Python 3.x 当我尝试使用HashingVectorizer时，Dask client.persist返回AssertionError

我正在尝试使用dask哈希向量器对dask.dataframe进行向量化。我希望矢量化结果留在集群（分布式系统）中。这就是我在尝试转换数据时使用

client.persist

的原因。但是由于某种原因，我得到了下面的错误

Traceback (most recent call last):
  File "/home/dodzilla/my_project/components_with_adapter/vectorizers/base_vectorizer.py", line 112, in hybrid_feature_vectorizer
    CLUSTERING_FEATURES=self.clustering_features)
  File "/home/dodzilla/my_project/components_with_adapter/vectorizers/text_vectorizer.py", line 143, in vectorize
    X = self.client.persist(fitted_vectorizer.transform, combined_data)
  File "/home/dodzilla/.local/lib/python3.6/site-packages/distributed/client.py", line 2860, in persist
    assert all(map(dask.is_dask_collection, collections))
AssertionError

我无法共享数据，但有关数据的所有必要信息如下：

>>>type(combined_data)
<class 'dask.dataframe.core.Series'>
>>>type(combined_data.compute())
<class 'pandas.core.series.Series'>
>>>combined_data.compute().shape
12

我希望这些信息足够了

---

重要提示：当我说

client.compute

时，我没有收到任何类型的错误，但据我所知，这在机器集群中不起作用，而是在本地机器中运行。它返回一个csr矩阵，而不是延迟计算的

dask.array

这不是我应该如何使用

client.persist

。我正在寻找的函数是

client.submit

和

client.map

。。。就我而言，

client.submit

解决了我的问题

from stop_words import get_stop_words
from dask_ml.feature_extraction.text import HashingVectorizer as daskHashingVectorizer
import pandas as pd
import dask
import dask.dataframe as dd
from dask.distributed import Client


def convert_dataframe_to_single_text(documents):
    """
    Combine all of the columns into 1 column.
    """
    if type(documents) is dask.dataframe.core.DataFrame:
        cols = documents.columns
        documents['combined'] = documents[cols].apply(func=(lambda row: ' '.join(row.values.astype(str))), axis=1,
                                                      meta=('str'))
        document_texts = documents.drop(cols, axis=1)
    else:
        raise TypeError('Wrong type of data. Expected Pandas DF or Dask DF but received ', type(documents))
    return document_texts

# Init the client.
client = Client('localhost:8786')

# Get stopwords
stopwords = get_stop_words(language="english")

# Create dask dataframe from pandas dataframe
data = {'Name':['Tom', 'nick', 'krish', 'jack'], 'Age':["twenty", "twentyone", "nineteen", "eighteen"]}
df = pd.DataFrame(data)
df = dd.from_pandas(df, npartitions=1)

# Init the vectorizer
vectorizer = daskHashingVectorizer(stop_words=stopwords, alternate_sign=False,
                       norm=None, binary=False,
                       n_features=10000)

# Combine all of to columns into 1 column.
combined_data = convert_dataframe_to_single_text(df)

# Fit the vectorizer.
fitted_vectorizer = client.persist(vectorizer.fit(combined_data))

# Transform the data.
X = client.persist(fitted_vectorizer.transform, combined_data)