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Dataset.reduce() allows to fix the random_state to have reproducible unit tests. This is required to ensure that the expected hyper-parameters are always chosen, independent of randomness

This commit is contained in:
Mirko Bunse 2024-04-17 14:46:37 +02:00
parent 72b43bd2f8
commit a64620c377
2 changed files with 14 additions and 6 deletions
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14
quapy/data/base.py
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@ -549,7 +549,7 @@ class Dataset:
yield Dataset(train, test, name=f'fold {(i % nfolds) + 1}/{nfolds} (round={(i // nfolds) + 1})')
def reduce(self, n_train=100, n_test=100):
def reduce(self, n_train=100, n_test=100, random_state=None):
"""
Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.
@ -557,6 +557,14 @@ class Dataset:
:param n_test: number of test documents to keep (default 100)
:return: self
"""
self.training = self.training.sampling(n_train, *self.training.prevalence())
self.test = self.test.sampling(n_test, *self.test.prevalence())
self.training = self.training.sampling(
n_train,
*self.training.prevalence(),
random_state = random_state
)
self.test = self.test.sampling(
n_test,
*self.test.prevalence(),
random_state = random_state
)
return self

6
quapy/tests/test_modsel.py
View File

@ -19,7 +19,7 @@ class ModselTestCase(unittest.TestCase):
q = PACC(LogisticRegression(random_state=1, max_iter=5000))
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce()
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(random_state=1)
training, validation = data.training.split_stratified(0.7, random_state=1)
param_grid = {'classifier__C': [0.000001, 10.]}
@ -41,7 +41,7 @@ class ModselTestCase(unittest.TestCase):
q = PACC(LogisticRegression(random_state=1, max_iter=5000))
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(n_train=500)
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(n_train=500, random_state=1)
training, validation = data.training.split_stratified(0.7, random_state=1)
param_grid = {'classifier__C': np.logspace(-3,3,7)}
@ -79,7 +79,7 @@ class ModselTestCase(unittest.TestCase):
q = PACC(SlowLR())
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce()
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10).reduce(random_state=1)
training, validation = data.training.split_stratified(0.7, random_state=1)
param_grid = {'classifier__C': np.logspace(-1,1,3)}