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allow max_train_instances be deactivated in UCI multiclass datasets

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Alejandro Moreo Fernandez 2024-07-10 10:45:03 +02:00
parent b06a1532c2
commit 2034845988
1 changed files with 6 additions and 4 deletions
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quapy/data/datasets.py
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@ -621,7 +621,8 @@ def fetch_UCIMulticlassDataset(
as a minimum proportion, meaning that the real proportion could be higher in case the training proportion
(1-`min_test_split`% of the instances) surpasses `max_train_instances`. In such case, only `max_train_instances`
are taken for training, and the rest (irrespective of `min_test_split`) is taken for test.
:param max_train_instances: maximum number of instances to keep for training (defaults to 25000)
:param max_train_instances: maximum number of instances to keep for training (defaults to 25000);
set to -1 or None to avoid this check
:param min_class_support: minimum number of istances per class. Classes with fewer instances
are discarded (deafult is 100)
:param verbose: set to True (default is False) to get information (stats) about the dataset
@ -631,9 +632,10 @@ def fetch_UCIMulticlassDataset(
data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, min_class_support, verbose=verbose)
n = len(data)
train_prop = (1.-min_test_split)
n_train = int(n*train_prop)
if n_train > max_train_instances:
train_prop = (max_train_instances / n)
if (max_train_instances is not None) and (max_train_instances > 0):
n_train = int(n*train_prop)
if n_train > max_train_instances:
train_prop = (max_train_instances / n)
return Dataset(*data.split_stratified(train_prop, random_state=0))