我对使用h204gpu对文本文档进行集群很感兴趣。作为参考，我遵循了本教程，但修改了代码以反映h204gpu。
from sklearn.feature_extraction.text import TfidfVectorizer
import h2o4gpu
documents = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user perceived response time to error measurement",
"The generation of random binary unordered trees",
"The intersection graph of paths in trees",
"Graph minors IV Widths of trees and well quasi ordering",
"Graph minors A survey"]
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)
true_k = 2
model = h2o4gpu.KMeans(n_gpus=1, n_clusters=true_k, init='k-means++',
max_iter=100, n_init=1)
model.fit(X)
然而，当运行上面的代码示例时，我收到以下错误：
Traceback (most recent call last):
File "dev.py", line 20, in <module>
model.fit(X)
File "/home/greg/anaconda3/lib/python3.6/site-packages/h2o4gpu/solvers/kmeans.py", line 810, in fit
res = self.model.fit(X, y)
File "/home/greg/anaconda3/lib/python3.6/site-packages/h2o4gpu/solvers/kmeans.py", line 303, in fit
X_np, _, _, _, _, _ = _get_data(X, ismatrix=True)
File "/home/greg/anaconda3/lib/python3.6/site-packages/h2o4gpu/solvers/utils.py", line 119, in _get_data
data, ismatrix=ismatrix, dtype=dtype, order=order)
File "/home/greg/anaconda3/lib/python3.6/site-packages/h2o4gpu/solvers/utils.py", line 79, in _to_np
outdata = outdata.astype(dtype, copy=False, order=nporder)
ValueError: setting an array element with a sequence.
我已经搜索了h2o4gpu.feature_extraction.text.TfidfVectorizer，但在h204gpu中没有找到。也就是说，有没有办法纠正这个问题？
软件版本
CUDA 9.0，V9.0.176
cuDNN 7.1.3
Python 3.6.4
h204gpu 0.2.0
Scikit Learn 0.19.1
X = TfidfVectorizer(stop_words='english').fit_transform(documents)
返回稀疏矩阵对象scipy.sparse.csr_matrix.
目前，在H2O4GPU中，我们只支持KMean的密集表示。这意味着您必须将X转换为2D Python香草列表或2D Numpy数组，用0填充缺失的元素。
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(documents)
X_dense = X.toarray()
true_k = 2
model = h2o4gpu.KMeans(n_gpus=1, n_clusters=true_k, init='k-means++',
max_iter=100, n_init=1)
model.fit(X_dense)
应该做这个把戏。这不是NLP的最佳解决方案，因为它可能需要更多的内存，但我们在路线图上还没有对KMean的稀疏支持。