模型融合
参考：台大机器学习技法 http://blog.csdn.net/lho2010/article/details/42927287
stacking&blending http://heamy.readthedocs.io/en/latest/usage.html
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
Blending：用不相交的数据训练不同的 Base Model，将它们的输出取（加权）平均。
Stacking：划分训练数据集为两个不相交的集合，在第一个集合上训练多个学习器，在第二个集合上测试这几个学习器，把第三步得到的预测结果作为输入，把正确的回应作为输出，训练一个高层学习器。
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