Python Scikit-learn多分类出现问题

Question

我正在尝试对Scikit-learn中可用的分类器做一个小比较。根据这个页面，除了支持向量机（svm）以外，所有分类器都应该可以正常工作。

这个操作是这样实现的：

clf['bayes'] = OneVsRestClassifier(MultinomialNB(
clf['lda'] = OneVsRestClassifier(LDA())
clf['decision tree'] = OneVsRestClassifier(DecisionTreeClassifier())
clf['rdc'] = OneVsRestClassifier(RandomForestClassifier())

y_supposes = {}
precision = {}
for classifier in clf:
    clf[classifier].fit(x_train, y_train)
    y_supposes[classifier] = clf[classifier].predict(x_test)
    precision[classifier] = calcul_precision(y_supposes[classifier], y_test)

问题是，唯一能正常工作的分类器是bayes分类器。

其他分类器在我尝试调用classifier['rdc'].fit(x_train, y_train)时给我报了这个错误：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Python27\lib\site-packages\sklearn\multiclass.py", line 201, in fit
    n_jobs=self.n_jobs)
  File "C:\Python27\lib\site-packages\sklearn\multiclass.py", line 92, in fit_ov
r
    for i in range(Y.shape[1]))
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 517, in __call__
    self.dispatch(function, args, kwargs)
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 312, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", lin
e 136, in __init__
    self.results = func(*args, **kwargs)
  File "C:\Python27\lib\site-packages\sklearn\multiclass.py", line 61, in _fit_b
inary
    estimator.fit(X, y)
  File "C:\Python27\lib\site-packages\sklearn\ensemble\forest.py", line 257, in
fit
    check_ccontiguous=True)
  File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 220, in
 check_arrays
    raise TypeError('A sparse matrix was passed, but dense '
TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray
() to convert to a dense numpy array.

我想补充一下，clf['rdc'].fit(x_train.toarray, y_train)（正如错误信息中所提到的）也给我报了错。

你能帮我找出我漏掉的步骤吗？

编辑：新进展

我觉得问题可能出在x_train的类型上。我是这样计算它的：

x = [{f1 : a, ... fn : jo}, ..., {f3 : 5}]
y_train = [('label1', ), ..., ('labelZ', 'label72')]
x_train = DictVectorizer.fit_transform(x)

type(x_train) ==  <class 'scipy.sparse.csr.csr_matrix'>

我还尝试了这个方法：MultinomialNB.fit(np.array(x), np.array(y))，结果给了我一个新的错误信息：

Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python27\lib\site-packages\sklearn\naive_bayes.py", line 308, in fit
X = X.astype(np.float)
TypeError: float() argument must be a string or a number

error handling machine learning scikit-learn data preprocessing multiclass classification support vector machine classifier comparison model evaluation

Python Scikit-learn多分类出现问题

编辑：新进展

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