Question

我正在尝试在scikit-learn上执行first exercise，但即使我运行他们的solution code（如下所示），我也会立即得到代码块中的错误。有谁知道为什么会这样？我该如何解决这个问题？

当尝试使用此数据集时，预测方法也会失败，出于某种原因，使用问题最底部的代码，它似乎对虹膜数据集工作正常。对不起，如果我遗漏了一些非常明显的东西，我不是真正的程序员。

Traceback (most recent call last):
  File "C:\Users\user2491873\Desktop\scikit_exercise.py", line 30, in <module>
    print(knn.fit(X_train, y_train).score(X_test, y_test))
  File "C:\Python33\lib\site-packages\sklearn\base.py", line 279, in score
    return accuracy_score(y, self.predict(X))
  File "C:\Python33\lib\site-packages\sklearn\neighbors\classification.py", line 131,     in predict
    neigh_dist, neigh_ind = self.kneighbors(X)
  File "C:\Python33\lib\site-packages\sklearn\neighbors\base.py", line 254, in kneighbors
warn_equidistant()
  File "C:\Python33\lib\site-packages\sklearn\neighbors\base.py", line 33, in warn_equidistant
    warnings.warn(msg, NeighborsWarning, stacklevel=3)
  File "C:\Python33\lib\idlelib\PyShell.py", line 59, in idle_showwarning
file.write(warnings.formatwarning(message, category, filename,
AttributeError: 'NoneType' object has no attribute 'write'

这是代码：

"""
================================
Digits Classification Exercise
================================

This exercise is used in the :ref:`clf_tut` part of the
:ref:`supervised_learning_tut` section of the
:ref:`stat_learn_tut_index`.
"""

from sklearn import datasets, neighbors, linear_model

digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target

n_samples = len(X_digits)

X_train = X_digits[:.9 * n_samples]
y_train = y_digits[:.9 * n_samples]
X_test = X_digits[.9 * n_samples:]
y_test = y_digits[.9 * n_samples:]

knn = neighbors.KNeighborsClassifier()
logistic = linear_model.LogisticRegression()

print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\
print('LogisticRegression score: %f'
      % logistic.fit(X_train, y_train).score(X_test, y_test))

这是Iris数据集的代码，似乎工作得很好......

import numpy as np
>>> from sklearn import datasets
>>> iris = datasets.load_iris()
>>> iris_X = iris.data
>>> iris_y = iris.target
>>> np.unique(iris_y)
array([0, 1, 2])

>>> # Split iris data in train and test data
>>> # A random permutation, to split the data randomly
>>> np.random.seed(0)
>>> indices = np.random.permutation(len(iris_X))
>>> iris_X_train = iris_X[indices[:-10]]
>>> iris_y_train = iris_y[indices[:-10]]
>>> iris_X_test  = iris_X[indices[-10:]]
>>> iris_y_test  = iris_y[indices[-10:]]
>>> # Create and fit a nearest-neighbor classifier
>>> from sklearn.neighbors import KNeighborsClassifier
>>> knn = KNeighborsClassifier()
>>> knn.fit(iris_X_train, iris_y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, n_neighbors=5, p=2,
           warn_on_equidistant=True, weights='uniform')
>>> knn.predict(iris_X_test)
array([1, 2, 1, 0, 0, 0, 2, 1, 2, 0])
>>> iris_y_test
array([1, 1, 1, 0, 0, 0, 2, 1, 2, 0])

Answer 1

如果您阅读了回溯消息，则表示表达式file中的变量file.write(warnings.formatwarning(message, category, filename, ...)设置为None而不是预期的通道（例如程序的标准输出或用户界面中的缓冲区。）

这意味着这可能是IDLE中的一个错误。如果你谷歌的错误消息，你会得到：

http://bugs.python.org/issue18030

反过来指向：

http://bugs.python.org/issue13582

所以这个bug确实与scikit-learn无关。我建议你：

通过输入cmd

python -m idlelib.idle

或使用其他Python IDE /环境。

如何解析'NoneType'对象与scikit-learn digits数据集没有属性'write'错误？

1 个答案: