[英]In python how to replace nan in sparse csr_matrix
I have hstacked a sprase matrix and a dataframe . 我已经堆叠了一个sprase矩阵和一个dataframe。 The resulting csr_matrix is containing NAN.
结果csr_matrix包含NAN。
My question is how to update these nan values to 0 . 我的问题是如何将这些nan值更新为0。
X_train_1hc = sp.sparse.hstack([X_train_1hc, X_train_df.values]).tocsr()
When I pass X_train_1hc to a clasifier I get error Input contains NaN or infinity or a value too large for dtype('float') 当我将X_train_1hc传递给分类器时,出现错误输入包含NaN或无穷大,或者对于dtype('float')而言值太大
1.Is there an option/function/hack to replace nan values in a sparse matrix. 1.是否有一个选项/功能/技巧来替换稀疏矩阵中的nan值。 This is a conceptual question and hence no data is being provided.
这是一个概念性问题,因此没有提供任何数据。
Expanding a bit on Martin's answer, here is one way to do it. 扩展一下马丁的答案,这是一种方法。 Assume you have a
csr_matrix
with some NaN
values: 假设您有一个带有某些
NaN
值的csr_matrix
:
>>> Asp.todense()
matrix([[0.37512508, nan, 0.34919696, 0.10321203],
[0.48744859, 0.07289436, 0.16881342, 0.57637166],
[0.37742037, 0.01425494, 0.38536847, 0.23799655],
[0.95520474, 0.97719059, nan, 0.22877082]])
Since the csr_matrix
stores the nonzeros in the data
attribute , you need to manipulate that array. 由于
csr_matrix
将非零csr_matrix
存储在data
属性中 ,因此您需要操作该数组。 The replacing all occurences of NaN
and inf
by 0 and some large number (in fact the largest one representable), you can do 您可以将
NaN
和inf
的所有出现替换为0和一个较大的数字(实际上是最大的可表示的数字),
>>> Asp.data = np.nan_to_num(Asp.data, copy=False)
>>> Asp.todense()
matrix([[0.37512508, 0. , 0.34919696, 0.10321203],
[0.48744859, 0.07289436, 0.16881342, 0.57637166],
[0.37742037, 0.01425494, 0.38536847, 0.23799655],
[0.95520474, 0.97719059, 0. , 0.22877082]])
Alternatively, you can replace just NaN
's manually like this: 另外,您可以像这样手动替换
NaN
:
>>> Asp.data[np.isnan(Asp.data)] = 0.0
>>> Asp.todense()
matrix([[0.37512508, 0. , 0.34919696, 0.10321203],
[0.48744859, 0.07289436, 0.16881342, 0.57637166],
[0.37742037, 0.01425494, 0.38536847, 0.23799655],
[0.95520474, 0.97719059, 0. , 0.22877082]])
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