Scipy hstack导致“ TypeError：类型不受支持的转换：（dtype（'float64'），dtype（'O'））” [英] Scipy hstack results in "TypeError: no supported conversion for types: (dtype('float64'), dtype('O'))"
问题描述
我正在尝试运行hstack将整数值的列连接到TF-IDF创建的列的列表中（这样，我最终可以在分类器中使用所有这些列/功能）。
I am trying to run hstack to join a column of integer values to a list of columns created by a TF-IDF (so I can eventually use all of these columns/features in a classifier).
我正在使用熊猫阅读该列，检查所有NA值并将其转换为数据框中的最大值，如下所示：
I'm reading in the column using pandas, checking for any NA values and converting them to the largest value in the dataframe like so :
OtherColumn = p.read_csv('file.csv', delimiter=";", na_values=['?'])[["OtherColumn"]]
OtherColumn = OtherColumn.fillna(OtherColumn.max())
OtherColumn = OtherColumn.convert_objects(convert_numeric=True)
然后我在文本列中阅读并运行TF-IDF来创建许多功能：
Then I read in my text column and run TF-IDF to create loads of features :
X = list(np.array(p.read_csv('file.csv', delimiter=";"))[:,2])
tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode',
analyzer='word',token_pattern=r'\w{1,}',ngram_range=(1, 2), use_idf=1,smooth_idf=1,sublinear_tf=1)
tfv.fit(X)
最后，我想将它们全部加入，这是我们发生错误的地方，程序无法运行，而且我不确定是否在这里正确使用了StandardScaler：
Finally, I want to join them all together, and this is where our error occurs and the program cannot run, and also I am unsure whether I am using the StandardScaler appropriately here :
X = sp.sparse.hstack((X, OtherColumn.values)) #error here
sc = preprocessing.StandardScaler().fit(X)
X = sc.transform(X)
X_test = sc.transform(X_test)
完整错误消息：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-79d1e70bc1bc> in <module>()
---> 47 X = sp.sparse.hstack((X, OtherColumn.values))
48 sc = preprocessing.StandardScaler().fit(X)
49 X = sc.transform(X)
C:\Users\Simon\Anaconda\lib\site-packages\scipy\sparse\construct.pyc in hstack(blocks, format, dtype)
421
422 """
--> 423 return bmat([blocks], format=format, dtype=dtype)
424
425
C:\Users\Simon\Anaconda\lib\site-packages\scipy\sparse\construct.pyc in bmat(blocks, format, dtype)
537 nnz = sum([A.nnz for A in blocks[block_mask]])
538 if dtype is None:
--> 539 dtype = upcast(*tuple([A.dtype for A in blocks[block_mask]]))
540
541 row_offsets = np.concatenate(([0], np.cumsum(brow_lengths)))
C:\Users\Simon\Anaconda\lib\site-packages\scipy\sparse\sputils.pyc in upcast(*args)
58 return t
59
---> 60 raise TypeError('no supported conversion for types: %r' % (args,))
61
62
TypeError: no supported conversion for types: (dtype('float64'), dtype('O'))
推荐答案
如numpy hstack- ValueError：所有输入数组必须具有相同数量的维数 -但是确实如此，您很多人需要将输入显式转换为 sparse.hstack 。 稀疏代码不如核心 numpy 代码健壮。
As discussed in Numpy hstack - "ValueError: all the input arrays must have same number of dimensions" - but they do you many need to explicitly cast the inputs to sparse.hstack. The sparse code is not as robust as the core numpy code.
如果 X 是具有 dtype = float 和 A 带有 dtype = object 密集，可能有几种选择。
If X is a sparse array with dtype=float, and A is dense with dtype=object, several options are possible.
sparse.hstack(X, A) # error
sparse.hstack(X.astype(object), A) # cast X to object; return object
sparse.hstack(X, A.astype(float)) # cast A to float; return float
hstack(X.A, A) # make X dense, result will be type object
A.astype（float）将在 A 包含某些 NaN 。请参见 http://pandas.pydata.org/pandas-docs/stable/gotchas。 html 关于NaN。如果 A 是由于其他原因（例如参差不齐的列表）而成为对象，则我们将不得不重新考虑该问题。
A.astype(float) will work if A contains some NaN. See http://pandas.pydata.org/pandas-docs/stable/gotchas.html regarding NaN. If A is object for some other reason (e.g. ragged lists), then we'll have to revisit the issue.
另一种可能性是使用Pandas的 concat 。 http://pandas.pydata.org/pandas-docs/stable/merging.html 。我认为熊猫比稀疏编码员更加关注这些问题。
Another possibility is to use Pandas's concat. http://pandas.pydata.org/pandas-docs/stable/merging.html. I assume Pandas has paid more attention to these issues than the sparse coders.
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