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jannis_seed_1_nrows_2000_nclasses_10_ncols_100_stratify_True

jannis_seed_1_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 jannis (44131) with seed=1 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, ) ```

55 features

class (target)nominal2 unique values
0 missing
V29numeric566 unique values
0 missing
V28numeric1998 unique values
0 missing
V30numeric1998 unique values
0 missing
V31numeric1997 unique values
0 missing
V32numeric1998 unique values
0 missing
V33numeric1990 unique values
0 missing
V34numeric1999 unique values
0 missing
V35numeric1999 unique values
0 missing
V36numeric1995 unique values
0 missing
V37numeric1994 unique values
0 missing
V38numeric606 unique values
0 missing
V39numeric1993 unique values
0 missing
V40numeric1995 unique values
0 missing
V41numeric2000 unique values
0 missing
V42numeric1996 unique values
0 missing
V43numeric1994 unique values
0 missing
V44numeric1992 unique values
0 missing
V45numeric1994 unique values
0 missing
V46numeric1994 unique values
0 missing
V47numeric1995 unique values
0 missing
V48numeric1995 unique values
0 missing
V49numeric1993 unique values
0 missing
V50numeric1997 unique values
0 missing
V51numeric1998 unique values
0 missing
V52numeric1992 unique values
0 missing
V53numeric1959 unique values
0 missing
V54numeric1994 unique values
0 missing
V15numeric1998 unique values
0 missing
V2numeric555 unique values
0 missing
V3numeric578 unique values
0 missing
V4numeric1997 unique values
0 missing
V5numeric1997 unique values
0 missing
V6numeric1991 unique values
0 missing
V7numeric1997 unique values
0 missing
V8numeric1992 unique values
0 missing
V9numeric1994 unique values
0 missing
V10numeric1993 unique values
0 missing
V11numeric1996 unique values
0 missing
V12numeric1993 unique values
0 missing
V13numeric1993 unique values
0 missing
V14numeric1994 unique values
0 missing
V1numeric1957 unique values
0 missing
V16numeric1999 unique values
0 missing
V17numeric1999 unique values
0 missing
V18numeric1999 unique values
0 missing
V19numeric1998 unique values
0 missing
V20numeric2000 unique values
0 missing
V21numeric1998 unique values
0 missing
V22numeric1998 unique values
0 missing
V23numeric1996 unique values
0 missing
V24numeric1999 unique values
0 missing
V25numeric1999 unique values
0 missing
V26numeric1998 unique values
0 missing
V27numeric1997 unique values
0 missing
Show all 55 features

19 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
55
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
54
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
PercentageOfSymbolicFeatures
1.82
Percentage of nominal attributes.
AutoCorrelation
0.5
Average class difference between consecutive instances.
PercentageOfNumericFeatures
98.18
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
1.82
Percentage of binary attributes.
NumberOfBinaryFeatures
1
Number of binary attributes.
MinorityClassSize
1000
Number of instances belonging to the least frequent class.
MinorityClassPercentage
50
Percentage of instances belonging to the least frequent class.
MajorityClassSize
1000
Number of instances belonging to the most frequent class.
MajorityClassPercentage
50
Percentage of instances belonging to the most frequent class.
Dimensionality
0.03
Number of attributes divided by the number of instances.
Show all 19 properties

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