#-*- coding:utf-8 -*-
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier,GradientBoostingRegressor
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_wine,load_boston,load_breast_cancer
from sklearn import tree


def read_data():
    boston = load_breast_cancer()
    Xtrain, Xtest, Ytrain, Ytest = train_test_split(boston.data, boston.target, test_size=0.3)
    for i in range(len(Ytrain)):
        if Ytrain[i] == 0:
            Ytrain[i] = -1
    for i in range(len(Ytest)):
        if Ytest[i] == 0:
            Ytest[i] = -1
    return Xtrain, Xtest, Ytrain, Ytest


def init(Ytrain):
    positive = sum(Ytrain == 1)
    negative = Ytrain.shape[0] - positive
    p = np.log2(positive/negative) # 可能是为了训练稍微快点
    return np.ones(Ytrain.shape[0])*p


def fit(Xtrain, Ytrain):
    print("init", Ytrain[:10])
    fx = []
    clf_tress = []


    fx0 = init(Ytrain)
    fx.append(fx0)

    print("0", fx0[:10])

    gx = fx0

    for i in range(10):
        # 求伪残差
        hx_0 = []
        for j in range(Ytrain.shape[0]):
            p = Ytrain[j] / (np.exp2(Ytrain[j]*gx[j]) + 1)
            hx_0.append(p)

        print("第", i, '轮 残差', gx[:10])
        clf = tree.DecisionTreeRegressor(criterion="mse", max_features=1, max_depth=1)
        clf.fit(Xtrain, np.array(hx_0))
        clf_tress.append(clf)

        fx_i = clf.predict(Xtrain)*0.7
        print("第", i, '轮 结果', fx_i[:10])
        fx.append(fx_i)
        gx = gx + fx_i


    gx = np.zeros(Ytrain.shape[0])
    for i in range(len(fx)):
        gx = gx + fx[i]

    print(gx[:10])

    gx = np.sign(gx)
    p = sum(gx==Ytrain)/Ytrain.shape[0]

    print("准确率", p)
    return clf_tress, fx0[0]


def score(Xtest, Ytest, trees, fx0):
    gx = np.ones(Ytest.shape[0])*fx0

    for i in range(len(trees)):
        gx = gx + trees[i].predict(Xtest)

    gx = np.sign(gx)
    p = sum(gx == Ytest) / Ytest.shape[0]
    print("准确率", p)

    gx = np.sign(trees[0].predict(Xtest))
    p = sum(gx == Ytest) / Ytest.shape[0]
    print("准确率0", p)


if __name__ == '__main__':
    Xtrain, Xtest, Ytrain, Ytest = read_data()
    trees,fx0 = fit(Xtrain, Ytrain)
    score(Xtest, Ytest, trees, fx0)

    gbm1 = GradientBoostingClassifier(n_estimators=10, max_depth=1, learning_rate=0.7,
                                      max_features='sqrt', random_state=10)
    gbm1.fit(Xtrain, Ytrain)
    print("gbdt", gbm1.score(Xtest, Ytest))