def fit(self, data):
# 掐头去尾， 删除id和label，得到特征名称
self.features = list(data.columns)[1: -1]
# 获取所有类别
self.classes = data['label'].unique().astype(str)
# 初始化多分类损失函数的参数 K
self.loss.init_classes(self.classes)
# 根据类别将‘label’列进行one-hot处理
for class_name in self.classes:
label_name = 'label_' + class_name
data[label_name] = data['label'].apply(lambda x: 1 if str(x) == class_name else 0)
# 初始化 f_0(x)
self.f_0[class_name] = self.loss.initialize_f_0(data, class_name)
# print(data)
# 对 m = 1, 2, ..., M
logger.handlers[0].setLevel(logging.INFO if self.is_log else logging.CRITICAL)
for iter in range(1, self.n_trees + 1):
if len(logger.handlers) > 1:
logger.removeHandler(logger.handlers[-1])
fh = logging.FileHandler('results/NO.{}_tree.log'.format(iter), mode='w', encoding='utf-8')
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
logger.info(('-----------------------------构建第%d颗树-----------------------------' % iter))
# 这里计算负梯度整体计算是为了计算p_sum的一致性
self.loss.calculate_residual(data, iter)
self.trees[iter] = {}
for class_name in self.classes:
target_name = 'res_' + class_name + '_' + str(iter)
self.trees[iter][class_name] = Tree(data, self.max_depth, self.min_samples_split,
self.features, self.loss, target_name, logger)
self.loss.update_f_m(data, self.trees, iter, class_name, self.learning_rate, logger)
if self.is_plot:
plot_multi(self.trees[iter], max_depth=self.max_depth, iter=iter)
if self.is_plot:
plot_all_trees(self.n_trees)
def fit(self, data):
"""
:param data: pandas.DataFrame, the features data of train training
"""
# 掐头去尾， 删除id和label，得到特征名称
self.features = list(data.columns)[1: -1]
# 初始化 f_0(x)
# 对于平方损失来说，初始化 f_0(x) 就是 y 的均值
self.f_0 = self.loss.initialize_f_0(data)
# 对 m = 1, 2, ..., M
logger.handlers[0].setLevel(logging.INFO if self.is_log else logging.CRITICAL)
for iter in range(1, self.n_trees+1):
if len(logger.handlers) > 1:
logger.removeHandler(logger.handlers[-1])
fh = logging.FileHandler('results/NO.{}_tree.log'.format(iter), mode='w', encoding='utf-8')
fh.setLevel(logging.DEBUG)
logger.addHandler(fh)
# 计算负梯度--对于平方误差来说就是残差
logger.info(('-----------------------------构建第%d颗树-----------------------------' % iter))
self.loss.calculate_residual(data, iter)
target_name = 'res_' + str(iter)
self.trees[iter] = Tree(data, self.max_depth, self.min_samples_split,
self.features, self.loss, target_name, logger)
self.loss.update_f_m(data, self.trees, iter, self.learning_rate, logger)
if self.is_plot:
plot_tree(self.trees[iter], max_depth=self.max_depth, iter=iter)
# print(self.trees)
if self.is_plot:
plot_all_trees(self.n_trees)
def fit(self, data):
'''
:param x: pandas.DataFrame, the features data of train training
:param y: list, the label of training
'''
# 去头掐尾， 删除id和label，得到特征名称
self.features = list(data.columns)[1:-1]
# 初始化 f_0(x)
# 对于平方损失来说，初始化 f_0(x) 就是 y 的均值
self.f_0 = self.loss_function.initialize_f_0(data)
# 对 m = 1, 2, ..., M
logger.setLevel(logging.INFO if self.is_log else logging.CRITICAL)
for iter in range(1, self.n_trees + 1):
# 计算负梯度--对于平方误差来说就是残差
logger.info((
'-----------------------------构建第%d颗树-----------------------------'
% iter))
self.loss_function.calculate_residual(data, iter)
self.trees[iter] = Tree(data, self.max_depth, self.features, iter,
logger)
self.loss_function.update_f_m(data, self.trees, iter,
self.learning_rate, logger)