def test_predict_example_data(test_input):
"""Test learn and predict."""
train, test = load_toy_cancer()
_bk = Background(modes=train.modes)
_dn = BoostedRDN(background=_bk, target="cancer", n_estimators=test_input)
_dn.fit(train)
assert_array_equal(_dn.predict(test), np.array([1.0, 1.0, 1.0, 0.0, 0.0]))
def test_predict_example_data(test_input):
"""Test learn and predict."""
_bk = Background(modes=example_data.train.modes,
use_std_logic_variables=True)
_dn = BoostedRDN(background=_bk, target="cancer", n_estimators=test_input)
_dn.fit(example_data.train)
assert_array_equal(_dn.predict(example_data.test),
np.array([1.0, 1.0, 1.0, 0.0, 0.0]))
def test_toy_cancer_predict_after_load(test_input):
"""Load a ToyCancer json file and predict."""
clf = BoostedRDN()
clf.from_json(
"srlearn/tests/regression_tests/json/toy_cancer_{0}.json".format(
test_input))
_, test = load_toy_cancer()
_predictions = clf.predict(test)
assert_array_equal(_predictions, np.array([1.0, 1.0, 1.0, 0.0, 0.0]))
def test_serialize_BoostedRDN(tmpdir):
"""Test that inference is possible after loading from json"""
output_json = tmpdir.join("ToyCancerRDN.json")
train, test = load_toy_cancer()
bkg = Background(modes=train.modes)
rdn = BoostedRDN(background=bkg, target="cancer", n_estimators=5)
rdn.fit(train)
rdn.to_json(output_json)
# New BoostedRDN instance, loading from file, and running.
rdn2 = BoostedRDN()
rdn2.from_json(output_json)
_predictions = rdn2.predict(test)
assert len(rdn2.estimators_) == 5
assert_array_equal(_predictions, np.array([1.0, 1.0, 1.0, 0.0, 0.0]))