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eye_movements_seed_3_nrows_2000_nclasses_10_ncols_100_stratify_True

eye_movements_seed_3_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 eye_movements (44130) 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, ) ```

21 features

label (target)nominal2 unique values
0 missing
leavingPosnumeric1801 unique values
0 missing
wordNonumeric10 unique values
0 missing
titleNonumeric10 unique values
0 missing
timePrtctgnumeric566 unique values
0 missing
pupilDiamLagnumeric1207 unique values
0 missing
pupilDiamMaxnumeric1433 unique values
0 missing
regressDurnumeric171 unique values
0 missing
regressLennumeric254 unique values
0 missing
meanFixDurnumeric104 unique values
0 missing
totalFixDurnumeric71 unique values
0 missing
lineNonumeric2000 unique values
0 missing
landingPosnumeric1831 unique values
0 missing
prevFixPosnumeric1795 unique values
0 missing
lastSaccLennumeric1948 unique values
0 missing
firstSaccLennumeric1889 unique values
0 missing
nextFixDurnumeric48 unique values
0 missing
firstPassFixDurnumeric72 unique values
0 missing
firstfixDurnumeric52 unique values
0 missing
prevFixDurnumeric44 unique values
0 missing
assgNonumeric316 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
2
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.
20
Number of numeric attributes.
1
Number of nominal attributes.
4.76
Percentage of nominal attributes.
0.5
Average class difference between consecutive instances.
95.24
Percentage of numeric attributes.
0
Percentage of missing values.
0
Percentage of instances having missing values.
4.76
Percentage of binary attributes.
1
Number of binary attributes.
1000
Number of instances belonging to the least frequent class.
50
Percentage of instances belonging to the least frequent class.
1000
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the most frequent class.
0.01
Number of attributes divided by the number of instances.

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