#读取数据集,至少包含变量和target两列
sample_set = pd.read_excel('/数据样本.xlsx')
def calc_score_median(sample_set, var):
'''
计算相邻评分的中位数,以便进行决策树二元切分
param sample_set: 待切分样本
param var: 分割变量名称
'''
var_list = list(np.unique(sample_set[var]))
var_median_list = []
for i in range(len(var_list) -1):
var_median = (var_list[i] + var_list[i+1]) / 2
var_median_list.append(var_median)
return var_median_list
def choose_best_split(sample_set, var, min_sample):
'''
使用CART分类决策树选择最好的样本切分点
返回切分点
param sample_set: 待切分样本
param var: 分割变量名称
param min_sample: 待切分样本的最小样本量(限制条件)
'''
# 根据样本评分计算相邻不同分数的中间值
score_median_list = calc_score_median(sample_set, var)
median_len = len(score_median_list)
sample_cnt = sample_set.shape[0]
sample1_cnt = sum(sample_set['target'])
sample0_cnt = sample_cnt- sample1_cnt
Gini = 1 - np.square(sample1_cnt / sample_cnt) - np.square(sample0_cnt / sample_cnt)
bestGini = 0.0; bestSplit_point = 0.0; bestSplit_position = 0.0
for i in range(median_len):
left = sample_set[sample_set[var] < score_median_list[i]]
right = sample_set[sample_set[var] > score_median_list[i]]
left_cnt = left.shape[0]; right_cnt = right.shape[0]
left1_cnt = sum(left['target']); right1_cnt = sum(right['target'])
left0_cnt = left_cnt - left1_cnt; right0_cnt = right_cnt - right1_cnt
left_ratio = left_cnt / sample_cnt; right_ratio = right_cnt / sample_cnt
if left_cnt < min_sample or right_cnt < min_sample:
continue
Gini_left = 1 - np.square(left1_cnt / left_cnt) - np.square(left0_cnt / left_cnt)
Gini_right = 1 - np.square(right1_cnt / right_cnt) - np.square(right0_cnt / right_cnt)
Gini_temp = Gini - (left_ratio * Gini_left + right_ratio * Gini_right)
if Gini_temp > bestGini:
bestGini = Gini_temp; bestSplit_point = score_median_list[i]
if median_len > 1:
bestSplit_position = i / (median_len - 1)
else:
bestSplit_position = i / median_len
else:
continue
Gini = Gini - bestGini
return bestSplit_point, bestSplit_position
def bining_data_split(sample_set, var, min_sample, split_list):
'''
划分数据找到最优分割点list
param sample_set: 待切分样本
param var: 分割变量名称
param min_sample: 待切分样本的最小样本量(限制条件)
param split_list: 最优分割点list
'''
split, position = choose_best_split(sample_set, var, min_sample)
if split != 0.0:
split_list.append(split)
# 根据分割点划分数据集,继续进行划分
sample_set_left = sample_set[sample_set[var] < split]
sample_set_right = sample_set[sample_set[var] > split]
# 如果左子树样本量超过2倍最小样本量,且分割点不是第一个分割点,则切分左子树
if len(sample_set_left) >= min_sample * 2 and position not in [0.0, 1.0]:
bining_data_split(sample_set_left, var, min_sample, split_list)
else:
None
# 如果右子树样本量超过2倍最小样本量,且分割点不是最后一个分割点,则切分右子树
if len(sample_set_right) >= min_sample * 2 and position not in [0.0, 1.0]:
bining_data_split(sample_set_right, var, min_sample, split_list)
else:
None
def get_bestsplit_list(sample_set, var):
'''
根据分箱得到最优分割点list
param sample_set: 待切分样本
param var: 分割变量名称
'''
# 计算最小样本阈值(终止条件)
min_df = sample_set.shape[0] * 0.05
split_list = []
# 计算第一个和最后一个分割点
bining_data_split(sample_set, var, min_df, split_list)
return split_list
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