{
    "file_diffs": [
        {
            "old_file_content": "import numpy as np\n\nfrom Orange.data import ContinuousVariable, Domain\nfrom Orange.statistics import distribution\nfrom Orange.util import Reprable\nfrom .preprocess import Normalize\nfrom .transformation import Normalizer as Norm\n__all__ = [\"Normalizer\"]\n\n\nclass Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)\n",
            "new_file_content": "import numpy as np\n\nfrom Orange.data import Domain\nfrom Orange.statistics import distribution\nfrom Orange.util import Reprable\nfrom .preprocess import Normalize\nfrom .transformation import Normalizer as Norm\n__all__ = [\"Normalizer\"]\n\n\nclass Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n        return var.copy(compute_value=compute_val)\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            compute_val = Norm(var, dmi, 1 / diff)\n        else:\n            compute_val = Norm(var, (dma + dmi) / 2, 2 / diff)\n        return var.copy(compute_value=compute_val)\n",
            "header": {
                "file": {
                    "path": "Orange/preprocess/normalize.py"
                },
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                "mode": "100644"
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            "binary_line": null,
            "minus_file": {
                "path": "a/Orange/preprocess/normalize.py"
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                    "line_group": {
                        "all_lines": [
                            {
                                "content": "import numpy as np",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.data import ContinuousVariable, Domain",
                                "type": "deleted"
                            },
                            {
                                "content": "from Orange.data import Domain",
                                "type": "added"
                            },
                            {
                                "content": "from Orange.statistics import distribution",
                                "type": "context"
                            },
                            {
                                "content": "from Orange.util import Reprable",
                                "type": "context"
                            },
                            {
                                "content": "from .preprocess import Normalize",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [],
                    "added_entities": [
                        {
                            "file_name": "Orange/preprocess/normalize.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.data import Domain",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 3,
                            "end_lineno": 3,
                            "parent": null
                        }
                    ],
                    "deleted_entities": [
                        {
                            "file_name": "Orange/preprocess/normalize.py",
                            "type": "statement",
                            "name": "",
                            "content": "from Orange.data import ContinuousVariable, Domain",
                            "ast_type_str": "ImportFrom",
                            "start_lineno": 3,
                            "end_lineno": 3,
                            "parent": null
                        }
                    ]
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                        "section": "class Normalizer(Reprable):"
                    },
                    "line_group": {
                        "all_lines": [
                            {
                                "content": "            compute_val = Norm(var, avg, 1 / sd)",
                                "type": "context"
                            },
                            {
                                "content": "        else:",
                                "type": "context"
                            },
                            {
                                "content": "            compute_val = Norm(var, 0, 1 / sd)",
                                "type": "context"
                            },
                            {
                                "content": "",
                                "type": "deleted"
                            },
                            {
                                "content": "        return ContinuousVariable(",
                                "type": "deleted"
                            },
                            {
                                "content": "            var.name,",
                                "type": "deleted"
                            },
                            {
                                "content": "            compute_value=compute_val,",
                                "type": "deleted"
                            },
                            {
                                "content": "            sparse=var.sparse,",
                                "type": "deleted"
                            },
                            {
                                "content": "        )",
                                "type": "deleted"
                            },
                            {
                                "content": "        return var.copy(compute_value=compute_val)",
                                "type": "added"
                            },
                            {
                                "content": "",
                                "type": "context"
                            },
                            {
                                "content": "    def normalize_by_span(self, dist, var):",
                                "type": "context"
                            },
                            {
                                "content": "        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)",
                                "type": "context"
                            }
                        ]
                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/preprocess/normalize.py",
                            "type": "class",
                            "name": "Normalizer",
                            "content": "class Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 11,
                            "end_lineno": 75,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/normalize.py",
                            "type": "method",
                            "name": "Normalizer.normalize_by_sd",
                            "content": "class Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 46,
                            "end_lineno": 59,
                            "parent": {
                                "file_name": "Orange/preprocess/normalize.py",
                                "type": "class",
                                "name": "Normalizer",
                                "content": "class Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 11,
                                "end_lineno": 75,
                                "parent": null
                            }
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                        "section": "class Normalizer(Reprable):"
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                    "line_group": {
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                            {
                                "content": "        if diff < 1e-15:",
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                            },
                            {
                                "content": "            diff = 1",
                                "type": "context"
                            },
                            {
                                "content": "        if self.zero_based:",
                                "type": "context"
                            },
                            {
                                "content": "            return ContinuousVariable(",
                                "type": "deleted"
                            },
                            {
                                "content": "                var.name,",
                                "type": "deleted"
                            },
                            {
                                "content": "                compute_value=Norm(var, dmi, 1 / diff),",
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                                "content": "                sparse=var.sparse)",
                                "type": "deleted"
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                            {
                                "content": "            compute_val = Norm(var, dmi, 1 / diff)",
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                                "content": "        else:",
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                                "content": "            return ContinuousVariable(",
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                            {
                                "content": "                var.name,",
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                            },
                            {
                                "content": "                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),",
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                            {
                                "content": "                sparse=var.sparse)",
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                            {
                                "content": "            compute_val = Norm(var, (dma + dmi) / 2, 2 / diff)",
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                            },
                            {
                                "content": "        return var.copy(compute_value=compute_val)",
                                "type": "added"
                            }
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                    },
                    "modified_entities": [
                        {
                            "file_name": "Orange/preprocess/normalize.py",
                            "type": "class",
                            "name": "Normalizer",
                            "content": "class Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 11,
                            "end_lineno": 75,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/preprocess/normalize.py",
                            "type": "method",
                            "name": "Normalizer.normalize_by_span",
                            "content": "class Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 61,
                            "end_lineno": 75,
                            "parent": {
                                "file_name": "Orange/preprocess/normalize.py",
                                "type": "class",
                                "name": "Normalizer",
                                "content": "class Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 11,
                                "end_lineno": 75,
                                "parent": null
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                        }
                    ],
                    "added_entities": [],
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        {
            "old_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.preprocess import Normalize\nfrom Orange.tests import test_filename\n\n\nclass TestNormalizer(unittest.TestCase):\n    def compare_tables(self, dataNorm, solution):\n        for i in range(len(dataNorm)):\n            for j in range(len(dataNorm[i])):\n                if type(solution[i][j]) == float:\n                    self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)\n                else:\n                    self.assertEqual(dataNorm[i, j], solution[i][j])\n        self.assertEqual([attr.name for attr in dataNorm.domain.attributes],\n                         [\"c1\", \"c2\", \"d1\", \"d2\", \"n1\", \"n2\", \"c3\", \"d3\", \"c4\"])\n        self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],\n                         [\"cl1\", \"cl2\"])\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/test5.tab\"))\n\n    def test_normalize_default(self):\n        normalizer = Normalize()\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_sd(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_class(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_sparse(self):\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        # pylint: disable=bad-whitespace\n        X = np.array([\n            [0, -1, -2],\n            [0,  1,  2],\n        ])\n        data = Table.from_numpy(domain, X).to_sparse()\n\n        # pylint: disable=bad-whitespace\n        solution = sp.csr_matrix(np.array([\n            [0, -1, -1],\n            [0,  1,  1],\n        ]))\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        self.assertEqual((normalized.X != solution).nnz, 0)\n\n        # raise error for non-zero offsets\n        data.X = sp.csr_matrix(np.array([\n            [0, 0, 0],\n            [0, 1, 3],\n            [0, 2, 4],\n        ]))\n        with self.assertRaises(ValueError):\n            normalizer(data)\n\n    def test_skip_normalization(self):\n        data = self.data.copy()\n        for attr in data.domain.attributes:\n            attr.attributes = {'skip-normalization': True}\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        np.testing.assert_array_equal(data.X, normalized.X)\n\n    def test_datetime_normalization(self):\n        data = Table(test_filename(\"datasets/test10.tab\"))\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(data)\n        solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n",
            "new_file_content": "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.preprocess import Normalize\nfrom Orange.tests import test_filename\n\n\nclass TestNormalizer(unittest.TestCase):\n    def compare_tables(self, dataNorm, solution):\n        for i in range(len(dataNorm)):\n            for j in range(len(dataNorm[i])):\n                if type(solution[i][j]) == float:\n                    self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)\n                else:\n                    self.assertEqual(dataNorm[i, j], solution[i][j])\n        self.assertEqual([attr.name for attr in dataNorm.domain.attributes],\n                         [\"c1\", \"c2\", \"d1\", \"d2\", \"n1\", \"n2\", \"c3\", \"d3\", \"c4\"])\n        self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],\n                         [\"cl1\", \"cl2\"])\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/test5.tab\"))\n\n    def test_normalize_default(self):\n        normalizer = Normalize()\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_sd(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_class(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_sparse(self):\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        # pylint: disable=bad-whitespace\n        X = np.array([\n            [0, -1, -2],\n            [0,  1,  2],\n        ])\n        data = Table.from_numpy(domain, X).to_sparse()\n\n        # pylint: disable=bad-whitespace\n        solution = sp.csr_matrix(np.array([\n            [0, -1, -1],\n            [0,  1,  1],\n        ]))\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        self.assertEqual((normalized.X != solution).nnz, 0)\n\n        # raise error for non-zero offsets\n        data.X = sp.csr_matrix(np.array([\n            [0, 0, 0],\n            [0, 1, 3],\n            [0, 2, 4],\n        ]))\n        with self.assertRaises(ValueError):\n            normalizer(data)\n\n    def test_skip_normalization(self):\n        data = self.data.copy()\n        for attr in data.domain.attributes:\n            attr.attributes = {'skip-normalization': True}\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        np.testing.assert_array_equal(data.X, normalized.X)\n\n    def test_datetime_normalization(self):\n        data = Table(test_filename(\"datasets/test10.tab\"))\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(data)\n        solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_retain_vars_attributes(self):\n        data = Table(\"iris\")\n        attributes = {\"foo\": \"foo\", \"baz\": 1}\n        data.domain.attributes[0].attributes = attributes\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySD)(\n                data).domain.attributes[0].attributes, attributes)\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySpan)(\n                data).domain.attributes[0].attributes, attributes)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n",
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                                "content": "    def test_retain_vars_attributes(self):",
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                                "content": "        data = Table(\"iris\")",
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                                "content": "        attributes = {\"foo\": \"foo\", \"baz\": 1}",
                                "type": "added"
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                                "type": "added"
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                            "type": "class",
                            "name": "TestNormalizer",
                            "content": "class TestNormalizer(unittest.TestCase):\n    def compare_tables(self, dataNorm, solution):\n        for i in range(len(dataNorm)):\n            for j in range(len(dataNorm[i])):\n                if type(solution[i][j]) == float:\n                    self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)\n                else:\n                    self.assertEqual(dataNorm[i, j], solution[i][j])\n        self.assertEqual([attr.name for attr in dataNorm.domain.attributes],\n                         [\"c1\", \"c2\", \"d1\", \"d2\", \"n1\", \"n2\", \"c3\", \"d3\", \"c4\"])\n        self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],\n                         [\"cl1\", \"cl2\"])\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/test5.tab\"))\n\n    def test_normalize_default(self):\n        normalizer = Normalize()\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_sd(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_class(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_sparse(self):\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        # pylint: disable=bad-whitespace\n        X = np.array([\n            [0, -1, -2],\n            [0,  1,  2],\n        ])\n        data = Table.from_numpy(domain, X).to_sparse()\n\n        # pylint: disable=bad-whitespace\n        solution = sp.csr_matrix(np.array([\n            [0, -1, -1],\n            [0,  1,  1],\n        ]))\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        self.assertEqual((normalized.X != solution).nnz, 0)\n\n        # raise error for non-zero offsets\n        data.X = sp.csr_matrix(np.array([\n            [0, 0, 0],\n            [0, 1, 3],\n            [0, 2, 4],\n        ]))\n        with self.assertRaises(ValueError):\n            normalizer(data)\n\n    def test_skip_normalization(self):\n        data = self.data.copy()\n        for attr in data.domain.attributes:\n            attr.attributes = {'skip-normalization': True}\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        np.testing.assert_array_equal(data.X, normalized.X)\n\n    def test_datetime_normalization(self):\n        data = Table(test_filename(\"datasets/test10.tab\"))\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(data)\n        solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_retain_vars_attributes(self):\n        data = Table(\"iris\")\n        attributes = {\"foo\": \"foo\", \"baz\": 1}\n        data.domain.attributes[0].attributes = attributes\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySD)(\n                data).domain.attributes[0].attributes, attributes)\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySpan)(\n                data).domain.attributes[0].attributes, attributes)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 14,
                            "end_lineno": 155,
                            "parent": null
                        }
                    ],
                    "added_entities": [
                        {
                            "file_name": "Orange/tests/test_normalize.py",
                            "type": "statement",
                            "name": "",
                            "content": "if __name__ == \"__main__\":\n    unittest.main()",
                            "ast_type_str": "If",
                            "start_lineno": 158,
                            "end_lineno": 159,
                            "parent": null
                        },
                        {
                            "file_name": "Orange/tests/test_normalize.py",
                            "type": "method",
                            "name": "TestNormalizer.test_retain_vars_attributes",
                            "content": "class TestNormalizer(unittest.TestCase):\n    def compare_tables(self, dataNorm, solution):\n        for i in range(len(dataNorm)):\n            for j in range(len(dataNorm[i])):\n                if type(solution[i][j]) == float:\n                    self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)\n                else:\n                    self.assertEqual(dataNorm[i, j], solution[i][j])\n        self.assertEqual([attr.name for attr in dataNorm.domain.attributes],\n                         [\"c1\", \"c2\", \"d1\", \"d2\", \"n1\", \"n2\", \"c3\", \"d3\", \"c4\"])\n        self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],\n                         [\"cl1\", \"cl2\"])\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/test5.tab\"))\n\n    def test_normalize_default(self):\n        normalizer = Normalize()\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_sd(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_class(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_sparse(self):\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        # pylint: disable=bad-whitespace\n        X = np.array([\n            [0, -1, -2],\n            [0,  1,  2],\n        ])\n        data = Table.from_numpy(domain, X).to_sparse()\n\n        # pylint: disable=bad-whitespace\n        solution = sp.csr_matrix(np.array([\n            [0, -1, -1],\n            [0,  1,  1],\n        ]))\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        self.assertEqual((normalized.X != solution).nnz, 0)\n\n        # raise error for non-zero offsets\n        data.X = sp.csr_matrix(np.array([\n            [0, 0, 0],\n            [0, 1, 3],\n            [0, 2, 4],\n        ]))\n        with self.assertRaises(ValueError):\n            normalizer(data)\n\n    def test_skip_normalization(self):\n        data = self.data.copy()\n        for attr in data.domain.attributes:\n            attr.attributes = {'skip-normalization': True}\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        np.testing.assert_array_equal(data.X, normalized.X)\n\n    def test_datetime_normalization(self):\n        data = Table(test_filename(\"datasets/test10.tab\"))\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(data)\n        solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_retain_vars_attributes(self):\n        data = Table(\"iris\")\n        attributes = {\"foo\": \"foo\", \"baz\": 1}\n        data.domain.attributes[0].attributes = attributes\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySD)(\n                data).domain.attributes[0].attributes, attributes)\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySpan)(\n                data).domain.attributes[0].attributes, attributes)",
                            "ast_type_str": "ClassDef",
                            "start_lineno": 146,
                            "end_lineno": 155,
                            "parent": {
                                "file_name": "Orange/tests/test_normalize.py",
                                "type": "class",
                                "name": "TestNormalizer",
                                "content": "class TestNormalizer(unittest.TestCase):\n    def compare_tables(self, dataNorm, solution):\n        for i in range(len(dataNorm)):\n            for j in range(len(dataNorm[i])):\n                if type(solution[i][j]) == float:\n                    self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)\n                else:\n                    self.assertEqual(dataNorm[i, j], solution[i][j])\n        self.assertEqual([attr.name for attr in dataNorm.domain.attributes],\n                         [\"c1\", \"c2\", \"d1\", \"d2\", \"n1\", \"n2\", \"c3\", \"d3\", \"c4\"])\n        self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],\n                         [\"cl1\", \"cl2\"])\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/test5.tab\"))\n\n    def test_normalize_default(self):\n        normalizer = Normalize()\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_sd(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_class(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_sparse(self):\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        # pylint: disable=bad-whitespace\n        X = np.array([\n            [0, -1, -2],\n            [0,  1,  2],\n        ])\n        data = Table.from_numpy(domain, X).to_sparse()\n\n        # pylint: disable=bad-whitespace\n        solution = sp.csr_matrix(np.array([\n            [0, -1, -1],\n            [0,  1,  1],\n        ]))\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        self.assertEqual((normalized.X != solution).nnz, 0)\n\n        # raise error for non-zero offsets\n        data.X = sp.csr_matrix(np.array([\n            [0, 0, 0],\n            [0, 1, 3],\n            [0, 2, 4],\n        ]))\n        with self.assertRaises(ValueError):\n            normalizer(data)\n\n    def test_skip_normalization(self):\n        data = self.data.copy()\n        for attr in data.domain.attributes:\n            attr.attributes = {'skip-normalization': True}\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        np.testing.assert_array_equal(data.X, normalized.X)\n\n    def test_datetime_normalization(self):\n        data = Table(test_filename(\"datasets/test10.tab\"))\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(data)\n        solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_retain_vars_attributes(self):\n        data = Table(\"iris\")\n        attributes = {\"foo\": \"foo\", \"baz\": 1}\n        data.domain.attributes[0].attributes = attributes\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySD)(\n                data).domain.attributes[0].attributes, attributes)\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySpan)(\n                data).domain.attributes[0].attributes, attributes)",
                                "ast_type_str": "ClassDef",
                                "start_lineno": 14,
                                "end_lineno": 155,
                                "parent": null
                            }
                        }
                    ],
                    "deleted_entities": []
                }
            ]
        }
    ],
    "old_commit_hash": "c0f8a6739223b049f28176675dc72f5bdbc49827^",
    "new_commit_hash": "c0f8a6739223b049f28176675dc72f5bdbc49827",
    "commit_message": "Normalizer: Retain attributes of attributes",
    "commit_date": "2019-11-22T13:47:06+01:00",
    "metadata": {}
}
{
    "repo_name": "orange3",
    "new_commit_hash": "c0f8a6739223b049f28176675dc72f5bdbc49827",
    "test_file_codes": [
        "# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring\n\nimport unittest\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom Orange.data import Table, Domain, ContinuousVariable\nfrom Orange.preprocess import Normalize\nfrom Orange.tests import test_filename\n\n\nclass TestNormalizer(unittest.TestCase):\n    def compare_tables(self, dataNorm, solution):\n        for i in range(len(dataNorm)):\n            for j in range(len(dataNorm[i])):\n                if type(solution[i][j]) == float:\n                    self.assertAlmostEqual(dataNorm[i, j], solution[i][j], places=3)\n                else:\n                    self.assertEqual(dataNorm[i, j], solution[i][j])\n        self.assertEqual([attr.name for attr in dataNorm.domain.attributes],\n                         [\"c1\", \"c2\", \"d1\", \"d2\", \"n1\", \"n2\", \"c3\", \"d3\", \"c4\"])\n        self.assertEqual([attr.name for attr in dataNorm.domain.class_vars],\n                         [\"cl1\", \"cl2\"])\n    @classmethod\n    def setUpClass(cls):\n        cls.data = Table(test_filename(\"datasets/test5.tab\"))\n\n    def test_normalize_default(self):\n        normalizer = Normalize()\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_sd(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1.225, 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 1.225],\n                    [0., -1.225, 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.225],\n                    [0., 0., 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=False)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 2.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 1.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_class(self):\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., -1., 'a', 'b', -1., '?', 0., 'b', '?', 'b', -1.],\n                    [0., 0., 'a', 'b', 1., 'b', -1., 'c', '?', 'c', 0.]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_transform_by_span_zero_class(self):\n        normalizer = Normalize(zero_based=True,\n                               norm_type=Normalize.NormalizeBySpan,\n                               transform_class=True)\n        data_norm = normalizer(self.data)\n        solution = [[0., 1., 'a', 'a', '?', 'a', 1., 'a', '?', 'a', 1.],\n                    [0., 0., 'a', 'b', 0., '?', 0.5, 'b', '?', 'b', 0.],\n                    [0., 0.5, 'a', 'b', 1., 'b', 0., 'c', '?', 'c', 0.5]]\n        self.compare_tables(data_norm, solution)\n\n    def test_normalize_sparse(self):\n        domain = Domain([ContinuousVariable(str(i)) for i in range(3)])\n        # pylint: disable=bad-whitespace\n        X = np.array([\n            [0, -1, -2],\n            [0,  1,  2],\n        ])\n        data = Table.from_numpy(domain, X).to_sparse()\n\n        # pylint: disable=bad-whitespace\n        solution = sp.csr_matrix(np.array([\n            [0, -1, -1],\n            [0,  1,  1],\n        ]))\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        self.assertEqual((normalized.X != solution).nnz, 0)\n\n        # raise error for non-zero offsets\n        data.X = sp.csr_matrix(np.array([\n            [0, 0, 0],\n            [0, 1, 3],\n            [0, 2, 4],\n        ]))\n        with self.assertRaises(ValueError):\n            normalizer(data)\n\n    def test_skip_normalization(self):\n        data = self.data.copy()\n        for attr in data.domain.attributes:\n            attr.attributes = {'skip-normalization': True}\n\n        normalizer = Normalize()\n        normalized = normalizer(data)\n        np.testing.assert_array_equal(data.X, normalized.X)\n\n    def test_datetime_normalization(self):\n        data = Table(test_filename(\"datasets/test10.tab\"))\n        normalizer = Normalize(zero_based=False,\n                               norm_type=Normalize.NormalizeBySD,\n                               transform_class=False)\n        data_norm = normalizer(data)\n        solution = [[0., '1995-01-21', 'a', 'a', '?', 'a', 1.225, 'a', '?', 'a', 2],\n                    [0., '2003-07-23', 'a', 'b', -1., '?', 0., 'b', '?', 'b', 0],\n                    [0., '1967-03-12', 'a', 'b', 1., 'b', -1.225, 'c', '?', 'c', 1]]\n        self.compare_tables(data_norm, solution)\n\n    def test_retain_vars_attributes(self):\n        data = Table(\"iris\")\n        attributes = {\"foo\": \"foo\", \"baz\": 1}\n        data.domain.attributes[0].attributes = attributes\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySD)(\n                data).domain.attributes[0].attributes, attributes)\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySpan)(\n                data).domain.attributes[0].attributes, attributes)\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\nrunning build_src\nbuild_src\nbuilding extension \"Orange.classification._simple_tree\" sources\nbuilding extension \"Orange.classification._tree_scorers\" sources\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._contingency\" sources\nbuilding extension \"Orange.data._io\" sources\nbuilding extension \"Orange.data._variable\" sources\nbuilding extension \"Orange.distance._distance\" sources\nbuilding extension \"Orange.preprocess._discretize\" sources\nbuilding extension \"Orange.preprocess._relieff\" sources\nbuilding extension \"Orange.projection._som\" sources\nbuilding extension \"Orange.widgets.utils._grid_density\" sources\nbuild_src: building npy-pkg config files\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmprg282_si/tmp\ncreating /tmp/tmprg282_si/tmp/tmprg282_si\ncompile options: '-MMD -MF /tmp/tmprg282_si/file.c.d -c'\ncc: /tmp/tmprg282_si/file.c\ncustomize UnixCCompiler using build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpukdw23v7/tmp\ncreating /tmp/tmpukdw23v7/tmp/tmpukdw23v7\ncompile options: '-MMD -MF /tmp/tmpukdw23v7/file.c.d -c'\ncc: /tmp/tmpukdw23v7/file.c\ncustomize UnixCCompiler using build_ext\nbuilding 'Orange.classification._simple_tree' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange\ncreating build/temp.linux-x86_64-3.7/Orange/classification\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/classification/_simple_tree.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_simple_tree.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/classification/_simple_tree.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.classification._tree_scorers' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/classification/_tree_scorers.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/classification/_tree_scorers.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/classification/_tree_scorers.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._valuecount' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/data\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_valuecount.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_valuecount.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_valuecount.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._contingency' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_contingency.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_contingency.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_contingency.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._io' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_io.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_io.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_io.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.data._variable' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/data/_variable.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/data/_variable.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/data/_variable.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.distance._distance' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/distance\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/distance/_distance.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/distance/_distance.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/distance/_distance.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._discretize' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/preprocess\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/preprocess/_discretize.c\ncc -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_discretize.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/preprocess/_discretize.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.preprocess._relieff' extension\ncompiling C++ sources\nC compiler: c++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\nc++: Orange/preprocess/_relieff.cpp\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/preprocess/_relieff.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/preprocess/_relieff.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.projection._som' extension\ncompiling C sources\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/projection\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\ncc: Orange/projection/_som.c\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 Orange/projection/_som.cpython-37m-x86_64-linux-gnu.so\nbuilding 'Orange.widgets.utils._grid_density' extension\ncompiling C++ sources\nC compiler: c++ -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating build/temp.linux-x86_64-3.7/Orange/widgets\ncreating build/temp.linux-x86_64-3.7/Orange/widgets/utils\ncompile options: '-I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include -I/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/include -I/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/include/python3.7m -c'\nc++: Orange/widgets/utils/_grid_density.cpp\nc++ -pthread -shared -L/tools/deps/lib build/temp.linux-x86_64-3.7/Orange/widgets/utils/_grid_density.o -L/home/gcpuser/.local/share/uv/python/cpython-3.7.9-linux-x86_64-gnu/lib -lm -lpython3.7m -o Orange/widgets/utils/_grid_density.cpython-37m-x86_64-linux-gnu.so\nrunning develop\nrunning build_scripts\nrunning egg_info\nrunning build_src\nbuild_src\nbuilding extension \"Orange.classification._simple_tree\" sources\nbuilding extension \"Orange.classification._tree_scorers\" sources\nbuilding extension \"Orange.data._valuecount\" sources\nbuilding extension \"Orange.data._contingency\" sources\nbuilding extension \"Orange.data._io\" sources\nbuilding extension \"Orange.data._variable\" sources\nbuilding extension \"Orange.distance._distance\" sources\nbuilding extension \"Orange.preprocess._discretize\" sources\nbuilding extension \"Orange.preprocess._relieff\" sources\nbuilding extension \"Orange.projection._som\" sources\nbuilding extension \"Orange.widgets.utils._grid_density\" sources\nbuild_src: building npy-pkg config files\ncreating Orange3.egg-info\nwriting Orange3.egg-info/PKG-INFO\nwriting dependency_links to Orange3.egg-info/dependency_links.txt\nwriting entry points to Orange3.egg-info/entry_points.txt\nwriting requirements to Orange3.egg-info/requires.txt\nwriting top-level names to Orange3.egg-info/top_level.txt\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest file 'Orange3.egg-info/SOURCES.txt'\nreading manifest template 'MANIFEST.in'\nwarning: no files found matching 'README*' under directory 'Orange'\nwarning: no files found matching 'LICENSE' under directory 'Orange'\nwarning: no files found matching '*.basket' under directory 'Orange/datasets'\nwarning: no files found matching '*.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 'COPYING'\nadding license file 'LICENSE'\nwriting manifest file 'Orange3.egg-info/SOURCES.txt'\nrunning build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpcyz45m3h/tmp\ncreating /tmp/tmpcyz45m3h/tmp/tmpcyz45m3h\ncompile options: '-MMD -MF /tmp/tmpcyz45m3h/file.c.d -c'\ncc: /tmp/tmpcyz45m3h/file.c\ncustomize UnixCCompiler using build_ext\ncustomize UnixCCompiler\nC compiler: cc -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/tools/deps/include -I/tools/deps/include/ncursesw -fPIC\n\ncreating /tmp/tmpj1gqj37y/tmp\ncreating /tmp/tmpj1gqj37y/tmp/tmpj1gqj37y\ncompile options: '-MMD -MF /tmp/tmpj1gqj37y/file.c.d -c'\ncc: /tmp/tmpj1gqj37y/file.c\ncustomize UnixCCompiler using build_ext\nCreating /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/Orange3.egg-link (link to .)\nAdding Orange3 3.24.0.dev0+c0f8a67 to easy-install.pth file\nInstalling orange-canvas script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nInstalled /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827\nProcessing dependencies for Orange3==3.24.0.dev0+c0f8a67\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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for xlrd==2.0.1\nBest match: xlrd 2.0.1\nAdding xlrd 2.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for setuptools==59.8.0\nBest match: setuptools 59.8.0\nAdding setuptools 59.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for serverfiles==0.3.1\nBest match: serverfiles 0.3.1\nAdding serverfiles 0.3.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for scipy==1.7.3\nBest match: scipy 1.7.3\nAdding scipy 1.7.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for scikit-learn==1.0.2\nBest match: scikit-learn 1.0.2\nAdding scikit-learn 1.0.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for requests==2.31.0\nBest match: requests 2.31.0\nAdding requests 2.31.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for PyYAML==6.0.1\nBest match: PyYAML 6.0.1\nAdding PyYAML 6.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for python-louvain==0.16\nBest match: python-louvain 0.16\nAdding python-louvain 0.16 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for pyqtgraph==0.12.4\nBest match: pyqtgraph 0.12.4\nAdding pyqtgraph 0.12.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for pip==24.0\nBest match: pip 24.0\nAdding pip 24.0 to easy-install.pth file\nInstalling pip script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling pip3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling pip3.10 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for pandas==1.3.5\nBest match: pandas 1.3.5\nAdding pandas 1.3.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for openTSNE==1.0.1\nBest match: openTSNE 1.0.1\nAdding openTSNE 1.0.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for numpy==1.17.5\nBest match: numpy 1.17.5\nAdding numpy 1.17.5 to easy-install.pth file\nInstalling f2py script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling f2py3 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling f2py3.7 script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for networkx==2.6.3\nBest match: networkx 2.6.3\nAdding networkx 2.6.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for matplotlib==3.5.3\nBest match: matplotlib 3.5.3\nAdding matplotlib 3.5.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for keyrings.alt==4.2.0\nBest match: keyrings.alt 4.2.0\nAdding keyrings.alt 4.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for keyring==24.1.1\nBest match: keyring 24.1.1\nAdding keyring 24.1.1 to easy-install.pth file\nInstalling keyring script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for joblib==1.3.2\nBest match: joblib 1.3.2\nAdding joblib 1.3.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for docutils==0.20.1\nBest match: docutils 0.20.1\nAdding docutils 0.20.1 to easy-install.pth file\nInstalling docutils script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for chardet==5.2.0\nBest match: chardet 5.2.0\nAdding chardet 5.2.0 to easy-install.pth file\nInstalling chardetect script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for Bottleneck==1.4.0\nBest match: Bottleneck 1.4.0\nAdding Bottleneck 1.4.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for AnyQt==0.2.0\nBest match: AnyQt 0.2.0\nAdding AnyQt 0.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for threadpoolctl==3.1.0\nBest match: threadpoolctl 3.1.0\nAdding threadpoolctl 3.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for charset-normalizer==3.4.1\nBest match: charset-normalizer 3.4.1\nAdding charset-normalizer 3.4.1 to easy-install.pth file\nInstalling normalizer script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for certifi==2024.12.14\nBest match: certifi 2024.12.14\nAdding certifi 2024.12.14 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for idna==3.10\nBest match: idna 3.10\nAdding idna 3.10 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for urllib3==2.0.7\nBest match: urllib3 2.0.7\nAdding urllib3 2.0.7 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for python-dateutil==2.9.0.post0\nBest match: python-dateutil 2.9.0.post0\nAdding python-dateutil 2.9.0.post0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for typing-extensions==4.7.1\nBest match: typing-extensions 4.7.1\nAdding typing-extensions 4.7.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for qasync==0.25.0\nBest match: qasync 0.25.0\nAdding qasync 0.25.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for requests-cache==1.1.1\nBest match: requests-cache 1.1.1\nAdding requests-cache 1.1.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for importlib-metadata==6.7.0\nBest match: importlib-metadata 6.7.0\nAdding importlib-metadata 6.7.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for packaging==24.0\nBest match: packaging 24.0\nAdding packaging 24.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for kiwisolver==1.4.5\nBest match: kiwisolver 1.4.5\nAdding kiwisolver 1.4.5 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for Pillow==9.5.0\nBest match: Pillow 9.5.0\nAdding Pillow 9.5.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for pyparsing==3.1.4\nBest match: pyparsing 3.1.4\nAdding pyparsing 3.1.4 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for fonttools==4.38.0\nBest match: fonttools 4.38.0\nAdding fonttools 4.38.0 to easy-install.pth file\nInstalling fonttools script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling pyftmerge script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling pyftsubset script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\nInstalling ttx script to /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/bin\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for cycler==0.11.0\nBest match: cycler 0.11.0\nAdding cycler 0.11.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for jaraco.classes==3.2.3\nBest match: jaraco.classes 3.2.3\nAdding jaraco.classes 3.2.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for SecretStorage==3.3.3\nBest match: SecretStorage 3.3.3\nAdding SecretStorage 3.3.3 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for importlib-resources==5.12.0\nBest match: importlib-resources 5.12.0\nAdding importlib-resources 5.12.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for jeepney==0.8.0\nBest match: jeepney 0.8.0\nAdding jeepney 0.8.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for six==1.17.0\nBest match: six 1.17.0\nAdding six 1.17.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for attrs==24.2.0\nBest match: attrs 24.2.0\nAdding attrs 24.2.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for cattrs==23.1.2\nBest match: cattrs 23.1.2\nAdding cattrs 23.1.2 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for platformdirs==4.0.0\nBest match: platformdirs 4.0.0\nAdding platformdirs 4.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for zipp==3.15.0\nBest match: zipp 3.15.0\nAdding zipp 3.15.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for more-itertools==9.1.0\nBest match: more-itertools 9.1.0\nAdding more-itertools 9.1.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for cryptography==44.0.0\nBest match: cryptography 44.0.0\nAdding cryptography 44.0.0 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for cffi==1.15.1\nBest match: cffi 1.15.1\nAdding cffi 1.15.1 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nSearching for pycparser==2.21\nBest match: pycparser 2.21\nAdding pycparser 2.21 to easy-install.pth file\n\nUsing /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages\nFinished processing dependencies for Orange3==3.24.0.dev0+c0f8a67\nVerifying Orange installation...\n✅ Orange installation successful!\nSuccessfully installed orange using Python 3.7\n",
    "setup_res_stderr": "Using CPython 3.7.9\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nResolved 18 packages in 110ms\nPrepared 18 packages in 2ms\nInstalled 18 packages in 151ms\n + cython==0.29.37\n + exceptiongroup==1.2.2\n + importlib-metadata==6.7.0\n + iniconfig==2.0.0\n + numpy==1.17.5\n + packaging==24.0\n + pluggy==1.2.0\n + pyqt5==5.15.10\n + pyqt5-qt5==5.15.16\n + pyqt5-sip==12.13.0\n + pyqtwebengine==5.15.6\n + pyqtwebengine-qt5==5.15.16\n + pytest==7.4.4\n + setuptools==59.8.0\n + tomli==2.0.1\n + typing-extensions==4.7.1\n + wheel==0.42.0\n + zipp==3.15.0\nResolved 38 packages in 175ms\nInstalled 33 packages in 700ms\n + bottleneck==1.4.0\n + certifi==2024.12.14\n + cffi==1.15.1\n + chardet==5.2.0\n + charset-normalizer==3.4.1\n + cryptography==44.0.0\n + idna==3.10\n + importlib-resources==5.12.0\n + jaraco-classes==3.2.3\n + jeepney==0.8.0\n + joblib==1.3.2\n + keyring==24.1.1\n + keyrings-alt==4.2.0\n + more-itertools==9.1.0\n + networkx==2.6.3\n + opentsne==1.0.1\n + pandas==1.3.5\n + pip==24.0\n + pycparser==2.21\n + python-dateutil==2.9.0.post0\n + python-louvain==0.16\n + pytz==2024.2\n + pyyaml==6.0.1\n + requests==2.31.0\n + scikit-learn==1.0.2\n + scipy==1.7.3\n + secretstorage==3.3.3\n + serverfiles==0.3.1\n + six==1.17.0\n + threadpoolctl==3.1.0\n + urllib3==2.0.7\n + xlrd==2.0.1\n + xlsxwriter==3.2.0\nResolved 34 packages in 148ms\nInstalled 19 packages in 292ms\n + anyqt==0.2.0\n + attrs==24.2.0\n + cattrs==23.1.2\n + commonmark==0.9.1\n + cycler==0.11.0\n + dictdiffer==0.9.0\n + docutils==0.20.1\n + fonttools==4.38.0\n + kiwisolver==1.4.5\n + matplotlib==3.5.3\n + orange-canvas-core==0.1.35\n + orange-widget-base==4.22.0\n + pillow==9.5.0\n + platformdirs==4.0.0\n + pyparsing==3.1.4\n + pyqtgraph==0.12.4\n + qasync==0.25.0\n + requests-cache==1.1.1\n + url-normalize==1.4.3\nResolved 2 packages in 2ms\nInstalled 2 packages in 78ms\n + psycopg2==2.9.9\n + pymssql==2.3.2\nAudited 1 package in 15ms\nAudited 2 packages in 3ms\nOrange/classification/_simple_tree.c: In function ‘gain_ratio_c’:\nOrange/classification/_simple_tree.c:63:9: warning: implicit declaration of function ‘qsort_r’; did you mean ‘qsort’? [-Wimplicit-function-declaration]\n   63 |         qsort_r(base, nel, size, compar, thunk)\n      |         ^~~~~~~\nOrange/classification/_simple_tree.c:147:9: note: in expansion of macro ‘QSORT_R_FUNC’\n  147 |         QSORT_R_FUNC(examples, size, sizeof(struct Example), (void*) &compar_attr, compar_examples);\n      |         ^~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/classification/_tree_scorers.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_2find_threshold_entropy’:\nOrange/classification/_tree_scorers.c:3115:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 3115 |   __pyx_t_7 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_6find_threshold_MSE’:\nOrange/classification/_tree_scorers.c:4619:27: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 4619 |   __pyx_t_1 = ((__pyx_v_N <= __pyx_v_min_leaf) != 0);\n      |                           ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_8find_binarization_MSE’:\nOrange/classification/_tree_scorers.c:5396:43: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5396 |           for (__pyx_t_22 = 0; __pyx_t_22 < __pyx_t_21; __pyx_t_22+=1) {\n      |                                           ^\nOrange/classification/_tree_scorers.c:5517:39: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5517 |           __pyx_t_27 = ((__pyx_v_left >= __pyx_v_min_leaf) != 0);\n      |                                       ^~\nOrange/classification/_tree_scorers.c:5523:53: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 5523 |           __pyx_t_27 = (((__pyx_v_N - __pyx_v_left) >= __pyx_v_min_leaf) != 0);\n      |                                                     ^~\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_14compute_predictions_csr’:\nOrange/classification/_tree_scorers.c:7051:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7051 |         for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c: In function ‘__pyx_pf_6Orange_14classification_13_tree_scorers_16compute_predictions_csc’:\nOrange/classification/_tree_scorers.c:7741:41: warning: comparison of integer expressions of different signedness: ‘unsigned int’ and ‘int’ [-Wsign-compare]\n 7741 |         for (__pyx_t_11 = 0; __pyx_t_11 < __pyx_t_10; __pyx_t_11+=1) {\n      |                                         ^\nOrange/classification/_tree_scorers.c:7801:143: warning: comparison of integer expressions of different signedness: ‘__pyx_t_5numpy_int32_t’ {aka ‘int’} and ‘unsigned int’ [-Wsign-compare]\n 7801 |               __pyx_t_17 = (((*((__pyx_t_5numpy_int32_t *) ( /* dim=0 */ (__pyx_v_indices.data + __pyx_t_18 * __pyx_v_indices.strides[0]) ))) != __pyx_v_i) != 0);\n      |                                                                                                                                               ^~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_valuecount.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/data/_contingency.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n   17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n      |  ^~~~~~~\nOrange/data/_io.c: In function ‘__pyx_pf_6Orange_4data_3_io_sparse_read_float.constprop’:\nOrange/data/_io.c:3881:24: warning: ‘__pyx_v_decs’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3881 |           __pyx_v_decs = (__pyx_v_decs / 10.0);\n      |           ~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:4070:24: warning: ‘__pyx_v_value’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4070 |           __pyx_t_10 = PyFloat_FromDouble(__pyx_v_value); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 221, __pyx_L5_error)\n      |                        ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:480:40: warning: ‘__pyx_v_attr_index’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n  480 |   #define PyInt_FromLong               PyLong_FromLong\n      |                                        ^~~~~~~~~~~~~~~\nOrange/data/_io.c:1301:7: note: ‘__pyx_v_attr_index’ was declared here\n 1301 |   int __pyx_v_attr_index;\n      |       ^~~~~~~~~~~~~~~~~~\nOrange/data/_io.c:2935:25: warning: ‘__pyx_v_atomp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2935 |           __pyx_v_atomp = (__pyx_v_atomp + 1);\n      |           ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\nIn file included from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/distance/_distance.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_discretize.c:567:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/preprocess/_relieff.cpp:581:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1830,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n                 from /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n                 from Orange/projection/_som.c:598:\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.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:3655:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3655 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:3654:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3654 |   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:4547:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4547 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_38, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_39, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4114:7: note: ‘__pyx_v_win_y’ was declared here\n 4114 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4536:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4536 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_36, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_37, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:4113:7: note: ‘__pyx_v_win_x’ was declared here\n 4113 |   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:4746:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4746 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:4745:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 4745 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_3update’:\nOrange/projection/_som.c:3141:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 3141 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c: In function ‘__pyx_pw_6Orange_10projection_4_som_1get_winners’:\nOrange/projection/_som.c:2964:223: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2964 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_y;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2665:7: note: ‘__pyx_v_win_y’ was declared here\n 2665 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:2953:223: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 2953 |           *__Pyx_BufPtrStrided2d(__pyx_t_5numpy_int16_t *, __pyx_pybuffernd_winners.rcbuffer->pybuffer.buf, __pyx_t_25, __pyx_pybuffernd_winners.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_winners.diminfo[1].strides) = __pyx_v_win_x;\n      |           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~\nOrange/projection/_som.c:2664:7: note: ‘__pyx_v_win_x’ was declared here\n 2664 |   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:5515:7: warning: ‘__pyx_v_win_y’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5515 |   int __pyx_v_win_y;\n      |       ^~~~~~~~~~~~~\nOrange/projection/_som.c:5514:7: warning: ‘__pyx_v_win_x’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n 5514 |   int __pyx_v_win_x;\n      |       ^~~~~~~~~~~~~\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.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_c0f8a6739223b049f28176675dc72f5bdbc49827/.venv/lib/python3.7/site-packages/setuptools/command/egg_info.py:628: SetuptoolsDeprecationWarning: Custom 'build_py' does not implement 'get_data_files_without_manifest'.\nPlease extend command classes from setuptools instead of distutils.\n  SetuptoolsDeprecationWarning\n",
    "new_commit_res_code": 1,
    "new_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827\ncollected 12 items\n\nr2e_tests/test_1.py E...........                                         [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/Orange/tests/__init__.py, line 65\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/Orange/tests/__init__.py:65\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestNormalizer::test_datetime_normalization\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_default\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_sparse\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_sd\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span_class\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span_zero\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span_zero_class\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_class\nPASSED r2e_tests/test_1.py::TestNormalizer::test_retain_vars_attributes\nPASSED r2e_tests/test_1.py::TestNormalizer::test_skip_normalization\nERROR r2e_tests/test_1.py::test_filename\n========================= 11 passed, 1 error in 1.53s ==========================\n",
    "new_commit_res_stderr": "",
    "old_commit_res_code": 1,
    "old_commit_res_stdout": "============================= test session starts ==============================\nplatform linux -- Python 3.7.9, pytest-7.4.4, pluggy-1.2.0\nrootdir: /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827\ncollected 12 items\n\nr2e_tests/test_1.py E.........F.                                         [100%]\n\n==================================== ERRORS ====================================\n_______________________ ERROR at setup of test_filename ________________________\nfile /home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/Orange/tests/__init__.py, line 65\n  def test_filename(path):\nE       fixture 'path' not found\n>       available fixtures: cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory\n>       use 'pytest --fixtures [testpath]' for help on them.\n\n/home/gcpuser/buckets/local_repoeval_bucket/repos/orange3_c0f8a6739223b049f28176675dc72f5bdbc49827/Orange/tests/__init__.py:65\n=================================== FAILURES ===================================\n__________________ TestNormalizer.test_retain_vars_attributes __________________\n\nself = <r2e_tests.test_1.TestNormalizer testMethod=test_retain_vars_attributes>\n\n    def test_retain_vars_attributes(self):\n        data = Table(\"iris\")\n        attributes = {\"foo\": \"foo\", \"baz\": 1}\n        data.domain.attributes[0].attributes = attributes\n        self.assertDictEqual(\n            Normalize(norm_type=Normalize.NormalizeBySD)(\n>               data).domain.attributes[0].attributes, attributes)\nE       AssertionError: {} != {'foo': 'foo', 'baz': 1}\nE       - {}\nE       + {'baz': 1, 'foo': 'foo'}\n\nr2e_tests/test_1.py:152: AssertionError\n==================================== PASSES ====================================\n=========================== short test summary info ============================\nPASSED r2e_tests/test_1.py::TestNormalizer::test_datetime_normalization\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_default\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_sparse\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_sd\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span_class\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span_zero\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_by_span_zero_class\nPASSED r2e_tests/test_1.py::TestNormalizer::test_normalize_transform_class\nPASSED r2e_tests/test_1.py::TestNormalizer::test_skip_normalization\nERROR r2e_tests/test_1.py::test_filename\nFAILED r2e_tests/test_1.py::TestNormalizer::test_retain_vars_attributes - Ass...\n==================== 1 failed, 10 passed, 1 error in 1.38s =====================\n",
    "old_commit_res_stderr": ""
}
Orange/preprocess/normalize.py Orange/tests/test_normalize.py
We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[ISSUE]

**Title:** Normalizer Fails to Retain Variable Attributes After Normalization

**Description:**
When using the `Normalizer` to preprocess data, the attributes of variables are not preserved. This results in the loss of important metadata associated with each variable, which can affect downstream analysis and processing that rely on these attributes.

**Example Code:**
```python
from Orange.data import Table
from Orange.preprocess import Normalize

# Load dataset
data = Table("iris")

# Assign custom attributes to the first variable
data.domain.attributes[0].attributes = {"foo": "foo", "baz": 1}

# Apply normalization
normalized_data = Normalize(norm_type=Normalize.NormalizeBySD)(data)

# Check if attributes are retained
print(normalized_data.domain.attributes[0].attributes)
```

**Expected Behavior:**
After normalization, the custom attributes of the first variable should be retained. The output should display:
```
{'foo': 'foo', 'baz': 1}
```

**Actual Behavior:**
The custom attributes are lost during normalization. The output displays an empty dictionary:
```
{}
```

[/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/normalize.py
import numpy as np

from Orange.data import ContinuousVariable, Domain
from Orange.statistics import distribution
from Orange.util import Reprable
from .preprocess import Normalize
from .transformation import Normalizer as Norm
__all__ = ["Normalizer"]


class Normalizer(Reprable):
    def __init__(self,
                 zero_based=True,
                 norm_type=Normalize.NormalizeBySD,
                 transform_class=False,
                 center=True,
                 normalize_datetime=False):
        self.zero_based = zero_based
        self.norm_type = norm_type
        self.transform_class = transform_class
        self.center = center
        self.normalize_datetime = normalize_datetime

    def __call__(self, data):
        dists = distribution.get_distributions(data)
        new_attrs = [self.normalize(dists[i], var) for
                     (i, var) in enumerate(data.domain.attributes)]

        new_class_vars = data.domain.class_vars
        if self.transform_class:
            attr_len = len(data.domain.attributes)
            new_class_vars = [self.normalize(dists[i + attr_len], var) for
                              (i, var) in enumerate(data.domain.class_vars)]

        domain = Domain(new_attrs, new_class_vars, data.domain.metas)
        return data.transform(domain)

    def normalize(self, dist, var):
        if not var.is_continuous or (var.is_time and not self.normalize_datetime):
            return var
        elif self.norm_type == Normalize.NormalizeBySD:
            return self.normalize_by_sd(dist, var)
        elif self.norm_type == Normalize.NormalizeBySpan:
            return self.normalize_by_span(dist, var)

    def normalize_by_sd(self, dist, var):
        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)
        if sd == 0:
            sd = 1
        if self.center:
            compute_val = Norm(var, avg, 1 / sd)
        else:
            compute_val = Norm(var, 0, 1 / sd)

        return ContinuousVariable(
            var.name,
            compute_value=compute_val,
            sparse=var.sparse,
        )

    def normalize_by_span(self, dist, var):
        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)
        diff = dma - dmi
        if diff < 1e-15:
            diff = 1
        if self.zero_based:
            return ContinuousVariable(
                var.name,
                compute_value=Norm(var, dmi, 1 / diff),
                sparse=var.sparse)
        else:
            return ContinuousVariable(
                var.name,
                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),
                sparse=var.sparse)

```
--- 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:** Normalizer Fails to Retain Variable Attributes After Normalization

**Description:**
When using the `Normalizer` to preprocess data, the attributes of variables are not preserved. This results in the loss of important metadata associated with each variable, which can affect downstream analysis and processing that rely on these attributes.

**Example Code:**
```python
from Orange.data import Table
from Orange.preprocess import Normalize

# Load dataset
data = Table("iris")

# Assign custom attributes to the first variable
data.domain.attributes[0].attributes = {"foo": "foo", "baz": 1}

# Apply normalization
normalized_data = Normalize(norm_type=Normalize.NormalizeBySD)(data)

# Check if attributes are retained
print(normalized_data.domain.attributes[0].attributes)
```

**Expected Behavior:**
After normalization, the custom attributes of the first variable should be retained. The output should display:
```
{'foo': 'foo', 'baz': 1}
```

**Actual Behavior:**
The custom attributes are lost during normalization. The output displays an empty dictionary:
```
{}
```

[/ISSUE]
{
    "TestNormalizer.test_datetime_normalization": "PASSED",
    "TestNormalizer.test_normalize_default": "PASSED",
    "TestNormalizer.test_normalize_sparse": "PASSED",
    "TestNormalizer.test_normalize_transform_by_sd": "PASSED",
    "TestNormalizer.test_normalize_transform_by_span": "PASSED",
    "TestNormalizer.test_normalize_transform_by_span_class": "PASSED",
    "TestNormalizer.test_normalize_transform_by_span_zero": "PASSED",
    "TestNormalizer.test_normalize_transform_by_span_zero_class": "PASSED",
    "TestNormalizer.test_normalize_transform_class": "PASSED",
    "TestNormalizer.test_retain_vars_attributes": "PASSED",
    "TestNormalizer.test_skip_normalization": "PASSED",
    "test_filename": "ERROR"
}
diff --git a/Orange/preprocess/normalize.py b/Orange/preprocess/normalize.py
index 00783ab89..d05504f22 100644
--- a/Orange/preprocess/normalize.py
+++ b/Orange/preprocess/normalize.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-from Orange.data import ContinuousVariable, Domain
+from Orange.data import Domain
 from Orange.statistics import distribution
 from Orange.util import Reprable
 from .preprocess import Normalize
@@ -51,12 +51,7 @@ class Normalizer(Reprable):
             compute_val = Norm(var, avg, 1 / sd)
         else:
             compute_val = Norm(var, 0, 1 / sd)
-
-        return ContinuousVariable(
-            var.name,
-            compute_value=compute_val,
-            sparse=var.sparse,
-        )
+        return var.copy(compute_value=compute_val)
 
     def normalize_by_span(self, dist, var):
         dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)
@@ -64,12 +59,7 @@ class Normalizer(Reprable):
         if diff < 1e-15:
             diff = 1
         if self.zero_based:
-            return ContinuousVariable(
-                var.name,
-                compute_value=Norm(var, dmi, 1 / diff),
-                sparse=var.sparse)
+            compute_val = Norm(var, dmi, 1 / diff)
         else:
-            return ContinuousVariable(
-                var.name,
-                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),
-                sparse=var.sparse)
+            compute_val = Norm(var, (dma + dmi) / 2, 2 / diff)
+        return var.copy(compute_value=compute_val)

{"Orange/preprocess/normalize.py": "import numpy as np\n\nfrom Orange.data import ContinuousVariable, Domain\nfrom Orange.statistics import distribution\nfrom Orange.util import Reprable\nfrom .preprocess import Normalize\nfrom .transformation import Normalizer as Norm\n__all__ = [\"Normalizer\"]\n\n\nclass Normalizer(Reprable):\n    def __init__(self,\n                 zero_based=True,\n                 norm_type=Normalize.NormalizeBySD,\n                 transform_class=False,\n                 center=True,\n                 normalize_datetime=False):\n        self.zero_based = zero_based\n        self.norm_type = norm_type\n        self.transform_class = transform_class\n        self.center = center\n        self.normalize_datetime = normalize_datetime\n\n    def __call__(self, data):\n        dists = distribution.get_distributions(data)\n        new_attrs = [self.normalize(dists[i], var) for\n                     (i, var) in enumerate(data.domain.attributes)]\n\n        new_class_vars = data.domain.class_vars\n        if self.transform_class:\n            attr_len = len(data.domain.attributes)\n            new_class_vars = [self.normalize(dists[i + attr_len], var) for\n                              (i, var) in enumerate(data.domain.class_vars)]\n\n        domain = Domain(new_attrs, new_class_vars, data.domain.metas)\n        return data.transform(domain)\n\n    def normalize(self, dist, var):\n        if not var.is_continuous or (var.is_time and not self.normalize_datetime):\n            return var\n        elif self.norm_type == Normalize.NormalizeBySD:\n            return self.normalize_by_sd(dist, var)\n        elif self.norm_type == Normalize.NormalizeBySpan:\n            return self.normalize_by_span(dist, var)\n\n    def normalize_by_sd(self, dist, var):\n        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)\n        if sd == 0:\n            sd = 1\n        if self.center:\n            compute_val = Norm(var, avg, 1 / sd)\n        else:\n            compute_val = Norm(var, 0, 1 / sd)\n\n        return ContinuousVariable(\n            var.name,\n            compute_value=compute_val,\n            sparse=var.sparse,\n        )\n\n    def normalize_by_span(self, dist, var):\n        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)\n        diff = dma - dmi\n        if diff < 1e-15:\n            diff = 1\n        if self.zero_based:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, dmi, 1 / diff),\n                sparse=var.sparse)\n        else:\n            return ContinuousVariable(\n                var.name,\n                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),\n                sparse=var.sparse)\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:** Normalizer Fails to Retain Variable Attributes After Normalization

**Description:**
When using the `Normalizer` to preprocess data, the attributes of variables are not preserved. This results in the loss of important metadata associated with each variable, which can affect downstream analysis and processing that rely on these attributes.

**Example Code:**
```python
from Orange.data import Table
from Orange.preprocess import Normalize

# Load dataset
data = Table("iris")

# Assign custom attributes to the first variable
data.domain.attributes[0].attributes = {"foo": "foo", "baz": 1}

# Apply normalization
normalized_data = Normalize(norm_type=Normalize.NormalizeBySD)(data)

# Check if attributes are retained
print(normalized_data.domain.attributes[0].attributes)
```

**Expected Behavior:**
After normalization, the custom attributes of the first variable should be retained. The output should display:
```
{'foo': 'foo', 'baz': 1}
```

**Actual Behavior:**
The custom attributes are lost during normalization. The output displays an empty dictionary:
```
{}
```

[/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/normalize.py
import numpy as np

from Orange.data import ContinuousVariable, Domain
from Orange.statistics import distribution
from Orange.util import Reprable
from .preprocess import Normalize
from .transformation import Normalizer as Norm
__all__ = ["Normalizer"]


class Normalizer(Reprable):
    def __init__(self,
                 zero_based=True,
                 norm_type=Normalize.NormalizeBySD,
                 transform_class=False,
                 center=True,
                 normalize_datetime=False):
        self.zero_based = zero_based
        self.norm_type = norm_type
        self.transform_class = transform_class
        self.center = center
        self.normalize_datetime = normalize_datetime

    def __call__(self, data):
        dists = distribution.get_distributions(data)
        new_attrs = [self.normalize(dists[i], var) for
                     (i, var) in enumerate(data.domain.attributes)]

        new_class_vars = data.domain.class_vars
        if self.transform_class:
            attr_len = len(data.domain.attributes)
            new_class_vars = [self.normalize(dists[i + attr_len], var) for
                              (i, var) in enumerate(data.domain.class_vars)]

        domain = Domain(new_attrs, new_class_vars, data.domain.metas)
        return data.transform(domain)

    def normalize(self, dist, var):
        if not var.is_continuous or (var.is_time and not self.normalize_datetime):
            return var
        elif self.norm_type == Normalize.NormalizeBySD:
            return self.normalize_by_sd(dist, var)
        elif self.norm_type == Normalize.NormalizeBySpan:
            return self.normalize_by_span(dist, var)

    def normalize_by_sd(self, dist, var):
        avg, sd = (dist.mean(), dist.standard_deviation()) if dist.size else (0, 1)
        if sd == 0:
            sd = 1
        if self.center:
            compute_val = Norm(var, avg, 1 / sd)
        else:
            compute_val = Norm(var, 0, 1 / sd)

        return ContinuousVariable(
            var.name,
            compute_value=compute_val,
            sparse=var.sparse,
        )

    def normalize_by_span(self, dist, var):
        dma, dmi = (dist.max(), dist.min()) if dist.shape[1] else (np.nan, np.nan)
        diff = dma - dmi
        if diff < 1e-15:
            diff = 1
        if self.zero_based:
            return ContinuousVariable(
                var.name,
                compute_value=Norm(var, dmi, 1 / diff),
                sparse=var.sparse)
        else:
            return ContinuousVariable(
                var.name,
                compute_value=Norm(var, (dma + dmi) / 2, 2 / diff),
                sparse=var.sparse)

```
--- 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