def test_whole_and_train_separete(self):
""" Applying a preprocessor before spliting data into train and test
and applying is just on train data should yield the same transformation of
the test data. """
for proc in PREPROCESSORS_INDEPENDENT_SAMPLES:
data = preprocessor_data(proc)
_, test1 = separate_learn_test(proc(data))
train, test = separate_learn_test(data)
train = proc(train)
test_transformed = test.transform(train.domain)
np.testing.assert_almost_equal(test_transformed.X,
test1.X,
err_msg="Preprocessor " + str(proc))
def test_slightly_different_domain(self):
""" If test data has a slightly different domain then (with interpolation)
we should obtain a similar classification score. """
# rows full of unknowns make LogisticRegression undefined
# we can obtain them, for example, with EMSC, if one of the badspectra
# is a spectrum from the data
learner = LogisticRegressionLearner(max_iter=1000,
preprocessors=[_RemoveNaNRows()])
for proc in PREPROCESSORS:
if hasattr(proc, "skip_add_zeros"):
continue
# LR that can not handle unknown values
train, test = separate_learn_test(preprocessor_data(proc))
train1 = proc(train)
aucorig = AUC(TestOnTestData()(train1, test, [learner]))
test = slightly_change_wavenumbers(test, 0.00001)
test = odd_attr(test)
# a subset of points for training so that all test sets points
# are within the train set points, which gives no unknowns
train = Interpolate(points=getx(train)[1:-3])(
train) # interpolatable train
train = proc(train)
# explicit domain conversion test to catch exceptions that would
# otherwise be silently handled in TestOnTestData
_ = test.transform(train.domain)
aucnow = AUC(TestOnTestData()(train, test, [learner]))
self.assertAlmostEqual(aucnow,
aucorig,
delta=0.03,
msg="Preprocessor " + str(proc))
test = Interpolate(points=getx(test) - 1.)(test) # also do a shift
_ = test.transform(train.domain) # explicit call again
aucnow = AUC(TestOnTestData()(train, test, [learner]))
# the difference should be slight
self.assertAlmostEqual(aucnow,
aucorig,
delta=0.05,
msg="Preprocessor " + str(proc))