186 lines
6.8 KiB
Python
186 lines
6.8 KiB
Python
import numpy
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import pytest
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import LinearSVC
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import quapy as qp
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from quapy.data import Dataset, LabelledCollection
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from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS
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from quapy.method.aggregative import ACC, PACC, HDy
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from quapy.method.meta import Ensemble
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datasets = [pytest.param(qp.datasets.fetch_twitter('hcr'), id='hcr'),
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pytest.param(qp.datasets.fetch_UCIDataset('ionosphere'), id='ionosphere')]
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learners = [LogisticRegression, LinearSVC]
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('aggregative_method', AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS))
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@pytest.mark.parametrize('learner', learners)
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def test_aggregative_methods(dataset: Dataset, aggregative_method, learner):
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model = aggregative_method(learner())
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {type(model)} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('elm_method', EXPLICIT_LOSS_MINIMIZATION_METHODS)
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def test_elm_methods(dataset: Dataset, elm_method):
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try:
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model = elm_method()
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except AssertionError as ae:
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if ae.args[0].find('does not seem to point to a valid path') > 0:
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print('Missing SVMperf binary program, skipping test')
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return
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('non_aggregative_method', NON_AGGREGATIVE_METHODS)
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def test_non_aggregative_methods(dataset: Dataset, non_aggregative_method):
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model = non_aggregative_method()
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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@pytest.mark.parametrize('base_method', AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS))
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@pytest.mark.parametrize('learner', learners)
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@pytest.mark.parametrize('dataset', datasets)
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@pytest.mark.parametrize('policy', Ensemble.VALID_POLICIES)
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def test_ensemble_method(base_method, learner, dataset: Dataset, policy):
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qp.environ['SAMPLE_SIZE'] = len(dataset.training)
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model = Ensemble(quantifier=base_method(learner()), size=5, policy=policy, n_jobs=-1)
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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def test_quanet_method():
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try:
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import quapy.classification.neural
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except ModuleNotFoundError:
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print('skipping QuaNet test due to missing torch package')
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return
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dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
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dataset = Dataset(dataset.training.sampling(100, *dataset.training.prevalence()),
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dataset.test.sampling(100, *dataset.test.prevalence()))
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qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
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from quapy.classification.neural import CNNnet
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cnn = CNNnet(dataset.vocabulary_size, dataset.training.n_classes)
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from quapy.classification.neural import NeuralClassifierTrainer
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learner = NeuralClassifierTrainer(cnn, device='cuda')
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from quapy.method.meta import QuaNet
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model = QuaNet(learner, sample_size=len(dataset.training), device='cuda')
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if model.binary and not dataset.binary:
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print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
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return
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model.fit(dataset.training)
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estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, estim_prevalences)
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assert type(error) == numpy.float64
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def models_to_test_for_str_label_names():
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models = list()
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learner = LogisticRegression
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for method in AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS):
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models.append(method(learner()))
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for method in NON_AGGREGATIVE_METHODS:
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models.append(method())
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return models
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@pytest.mark.parametrize('model', models_to_test_for_str_label_names())
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def test_str_label_names(model):
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if type(model) in {ACC, PACC, HDy}:
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print(
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f'skipping the test of binary model {type(model)} because it currently does not support random seed control.')
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return
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dataset = qp.datasets.fetch_reviews('imdb', pickle=True)
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dataset = Dataset(dataset.training.sampling(1000, *dataset.training.prevalence()),
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dataset.test.sampling(1000, *dataset.test.prevalence()))
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qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
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model.fit(dataset.training)
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int_estim_prevalences = model.quantify(dataset.test.instances)
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true_prevalences = dataset.test.prevalence()
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error = qp.error.mae(true_prevalences, int_estim_prevalences)
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assert type(error) == numpy.float64
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dataset_str = Dataset(LabelledCollection(dataset.training.instances,
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['one' if label == 1 else 'zero' for label in dataset.training.labels]),
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LabelledCollection(dataset.test.instances,
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['one' if label == 1 else 'zero' for label in dataset.test.labels]))
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model.fit(dataset_str.training)
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str_estim_prevalences = model.quantify(dataset_str.test.instances)
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true_prevalences = dataset_str.test.prevalence()
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error = qp.error.mae(true_prevalences, str_estim_prevalences)
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assert type(error) == numpy.float64
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print(true_prevalences)
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print(int_estim_prevalences)
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print(str_estim_prevalences)
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numpy.testing.assert_almost_equal(int_estim_prevalences[1],
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str_estim_prevalences[list(model.classes_).index('one')])
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