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ml_GBDT.py
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ml_GBDT.py
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# -*- coding: utf-8 -*-
"""Module docstring.
author: ferris
update: 2015-11-22
function: Gradient Boosting Decision Tree
A regression model based on ml_decision_tree. Here I present the most common situation of
gradient boosting, in which the loos function is sum of squared errors.
对于广义的GB, 梯度(伪残差)和回归树的分裂条件都要修改.
ref:
1. 李航: 统计学习方法 P151
2. The Elements of Statistical Learning P380
"""
import numpy as np
import ml_DTS as dts
class GBDT:
def __init__(self, rows):
self.rows = rows
self.y = np.array([r[len(rows[0]) - 1] for r in rows], dtype='float64') # 样本y值
self.trees = []
self.m = len(rows) # 样本容量
self.resid = self.y # 初始化残差
self.rows_updated = self.rows # 初始化提升训练数据
def train(self, d=1, k=20):
"""
回归问题的提升树算法
:param d: 每棵树的深度
:param k: 树的数量
:return:
"""
def update_y(rows, y_new):
"""
替换数据集rows的y值, 返回一个新的数据集
:param rows: 数据集
:param y_new: 新的y值, list-like
:return:
"""
updated = []
col_id = len(rows[0]) - 1 # 列数
n_rows = len(rows) # 行数
for l in range(n_rows):
r_new = rows[l][:col_id]
r_new.append(y_new[l])
updated.append(r_new)
return updated
for i in range(k):
# 用残差更新训练数据的y值
self.rows_updated = update_y(self.rows_updated, self.resid)
# 训练新的weak learner, 入栈
t = dts.train_cart(self.rows_updated, target="regression", d=d)
self.trees.append(t)
# 更新残差
pred = dts.predict(t, self.rows_updated)
# print(np.array(pred))
# print(self.resid)
self.resid -= np.array(pred)
# 输出当前步骤均方误差
mse = np.dot(self.resid, self.resid) / len(self.resid)
print("training step {0} finished, current mse: {1}".format(str(i), str(mse)))
def predict(self, rows_new):
"""
将self.trees的每个预测结果累加, 得到最终预测值
:param rows_new: 待预测数据, 嵌套的list或tuple
:return: gbdt预测的y值
"""
result = []
for r in rows_new:
y_new = 0.0
# 预测结果为所有树的结果累加
for t in self.trees:
y_new += dts.predict_single(t, r)
result.append(y_new)
return result
def test(self, rows_test):
"""
:param rows_test: 待测试数据, 嵌套的list或tuple
:return: 均方误差
"""
l = len(rows_test[0]) - 1
predicted = self.predict(rows_test)
actual = [r[l] for r in rows_test]
error = np.array(predicted) - np.array(actual)
mse = np.dot(error, error) / len(error)
print("test mse: {0}".format(str(mse)))
return mse
# # test
# new_data = update_y(dts.my_data, [0, 2, 1, 1, 2, 0, 1, 2, 0, 0, 0, 0, 1, 0, 1, 1])
# g = GBDT(new_data)
# g.train(k=20)
# g.predict(new_data)
# g.test(new_data)