Data
jungle_chess_2pcs_raw_endgame_complete_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

jungle_chess_2pcs_raw_endgame_complete_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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 jungle_chess_2pcs_raw_endgame_complete (41027) with seed=3 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, ) ```

7 features

class (target)nominal3 unique values
0 missing
white_piece0_strengthnumeric5 unique values
0 missing
white_piece0_filenumeric7 unique values
0 missing
white_piece0_ranknumeric9 unique values
0 missing
black_piece0_strengthnumeric5 unique values
0 missing
black_piece0_filenumeric7 unique values
0 missing
black_piece0_ranknumeric9 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.42
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
85.71
Percentage of numeric attributes.
51.45
Percentage of instances belonging to the most frequent class.
14.29
Percentage of nominal attributes.
1029
Number of instances belonging to the most frequent class.
9.7
Percentage of instances belonging to the least frequent class.
194
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

0 tasks

Define a new task