K-means with H2O

Here, we're comparing the K-means implementation of H2O with Scikit-learn. More specifically, we will run the mini-batch experiment using H2OKMeansEstimator, the object for K-means available in H2O. The setup is similar to the one shown in the PCA with H2O section, and the experiment is the same as seen in the preceding section:

In:import h2o
from h2o.estimators.kmeans import H2OKMeansEstimator
h2o.init(max_mem_size_GB=4)

def testH2O_kmeans(X, k):

    temp_file = tempfile.NamedTemporaryFile().name
    np.savetxt(temp_file, np.c_[X], delimiter=",")

    cls = H2OKMeansEstimator(k=k, standardize=True)
    blobdata = h2o.import_file(temp_file) 

    tik = time.time()
    cls.train(x=range(blobdata.ncol), training_frame=blobdata)
    fit_time = time.time() - tik

    os.remove(temp_file)

    return fit_time

piece_of_dataset = pd.read_csv(census_csv_file, iterator=True).get_chunk(500000).drop('caseid', axis=1).as_matrix()
time_results = {4: [], 8:[], 12:[]}
dataset_sizes ...