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- #-*- 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))
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