def test_logitboost_hastie_fitting():
c = LogitBoostClassifier(base_estimator=DecisionTreeRegressor(max_depth=1),
n_estimators=30,
learning_rate=1.0)
data = Hastie_10_2()
c.fit(data.data, np.sign(data.labels))
assert_array_less(c.estimator_errors_, 0.5)
assert zero_one_loss(np.sign(data.labels), c.predict(data.data)) < 0.2
def test_logitboost_hastie_fitting():
c = LogitBoostClassifier(
base_estimator=DecisionTreeRegressor(max_depth=1),
n_estimators=30,
learning_rate=1.0
)
data = Hastie_10_2()
c.fit(data.data, np.sign(data.labels))
assert_array_less(c.estimator_errors_, 0.5)
assert zero_one_loss(np.sign(data.labels), c.predict(data.data)) < 0.2
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import AdaBoostClassifier
from skboost.logitboost import LogitBoostClassifier
from skboost.datasets import MUSK1, MUSK2, Hastie_10_2
from skboost.milboost.softmax import *
#ds_train = MUSK2()
#print(ds_train)
#ds_test = MUSK1()
#print(ds_test)
ds_train = Hastie_10_2()
ds_test = Hastie_10_2(random_state=43143)
classifier = LogitBoostClassifier(
base_estimator=DecisionTreeRegressor(max_depth=3),
n_estimators=30,
learning_rate=1.0)
classifier.fit(ds_train.data, np.sign(ds_train.labels))
print(classifier)
classifier.predict_proba(ds_train.data)
# Calculate step-by-step results per instance on training data.
milboost_train_error = np.zeros((len(classifier.estimators_), ))
for i, y_pred in enumerate(classifier.staged_predict(ds_train.data)):
milboost_train_error[i] = zero_one_loss(y_pred, np.sign(ds_train.labels))
# Calculate step-by-step results per instance on test data.
milboost_test_error = np.zeros((len(classifier.estimators_), ))
for i, y_pred in enumerate(classifier.staged_predict(ds_test.data)):
milboost_test_error[i] = zero_one_loss(y_pred, np.sign(ds_test.labels))
from sklearn.ensemble import AdaBoostClassifier
from skboost.logitboost import LogitBoostClassifier
from skboost.datasets import MUSK1, MUSK2, Hastie_10_2
from skboost.milboost.softmax import *
#ds_train = MUSK2()
#print(ds_train)
#ds_test = MUSK1()
#print(ds_test)
ds_train = Hastie_10_2()
ds_test = Hastie_10_2(random_state=43143)
classifier = LogitBoostClassifier(
base_estimator=DecisionTreeRegressor(max_depth=3),
n_estimators=30,
learning_rate=1.0)
classifier.fit(ds_train.data, np.sign(ds_train.labels))
print(classifier)
classifier.predict_proba(ds_train.data)
# Calculate step-by-step results per instance on training data.
milboost_train_error = np.zeros((len(classifier.estimators_),))
for i, y_pred in enumerate(classifier.staged_predict(ds_train.data)):
milboost_train_error[i] = zero_one_loss(y_pred, np.sign(ds_train.labels))
# Calculate step-by-step results per instance on test data.
milboost_test_error = np.zeros((len(classifier.estimators_),))
for i, y_pred in enumerate(classifier.staged_predict(ds_test.data)):
milboost_test_error[i] = zero_one_loss(y_pred, np.sign(ds_test.labels))
