def reduce_error(self, pool, score_index, x_train, y_train, x_validation,
y_validation, x_test, y_test):
BagPercepCurrent = BaggingClassifier(
linear_model.Perceptron(max_iter=5), self.pool_size)
BagPercepCurrent.fit(x_train, y_train)
ensemble_index = set()
ensemble_index.add(score_index[0])
ensemble = []
ensemble.append(pool.estimators_[score_index[0]])
BagPercepCurrent.estimators_ = ensemble
best_score = BagPercepCurrent.score(x_validation, y_validation)
# metrics = (None, None, None, None)
while (True):
index_best_score = 0
BagPercepCurrent.estimators_ = ensemble
best_score_test = BagPercepCurrent.score(x_test, y_test)
metrics = (best_score_test, ) + self.calc_metrics(
BagPercepCurrent.predict(x_test), y_test)
for i in list(score_index):
if i not in ensemble_index:
BagPercepCurrent.estimators_ = ensemble + [
pool.estimators_[i]
]
score_current = BagPercepCurrent.score(
x_validation, y_validation)
if best_score < score_current:
best_score = score_current
index_best_score = i
if index_best_score != 0:
ensemble_index.add(index_best_score)
ensemble.append(pool.estimators_[index_best_score])
else:
# print("best index", len(ensemble), best_score, best_score_test)
kappa_diversity = self.pairwise_diversity_measure(
BagPercepCurrent, len(BagPercepCurrent.estimators_),
x_test)
disagreement_diversity_ = self.disagreement_diversity_measure(
BagPercepCurrent, len(BagPercepCurrent.estimators_),
x_test)
return (metrics) + (
kappa_diversity,
disagreement_diversity_,
)
if len(ensemble_index) == self.pool_size:
return (metrics) + (
kappa_diversity,
disagreement_diversity_,
)
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(
base_estimator=Perceptron(tol=1e-3, random_state=None), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
random_state = np.random.RandomState(3)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(random_state=random_state)
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
assert_equal(ensemble[0].random_state, None)
assert_true(isinstance(ensemble[1].random_state, int))
assert_true(isinstance(ensemble[2].random_state, int))
assert_not_equal(ensemble[1].random_state, ensemble[2].random_state)
np_int_ensemble = BaggingClassifier(base_estimator=Perceptron(tol=1e-3),
n_estimators=np.int32(3))
np_int_ensemble.fit(iris.data, iris.target)
def best_first(self, pool, score_index, x_train, y_train, x_validation,
y_validation, x_test, y_test):
BagPercepCurrent = BaggingClassifier(
linear_model.Perceptron(max_iter=5), self.pool_size)
BagPercepCurrent.fit(x_train, y_train)
BagPercepCurrent.estimators_ = [pool.estimators_[score_index[0]]]
best_score = BagPercepCurrent.score(x_validation, y_validation)
best_score_test = BagPercepCurrent.score(x_test, y_test)
metrics = (best_score_test, ) + self.calc_metrics(
BagPercepCurrent.predict(x_test), y_test)
best_index = 1
best_score_test = 0
diversity_kappa = 0
for i, j in enumerate(list(score_index[1:])):
BagPercepCurrent.estimators_ += [pool.estimators_[j]]
score_current = BagPercepCurrent.score(x_validation, y_validation)
if best_score < score_current:
best_score = score_current
best_index = i
best_score_test = BagPercepCurrent.score(x_test, y_test)
metrics = (best_score_test, ) + self.calc_metrics(
BagPercepCurrent.predict(x_test), y_test)
diversity_kappa = self.pairwise_diversity_measure(
BagPercepCurrent, len(BagPercepCurrent.estimators_),
x_teste)
best_index += 2
# print("best index", best_index, best_score, best_score_test)
return (metrics) + (diversity_kappa, )
def test_base():
# Check BaseEnsemble methods.
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
def test_base():
"""Check BaseEnsemble methods."""
ensemble = BaggingClassifier(base_estimator=Perceptron(), n_estimators=3)
iris = load_iris()
ensemble.fit(iris.data, iris.target)
ensemble.estimators_ = [] # empty the list and create estimators manually
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator()
ensemble._make_estimator(append=False)
assert_equal(3, len(ensemble))
assert_equal(3, len(ensemble.estimators_))
assert_true(isinstance(ensemble[0], Perceptron))
