{ "data_id": "44361", "name": "credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True", "exact_name": "credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True", "version": 2, "version_label": "c8c1706a-9be9-44d6-9860-17df02da7773", "description": "Subsampling of the dataset credit (44089) 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 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": "Eddie Bergman", "uploader_id": 32840, "visibility": "public", "creator": "\"Eddie Bergman\"", "contributor": null, "date": "2022-11-17 17:23:28", "update_comment": null, "last_update": "2022-11-17 17:23:28", "licence": "See source", "status": "active", "error_message": null, "url": "https:\/\/api.openml.org\/data\/download\/22111123\/dataset", "kaggle_url": null, "default_target_attribute": "SeriousDlqin2yrs", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "credit_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True", "Subsampling of the dataset credit (44089) 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.choice( c " ], "weight": 5 }, "qualities": { "NumberOfInstances": 2000, "NumberOfFeatures": 11, "NumberOfClasses": 2, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 10, "NumberOfSymbolicFeatures": 1, "PercentageOfBinaryFeatures": 9.090909090909092, "PercentageOfInstancesWithMissingValues": 0, "PercentageOfMissingValues": 0, "AutoCorrelation": 0.5042521260630315, "PercentageOfNumericFeatures": 90.9090909090909, "Dimensionality": 0.0055, "PercentageOfSymbolicFeatures": 9.090909090909092, "MajorityClassPercentage": 50, "MajorityClassSize": 1000, "MinorityClassPercentage": 50, "MinorityClassSize": 1000, "NumberOfBinaryFeatures": 1 }, "tags": [], "features": [ { "name": "SeriousDlqin2yrs", "index": "10", "type": "nominal", "distinct": "2", "missing": "0", "target": "1", "distr": [ [ "0", "1" ], [ [ "1000", "0" ], [ "0", "1000" ] ] ] }, { "name": "RevolvingUtilizationOfUnsecuredLines", "index": "0", "type": "numeric", "distinct": "1644", "missing": "0", "min": "0", "max": "6726", "mean": "5", "stdev": "158" }, { "name": "age", "index": "1", "type": "numeric", "distinct": "70", "missing": "0", "min": "21", "max": "94", "mean": "49", "stdev": "14" }, { "name": "NumberOfTime30-59DaysPastDueNotWorse", "index": "2", "type": "numeric", "distinct": "12", "missing": "0", "min": "0", "max": "98", "mean": "1", "stdev": "8" }, { "name": "DebtRatio", "index": "3", "type": "numeric", "distinct": "1945", "missing": "0", "min": "0", "max": "5775", "mean": "19", "stdev": "238" }, { "name": "MonthlyIncome", "index": "4", "type": "numeric", "distinct": "1047", "missing": "0", "min": "0", "max": "234600", "mean": "6168", "stdev": "7653" }, { "name": "NumberOfOpenCreditLinesAndLoans", "index": "5", "type": "numeric", "distinct": "34", "missing": "0", "min": "0", "max": "43", "mean": "8", "stdev": "5" }, { "name": "NumberOfTimes90DaysLate", "index": "6", "type": "numeric", "distinct": "13", "missing": "0", "min": "0", "max": "98", "mean": "1", "stdev": "8" }, { "name": "NumberRealEstateLoansOrLines", "index": "7", "type": "numeric", "distinct": "12", "missing": "0", "min": "0", "max": "17", "mean": "1", "stdev": "1" }, { "name": "NumberOfTime60-89DaysPastDueNotWorse", "index": "8", "type": "numeric", "distinct": "9", "missing": "0", "min": "0", "max": "98", "mean": "1", "stdev": "8" }, { "name": "NumberOfDependents", "index": "9", "type": "numeric", "distinct": "7", "missing": "0", "min": "0", "max": "6", "mean": "1", "stdev": "1" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }