def test_fit(self):
bag_params = {'model_params': {'ind': 0}, 'n_models_fit': 10, 'seed': 23}
bag = ens.BaggingBinaryClassifier(SimpleClassifier, **bag_params)
bag.fit(self.X, self.Y)
assert bag.params == bag_params
assert bag.n_models == bag_params['n_models_fit'] == bag.n_models_fit
def test_oob_error_unused_obs(self):
bag = ens.BaggingBinaryClassifier(SimpleClassifier, seed=25,
n_models_fit=1)
with warnings.catch_warnings(record=True) as w:
bag.fit(self.X, self.Y)
assert len(w) == 0
def test_init1(self):
model_params = {'a': 5, 'b': True}
bag_params = {'model_params': model_params, 'n_models_fit': 5,
'seed': 23}
bag = ens.BaggingBinaryClassifier(ExBinaryClassifier1,
model_params=model_params,
n_models_fit=5, seed=23)
bag.params == bag_params
def test_classify_easy(self):
bag_params = {'model_params': {'ind': 1}, 'n_models_fit': 10,
'seed': 23}
bag = ens.BaggingBinaryClassifier(SimpleClassifier, **bag_params)
bag.fit(self.X, self.Y)
assert np.all(bag.classify(self.X) == self.Y)
def test_oob_error(self):
bag = ens.BaggingBinaryClassifier(SimpleClassifier, seed=25)
bag.fit(self.X, self.Y)
assert np.isfinite(bag.oob_error)
assert 0. <= bag.oob_error <= 1.
def test_fit_verbose(self):
bag_params = {'model_params': {'ind': 0}, 'n_models_fit': 10, 'seed': 23}
bag = ens.BaggingBinaryClassifier(SimpleClassifier, **bag_params)
bag.fit(self.X, self.Y, verbose=True)
def test_fit_param(self):
bag = ens.BaggingBinaryClassifier(SimpleClassifier)
bag.fit(self.X, self.Y, fit_params={'param': 2})
def test_fit_oob_noerror(self):
bag = ens.BaggingBinaryClassifier(NotBinaryClassifier)
bag.fit(self.X, self.Y)
def test_oob_nofit(self):
bag = ens.BaggingBinaryClassifier(ExBinaryClassifier1)
bag.oob_error
def test_init2(self):
bag_params = {'model_params': {}, 'n_models_fit': 5, 'seed': None}
bag = ens.BaggingBinaryClassifier(ExBinaryClassifier1, n_models_fit=5)
bag.params == bag_params