def test_store_models(self):
nrows, ncols = 50, 10
t = random_data(nrows, ncols)
fitters = [naive_bayes.BayesLearner(), majority.MajorityFitter()]
cv = testing.LeaveOneOut()
res = cv(t, fitters)
self.assertIsNone(res.models)
cv = testing.LeaveOneOut(store_models=True)
res = cv(t, fitters)
self.assertEqual(len(res.models), 50)
for models in res.models:
self.assertEqual(len(models), 2)
self.assertIsInstance(models[0], naive_bayes.BayesClassifier)
self.assertIsInstance(models[1], majority.ConstantClassifier)
cv = testing.LeaveOneOut()
res = cv(t, fitters)
self.assertIsNone(res.models)
res = testing.LeaveOneOut(t, fitters, store_models=True)
self.assertEqual(len(res.models), 50)
for models in res.models:
self.assertEqual(len(models), 2)
self.assertIsInstance(models[0], naive_bayes.BayesClassifier)
self.assertIsInstance(models[1], majority.ConstantClassifier)
def test_miss_majority(self):
x = np.zeros((50, 3))
y = x[:, -1]
x[49] = 1
data = Table(x, y)
res = testing.LeaveOneOut(data, [majority.MajorityFitter()])
np.testing.assert_equal(res.predicted[0][:49], 0)
x[49] = 0
res = testing.LeaveOneOut(data, [majority.MajorityFitter()])
np.testing.assert_equal(res.predicted[0][:49], 0)
x[25:] = 1
y = x[:, -1]
data = Table(x, y)
res = testing.LeaveOneOut(data, [majority.MajorityFitter()])
np.testing.assert_equal(res.predicted[0],
1 - data.Y[res.row_indices].flatten())
def test_store_data(self):
nrows, ncols = 50, 10
t = random_data(nrows, ncols)
fitters = [naive_bayes.BayesLearner()]
cv = testing.LeaveOneOut()
res = cv(t, fitters)
self.assertIsNone(res.data)
cv = testing.LeaveOneOut(store_data=True)
res = cv(t, fitters)
self.assertIs(res.data, t)
res = testing.LeaveOneOut(t, fitters)
self.assertIsNone(res.data)
res = testing.LeaveOneOut(t, fitters, store_data=True)
self.assertIs(res.data, t)
def test_probs(self):
data = Table('iris')[30:130]
fitters = [majority.MajorityFitter(), majority.MajorityFitter()]
results = testing.LeaveOneOut(k=10)(data, fitters)
self.assertEqual(results.predicted.shape, (2, len(data)))
np.testing.assert_equal(results.predicted, np.ones((2, 100)))
probs = results.probabilities
self.assertTrue((probs[:, :, 0] < probs[:, :, 2]).all())
self.assertTrue((probs[:, :, 2] < probs[:, :, 1]).all())
def test_call(self):
cv = testing.LeaveOneOut()
nrows, ncols = 100, 10
t = random_data(nrows, ncols)
res = cv(t, [naive_bayes.BayesLearner()])
y = t.Y
np.testing.assert_equal(res.actual, y[res.row_indices].reshape(nrows))
np.testing.assert_equal(res.predicted[0],
y[res.row_indices].reshape(nrows))
np.testing.assert_equal(np.argmax(res.probabilities[0], axis=1),
y[res.row_indices].reshape(nrows))
def update_results(self):
# items in need of an update
items = [(key, input) for key, input in self.learners.items()
if input.results is None]
learners = [input.learner for _, input in items]
self.setStatusMessage("Running")
# TODO: Test each learner individually
if self.resampling == OWTestLearners.KFold:
results = testing.CrossValidation(self.train_data,
learners,
k=self.k_folds,
store_data=True)
elif self.resampling == OWTestLearners.LeaveOneOut:
results = testing.LeaveOneOut(self.train_data,
learners,
store_data=True)
elif self.resampling == OWTestLearners.Bootstrap:
p = self.sample_p / 100.0
results = testing.Bootstrap(self.train_data,
learners,
n_resamples=self.n_repeat,
p=p,
store_data=True)
elif self.resampling == OWTestLearners.TestOnTrain:
results = testing.TestOnTrainingData(self.train_data,
learners,
store_data=True)
elif self.resampling == OWTestLearners.TestOnTest:
assert self.test_data is not None
results = testing.TestOnTestData(self.train_data,
self.test_data,
learners,
store_data=True)
else:
assert False
results = list(split_by_model(results))
class_var = self.train_data.domain.class_var
if is_discrete(class_var):
test_stats = classification_stats
else:
test_stats = regression_stats
stats = [test_stats(res) for res in results]
for (key, input), res, stat in zip(items, results, stats):
self.learners[key] = input._replace(results=res, stats=stat)
self.setStatusMessage("")
self._update_stats_model()
def update_results(self):
self.warning([1, 2])
self.error(2)
if self.train_data is None:
return
if self.resampling == OWTestLearners.TestOnTest:
if self.test_data is None:
self.warning(2, "Missing separate test data input")
return
elif self.test_data.domain.class_var != \
self.train_data.domain.class_var:
self.error(2, ("Inconsistent class variable between test " +
"and train data sets"))
return
# items in need of an update
items = [(key, input) for key, input in self.learners.items()
if input.results is None]
learners = [input.learner for _, input in items]
self.setStatusMessage("Running")
if self.test_data is not None and \
self.resampling != OWTestLearners.TestOnTest:
self.warning(1, "Test data is present but unused. "
"Select 'Test on test data' to use it.")
# TODO: Test each learner individually
if self.resampling == OWTestLearners.KFold:
results = testing.CrossValidation(
self.train_data, learners, k=self.k_folds, store_data=True
)
elif self.resampling == OWTestLearners.LeaveOneOut:
results = testing.LeaveOneOut(
self.train_data, learners, store_data=True
)
elif self.resampling == OWTestLearners.Bootstrap:
p = self.sample_p / 100.0
results = testing.Bootstrap(
self.train_data, learners, n_resamples=self.n_repeat, p=p,
store_data=True
)
elif self.resampling == OWTestLearners.TestOnTrain:
results = testing.TestOnTrainingData(
self.train_data, learners, store_data=True
)
elif self.resampling == OWTestLearners.TestOnTest:
if self.test_data is None:
return
results = testing.TestOnTestData(
self.train_data, self.test_data, learners, store_data=True
)
else:
assert False
results = list(split_by_model(results))
class_var = self.train_data.domain.class_var
if is_discrete(class_var):
test_stats = classification_stats
else:
test_stats = regression_stats
self._update_header()
stats = [test_stats(res) for res in results]
for (key, input), res, stat in zip(items, results, stats):
self.learners[key] = input._replace(results=res, stats=stat)
self.setStatusMessage("")
self._update_stats_model()
