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

Question

我最近遇到一个要求，即我有一个.fit()训练有素的scikit-learn SVC分类器实例并且需要.predict()很多实例。

有没有办法.predict()通过任何scikit-learn内置工具仅并行化此方法？

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，如果您能分享它，我会非常高兴。

Answer 1

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])

Answer 2

这可能有问题，但类似这样的事情应该可以解决问题。基本上，将数据分成块，并在循环中分别在每个块上运行模型joblib.Parallel。

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)
    ))