def init_hyperparameters(self, trial, X, y):
self.name = id_name('SelectKBest')
self.k_fraction = trial.suggest_uniform(self.name + 'k_fraction', 0.0,
1.0)
self.sparse = False
score_func = trial.suggest_categorical(self.name + 'score_func', [
'chi2', 'f_classif', 'mutual_info', 'ExtraTreesClassifier',
'LinearSVC'
])
if score_func == "chi2":
self.score_func = sklearn.feature_selection.chi2
elif score_func == "f_classif":
self.score_func = sklearn.feature_selection.f_classif
elif score_func == "mutual_info":
self.score_func = sklearn.feature_selection.mutual_info_classif
elif score_func == 'ExtraTreesClassifier':
new_name = self.name + '_' + score_func + '_'
model = ExtraTreesClassifier()
model.n_estimators = 100
model.criterion = trial.suggest_categorical(
new_name + "criterion", ["gini", "entropy"])
model.max_features = trial.suggest_uniform(
new_name + "max_features", 0, 1)
model.max_depth = None
model.max_leaf_nodes = None
model.min_samples_split = trial.suggest_int(new_name +
"min_samples_split",
2,
20,
log=False)
model.min_samples_leaf = trial.suggest_int(new_name +
"min_samples_leaf",
1,
20,
log=False)
model.min_weight_fraction_leaf = 0.
model.min_impurity_decrease = 0.
model.bootstrap = trial.suggest_categorical(
new_name + "bootstrap", [True, False])
self.score_func = functools.partial(
model_score, estimator=model) #bindFunction1(model)
elif score_func == 'LinearSVC':
new_name = self.name + '_' + score_func + '_'
model = sklearn.svm.LinearSVC()
model.penalty = "l1"
model.loss = "squared_hinge"
model.dual = False
model.tol = trial.suggest_loguniform(new_name + "tol", 1e-5, 1e-1)
model.C = trial.suggest_loguniform(new_name + "C", 0.03125, 32768)
model.multi_class = "ovr"
model.fit_intercept = True
model.intercept_scaling = 1
self.score_func = functools.partial(model_score, estimator=model)
