def EBM(X,
Y,
learning_rate=None,
depth=None,
estimators=None,
holdout_split=None,
seed=None):
### model & parameters
ebm = ExplainableBoostingClassifier(random_state=seed)
c_grid = {
"n_estimators": estimators,
"max_tree_splits": depth,
"learning_rate": learning_rate,
"holdout_split": holdout_split
}
c_grid = {k: v for k, v in c_grid.items() if v is not None}
summary = nested_cross_validate(X=X,
Y=Y,
estimator=ebm,
c_grid=c_grid,
seed=seed)
return summary
def XGB(X,
Y,
learning_rate=None,
depth=None,
estimators=None,
gamma=None,
child_weight=None,
subsample=None,
seed=None):
### model & parameters
xgboost = xgb.XGBClassifier(random_state=seed)
c_grid = {
"learning_rate": learning_rate,
"max_depth": depth,
"n_estimators": estimators,
"gamma": gamma,
"min_child_weight": child_weight,
"subsample": subsample
}
c_grid = {k: v for k, v in c_grid.items() if v is not None}
summary = nested_cross_validate(X=X,
Y=Y,
estimator=xgboost,
c_grid=c_grid,
seed=seed)
return summary
def LinearSVM(X, Y, C, seed=None):
### model & parameters
svm = LinearSVC(dual=False, max_iter=1e8, random_state=seed)
c_grid = {"C": C}
c_grid = {k: v for k, v in c_grid.items() if v is not None}
index = 'svm'
summary = nested_cross_validate(X=X,
Y=Y,
estimator=svm,
c_grid=c_grid,
seed=seed,
index=index)
return summary
def Logistic(X, Y, C, seed=None):
### model & parameters
lr = LogisticRegression(class_weight='balanced',
solver='liblinear',
random_state=seed,
penalty='l2')
c_grid = {"C": C}
c_grid = {k: v for k, v in c_grid.items() if v is not None}
summary = nested_cross_validate(X=X,
Y=Y,
estimator=lr,
c_grid=c_grid,
seed=seed)
return summary
def CART(X, Y, depth=None, split=None, impurity=None, seed=None):
### model & parameters
cart = DecisionTreeClassifier(random_state=seed)
c_grid = {
"max_depth": depth,
"min_samples_split": split,
"min_impurity_decrease": impurity
}
c_grid = {k: v for k, v in c_grid.items() if v is not None}
summary = nested_cross_validate(X=X,
Y=Y,
estimator=cart,
c_grid=c_grid,
seed=seed)
return summary
def RF(X, Y, depth=None, estimators=None, impurity=None, seed=None):
### model & parameters
rf = RandomForestClassifier(bootstrap=True, random_state=seed)
c_grid = {
"n_estimators": estimators,
"max_depth": depth,
"min_impurity_decrease": impurity
}
c_grid = {k: v for k, v in c_grid.items() if v is not None}
summary = nested_cross_validate(X=X,
Y=Y,
estimator=rf,
c_grid=c_grid,
seed=seed)
return summary