DEVELOPMENT... { "data_id": "44772", "name": "okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True", "exact_name": "okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True", "version": 1, "version_label": "395ca19d-61fd-4a5b-b9c5-bc6e932333c6", "description": "Subsampling of the dataset okcupid-stem (42734) with\n\nseed=4\nargs.nrows=2000\nargs.ncols=100\nargs.nclasses=10\nargs.no_stratify=True\nGenerated with the following source code:\n\n\n```python\n def subsample(\n self,\n seed: int,\n nrows_max: int = 2_000,\n ncols_max: int = 100,\n nclasses_max: int = 10,\n stratified: bool = True,\n ) -> Dataset:\n rng = np.random.default_rng(seed)\n\n x = self.x\n y = self.y\n\n # Uniformly sample\n classes = y.unique()\n if len(classes) > nclasses_max:\n vcs = y.value_counts()\n selected_classes = rng.choice(\n classes,\n size=nclasses_max,\n replace=False,\n p=vcs \/ sum(vcs),\n )\n\n # Select the indices where one of these classes is present\n idxs = y.index[y.isin(classes)]\n x = x.iloc[idxs]\n y = y.iloc[idxs]\n\n # Uniformly sample columns if required\n if len(x.columns) > ncols_max:\n columns_idxs = rng.choice(\n list(range(len(x.columns))), size=ncols_max, replace=False\n )\n sorted_column_idxs = sorted(columns_idxs)\n selected_columns = list(x.columns[sorted_column_idxs])\n x = x[selected_columns]\n else:\n sorted_column_idxs = list(range(len(x.columns)))\n\n if len(x) > nrows_max:\n # Stratify accordingly\n target_name = y.name\n data = pd.concat((x, y), axis=\"columns\")\n _, subset = train_test_split(\n data,\n test_size=nrows_max,\n stratify=data[target_name],\n shuffle=True,\n random_state=seed,\n )\n x = subset.drop(target_name, axis=\"columns\")\n y = subset[target_name]\n\n # We need to convert categorical columns to string for openml\n categorical_mask = [self.categorical_mask[i] for i in sorted_column_idxs]\n columns = list(x.columns)\n\n return Dataset(\n # Technically this is not the same but it's where it was derived from\n dataset=self.dataset,\n x=x,\n y=y,\n categorical_mask=categorical_mask,\n columns=columns,\n )\n```", "format": "arff", "uploader": "David Wilson", "uploader_id": 32840, "visibility": "public", "creator": "\"Eddie Bergman\"", "contributor": null, "date": "2022-11-17 18:50:09", "update_comment": null, "last_update": "2022-11-17 18:50:09", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/api.openml.org\/data\/download\/22111534\/dataset", "default_target_attribute": "job", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True", "Subsampling of the dataset okcupid-stem (42734) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10, stratified: bool = True, ) -> Dataset: rng = np.random.default_rng(seed) x = self.x y = self.y # Uniformly sample classes = y.unique() if len(classes) > nclasses_max: vcs = y.value_counts() selected_classes = rng.cho " ], "weight": 5 }, "qualities": { "NumberOfInstances": 2000, "NumberOfFeatures": 20, "NumberOfClasses": 3, "NumberOfMissingValues": 6050, "NumberOfInstancesWithMissingValues": 1910, "NumberOfNumericFeatures": 2, "NumberOfSymbolicFeatures": 18, "PercentageOfSymbolicFeatures": 90, "AutoCorrelation": 0.5512756378189094, "PercentageOfNumericFeatures": 10, "PercentageOfMissingValues": 15.125, "PercentageOfInstancesWithMissingValues": 95.5, "PercentageOfBinaryFeatures": 5, "NumberOfBinaryFeatures": 1, "MinorityClassSize": 192, "MinorityClassPercentage": 9.6, "MajorityClassSize": 1432, "MajorityClassPercentage": 71.6, "Dimensionality": 0.01 }, "tags": [], "features": [ { "name": "job", "index": "19", "type": "nominal", "distinct": "3", "missing": "0", "target": "1", "distr": [ [ "stem", "non_stem", "student" 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"10" ] ] ] }, { "name": "sex", "index": "14", "type": "nominal", "distinct": "2", "missing": "0", "distr": [ [ "f", "m" ], [ [ "54", "652", "73" ], [ "322", "780", "119" ] ] ] }, { "name": "religion", "index": "13", "type": "nominal", "distinct": "42", "missing": "585", "distr": [ [ "agnosticism", "agnosticism and laughing about it", "agnosticism and somewhat serious about it", "agnosticism and very serious about it", "agnosticism but not too serious about it", "atheism", "atheism and laughing about it", "atheism and somewhat serious about it", "atheism and very serious about it", "atheism but not too serious about it", "buddhism", "buddhism and laughing about it", "buddhism and somewhat serious about it", "buddhism and very serious about it", "buddhism but not too serious about it", "catholicism", "catholicism and laughing about it", "catholicism and somewhat serious about it", "catholicism and very serious about it", "catholicism but not too serious about it", "christianity", 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Filename: Unknown
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