pythonmkl有什么⽤_pythonMKLSVM（多核学习）import math
import pandas as pd
from sklearn.linear_model import LassoCV
from sklearn.feature_lection import SelectFromModel
from sklearn import preprocessing
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
del_lection import train_test_split
import itertools
import datetime
del_lection import cross_val_score
"定义核函数"
"(求范数)"
"范数的定义 eg向量x=[2,4,8]T(转⾄)的范数为:||x||=根号(2*2+4*4+8*8)=9.165"
"p(1)返回e的⼀次⽅"
def rbf(gamma=1.0):
def rbf_fun(x1,x2):
p(((x1-x2))*(-1.0*gamma))
return rbf_fun
"x2.transpo()是对矩阵的转置"
def lin(offt=0):
def lin_fun(x1,x2):
return x1.anspo())+offt
return lin_fun
"pow(x1.anspo())+offt,power)指的是对得到x1.anspo())+offt的power次⽅"
def poly(power=2,offt=0):
def poly_fun(x1,x2):
return pow(x1.anspo())+offt,power)
return poly_fun
def sig(alpha=1.0,offt=0):
def sig_fun(x1,x2):
return math.tanh(alpha*1.0*x1.anspo())+offt)
"根据输⼊X的⼤⼩构造核矩阵"
def kernel_matrix(x,kernel):
s((x.shape[0],x.shape[0]))
for a in range(x.shape[0]):
for b in range(x.shape[0]):
mat[a][b]=kernel(x[a],x[b])
return mat
".trace()得到矩阵的迹eg a=[[a11,a12],[a21,a22]] a的迹就是a11+a22的值"
"f_dot函数最后得到⼀个值"
def f_dot(kernel_mat1,kernel_mat2):
return (kernel_mat1.dot(anspo())).trace()
def A(kernel_mat1,kernel_mat2):
return (f_dot(kernel_mat1,kernel_mat2))/(math.sqrt(f_dot(kernel_mat1,kernel_mat1)*f_dot(kernel_mat2,kernel_mat2))) '''
求betas
1.形成⼀个y⾏y列的矩阵yyT，由y*yT得到
2.通过kernel_matrix，得到对X数据进⾏核函数的映射后的矩阵，
和X的⾏数列数相同，设为data
3.通过f_dot函数，将data和y相乘，返回相乘得到的矩阵f_mat，再返回矩阵的迹，记为J
4.通过A函数将f_mat和yyT相乘得到的迹，再除以根号下(f_mat*f_matT)*(yyT*yyTT)得到的矩阵的迹
5.将不同核函数在第四步得到的值相加，得到deno值
6.得到使⽤不同核函数情况下的betas值，
通过A函数将f_mat和yyT相乘得到的迹，再除以根号下
(f_mat*f_matT)*(yyT*yyTT)得到的矩阵的迹，最后除以deno
就的到每个核函数的betas值了
betas值是每个核函数的⽐重
'''
def beta_finder(x,y,kernel_list):
y=np.matrix(y)
yyT=y.anspo())
deno=sum([A(kernel_matrix(x,kernel),yyT) for kernel in kernel_list])
betas=[A(kernel_matrix(x,kernel),yyT)/deno for kernel in kernel_list]
print (betas)
"产⽣multi核"
'''
1.得到betas
2.⽣成矩阵XxY维的矩阵
3.得到不同核函数对X数据映射后的数据data，再乘以该核函数对应的beta值(⽐重)，再
4.将上述得到的矩阵相加得到最融合的矩阵
'''
def multi_kernel_maker(x,y,kernel_list):
betas=[float(b) for b in beta_finder(x,y,kernel_list)]
#print " ",betas
def multi_kernal(x1,x2):
s((x1.shape[0],x2.shape[0]))
for a in range(x1.shape[0]):
for b in range(x2.shape[0]):
mat[a][b]=sum([betas[i]*kernel(x1[a],x2[b]) for i,kernel in enumerate(kernel_list)])
return mat
return multi_kernal
"制造多核"
#kernels = [lin(),lin(2),poly(),poly(3),poly(4),rbf(),rbf(1.5),sig(),sig(1.5)]
kernels = [lin(),poly(),rbf(),sig(),rbf(10)]
kernel_numbers=5
multi_kernels = [mult for mult binations(kernels, kernel_numbers)]#binati
ons迭代器eg。(combinations('ABC', 2))得到[('A', 'B'), ('A', 'C'), ('B', 'C')]
"训练模型"
def mk_train(x_train,y_train,multi_kernels):
y=[[t] for t in y_train[:]]
# y=[[t] for t in y_train[:,i]]
for k_list in multi_kernels:
mk_train_start_time=w()
multi_kernel=multi_kernel_maker(x_train,y,k_list)
print(k_list,'multi kernel maked! !')
clf=svm.SVC(kernel=multi_kernel)
results=cross_val_score(clf,x_train, y_train, scoring='accuracy',cv=10)
an())
mk_train_end_time=w()
print('mk_train_time:',(mk_train_end_time-mk_train_start_time).conds,'conds') "导⼊数据"
file_path=r'C:\\Urs\\DengBY\\Desktop\\liver_Lasso_features\\ALL_V.csv' data = pd.read_csv(file_path)
a=pd.DataFrame(data)
X=a.values[:,1:597]
y=a.values[:,598]
min_max_scaler = preprocessing.MinMaxScaler()#范围0-1缩放标准化
X=min_max_scaler.fit_transform(X)
"基于Lasso的特征选择"
lsvc=LassoCV().fit(X, y)
model = SelectFromModel(lsvc, prefit=True)
X_lsvc = ansform(X)
df_X_lsvc=pd.DataFrame(X_lsvc)
y=pd.DataFrame(y)
b=df_X_lsvc
objs=[b,y]
"features lect 后的数据"
at(objs, axis=1, join='outer', join_axes=None, ignore_index=Fal, keys=None, levels=None, names=None, verify_integrity=Fal
'打乱数据，重新排序'
data=data.sample(frac=1)
X=data.values[:,:14]
y=data.values[:,15]
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=200)
print('model training starting')
mk_train(X_train,y_train,multi_kernels)
print('model training finishing')
#保存⽇志
#import sys
#f_handler=open('out.log', 'w')
#sys.stdout=f_handler
