def trained_decision_tree(max_depth, x, y):
# Create decision tree
# print("Called trained_decision_tree") # debug
dcsn_tree = DecisionTree(max_depth=max_depth)
x_sample, y_sample = Utils.bootstrap_samples(x, y) # bootstrapping samples
dcsn_tree.train_decision_tree(x_sample, y_sample)
return dcsn_tree
def train_classifiers(self, svm_param_dict=None, rf_param_dict=None, mlp_param_dict=None,
adab_param_dict=None):
self.__x_train, self.__x_test, self.__y_train, self.__y_test = prepare_data_for_training(Training_Data_Dir)
save_mdl = True
# ******************************************************************************Setting Up SVM for training
x_temp, y_temp = Utils.bootstrap_samples(self.__x_train, self.__y_train) # bootstrap samples
if svm_param_dict is not None:
self.svm.train_support_vector_machine(x_temp, self.__x_test, y_temp, self.__y_test, save_mdl=save_mdl,
**svm_param_dict)
else:
self.svm.train_support_vector_machine(x_temp, self.__x_test, y_temp, self.__y_test)
# ******************************************************************************Setting Up RF for training
x_temp, y_temp = Utils.bootstrap_samples(self.__x_train, self.__y_train) # bootstrap samples
if rf_param_dict is not None:
self.random_forest.train_random_forest(x_temp, self.__x_test, y_temp, self.__y_test, save_mdl=save_mdl,
**rf_param_dict)
else:
self.random_forest.train_random_forest(x_temp, self.__x_test, y_temp, self.__y_test, save_mdl=save_mdl)
# ******************************************************************************Setting Up MLP for training
x_temp, y_temp = Utils.bootstrap_samples(self.__x_train, self.__y_train)
if mlp_param_dict is not None:
self.multi_layer_perceptron.train_MultiLayerPerceptron(x_temp, self.__x_test, y_temp, self.__y_test,
save_mdl=save_mdl, **mlp_param_dict)
else:
self.multi_layer_perceptron.train_MultiLayerPerceptron(x_temp, self.__x_test, y_temp, self.__y_test,
save_mdl=save_mdl)
# ******************************************************************************Setting Up Adaboost for training
x_temp, y_temp = Utils.bootstrap_samples(self.__x_train, self.__y_train)
if adab_param_dict is not None:
self.adaboost.train_adaboost(x_temp, self.__x_test, y_temp, self.__y_test, save_classifiers=save_mdl,
**adab_param_dict)
else:
self.adaboost.train_adaboost(x_temp, self.__x_test, y_temp, self.__y_test, save_classifiers=save_mdl)
# self.__models_loaded = True
# self.test_main_classifier()
return 0