unknown values with predicted values using a `Orange.base.Model`

    Parameters
    ----------
    variable : Orange.data.Variable
        The target variable for the imputation.
    model : Orange.base.Model
        A fitted model predicting `variable`.
    """
    def __init__(self, variable, model):
        assert model.domain.class_var == variable
        self.variable = variable
        self.model = model

    def __call__(self, data):
        if isinstance(data, Orange.data.Instance):
            data = Orange.data.Table.from_list(data.domain, [data])
        domain = data.domain
        column = data.get_column(self.variable, copy=True)

        mask = np.isnan(column)
        if not np.any(mask):
            return column

        if domain.class_vars:
            # cannot have class var in domain (due to backmappers in model)
            data = data.transform(
                Orange.data.Domain(domain.attributes, None, domain.metas)
            )
        predicted = self.model(data[mask])
        column[mask] = predicted
        return column

    def __eq__(self, other):
        return type(self) is type(other) \
               and self.variable == other.variable \
               and self.model == other.model

    def __hash__(self):
        return hash((super().__hash__(), hash(self.variable), hash(self.model)))


class Model(BaseImputeMethod):
    _name = "Model-based imputer"
    short_name = "model"
    description = ""
    format = BaseImputeMethod.format + " ({self.learner.name})"
    @property
    def name(self):
        return "{} ({})".format(self._name, getattr(self.learner, 'name', ''))

    def __init__(self, learner):
        self.learner = learner

    def __call__(self, data, variable):
        variable = data.domain[variable]
        domain = domain_with_class_var(data.domain, variable)

        incompatibility_reason = self.learner.incompatibility_reason(domain)
        if incompatibility_reason is None:
            data = data.transform(domain)
            model = self.learner(data)
            assert model.domain.class_var == variable
            return variable.copy(
                compute_value=ReplaceUnknownsModel(variable, model))
        else:
            raise ValueError("`{}` doesn't support domain type"
                             .format(self.learner.name))

    def copy(self):
        return Model(self.learner)

    def supports_variable(self, variable):
        domain = Orange.data.Domain([], class_vars=variable)
        return self.learner.incompatibility_reason(domain) is None


def domain_with_class_var(domain, class_var):
    """
    Return a domain with class_var as output domain.class_var.

    If class_var is in the input domain's attributes it is removed from the
    output's domain.attributes.
    """
    if domain.class_var is class_var:
        return domain
    elif class_var in domain.attributes:
        attrs = [var for var in domain.attributes
                 if var is not class_var]
    else:
        attrs = domain.attributes
    return Orange.data.Domain(attrs, class_var)


class IsDefined(Transformation):
    def transform(self, c):
        if sp.issparse(c):
            c = c.toarray()
        return ~np.isnan(c)


class AsValue(BaseImputeMethod):
    name = "As a distinct value"
    short_name = "new value"
    description = ""

    def __call__(self, data, variable):
        variable = data.domain[variable]
        if variable.is_discrete:
            fmt = "{var.name}"
            value = "N/A"
            var = Orange.data.DiscreteVariable(
                fmt.format(var=variable),
                values=variable.values + (value, ),
                compute_value=Lookup(
                    variable,
                    np.arange(len(variable.values), dtype=int),
                    unknown=len(variable.values)),
                sparse=variable.sparse,
                )
            return var

        elif variable.is_continuous:
            fmt = "{var.name}_def"
            indicator_var = Orange.data.DiscreteVariable(
                fmt.format(var=variable),
                values=("undef", "def"),
                compute_value=IsDefined(variable),
                sparse=variable.sparse,
            )
            stats = basic_stats.BasicStats(data, variable)
            return (variable.copy(compute_value=ReplaceUnknowns(variable,
                                                                stats.mean)),
                    indicator_var)
        else:
            raise TypeError(type(variable))

    @staticmethod
    def supports_variable(variable):
        return variable.is_primitive()

class ReplaceUnknownsRandom(Transformation):
    """
    A column transformation replacing unknowns with values drawn randomly from
    an empirical distribution.

    Parameters
    ----------
    variable : Orange.data.Variable
        The target variable for imputation.
    distribution : Orange.statistics.distribution.Distribution
        The corresponding sampling distribution
    """
    def __init__(self, variable, distribution):
        assert distribution.size > 0
        assert distribution.variable == variable
        super().__init__(variable)
        self.distribution = distribution

        if variable.is_discrete:
            counts = np.array(distribution)
        elif variable.is_continuous:
            counts = np.array(distribution)[1, :]
        else:
            raise TypeError("Only categorical and numeric "
                            "variables are supported.")
        csum = np.sum(counts)
        if csum > 0:
            self.sample_prob = counts / csum
        else:
            self.sample_prob = np.ones_like(counts) / len(counts)

    def transform(self, c):
        if not sp.issparse(c):
            c = np.array(c, copy=True)
        else:
            c = c.toarray().ravel()
        nanindices = np.flatnonzero(np.isnan(c))

        if self.variable.is_discrete:
            sample = np.random.choice(
                len(self.variable.values), size=len(nanindices),
                replace=True, p=self.sample_prob)
        else:
            sample = np.random.choice(
                np.asarray(self.distribution)[0, :], size=len(nanindices),
                replace=True, p=self.sample_prob)

        c[nanindices] = sample
        return c

    def __eq__(self, other):
        return super().__eq__(other) and self.distribution == other.distribution

    def __hash__(self):
        return hash((type(self), self.variable, self.distribution))


class Random(BaseImputeMethod):
    name = "Random values"
    short_name = "random"
    description = "Replace with a random value"

    def __call__(self, data, variable):
        variable = data.domain[variable]
        dist = distribution.get_distribution(data, variable)
        # A distribution is invalid if a continuous variable's column does not
        # contain any known values or if a discrete variable's .values == []
        isinvalid = dist.size == 0
        if isinvalid and variable.is_discrete:
            assert len(variable.values) == 0
            raise ValueError("'{}' has no values".format(variable))
        elif isinvalid and variable.is_continuous:
            raise ValueError("'{}' has an unknown distribution"
                             .format(variable))

        if variable.is_discrete and np.sum(dist) == 0:
            dist += 1 / len(dist)
        elif variable.is_continuous and np.sum(dist[1, :]) == 0:
            dist[1, :] += 1 / dist.shape[1]
        return variable.copy(
            compute_value=ReplaceUnknownsRandom(variable, dist))

    @staticmethod
    def supports_variable(variable):
        return variable.is_primitive()

```
--- 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