def brbm_rf(Xtr, ytr, Xte=None, yte=None):
randomforest = ensemble.RandomForestClassifier(n_jobs=-1, n_estimators=100)
rbm = BernoulliRBM(random_state=0)
classifier = Pipeline(steps=[('rbm', rbm), ('randomforest', randomforest)])
rbm.learning_rate = 0.025
rbm.n_iter = 250
rbm.n_components = 100
return simple_classification(classifier, Xtr, ytr, Xte, yte)
def logistic_regression_cv(Xtr, ytr, Xte=None, yte=None):
return simple_classification(linear_model.LogisticRegressionCV(), Xtr, ytr,
Xte, yte)
def log_sgd(Xtr, ytr, Xte=None, yte=None):
return simple_classification(linear_model.SGDClassifier(loss="log"), Xtr,
ytr, Xte, yte)
def random_forest(Xtr, ytr, Xte=None, yte=None):
return simple_classification(
ensemble.RandomForestClassifier(n_jobs=-1, n_estimators=100), Xtr, ytr,
Xte, yte)
def bagging(Xtr, ytr, Xte=None, yte=None):
return simple_classification(ensemble.BaggingClassifier(), Xtr, ytr, Xte,
yte)
def ada_boost(Xtr, ytr, Xte=None, yte=None):
return simple_classification(ensemble.AdaBoostClassifier(), Xtr, ytr, Xte,
yte)