Question

我最近发现要求我有.fit()经过培训的scikit-learn SVC 分类器实例并需要 .predict() < / strong>很多实例。

是否有办法通过任何.predict()内置工具仅对此scikit-learn方法进行并行化？

from sklearn import svm data_train = [[0,2,3],[1,2,3],[4,2,3]] targets_train = [0,1,0] clf = svm.SVC(kernel='rbf', degree=3, C=10, gamma=0.3, probability=True) clf.fit(data_train, targets_train) # this can be very large (~ a million records) to_be_predicted = [[1,3,4]] clf.predict(to_be_predicted)

如果有人确实知道解决方案，如果你能分享它，我会非常高兴。

Answer 1

这可能是错误的，但这样的事情应该可以解决问题。基本上，将数据分成块并在joblib.Parallel循环中分别在每个块上运行模型。

from sklearn.externals.joblib import Parallel, delayed

n_cores = 2
n_samples = to_be_predicted.shape[0]
slices = [
    (n_samples*i/n_cores, n_samples*(i+1)/n_cores))
    for i in range(n_cores)
    ]

results = np.vstack( Parallel( n_jobs = n_cores )( 
    delayed(clf.predict)( to_be_predicted[slices[i_core][0]:slices[i_core][1]
    for i_core in range(n_cores)
    ))

Answer 2

上面的工作示例...

from joblib import Parallel, delayed
from sklearn import svm

data_train = [[0,2,3],[1,2,3],[4,2,3]]
targets_train = [0,1,0]

clf = svm.SVC(kernel='rbf', degree=3, C=10, gamma=0.3, probability=True)
clf.fit(data_train, targets_train)

to_be_predicted = np.array([[1,3,4], [1,3,4], [1,3,5]])
clf.predict(to_be_predicted)

n_cores = 3

parallel = Parallel(n_jobs=n_cores)
results = parallel(delayed(clf.predict)(to_be_predicted[i].reshape(-1,3))
    for i in range(n_cores))

np.vstack(results).flatten()

array([1, 1, 0])

如何并行scikit-learn SVM（SVC）分类器的.predict（）方法？

2 个答案: