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

riccardo_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 riccardo (41161) 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	1033 unique values 0 missing
V169	numeric	1143 unique values 0 missing
V233	numeric	699 unique values 0 missing
V245	numeric	905 unique values 0 missing
V328	numeric	962 unique values 0 missing
V387	numeric	779 unique values 0 missing
V415	numeric	359 unique values 0 missing
V433	numeric	799 unique values 0 missing
V445	numeric	938 unique values 0 missing
V457	numeric	379 unique values 0 missing
V459	numeric	901 unique values 0 missing
V634	numeric	864 unique values 0 missing
V792	numeric	839 unique values 0 missing
V794	numeric	557 unique values 0 missing
V849	numeric	940 unique values 0 missing
V860	numeric	824 unique values 0 missing
V911	numeric	801 unique values 0 missing
V929	numeric	955 unique values 0 missing
V951	numeric	1038 unique values 0 missing
V1098	numeric	795 unique values 0 missing
V1099	numeric	977 unique values 0 missing
V1111	numeric	517 unique values 0 missing
V1159	numeric	801 unique values 0 missing
V1254	numeric	965 unique values 0 missing
V1290	numeric	1029 unique values 0 missing
V1351	numeric	773 unique values 0 missing
V1409	numeric	1003 unique values 0 missing
V1411	numeric	911 unique values 0 missing
V1454	numeric	1032 unique values 0 missing
V1467	numeric	989 unique values 0 missing
V1610	numeric	911 unique values 0 missing
V1659	numeric	918 unique values 0 missing
V1736	numeric	1010 unique values 0 missing
V1739	numeric	985 unique values 0 missing
V1787	numeric	716 unique values 0 missing
V1827	numeric	325 unique values 0 missing
V1874	numeric	548 unique values 0 missing
V1885	numeric	823 unique values 0 missing
V1897	numeric	654 unique values 0 missing
V1904	numeric	1073 unique values 0 missing
V1913	numeric	385 unique values 0 missing
V1937	numeric	483 unique values 0 missing
V2002	numeric	698 unique values 0 missing
V2016	numeric	445 unique values 0 missing
V2030	numeric	1105 unique values 0 missing
V2080	numeric	915 unique values 0 missing
V2129	numeric	836 unique values 0 missing
V2134	numeric	1087 unique values 0 missing
V2153	numeric	939 unique values 0 missing
V2167	numeric	736 unique values 0 missing
V2206	numeric	827 unique values 0 missing
V2226	numeric	923 unique values 0 missing
V2243	numeric	989 unique values 0 missing
V2352	numeric	845 unique values 0 missing
V2372	numeric	489 unique values 0 missing
V2421	numeric	808 unique values 0 missing
V2458	numeric	970 unique values 0 missing
V2525	numeric	830 unique values 0 missing
V2534	numeric	954 unique values 0 missing
V2535	numeric	443 unique values 0 missing
V2536	numeric	1181 unique values 0 missing
V2627	numeric	703 unique values 0 missing
V2678	numeric	896 unique values 0 missing
V2714	numeric	791 unique values 0 missing
V2750	numeric	977 unique values 0 missing
V2772	numeric	979 unique values 0 missing
V2828	numeric	886 unique values 0 missing
V2867	numeric	903 unique values 0 missing
V2891	numeric	4 unique values 0 missing
V2898	numeric	886 unique values 0 missing
V2937	numeric	817 unique values 0 missing
V2950	numeric	724 unique values 0 missing
V2969	numeric	829 unique values 0 missing
V2979	numeric	466 unique values 0 missing
V3066	numeric	1024 unique values 0 missing
V3163	numeric	919 unique values 0 missing
V3292	numeric	575 unique values 0 missing
V3299	numeric	925 unique values 0 missing
V3352	numeric	767 unique values 0 missing
V3422	numeric	1178 unique values 0 missing
V3516	numeric	718 unique values 0 missing
V3552	numeric	702 unique values 0 missing
V3622	numeric	1136 unique values 0 missing
V3673	numeric	553 unique values 0 missing
V3685	numeric	938 unique values 0 missing
V3707	numeric	532 unique values 0 missing
V3722	numeric	495 unique values 0 missing
V3768	numeric	797 unique values 0 missing
V3781	numeric	1046 unique values 0 missing
V3818	numeric	932 unique values 0 missing
V3854	numeric	603 unique values 0 missing
V3927	numeric	1081 unique values 0 missing
V3954	numeric	1056 unique values 0 missing
V3994	numeric	996 unique values 0 missing
V4078	numeric	960 unique values 0 missing
V4093	numeric	961 unique values 0 missing
V4146	numeric	1766 unique values 0 missing
V4179	numeric	1779 unique values 0 missing
V4207	numeric	1760 unique values 0 missing
V4292	numeric	1776 unique values 0 missing