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(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(self.collagen)
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
_ = Orange.data.Table(train.domain, test)
aucnow = AUC(TestOnTestData()(train, test, [learner]))
self.assertAlmostEqual(aucnow, aucorig, delta=0.02, msg="Preprocessor " + str(proc))
test = Interpolate(points=getx(test) - 1.)(test) # also do a shift
_ = Orange.data.Table(train.domain, test) # 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))
def test_predict_samename_domain_interpolation(self):
train, test = separate_learn_test(self.collagen)
aucorig = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = destroy_atts_conversion(test)
train = Interpolate(points=getx(train))(train) # make train capable of interpolation
auc = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertEqual(aucorig, auc)
def test_predict_savgol_another_interpolate(self):
train, test = separate_learn_test(self.collagen)
train = SavitzkyGolayFiltering(window=9, polyorder=2, deriv=2)(train)
auc = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
train = Interpolate(points=getx(train))(train)
aucai = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertAlmostEqual(auc, aucai, delta=0.02)
def test_slightly_different_domain(self):
""" If test data has a slightly different domain then (with interpolation)
we should obtain a similar classification score. """
learner = LogisticRegressionLearner(preprocessors=[])
for proc in PREPROCESSORS:
# LR that can not handle unknown values
train, test = separate_learn_test(self.collagen)
train1 = proc(train)
aucorig = AUC(TestOnTestData(train1, test, [learner]))
test = destroy_atts_conversion(test)
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) # make train capable of interpolation
train = proc(train)
# explicit domain conversion test to catch exceptions that would
# otherwise be silently handled in TestOnTestData
_ = Orange.data.Table(train.domain, test)
aucnow = AUC(TestOnTestData(train, test, [learner]))
self.assertAlmostEqual(aucnow, aucorig, delta=0.02)
test = Interpolate(points=getx(test) - 1.)(test) # also do a shift
_ = Orange.data.Table(train.domain, test) # explicit call again
aucnow = AUC(TestOnTestData(train, test, [learner]))
self.assertAlmostEqual(
aucnow, aucorig, delta=0.05) # the difference should be slight
def test_predict_different_domain_interpolation(self):
train, test = separate_learn_test(self.collagen)
aucorig = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = Interpolate(points=getx(test) - 1.)(test) # other test domain
train = Interpolate(points=getx(train))(train) # make train capable of interpolation
aucshift = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertAlmostEqual(aucorig, aucshift, delta=0.01) # shift can decrease AUC slightly
test = Cut(1000, 1700)(test)
auccut1 = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = Cut(1100, 1600)(test)
auccut2 = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = Cut(1200, 1500)(test)
auccut3 = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
# the more we cut the lower precision we get
self.assertTrue(aucorig > auccut1 > auccut2 > auccut3)
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. """
data = self.collagen
for proc in PREPROCESSORS_INDEPENDENT_SAMPLES:
train1, test1 = separate_learn_test(proc(data))
train, test = separate_learn_test(data)
train = proc(train)
test_transformed = Orange.data.Table(train.domain, test)
np.testing.assert_equal(test_transformed.X, test1.X)
aucorig = AUC(
TestOnTestData(train1, test1, [LogisticRegressionLearner()]))
aucnow = AUC(
TestOnTestData(train, test, [LogisticRegressionLearner()]))
self.assertEqual(aucorig, aucnow)
def test_predict_different_domain(self):
train, test = separate_learn_test(self.collagen)
test = Interpolate(points=getx(test) - 1)(test) # other test domain
try:
from Orange.data.table import DomainTransformationError
with self.assertRaises(DomainTransformationError):
LogisticRegressionLearner()(train)(test)
except ImportError: # until Orange 3.19
aucdestroyed = AUC(TestOnTestData(train, test, [LogisticRegressionLearner()]))
self.assertTrue(0.45 < aucdestroyed < 0.55)
def test_predict_samename_domain(self):
train, test = separate_learn_test(self.collagen)
test = destroy_atts_conversion(test)
try:
from Orange.data.table import DomainTransformationError
with self.assertRaises(DomainTransformationError):
LogisticRegressionLearner()(train)(test)
except ImportError: # until Orange 3.19
aucdestroyed = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertTrue(0.45 < aucdestroyed < 0.55)
def test_slightly_different_domain(self):
""" If test data has a slightly different domain then (with interpolation)
we should obtain a similar classification score. """
for proc in PREPROCESSORS:
train, test = separate_learn_test(self.collagen)
train1 = proc(train)
aucorig = AUC(
TestOnTestData(train1, test, [LogisticRegressionLearner()]))
test = destroy_atts_conversion(test)
test = odd_attr(test)
train = Interpolate(points=getx(train))(
train) # make train capable of interpolation
train = proc(train)
aucnow = AUC(
TestOnTestData(train, test, [LogisticRegressionLearner()]))
self.assertAlmostEqual(aucnow, aucorig, delta=0.02)
test = Interpolate(points=getx(test) - 1.)(test) # also do a shift
aucnow = AUC(
TestOnTestData(train, test, [LogisticRegressionLearner()]))
self.assertAlmostEqual(
aucnow, aucorig, delta=0.05) # the difference should be slight
def test_predict_savgov_same_domain(self):
data = SavitzkyGolayFiltering(window=9, polyorder=2, deriv=2)(self.collagen)
train, test = separate_learn_test(data)
auc = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertGreater(auc, 0.85)
def test_predict_same_domain(self):
train, test = separate_learn_test(self.collagen)
auc = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertGreater(auc, 0.9) # easy dataset
def test_predict_different_domain(self):
train, test = separate_learn_test(self.collagen)
test = Interpolate(points=getx(test) - 1)(test) # other test domain
aucdestroyed = AUC(
TestOnTestData(train, test, [LogisticRegressionLearner()]))
self.assertTrue(0.45 < aucdestroyed < 0.55)
def test_predict_samename_domain(self):
train, test = separate_learn_test(self.collagen)
test = destroy_atts_conversion(test)
aucdestroyed = AUC(
TestOnTestData(train, test, [LogisticRegressionLearner()]))
self.assertTrue(0.45 < aucdestroyed < 0.55)
""" Documentation script """
from Orange.classification import LogisticRegressionLearner
from Orange.evaluation.testing import CrossValidation
from Orange.evaluation.scoring import AUC
from orangecontrib.bioinformatics.geo.dataset import GDS
gds = GDS("GDS2960")
data = gds.get_data(sample_type="disease state", transpose=True, report_genes=True)
print("Samples: %d, Genes: %d" % (len(data), len(data.domain.attributes)))
learners = [LogisticRegressionLearner()]
results = CrossValidation(data, learners, k=10)
print("AUC = %.3f" % AUC(results)[0])
