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QuaPy
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Tests

This commit is contained in:
Andrea Esuli 2021-05-10 10:26:51 +02:00
parent 147b2f2212
commit 32b25146c1
2 changed files with 8 additions and 2 deletions
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1
TODO.txt
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@ -20,6 +20,7 @@ an instance of single-label with 2 labels. Check
Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps) Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
Add random seed management to support replicability (see temp_seed in util.py).
Improvements: Improvements:
========================================== ==========================================

9
quapy/tests/test_methods.py
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@ -6,6 +6,7 @@ from sklearn.svm import LinearSVC
import quapy as qp import quapy as qp
from quapy.data import Dataset, LabelledCollection from quapy.data import Dataset, LabelledCollection
from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS
from quapy.method.aggregative import ACC, PACC, HDy
from quapy.method.meta import Ensemble from quapy.method.meta import Ensemble
datasets = [pytest.param(qp.datasets.fetch_twitter('hcr'), id='hcr'), datasets = [pytest.param(qp.datasets.fetch_twitter('hcr'), id='hcr'),
@ -21,7 +22,7 @@ def test_aggregative_methods(dataset: Dataset, aggregative_method, learner):
model = aggregative_method(learner()) model = aggregative_method(learner())
if model.binary and not dataset.binary: if model.binary and not dataset.binary:
print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') print(f'skipping the test of binary model {type(model)} on non-binary dataset {dataset}')
return return
model.fit(dataset.training) model.fit(dataset.training)
@ -139,6 +140,11 @@ def models_to_test_for_str_label_names():
@pytest.mark.parametrize('model', models_to_test_for_str_label_names()) @pytest.mark.parametrize('model', models_to_test_for_str_label_names())
def test_str_label_names(model): def test_str_label_names(model):
if type(model) in {ACC, PACC, HDy}:
print(
f'skipping the test of binary model {type(model)} because it currently does not support random seed control.')
return
dataset = qp.datasets.fetch_reviews('imdb', pickle=True) dataset = qp.datasets.fetch_reviews('imdb', pickle=True)
dataset = Dataset(dataset.training.sampling(1000, *dataset.training.prevalence()), dataset = Dataset(dataset.training.sampling(1000, *dataset.training.prevalence()),
dataset.test.sampling(1000, *dataset.test.prevalence())) dataset.test.sampling(1000, *dataset.test.prevalence()))
@ -171,4 +177,3 @@ def test_str_label_names(model):
numpy.testing.assert_almost_equal(int_estim_prevalences[1], numpy.testing.assert_almost_equal(int_estim_prevalences[1],
str_estim_prevalences[list(model.classes_).index('one')]) str_estim_prevalences[list(model.classes_).index('one')])