如何在普通的笔记本电脑上成功地运行一个带有中等大小数据集的ML算法？

Question

我有一台联想IdeaPad笔记本电脑，8GB内存和Intel Core I5处理器。我有6万个数据点，每个100维。我想做KNN，为此我运行LMNN算法来寻找马氏度量。
问题是运行2小时后，我的ubuntu上出现了一个空白屏幕。我不明白是什么问题！我的记性是变满了还是别的什么？
那么有什么方法可以优化我的代码吗？

我的数据集：data
我的LMNN实现：

import numpy as np
import sys
from modshogun import LMNN, RealFeatures, MulticlassLabels
from sklearn.datasets import load_svmlight_file

def main(): 

    # Get training file name from the command line
    traindatafile = sys.argv[1]

    # The training file is in libSVM format
    tr_data = load_svmlight_file(traindatafile);

    Xtr = tr_data[0].toarray(); # Converts sparse matrices to dense
    Ytr = tr_data[1]; # The trainig labels

    # Cast data to Shogun format to work with LMNN
    features = RealFeatures(Xtr.T)
    labels = MulticlassLabels(Ytr.astype(np.float64))



    # Number of target neighbours per example - tune this using validation
    k = 18

    # Initialize the LMNN package
    lmnn = LMNN(features, labels, k)
    init_transform = np.eye(Xtr.shape[1])

    # Choose an appropriate timeout
    lmnn.set_maxiter(200000)
    lmnn.train(init_transform)

    # Let LMNN do its magic and return a linear transformation
    # corresponding to the Mahalanobis metric it has learnt
    L = lmnn.get_linear_transform()
    M = np.matrix(np.dot(L.T, L))

    # Save the model for use in testing phase
    # Warning: do not change this file name
    np.save("model.npy", M) 

if __name__ == '__main__':
    main()

Answer 1

精确k-NN存在可伸缩性问题。你知道吗

Scikit learn有documentation page（缩放策略）来说明在这种情况下应该做什么（许多算法有partial_fit方法，但不幸的是kNN没有它）。你知道吗

如果你愿意用精确度来换取速度，你可以运行类似approximate nearest neighbors的程序。你知道吗