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guillermo_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 guillermo (41159) 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	344 unique values 0 missing
V23	numeric	721 unique values 0 missing
V36	numeric	385 unique values 0 missing
V70	numeric	192 unique values 0 missing
V94	numeric	591 unique values 0 missing
V121	numeric	854 unique values 0 missing
V142	numeric	641 unique values 0 missing
V173	numeric	539 unique values 0 missing
V209	numeric	657 unique values 0 missing
V312	numeric	760 unique values 0 missing
V317	numeric	690 unique values 0 missing
V341	numeric	584 unique values 0 missing
V361	numeric	703 unique values 0 missing
V378	numeric	650 unique values 0 missing
V527	numeric	848 unique values 0 missing
V576	numeric	393 unique values 0 missing
V737	numeric	718 unique values 0 missing
V743	numeric	555 unique values 0 missing
V975	numeric	784 unique values 0 missing
V1090	numeric	834 unique values 0 missing
V1093	numeric	800 unique values 0 missing
V1132	numeric	735 unique values 0 missing
V1134	numeric	626 unique values 0 missing
V1170	numeric	741 unique values 0 missing
V1269	numeric	405 unique values 0 missing
V1294	numeric	651 unique values 0 missing
V1325	numeric	133 unique values 0 missing
V1378	numeric	692 unique values 0 missing
V1407	numeric	367 unique values 0 missing
V1448	numeric	411 unique values 0 missing
V1530	numeric	784 unique values 0 missing
V1602	numeric	761 unique values 0 missing
V1615	numeric	683 unique values 0 missing
V1620	numeric	866 unique values 0 missing
V1630	numeric	275 unique values 0 missing
V1657	numeric	721 unique values 0 missing
V1664	numeric	495 unique values 0 missing
V1679	numeric	729 unique values 0 missing
V1709	numeric	526 unique values 0 missing
V1791	numeric	434 unique values 0 missing
V1806	numeric	796 unique values 0 missing
V1959	numeric	930 unique values 0 missing
V2038	numeric	663 unique values 0 missing
V2075	numeric	223 unique values 0 missing
V2120	numeric	774 unique values 0 missing
V2147	numeric	408 unique values 0 missing
V2161	numeric	543 unique values 0 missing
V2231	numeric	758 unique values 0 missing
V2242	numeric	803 unique values 0 missing
V2272	numeric	673 unique values 0 missing
V2291	numeric	683 unique values 0 missing
V2292	numeric	667 unique values 0 missing
V2341	numeric	623 unique values 0 missing
V2361	numeric	752 unique values 0 missing
V2446	numeric	638 unique values 0 missing
V2468	numeric	589 unique values 0 missing
V2545	numeric	743 unique values 0 missing
V2554	numeric	367 unique values 0 missing
V2613	numeric	565 unique values 0 missing
V2664	numeric	771 unique values 0 missing
V2674	numeric	257 unique values 0 missing
V2675	numeric	363 unique values 0 missing
V2733	numeric	614 unique values 0 missing
V2747	numeric	394 unique values 0 missing
V2769	numeric	828 unique values 0 missing
V2831	numeric	632 unique values 0 missing
V2845	numeric	767 unique values 0 missing
V2878	numeric	562 unique values 0 missing
V2917	numeric	363 unique values 0 missing
V2932	numeric	783 unique values 0 missing
V2999	numeric	709 unique values 0 missing
V3065	numeric	840 unique values 0 missing
V3071	numeric	24 unique values 0 missing
V3073	numeric	321 unique values 0 missing
V3077	numeric	498 unique values 0 missing
V3081	numeric	852 unique values 0 missing
V3084	numeric	449 unique values 0 missing
V3232	numeric	536 unique values 0 missing
V3245	numeric	679 unique values 0 missing
V3260	numeric	515 unique values 0 missing
V3421	numeric	764 unique values 0 missing
V3423	numeric	334 unique values 0 missing
V3442	numeric	517 unique values 0 missing
V3571	numeric	590 unique values 0 missing
V3578	numeric	441 unique values 0 missing
V3579	numeric	825 unique values 0 missing
V3606	numeric	901 unique values 0 missing
V3620	numeric	502 unique values 0 missing
V3652	numeric	518 unique values 0 missing
V3730	numeric	767 unique values 0 missing
V3800	numeric	868 unique values 0 missing
V3818	numeric	798 unique values 0 missing
V3841	numeric	840 unique values 0 missing
V3943	numeric	646 unique values 0 missing
V3993	numeric	449 unique values 0 missing
V4043	numeric	521 unique values 0 missing
V4088	numeric	648 unique values 0 missing
V4171	numeric	1222 unique values 0 missing
V4239	numeric	1221 unique values 0 missing
V4296	numeric	1217 unique values 0 missing