OpenML

JavaScript is required to properly view the contents of this page!

Explore
- Data
- Task
- Flow
- Run
- Study
- Task type
- Measure
- People
Help
Blog
Contact
Please cite us

guillermo_seed_2_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by David Wilson
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes

Issue	#Downvotes for this reason	By

Loading wiki

Help us complete this description Edit

Subsampling of the dataset guillermo (41159) with seed=2 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
V166	numeric	845 unique values 0 missing
V169	numeric	812 unique values 0 missing
V233	numeric	545 unique values 0 missing
V245	numeric	832 unique values 0 missing
V328	numeric	513 unique values 0 missing
V387	numeric	485 unique values 0 missing
V415	numeric	286 unique values 0 missing
V433	numeric	713 unique values 0 missing
V445	numeric	870 unique values 0 missing
V457	numeric	330 unique values 0 missing
V459	numeric	711 unique values 0 missing
V634	numeric	589 unique values 0 missing
V792	numeric	491 unique values 0 missing
V794	numeric	565 unique values 0 missing
V849	numeric	618 unique values 0 missing
V860	numeric	425 unique values 0 missing
V911	numeric	393 unique values 0 missing
V929	numeric	614 unique values 0 missing
V951	numeric	859 unique values 0 missing
V1098	numeric	396 unique values 0 missing
V1099	numeric	744 unique values 0 missing
V1111	numeric	418 unique values 0 missing
V1159	numeric	566 unique values 0 missing
V1254	numeric	814 unique values 0 missing
V1290	numeric	790 unique values 0 missing
V1351	numeric	437 unique values 0 missing
V1409	numeric	467 unique values 0 missing
V1411	numeric	479 unique values 0 missing
V1454	numeric	579 unique values 0 missing
V1467	numeric	617 unique values 0 missing
V1610	numeric	683 unique values 0 missing
V1659	numeric	859 unique values 0 missing
V1736	numeric	816 unique values 0 missing
V1739	numeric	852 unique values 0 missing
V1787	numeric	297 unique values 0 missing
V1827	numeric	121 unique values 0 missing
V1874	numeric	272 unique values 0 missing
V1885	numeric	728 unique values 0 missing
V1897	numeric	582 unique values 0 missing
V1904	numeric	860 unique values 0 missing
V1913	numeric	183 unique values 0 missing
V1937	numeric	349 unique values 0 missing
V2002	numeric	282 unique values 0 missing
V2016	numeric	196 unique values 0 missing
V2030	numeric	871 unique values 0 missing
V2080	numeric	813 unique values 0 missing
V2129	numeric	598 unique values 0 missing
V2134	numeric	804 unique values 0 missing
V2153	numeric	581 unique values 0 missing
V2167	numeric	580 unique values 0 missing
V2206	numeric	704 unique values 0 missing
V2226	numeric	575 unique values 0 missing
V2243	numeric	827 unique values 0 missing
V2352	numeric	699 unique values 0 missing
V2372	numeric	449 unique values 0 missing
V2421	numeric	528 unique values 0 missing
V2458	numeric	787 unique values 0 missing
V2525	numeric	828 unique values 0 missing
V2534	numeric	489 unique values 0 missing
V2535	numeric	230 unique values 0 missing
V2536	numeric	883 unique values 0 missing
V2627	numeric	527 unique values 0 missing
V2678	numeric	917 unique values 0 missing
V2714	numeric	740 unique values 0 missing
V2750	numeric	900 unique values 0 missing
V2772	numeric	590 unique values 0 missing
V2828	numeric	585 unique values 0 missing
V2867	numeric	676 unique values 0 missing
V2891	numeric	1 unique values 0 missing
V2898	numeric	840 unique values 0 missing
V2937	numeric	764 unique values 0 missing
V2950	numeric	787 unique values 0 missing
V2969	numeric	551 unique values 0 missing
V2979	numeric	430 unique values 0 missing
V3066	numeric	1038 unique values 0 missing
V3163	numeric	809 unique values 0 missing
V3292	numeric	309 unique values 0 missing
V3299	numeric	523 unique values 0 missing
V3352	numeric	492 unique values 0 missing
V3422	numeric	780 unique values 0 missing
V3516	numeric	605 unique values 0 missing
V3552	numeric	498 unique values 0 missing
V3622	numeric	934 unique values 0 missing
V3673	numeric	647 unique values 0 missing
V3685	numeric	822 unique values 0 missing
V3707	numeric	373 unique values 0 missing
V3722	numeric	308 unique values 0 missing
V3768	numeric	354 unique values 0 missing
V3781	numeric	916 unique values 0 missing
V3818	numeric	849 unique values 0 missing
V3854	numeric	299 unique values 0 missing
V3927	numeric	865 unique values 0 missing
V3954	numeric	805 unique values 0 missing
V3994	numeric	746 unique values 0 missing
V4078	numeric	711 unique values 0 missing
V4093	numeric	804 unique values 0 missing
V4146	numeric	1231 unique values 0 missing
V4179	numeric	1234 unique values 0 missing
V4207	numeric	1231 unique values 0 missing
V4292	numeric	1229 unique values 0 missing