forked from moreo/QuaPy
30 lines
917 B
Python
30 lines
917 B
Python
|
import numpy
|
||
|
|
import pytest
|
||
|
|
from sklearn.linear_model import LogisticRegression
|
||
|
|
from sklearn.naive_bayes import MultinomialNB
|
||
|
|
from sklearn.svm import LinearSVC
|
||
|
|
|
||
|
|
import quapy as qp
|
||
|
|
|
||
|
|
datasets = [qp.datasets.fetch_twitter('semeval16')]
|
||
|
|
|
||
|
|
aggregative_methods = [qp.method.aggregative.CC, qp.method.aggregative.ACC, qp.method.aggregative.ELM]
|
||
|
|
|
||
|
|
learners = [LogisticRegression, MultinomialNB, LinearSVC]
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('dataset', datasets)
|
||
|
|
@pytest.mark.parametrize('aggregative_method', aggregative_methods)
|
||
|
|
@pytest.mark.parametrize('learner', learners)
|
||
|
|
def test_aggregative_methods(dataset, aggregative_method, learner):
|
||
|
|
model = aggregative_method(learner())
|
||
|
|
|
||
|
|
model.fit(dataset.training)
|
||
|
|
|
||
|
|
estim_prevalences = model.quantify(dataset.test.instances)
|
||
|
|
|
||
|
|
true_prevalences = dataset.test.prevalence()
|
||
|
|
error = qp.error.mae(true_prevalences, estim_prevalences)
|
||
|
|
|
||
|
|
assert type(error) == numpy.float64
|