模型融合

1.blending

比如数据分成train和test，对于model_i（比如xgboost） ，即对所有的数据训练模型model_i，预测test数据生成预测向量v_i， 然后对train做CV fold=5，然后对其他4份做训练数据，另外一份作为val数据，得出模型model_i_j，然后对val预测生成向量t_i_j， 然后将5分向量concat生成t_i，这是对应t_i与v_i对应，每个模型都能生成这样一组向量，然后在顶层的模型比如LR或者线性对t向量进行训练，生成blender模型对v向量进行预测
   也就是需要生成如下的一个表，训练集数据为把数据切分交叉生成，测试集为训练数据全部训练对测试集预测生成

id	model_1	model_2	model_3	model_4	label
1	0.1	0.2	0.14	0.15	0
2	0.2	0.22	0.18	0.3	1
3	0.8	0.7	0.88	0.6	1
4	0.3	0.3	0.2	0.22	0
5	0.5	0.3	0.6	0.5	1

blending 的优点是：比stacking简单，不会造成数据穿越，generalizers和stackers使用不同的数据，可以随时添加其他模型到blender中。

与stacking的区别是：
stacking在预测 测试集上时直接基于训练数据的
blender在预测 测试集上每次cv的子集都会预测下预测集， n次cv取平均

from __future__ import division
import numpy as np
import load_data
from sklearn.cross_validation import StratifiedKFold
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from utility import *
from evaluator import *



def logloss(attempt, actual, epsilon=1.0e-15):
    """Logloss, i.e. the score of the bioresponse competition.
    """
    attempt = np.clip(attempt, epsilon, 1.0-epsilon)
    return - np.mean(actual * np.log(attempt) + (1.0 - actual) * np.log(1.0 - attempt))


if __name__ == '__main__':

    np.random.seed(0) # seed to shuffle the train set

    # n_folds = 10
    n_folds = 5
    verbose = True
    shuffle = False


    # X, y, X_submission = load_data.load()

    train_x_id, train_x, train_y = preprocess_train_input()
    val_x_id, val_x, val_y = preprocess_val_input()

    X = train_x
    y = train_y
    X_submission = val_x
    X_submission_y = val_y

    if shuffle:
        idx = np.random.permutation(y.size)
        X = X[idx]
        y = y[idx]


    skf = list(StratifiedKFold(y, n_folds))

    clfs = [RandomForestClassifier(n_estimators=100, n_jobs=-1, criterion='gini'),
            RandomForestClassifier(n_estimators=100, n_jobs=-1, criterion='entropy'),
            ExtraTreesClassifier(n_estimators=100, n_jobs=-1, criterion='gini'),
            ExtraTreesClassifier(n_estimators=100, n_jobs=-1, criterion='entropy'),
            GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=50)]

    print "Creating train and test sets for blending."
    
    dataset_blend_train = np.zeros((X.shape[0], len(clfs)))
    dataset_blend_test = np.zeros((X_submission.shape[0], len(clfs)))
    
    for j, clf in enumerate(clfs):
        print j, clf
        dataset_blend_test_j = np.zeros((X_submission.shape[0], len(skf)))
        for i, (train, test) in enumerate(skf):
            print "Fold", i
            X_train = X[train]
            y_train = y[train]
            X_test = X[test]
            y_test = y[test]
            clf.fit(X_train, y_train)
            y_submission = clf.predict_proba(X_test)[:,1]
            dataset_blend_train[test, j] = y_submission
            dataset_blend_test_j[:, i] = clf.predict_proba(X_submission)[:,1]
        dataset_blend_test[:,j] = dataset_blend_test_j.mean(1)
        print("val auc Score: %0.5f" % (evaluate2(dataset_blend_test[:,j], X_submission_y)))

    print
    print "Blending."
    # clf = LogisticRegression()
    clf = GradientBoostingClassifier(learning_rate=0.02, subsample=0.5, max_depth=6, n_estimators=100)
    clf.fit(dataset_blend_train, y)
    y_submission = clf.predict_proba(dataset_blend_test)[:,1]

    print "Linear stretch of predictions to [0,1]"
    y_submission = (y_submission - y_submission.min()) / (y_submission.max() - y_submission.min())
    print "blend result"
    print("val auc Score: %0.5f" % (evaluate2(y_submission, X_submission_y)))
    print "Saving Results."
    np.savetxt(fname='blend_result.csv', X=y_submission, fmt='%0.9f')

2.rank_avg

这种融合方法适合排序评估指标，比如auc之类的

其中weight_i为该模型权重，权重为1表示平均融合
rank_i表示样本的升序排名 ，也就是越靠前的样本融合后也越靠前
能较快的利用排名融合多个模型之间的差异，而不用去加权样本的概率值融合

3.weighted

加权融合，给模型一个权重weight，然后加权得到最终结果
weight为1时为均值融合，result_i为模型i的输出

4.bagging

从特征，参数，样本的多样性差异性来做多模型融合，参考随机森林

转自：http://blog.csdn.net/bryan__/article/details/51229032