我收到一条错误，指出“数组包含NaN或无穷大”。我检查了我的数据，无论是火车/测试缺失值，都没有遗漏。
我可能对“数组包含NaN或无穷大”的含义有错误的解释。
import numpy as np
from sklearn import linear_model
from numpy import genfromtxt, savetxt
def main():
#create the training & test sets, skipping the header row with [1:]
dataset = genfromtxt(open('C:\\Users\\Owner\\training.csv','r'), delimiter=',')[0:50]
target = [x[0] for x in dataset]
train = [x[1:50] for x in dataset]
test = genfromtxt(open('C:\\Users\\Owner\\test.csv','r'), delimiter=',')[0:50]
#create and train the SGD
sgd = linear_model.SGDClassifier()
sgd.fit(train, target)
predictions = [x[1] for x in sgd.predict(test)]
savetxt('C:\\Users\\Owner\\Desktop\\preds.csv', predictions, delimiter=',', fmt='%f')
if __name__=="__main__":
main()
我认为数据类型可能会抛出一个循环算法（它们是浮点数）。
我知道SGD可以处理浮点数，所以我不确定这个设置是否要求我声明数据类型。
如下列之一：
>>> dt = np.dtype('i4') # 32-bit signed integer
>>> dt = np.dtype('f8') # 64-bit floating-point number
>>> dt = np.dtype('c16') # 128-bit complex floating-point number
>>> dt = np.dtype('a25') # 25-character string
以下是完整的错误消息：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-62-af5537e7802b> in <module>()
19
20 if __name__=="__main__":
---> 21 main()
<ipython-input-62-af5537e7802b> in main()
13 #create and train the SGD
14 sgd = linear_model.SGDClassifier()
---> 15 sgd.fit(train, target)
16 predictions = [x[1] for x in sgd.predict(test)]
17
C:\Anaconda\lib\site-packages\sklearn\linear_model\stochastic_gradient.pyc in fi
t(self, X, y, coef_init, intercept_init, class_weight, sample_weight)
518 coef_init=coef_init, intercept_init=intercept_i
nit,
519 class_weight=class_weight,
--> 520 sample_weight=sample_weight)
521
522
C:\Anaconda\lib\site-packages\sklearn\linear_model\stochastic_gradient.pyc in _f
it(self, X, y, alpha, C, loss, learning_rate, coef_init, intercept_init, class_w
eight, sample_weight)
397 self.class_weight = class_weight
398
--> 399 X = atleast2d_or_csr(X, dtype=np.float64, order="C")
400 n_samples, n_features = X.shape
401
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in atleast2d_or_csr(X
, dtype, order, copy)
114 """
115 return _atleast2d_or_sparse(X, dtype, order, copy, sparse.csr_matrix
,
--> 116 "tocsr")
117
118
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in _atleast2d_or_spar
se(X, dtype, order, copy, sparse_class, convmethod)
94 _assert_all_finite(X.data)
95 else:
---> 96 X = array2d(X, dtype=dtype, order=order, copy=copy)
97 _assert_all_finite(X)
98 return X
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in array2d(X, dtype,
order, copy)
79 'is required. Use X.toarray() to convert to dens
e.')
80 X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
---> 81 _assert_all_finite(X_2d)
82 if X is X_2d and copy:
83 X_2d = safe_copy(X_2d)
C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in _assert_all_finite
(X)
16 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.s
um())
17 and not np.isfinite(X).all()):
---> 18 raise ValueError("Array contains NaN or infinity.")
19
20
ValueError: Array contains NaN or infinity.
任何想法都会受到赞赏。
答案 0 :(得分：0)
如错误报告，您的数据中某处有np.nan或np.inf或-np.inf。由于您正在从文本文件中读取并且您说数据不包含缺失值，因此可能是由列标题或文件中无法自动解释的其他值引起的。
genfromtxt的文档显示，读入数组的默认dtype为float，这意味着您读取的所有值都必须等同于float(x)。
如果您不确定这是否导致错误，您可以从numpy数组中删除非限定数字，如下所示：
dataset[ ~np.isfinite(dataset) ] = 0 # Set non-finite (nan, inf, -inf) to zero
如果这样可以消除错误，您可以确定变量某处中包含无效值。要查找 where ，您可以使用以下内容：
np.where(~np.isfinite(dataset))
这将返回无效值所在的索引列表，例如
>>> import numpy as np
>>> dataset = np.array([[0,1,1],[np.nan,0,0],[1,2,np.inf]])
>>> dataset
array([[ 0., 1., 1.],
[ nan, 0., 0.],
[ 1., 2., inf]])
>>> np.where(~np.isfinite(dataset))
(array([1, 2]), array([0, 2]))