def testEntireEnsembleBuilder(self):
ensbuilder = EnsembleBuilder(
backend=self.backend,
dataset_name="TEST",
task_type=1, # Binary Classification
metric=roc_auc,
limit=-1, # not used,
seed=0, # important to find the test files
ensemble_nbest=2,
)
ensbuilder.SAVE2DISC = False
ensbuilder.score_ensemble_preds()
d2 = os.path.join(
self.backend.temporary_directory,
".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy"
)
sel_keys = ensbuilder.get_n_best_preds()
self.assertGreater(len(sel_keys), 0)
ensemble = ensbuilder.fit_ensemble(selected_keys=sel_keys)
print(ensemble, sel_keys)
n_sel_valid, n_sel_test = ensbuilder.get_valid_test_preds(
selected_keys=sel_keys)
# both valid and test prediction files are available
self.assertGreater(len(n_sel_valid), 0)
self.assertEqual(n_sel_valid, n_sel_test)
y_valid = ensbuilder.predict(
set_="valid",
ensemble=ensemble,
selected_keys=n_sel_valid,
n_preds=len(sel_keys),
index_run=1,
)
y_test = ensbuilder.predict(
set_="test",
ensemble=ensemble,
selected_keys=n_sel_test,
n_preds=len(sel_keys),
index_run=1,
)
# predictions for valid and test are the same
# --> should results in the same predictions
np.testing.assert_array_almost_equal(y_valid, y_test)
# since d2 provides perfect predictions
# it should get a higher weight
# so that y_valid should be exactly y_valid_d2
y_valid_d2 = ensbuilder.read_preds[d2][Y_VALID][:, 1]
np.testing.assert_array_almost_equal(y_valid, y_valid_d2)
def testEntireEnsembleBuilder(self):
ensbuilder = EnsembleBuilder(
backend=self.backend,
dataset_name="TEST",
task_type=1, #Binary Classification
metric=roc_auc,
limit=-1, # not used,
seed=0, # important to find the test files
ensemble_nbest=2,
)
ensbuilder.SAVE2DISC = False
ensbuilder.read_ensemble_preds()
d2 = os.path.join(
self.backend.temporary_directory,
".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2.npy"
)
sel_keys = ensbuilder.get_n_best_preds()
self.assertGreater(len(sel_keys), 0)
ensemble = ensbuilder.fit_ensemble(selected_keys=sel_keys)
print(ensemble, sel_keys)
n_sel_valid, n_sel_test = ensbuilder.get_valid_test_preds(selected_keys=sel_keys)
# both valid and test prediction files are available
self.assertGreater(len(n_sel_valid), 0)
self.assertEqual(n_sel_valid, n_sel_test)
y_valid = ensbuilder.predict(
set_="valid",
ensemble=ensemble,
selected_keys=n_sel_valid,
n_preds=len(sel_keys),
index_run=1,
)
y_test = ensbuilder.predict(
set_="test",
ensemble=ensemble,
selected_keys=n_sel_test,
n_preds=len(sel_keys),
index_run=1,
)
# predictions for valid and test are the same
# --> should results in the same predictions
np.testing.assert_array_almost_equal(y_valid, y_test)
# since d2 provides perfect predictions
# it should get a higher weight
# so that y_valid should be exactly y_valid_d2
y_valid_d2 = ensbuilder.read_preds[d2][Y_VALID][:, 1]
np.testing.assert_array_almost_equal(y_valid, y_valid_d2)
def testEntireEnsembleBuilder(ensemble_backend):
ensbuilder = EnsembleBuilder(
backend=ensemble_backend,
dataset_name="TEST",
task_type=BINARY_CLASSIFICATION,
metric=roc_auc,
seed=0, # important to find the test files
ensemble_nbest=2,
)
ensbuilder.SAVE2DISC = False
ensbuilder.compute_loss_per_model()
d2 = os.path.join(
ensemble_backend.temporary_directory,
".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy")
sel_keys = ensbuilder.get_n_best_preds()
assert len(sel_keys) > 0
ensemble = ensbuilder.fit_ensemble(selected_keys=sel_keys)
print(ensemble, sel_keys)
n_sel_valid, n_sel_test = ensbuilder.get_valid_test_preds(
selected_keys=sel_keys)
# both valid and test prediction files are available
assert len(n_sel_valid) > 0
assert n_sel_valid == n_sel_test
y_valid = ensbuilder.predict(
set_="valid",
ensemble=ensemble,
selected_keys=n_sel_valid,
n_preds=len(sel_keys),
index_run=1,
)
y_test = ensbuilder.predict(
set_="test",
ensemble=ensemble,
selected_keys=n_sel_test,
n_preds=len(sel_keys),
index_run=1,
)
# predictions for valid and test are the same
# --> should results in the same predictions
np.testing.assert_array_almost_equal(y_valid, y_test)
# since d2 provides perfect predictions
# it should get a higher weight
# so that y_valid should be exactly y_valid_d2
y_valid_d2 = ensbuilder.read_preds[d2][Y_VALID][:, 1]
np.testing.assert_array_almost_equal(y_valid, y_valid_d2)