├── README ├── README.md ├── gbdt.py ├── gbdt_lr.py └── heart_scale /README: -------------------------------------------------------------------------------- 1 | 参数说明： 2 | ls： loss默认是deviance,包括（deviance、exponential）,deviance 其实就是logistic regression，exponential 指数损失函数，其实这时候就编程adaboost了 3 | lr： learning_rate,代表每个tree的学习速率，默认是0.1,具体可以自己试验看效果 4 | ns: n_estimators，代表 boosting的次数，默认是100，次数越多效果肯定越好，而且也不用担心over-fitting的问题 5 | md: max_depth, 代表每一棵树的深度， 默认是3. 6 | sub： subsample，每次随机选取训练数据集的比率，默认是1.就是全部用来训练。如果sub小于1的话，就变成stochastic gradient boosting. 7 | cv: cross Validation,交叉验证的大小，这个k-fold要设置. 8 | dataset： 训练数据集，注意这边目前只输入libsvm格式的数据 9 | 10 | 只要dataset放最后面，其他参数顺序无所谓！效果不好，就ns设高点~ 11 | 12 | command： 13 | （1） python gbdt.py -cv 10 heart_scale 14 | （2） python gbdt.py -ns 100 -md 5 -cv 10 heart_scale 15 | output： 16 | （1）会输出交叉验证的Acc 17 | （2）为了应对不平衡数据，还输出了Auc。 18 | （3）输出混淆矩阵，虽然样式没那么好看，但是还是能ok的。 19 | （4）输出feature的重要性打分，这个还是挺有用的，分值越高表示这个feature的在这个过程起到的 20 | 21 | 22 | 2.对文件进行predict 23 | 当我们有测试文件 test 和 训练文化train的时候，我们用train训练model，用这个model来预测test的文件，输出文件为当前目录下的predict文件。 24 | python gbdt.py -p testFile trainFile 25 | 26 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # gbdt 2 | gdbt implement by scikit-learn 3 | 4 | 参数说明： 5 | ls： loss默认是deviance,包括（deviance、exponential）,deviance 其实就是logistic regression，exponential 指数损失函数，其实这时候就编程adaboost了 6 | lr： learning_rate,代表每个tree的学习速率，默认是0.1,具体可以自己试验看效果 7 | ns: n_estimators，代表 boosting的次数，默认是100，次数越多效果肯定越好，而且也不用担心over-fitting的问题 8 | md: max_depth, 代表每一棵树的深度， 默认是3. 9 | sub： subsample，每次随机选取训练数据集的比率，默认是1.就是全部用来训练。如果sub小于1的话，就变成stochastic gradient boosting. 10 | cv: cross Validation,交叉验证的大小，这个k-fold要设置. 11 | dataset： 训练数据集，注意这边目前只输入libsvm格式的数据 12 | 13 | 只要dataset放最后面，其他参数顺序无所谓！效果不好，就ns设高点~ 14 | 15 | command： 16 | （1） python gbdt.py -cv 10 heart_scale 17 | （2） python gbdt.py -ns 100 -md 5 -cv 10 heart_scale 18 | output： 19 | （1）会输出交叉验证的Acc 20 | （2）为了应对不平衡数据，还输出了Auc。 21 | （3）输出混淆矩阵，虽然样式没那么好看，但是还是能ok的。 22 | （4）输出feature的重要性打分，这个还是挺有用的，分值越高表示这个feature的在这个过程起到的 23 | 24 | 25 | 2.对文件进行predict 26 | 当我们有测试文件 test 和 训练文化train的时候，我们用train训练model，用这个model来预测test的文件，输出文件为当前目录下的predict文件。 27 | python gbdt.py -p testFile trainFile 28 | 29 | 30 | # gbdt+Logistic 模型 31 | implement by scikit-learn 32 | 说明：利用GBDT模型构造新特征，比如使用N个树，n_estimators=n，对于一个输入样本点x，如果它在第一棵树最后落在其中的第二个叶子结点，而在第二棵树里最后落在其中的第一个叶子结点。那么通过GBDT获得的新特征向量为[0, 1, 0, 1, 0]，其中向量中的前三位对应第一棵树的3个叶子结点，后两位对应第二棵树的2个叶子结点。然后把这些构造的新特征做完LR的输入特征 33 | python gbdt_lr.py 34 | 35 | -------------------------------------------------------------------------------- /gbdt.py: -------------------------------------------------------------------------------- 1 | from sklearn.datasets import load_svmlight_file 2 | from sklearn.cross_validation import * 3 | from sklearn import metrics 4 | import numpy as np 5 | import sys 6 | import os 7 | import time 8 | from sklearn.ensemble import GradientBoostingClassifier 9 | from sklearn.linear_model import LogisticRegression 10 | 11 | 12 | class gbdt_paramter(): 13 | _nams = ["loss","learning_rate","n_estimators","max_depth","subsample","cv","p"] 14 | 15 | def __init__(self,option = None): 16 | if option == None: 17 | option = '' 18 | self.parse_option(option); 19 | 20 | def parse_option(self,option): 21 | if isinstance(option,list): 22 | argv = option 23 | elif isinstance(option,str): 24 | argv = option.split(); 25 | else: 26 | raise TypeError("arg 1 should be a list or a str ."); 27 | self.set_to_default_values(); 28 | 29 | i=0 30 | while i< len(argv): 31 | if argv[i] == "-ls": 32 | i = i+1 33 | self.loss = argv[i] 34 | elif argv[i] == "-lr": 35 | i = i+1 36 | self.learning_rate = float(argv[i]) 37 | elif argv[i] == "-ns": 38 | i = i+1 39 | self.n_estimators = int(argv[i]) 40 | elif argv[i] == "-md": 41 | i = i+1 42 | self.max_depth = int(argv[i]) 43 | elif argv[i] == "-sub": 44 | i = i+1 45 | self.subsample = float(argv[i]) 46 | elif argv[i] == "-cv": 47 | i = i+1 48 | self.cv = int(argv[i]) 49 | elif argv[i] == "-p": 50 | i = i+1 51 | self.p = argv[i] 52 | else: 53 | raise ValueError("Wrong options.Only -ls(loss) -lr(learning_rate) -ns(n_estimators) -md(max_depth) -sub(subssample),-cv,-p(testFile)") 54 | i += 1 55 | 56 | def set_to_default_values(self): 57 | self.loss = "deviance" 58 | self.learninig_rate = 0.1 59 | self.n_estimators = 100 60 | self.max_depth = 3 61 | self.subsample = 1 62 | self.cv=0 63 | self.p="" 64 | 65 | def read_data(data_file): 66 | try: 67 | 68 | t_X,t_y=load_svmlight_file(data_file) 69 | return t_X,t_y 70 | except ValueError as e: 71 | print(e) 72 | 73 | 74 | # GBDT(Gradient Boosting Decision Tree) Classifier 75 | def gradient_boosting_classifier(train_x, train_y,para): 76 | model = GradientBoostingClassifier(n_estimators=para.n_estimators) 77 | model.fit(train_x, train_y) 78 | return model 79 | 80 | if __name__ == '__main__': 81 | def exit_with_help(): 82 | print("Usage: gbdt.py [-ls (loss: deviance,exponential),-lr(learning_rate 0.1),-ns(n_estimators 100),-md(max_depth 3),-sub(subsample 1),-cv (10),-p testFile] dataset") 83 | sys.exit(1) 84 | if len(sys.argv)<2: 85 | exit_with_help(); 86 | dataset_path_name = sys.argv[-1] 87 | option = sys.argv[1:-1] 88 | try: 89 | train_X,train_Y=read_data(dataset_path_name) 90 | train_X=train_X.todense() 91 | para = gbdt_paramter(option) 92 | gbdt=gradient_boosting_classifier(train_X, train_Y,para) 93 | 94 | if para.cv>0: 95 | accuracy = cross_val_score(gbdt, train_X, train_Y, cv=10, scoring='accuracy') 96 | roc = cross_val_score(gbdt, train_X, train_Y, cv=10, scoring='roc_auc') 97 | print "10 cross validation result" 98 | print "ACC:"+str(accuracy.mean()); 99 | print "AUC:"+str(roc.mean()); 100 | predicted = cross_val_predict(gbdt, train_X,train_Y, cv=10) 101 | print "confusion_matrix" 102 | print metrics.confusion_matrix(train_Y, predicted) 103 | print "The feature importances (the higher, the more important the feature)" 104 | print gbdt.feature_importances_ 105 | if para.p!="": 106 | test_x,test_y = read_data(para.p); 107 | predict = gbdt.predict(test_x.todense()); 108 | prob = gbdt.predict_proba(test_x.todense()); 109 | out = open('predict','wb'); 110 | out.write("origin"+"\t"+"predict"+"\t"+"prob"+"\n") 111 | for i in range(predict.shape[0]): 112 | if (i%1000==0): 113 | print "instance:"+str(i); 114 | out.write(str(test_y[i])+"\t"+str(predict[i])+"\t"+str(prob[i])+'\n') 115 | out.close(); 116 | except(IOError,ValueError) as e: 117 | sys.stderr.write(str(e) + '\n') 118 | sys.exit(1) 119 | -------------------------------------------------------------------------------- /gbdt_lr.py: -------------------------------------------------------------------------------- 1 | from sklearn.datasets import load_iris 2 | import numpy as np 3 | import pandas as pd 4 | from sklearn import linear_model 5 | from sklearn.preprocessing import OneHotEncoder 6 | from sklearn.ensemble import GradientBoostingRegressor 7 | from sklearn.datasets import load_svmlight_file 8 | from sklearn.linear_model import LogisticRegression 9 | from sklearn import metrics 10 | from sklearn.model_selection import train_test_split 11 | import numpy as np 12 | 13 | 14 | def read_data(data_file): 15 | try: 16 | 17 | t_X,t_y=load_svmlight_file(data_file) 18 | return t_X.todense(),t_y 19 | except ValueError as e: 20 | print(e) 21 | 22 | 23 | def oneHot(datasets): 24 | encode = OneHotEncoder() 25 | encode.fit(datasets) 26 | return encode 27 | 28 | 29 | def gbdt(train_X,train_Y): 30 | gbdt=GradientBoostingRegressor(n_estimators=500,learning_rate=0.1) 31 | gbdt.fit(train_X,train_Y) 32 | return gbdt 33 | 34 | 35 | def gbdt_lr(train_X,train_Y,test_X,test_Y): 36 | gbdt_model = gbdt(train_X,train_Y) 37 | tree_feature = gbdt_model.apply(train_X) 38 | encode = oneHot(tree_feature) 39 | tree_feature = encode.transform(tree_feature).toarray() 40 | 41 | lr = LogisticRegression() 42 | lr.fit(tree_feature, train_Y) 43 | 44 | test_X = gbdt_model.apply(test_X) 45 | tree_feature_test = encode.transform(test_X) 46 | y_pred = lr.predict_proba(tree_feature_test)[:,1] 47 | auc = metrics.roc_auc_score(test_Y, y_pred) 48 | print "gbdt+lr:",auc 49 | 50 | def lr(train_X,train_Y,test_X,test_Y): 51 | lr = LogisticRegression() 52 | lr.fit(train_X, train_Y) 53 | y_pred = lr.predict_proba(test_X)[:,1] 54 | auc = metrics.roc_auc_score(test_Y, y_pred) 55 | print "only lr:",auc 56 | 57 | def gbdt_train(train_X,train_Y,test_X,test_Y): 58 | model = gbdt(train_X,train_Y) 59 | y_pred = model.predict(test_X) 60 | auc = metrics.roc_auc_score(test_Y, y_pred) 61 | print "only gbdt:",auc 62 | 63 | X,Y=read_data("heart_scale") 64 | train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.3) 65 | gbdt_lr(train_X,train_Y,test_X,test_Y) 66 | lr(train_X,train_Y,test_X,test_Y) 67 | gbdt_train(train_X,train_Y,test_X,test_Y) 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 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