python运行速度比较快_使SVM在python中运行速度更快

在python中使用svm的代码：

from sklearn import datasets

from sklearn.multiclass import OneVsRestClassifier

from sklearn.svm import SVC

iris = datasets.load_iris()

X, y = iris.data, iris.target

clf = OneVsRestClassifier(SVC(kernel='linear', probability=True, class_weight='auto'))

clf.fit(X, y)

proba = clf.predict_proba(X)

但是它花费了大量的时间。

实际数据尺寸：

train-set (1422392,29)

test-set (233081,29)

如何加速(平行或其他方式)？请帮忙。

我已经尝试过PCA和下采样。

编辑：

from sklearn import datasets

from sklearn.multiclass import OneVsRestClassifier

from sklearn.svm import SVC,LinearSVC

from sklearn.linear_model import SGDClassifier

import joblib

import numpy as np

from sklearn import grid_search

import multiprocessing

import numpy as np

import math

def new_func(a): #converts array(x) elements to (1/(1 + e(-x)))

a=1/(1 + math.exp(-a))

return a

if __name__ == '__main__':

iris = datasets.load_iris()

cores=multiprocessing.cpu_count()-2

X, y = iris.data, iris.target #loading dataset

C_range = 10.0 ** np.arange(-4, 4); #c value range

param_grid = dict(estimator__C=C_range.tolist())

svr = OneVsRestClassifier(LinearSVC(class_weight='auto'),n_jobs=cores) ################LinearSVC Code faster

#svr = OneVsRestClassifier(SVC(kernel='linear', probability=True, ##################SVC code slow

# class_weight='auto'),n_jobs=cores)

clf = grid_search.GridSearchCV(svr, param_grid,n_jobs=cores,verbose=2) #grid search

clf.fit(X, y) #training svm model

decisions=clf.decision_function(X) #outputs decision functions

#prob=clf.predict_proba(X) #only for SVC outputs probablilites

print decisions[:5,:]

vecfunc = np.vectorize(new_func)

prob=vecfunc(decisions) #converts deicision to (1/(1 + e(-x)))

print prob[:5,:]

编辑2：

用户3914041的答案产生非常差的概率估计。