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riccardo_seed_0_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
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Subsampling of the dataset riccardo (41161) with seed=0 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( classes, size=nclasses_max, replace=False, p=vcs / sum(vcs), ) # Select the indices where one of these classes is present idxs = y.index[y.isin(classes)] x = x.iloc[idxs] y = y.iloc[idxs] # Uniformly sample columns if required if len(x.columns) > ncols_max: columns_idxs = rng.choice( list(range(len(x.columns))), size=ncols_max, replace=False ) sorted_column_idxs = sorted(columns_idxs) selected_columns = list(x.columns[sorted_column_idxs]) x = x[selected_columns] else: sorted_column_idxs = list(range(len(x.columns))) if len(x) > nrows_max: # Stratify accordingly target_name = y.name data = pd.concat((x, y), axis="columns") _, subset = train_test_split( data, test_size=nrows_max, stratify=data[target_name], shuffle=True, random_state=seed, ) x = subset.drop(target_name, axis="columns") y = subset[target_name] # We need to convert categorical columns to string for openml categorical_mask = [self.categorical_mask[i] for i in sorted_column_idxs] columns = list(x.columns) return Dataset( # Technically this is not the same but it's where it was derived from dataset=self.dataset, x=x, y=y, categorical_mask=categorical_mask, columns=columns, ) ```

101 features

class (target)	nominal	2 unique values 0 missing
V12	numeric	751 unique values 0 missing
V23	numeric	1062 unique values 0 missing
V36	numeric	830 unique values 0 missing
V70	numeric	437 unique values 0 missing
V94	numeric	719 unique values 0 missing
V121	numeric	897 unique values 0 missing
V142	numeric	893 unique values 0 missing
V173	numeric	982 unique values 0 missing
V209	numeric	806 unique values 0 missing
V312	numeric	844 unique values 0 missing
V317	numeric	950 unique values 0 missing
V341	numeric	695 unique values 0 missing
V361	numeric	822 unique values 0 missing
V378	numeric	607 unique values 0 missing
V527	numeric	965 unique values 0 missing
V576	numeric	769 unique values 0 missing
V737	numeric	899 unique values 0 missing
V743	numeric	1001 unique values 0 missing
V975	numeric	1087 unique values 0 missing
V1090	numeric	966 unique values 0 missing
V1093	numeric	890 unique values 0 missing
V1132	numeric	872 unique values 0 missing
V1134	numeric	694 unique values 0 missing
V1170	numeric	1159 unique values 0 missing
V1269	numeric	548 unique values 0 missing
V1294	numeric	1032 unique values 0 missing
V1325	numeric	202 unique values 0 missing
V1378	numeric	863 unique values 0 missing
V1407	numeric	645 unique values 0 missing
V1448	numeric	793 unique values 0 missing
V1530	numeric	858 unique values 0 missing
V1602	numeric	969 unique values 0 missing
V1615	numeric	998 unique values 0 missing
V1620	numeric	1191 unique values 0 missing
V1630	numeric	502 unique values 0 missing
V1657	numeric	917 unique values 0 missing
V1664	numeric	878 unique values 0 missing
V1679	numeric	675 unique values 0 missing
V1709	numeric	733 unique values 0 missing
V1791	numeric	915 unique values 0 missing
V1806	numeric	1112 unique values 0 missing
V1959	numeric	887 unique values 0 missing
V2038	numeric	922 unique values 0 missing
V2075	numeric	555 unique values 0 missing
V2120	numeric	910 unique values 0 missing
V2147	numeric	681 unique values 0 missing
V2161	numeric	771 unique values 0 missing
V2231	numeric	1023 unique values 0 missing
V2242	numeric	805 unique values 0 missing
V2272	numeric	1119 unique values 0 missing
V2291	numeric	875 unique values 0 missing
V2292	numeric	920 unique values 0 missing
V2341	numeric	784 unique values 0 missing
V2361	numeric	1047 unique values 0 missing
V2446	numeric	989 unique values 0 missing
V2468	numeric	967 unique values 0 missing
V2545	numeric	1081 unique values 0 missing
V2554	numeric	609 unique values 0 missing
V2613	numeric	882 unique values 0 missing
V2664	numeric	1041 unique values 0 missing
V2674	numeric	632 unique values 0 missing
V2675	numeric	696 unique values 0 missing
V2733	numeric	802 unique values 0 missing
V2747	numeric	878 unique values 0 missing
V2769	numeric	1183 unique values 0 missing
V2831	numeric	931 unique values 0 missing
V2845	numeric	965 unique values 0 missing
V2878	numeric	1052 unique values 0 missing
V2917	numeric	728 unique values 0 missing
V2932	numeric	857 unique values 0 missing
V2999	numeric	793 unique values 0 missing
V3065	numeric	987 unique values 0 missing
V3071	numeric	49 unique values 0 missing
V3073	numeric	523 unique values 0 missing
V3077	numeric	862 unique values 0 missing
V3081	numeric	952 unique values 0 missing
V3084	numeric	656 unique values 0 missing
V3232	numeric	343 unique values 0 missing
V3245	numeric	1013 unique values 0 missing
V3260	numeric	1045 unique values 0 missing
V3421	numeric	884 unique values 0 missing
V3423	numeric	441 unique values 0 missing
V3442	numeric	1049 unique values 0 missing
V3571	numeric	931 unique values 0 missing
V3578	numeric	868 unique values 0 missing
V3579	numeric	1177 unique values 0 missing
V3606	numeric	1239 unique values 0 missing
V3620	numeric	773 unique values 0 missing
V3652	numeric	791 unique values 0 missing
V3730	numeric	1048 unique values 0 missing
V3800	numeric	950 unique values 0 missing
V3818	numeric	950 unique values 0 missing
V3841	numeric	1005 unique values 0 missing
V3943	numeric	773 unique values 0 missing
V3993	numeric	736 unique values 0 missing
V4043	numeric	734 unique values 0 missing
V4088	numeric	763 unique values 0 missing
V4171	numeric	1765 unique values 0 missing
V4239	numeric	1763 unique values 0 missing
V4296	numeric	1752 unique values 0 missing