def test_error_model(self):
for loo in [False, True]:
icr = InductiveRegressor(
ErrorModelNC(LinearRegressionLearner(),
LinearRegressionLearner(),
loo=loo), self.train, self.calibrate)
lo, hi = icr(self.test.x, 0.1)
self.assertLess(hi - lo, 30.0)
icr = InductiveRegressor(AbsError(RandomForestRegressionLearner()))
r = run(icr, 0.1, CrossSampler(Table('housing'), 20))
self.assertGreater(r.accuracy(), 0.85)
print(r.accuracy(), r.median_range(), r.interdecile_mean())
icr = InductiveRegressor(
ErrorModelNC(RandomForestRegressionLearner(),
LinearRegressionLearner()))
r = run(icr, 0.1, CrossSampler(Table('housing'), 20))
self.assertGreater(r.accuracy(), 0.85)
print(r.accuracy(), r.median_range(), r.interdecile_mean())
icr = InductiveRegressor(
ErrorModelNC(RandomForestRegressionLearner(),
LinearRegressionLearner(),
loo=True))
r = run(icr, 0.1, CrossSampler(Table('housing'), 20))
self.assertGreater(r.accuracy(), 0.85)
print(r.accuracy(), r.median_range(), r.interdecile_mean())
def test_cross(self):
folds = 7
s = CrossSampler(self.data, k=folds)
l = [(len(train), len(test)) for train, test in s]
self.assertEqual(len(l), folds)
self.assertTrue(all(a + b == len(self.data) for a, b in l))
t = [b for a, b in l]
self.assertLessEqual(max(t) - min(t), 1)
s = CrossSampler(self.data, k=folds)
ids = frozenset(
frozenset(inst.id for inst in test) for train, test in s)
self.assertEqual(len(ids), folds)
def evaluate_ncs(tab, cp, nc_strs, id):
res = {}
for nc_str in nc_strs:
nc = eval(nc_str)
r = run(cp(nc), 0.1, CrossSampler(tab, 5), rep=5)
res[nc_str] = r
print(id, nc_str)
with open('results/%s.p' % id, 'wb') as f:
pickle.dump(res, f)
def test_abs_error_normalized(self):
tab = Table('housing')
normalizer = Normalize(zero_based=True,
norm_type=Normalize.NormalizeBySpan)
tab = normalizer(tab)
icr = InductiveRegressor(AbsError(LinearRegressionLearner()))
icr_knn = InductiveRegressor(AbsError(KNNRegressionLearner(4)))
icr_norm = InductiveRegressor(
AbsErrorNormalized(KNNRegressionLearner(4),
Euclidean,
4,
exp=False))
icr_norm_exp = InductiveRegressor(
AbsErrorNormalized(KNNRegressionLearner(4), Euclidean, 4,
exp=True))
icr_norm_rf = InductiveRegressor(
AbsErrorNormalized(KNNRegressionLearner(4),
Euclidean,
4,
rf=RandomForestRegressor()))
r, r_knn, r_norm, r_norm_exp, r_norm_rf = ResultsRegr(), ResultsRegr(
), ResultsRegr(), ResultsRegr(), ResultsRegr()
eps = 0.05
for rep in range(10):
for train, test in CrossSampler(tab, 10):
train, calibrate = next(
RandomSampler(train,
len(train) - 100, 100))
r.concatenate(run_train_test(icr, eps, train, test, calibrate))
r_knn.concatenate(
run_train_test(icr_knn, eps, train, test, calibrate))
r_norm.concatenate(
run_train_test(icr_norm, eps, train, test, calibrate))
r_norm_exp.concatenate(
run_train_test(icr_norm_exp, eps, train, test, calibrate))
r_norm_rf.concatenate(
run_train_test(icr_norm_rf, eps, train, test, calibrate))
print(r.median_range(), r.interdecile_mean(), 1 - r.accuracy())
print(r_knn.median_range(), r_knn.interdecile_mean(),
1 - r_knn.accuracy())
print(r_norm.median_range(), r_norm.interdecile_mean(),
1 - r_norm.accuracy())
print(r_norm_exp.median_range(), r_norm_exp.interdecile_mean(),
1 - r_norm_exp.accuracy())
print(r_norm_rf.median_range(), r_norm_rf.interdecile_mean(),
1 - r_norm_rf.accuracy())
self.assertGreater(r.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_knn.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_norm.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_norm_exp.accuracy(), 1 - eps - 0.03)
self.assertGreater(r_norm_rf.accuracy(), 1 - eps - 0.03)
"""
def test_run(self):
tab = Table('iris')
cp = CrossClassifier(InverseProbability(LogisticRegressionLearner()),
5)
r = run(cp, 0.1, RandomSampler(tab, 4, 1), rep=3)
self.assertEqual(len(r.preds), 3 * 1 / 5 * len(tab))
tab = Table('housing')
cr = InductiveRegressor(AbsError(LinearRegressionLearner()))
r = run(cr, 0.1, CrossSampler(tab, 4), rep=3)
self.assertEqual(len(r.preds), 3 * len(tab))
def test_repeat(self):
rep = 5
s = RandomSampler(self.data, 3, 2)
ids = frozenset(
frozenset(inst.id for inst in test) for train, test in s.repeat(5))
self.assertEqual(len(ids), 5)
s = CrossSampler(self.data, 3)
ids = frozenset(
frozenset(inst.id for inst in test) for train, test in s.repeat(5))
self.assertEqual(len(ids), 15)
def evaluate_datasets(datasets, cp, nc_str, id):
res = {}
for dataset in datasets:
dataset_id = dataset.split('/')[-1].split('.')[0]
imp = Orange.preprocess.Impute()
rc = Orange.preprocess.preprocess.RemoveConstant()
tab = rc(imp(Table(dataset)))
nc = eval(nc_str)
r = run(cp(nc), 0.1, CrossSampler(tab, 5), rep=5)
res[dataset_id] = r
print(nc_str, dataset_id)
print(nc_str.upper())
with open('results/nc/%d.p' % id, 'wb') as f:
pickle.dump(res, f)
def test_abs_error_rf(self):
icr = InductiveRegressor(
AbsErrorRF(RandomForestRegressionLearner(),
RandomForestRegressor()), self.train, self.calibrate)
lo, hi = icr(self.test.x, 0.1)
self.assertLess(hi - lo, 30.0)
icr = InductiveRegressor(
AbsErrorRF(LinearRegressionLearner(), RandomForestRegressor()),
self.train, self.calibrate)
lo, hi = icr(self.test.x, 0.1)
self.assertLess(hi - lo, 30.0)
icr = InductiveRegressor(
AbsErrorRF(RandomForestRegressionLearner(),
RandomForestRegressor()))
r = run(icr, 0.1, CrossSampler(Table('housing'), 10))
self.assertGreater(r.accuracy(), 0.85)
print(r.median_range(), r.interdecile_mean())
def test_LOOClassNC(self):
for incl in [False, True]:
for rel in [False, True]:
for neigh in ['fixed', 'variable']:
nc = LOOClassNC(NaiveBayesLearner(),
Euclidean,
20,
relative=rel,
include=incl,
neighbourhood=neigh)
icp = InductiveClassifier(nc, self.train, self.calibrate)
pred = icp(self.test.x, 0.1)
print(pred)
self.assertEqual(pred, ['Iris-setosa'])
icp = InductiveClassifier(
LOOClassNC(NaiveBayesLearner(), Euclidean, 20))
r = run(icp, 0.1, CrossSampler(Table('iris'), 4))
self.assertGreater(r.accuracy(), 0.85)
self.assertGreater(r.singleton_criterion(), 0.8)
def test_LOORegrNC(self):
for incl in [False, True]:
for rel in [False, True]:
for neigh in ['fixed', 'variable']:
nc = LOORegrNC(LinearRegressionLearner,
Euclidean,
150,
relative=rel,
include=incl,
neighbourhood=neigh)
icr = InductiveRegressor(nc, self.train, self.calibrate)
lo, hi = icr(self.test.x, 0.1)
print(lo, hi)
self.assertLess(hi - lo, 20.0)
tab = Table('housing')
icr = InductiveRegressor(
LOORegrNC(LinearRegressionLearner, Euclidean, 150))
r = run(icr, 0.1, CrossSampler(tab, 4))
self.assertGreater(r.accuracy(), 0.85)
self.assertLess(r.mean_range(), 15.0)
def test_experimental(self):
icr = InductiveRegressor(
ExperimentalNC(RandomForestRegressor(n_estimators=20)), self.train,
self.calibrate)
r = run(icr, 0.1, CrossSampler(Table('housing'), 10))
print(r.accuracy(), r.median_range())