#!/usr/bin/python
# -*- coding: UTF-8 -*-
from numpy import ndarray
import numpy as np
from sklearn.tree import DecisionTreeClassifier
class GBDT():
def __init__(self, max_tree_num = 3):
"""
In this function you need to initialize the Gradient Boosting Decision Tree
:param max_tree_num: Maximum number of boosting tree,default = 3
"""
self.max_tree_num = max_tree_num
self.tree_list = []
def fit(self, x, y):
"""
In this function you need to fit the input data and label to the decision Tree
:param x: Training set X
:param y: Training set Y
"""
for i in range(self.max_tree_num):
model = DecisionTreeClassifier()
model.fit(x, y)
self.tree_list.append(model)
y -= model.predict(x)
def predict(self, x):
"""
In this function you need to use the classifier to predict input test data
:param x: Input test data which format is ndarray
:return: Return the prediction you got from model
"""
y_pred = np.zeros(x.shape[0])
for model in self.tree_list:
y_pred += model.predict(x)
return y_pred正文完
