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guillermo_seed_4_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=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( 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
V115	numeric	550 unique values 0 missing
V150	numeric	335 unique values 0 missing
V256	numeric	809 unique values 0 missing
V338	numeric	698 unique values 0 missing
V340	numeric	464 unique values 0 missing
V383	numeric	540 unique values 0 missing
V493	numeric	620 unique values 0 missing
V549	numeric	194 unique values 0 missing
V567	numeric	720 unique values 0 missing
V589	numeric	588 unique values 0 missing
V702	numeric	382 unique values 0 missing
V733	numeric	581 unique values 0 missing
V736	numeric	990 unique values 0 missing
V753	numeric	681 unique values 0 missing
V770	numeric	833 unique values 0 missing
V817	numeric	327 unique values 0 missing
V855	numeric	915 unique values 0 missing
V882	numeric	299 unique values 0 missing
V926	numeric	481 unique values 0 missing
V928	numeric	614 unique values 0 missing
V950	numeric	601 unique values 0 missing
V953	numeric	431 unique values 0 missing
V1143	numeric	728 unique values 0 missing
V1192	numeric	730 unique values 0 missing
V1288	numeric	414 unique values 0 missing
V1427	numeric	691 unique values 0 missing
V1489	numeric	755 unique values 0 missing
V1548	numeric	783 unique values 0 missing
V1551	numeric	799 unique values 0 missing
V1564	numeric	818 unique values 0 missing
V1572	numeric	243 unique values 0 missing
V1585	numeric	600 unique values 0 missing
V1643	numeric	633 unique values 0 missing
V1731	numeric	474 unique values 0 missing
V1818	numeric	681 unique values 0 missing
V1908	numeric	188 unique values 0 missing
V1966	numeric	156 unique values 0 missing
V1971	numeric	877 unique values 0 missing
V2014	numeric	654 unique values 0 missing
V2035	numeric	815 unique values 0 missing
V2053	numeric	438 unique values 0 missing
V2097	numeric	684 unique values 0 missing
V2100	numeric	666 unique values 0 missing
V2114	numeric	643 unique values 0 missing
V2117	numeric	500 unique values 0 missing
V2126	numeric	631 unique values 0 missing
V2127	numeric	166 unique values 0 missing
V2148	numeric	731 unique values 0 missing
V2213	numeric	586 unique values 0 missing
V2224	numeric	780 unique values 0 missing
V2278	numeric	791 unique values 0 missing
V2294	numeric	418 unique values 0 missing
V2304	numeric	725 unique values 0 missing
V2427	numeric	610 unique values 0 missing
V2447	numeric	927 unique values 0 missing
V2453	numeric	771 unique values 0 missing
V2482	numeric	317 unique values 0 missing
V2495	numeric	871 unique values 0 missing
V2555	numeric	478 unique values 0 missing
V2580	numeric	621 unique values 0 missing
V2639	numeric	789 unique values 0 missing
V2732	numeric	712 unique values 0 missing
V2824	numeric	1 unique values 0 missing
V2833	numeric	719 unique values 0 missing
V2848	numeric	768 unique values 0 missing
V2888	numeric	951 unique values 0 missing
V2963	numeric	341 unique values 0 missing
V2988	numeric	582 unique values 0 missing
V3049	numeric	732 unique values 0 missing
V3099	numeric	365 unique values 0 missing
V3299	numeric	524 unique values 0 missing
V3333	numeric	1 unique values 0 missing
V3377	numeric	509 unique values 0 missing
V3452	numeric	812 unique values 0 missing
V3464	numeric	834 unique values 0 missing
V3636	numeric	535 unique values 0 missing
V3674	numeric	395 unique values 0 missing
V3701	numeric	731 unique values 0 missing
V3760	numeric	325 unique values 0 missing
V3789	numeric	579 unique values 0 missing
V3797	numeric	626 unique values 0 missing
V3806	numeric	578 unique values 0 missing
V3831	numeric	393 unique values 0 missing
V3931	numeric	579 unique values 0 missing
V3932	numeric	611 unique values 0 missing
V3951	numeric	742 unique values 0 missing
V3959	numeric	935 unique values 0 missing
V3969	numeric	615 unique values 0 missing
V3998	numeric	349 unique values 0 missing
V4003	numeric	380 unique values 0 missing
V4075	numeric	754 unique values 0 missing
V4076	numeric	753 unique values 0 missing
V4079	numeric	686 unique values 0 missing
V4099	numeric	1233 unique values 0 missing
V4103	numeric	1234 unique values 0 missing
V4132	numeric	1230 unique values 0 missing
V4160	numeric	1225 unique values 0 missing
V4175	numeric	1232 unique values 0 missing
V4253	numeric	1231 unique values 0 missing
V4272	numeric	1235 unique values 0 missing