def optimization(self):
"""
grid search for optimized parameters
:return:
"""
self.get_default_model()
self.best_params, self.best_scores = search(self.model,
self.get_model_name(),
self.X, self.y,
self.params)
def optimization(self):
"""
function to perform grid search of best parameters
:return:
"""
self.get_default_model()
self.best_params, self.best_scores = search(self.model,
self.get_model_name(),
self.X, self.y,
self.params)
def select_ensemble_model(self, P_train, Y_train):
"""
function to select ensemble parameters
:param P_train:
:param Y_train:
:return: best parameters
"""
ensemble_clf = LogisticRegression(random_state=RANDOM_STATE)
best_params, self.best_scores = search(ensemble_clf, "ensemble",
P_train, Y_train,
self.params["ensemble"])
logging.info("Best parameters for ensemble: {}".format(best_params))
return best_params
def optimization(self):
self.get_default_model()
self.best_params, self.best_scores = search(self.model,
self.get_model_name(),
self.X, self.y,
self.params)