尝试使用GridSearchCV或RandomizedSearchCV填充我的训练数据时，我不断收到以下错误：
TypeError：类型不支持转换：（dtype（＆＃39; O＆＃39;），dtype（＆＃39; O＆＃39;））
以下是相关代码的示例：
from xgboost.sklearn import XGBRegressor as XGR
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
xgbRegModel = XGR()
params = {'max_depth':[3, 6, 9], 'learning_rate':[.05, .1, .5], 'n_estimators': [50, 100, 200]}
rscv = RandomizedSearchCV(xgbRegModel, params)
rscv.fit(X, y)
rscv.best_model_
其中X是（39942,11257）scipy.sparse.csr.csr_matrix而y是（39942，）numpy.ndarray。
dtypes都是int64或float64，我尝试使用np.nan值并在将np.nan值填充为0后运行它。 ..（我认为这可能是问题，但不是。）
谁能告诉我这里发生了什么？当我在不使用GridSearchCV或RandomizedSearchCV的情况下训练模型时，它可以正常工作。
任何想法都将不胜感激 - 谢谢！
ps - 错误的追溯真的很长，但是如果有帮助的话，就在这里..
TypeError Traceback (most recent call last)
<ipython-input-54-63d54d4cd03e> in <module>()
3 xgbRegModel = XGR()
4 rscv = RandomizedSearchCV(xgbRegModel, params)
----> 5 rscv.fit(X, y)
6 rscv.best_model_
~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
636 error_score=self.error_score)
637 for parameters, (train, test) in product(candidate_params,
--> 638 cv.split(X, y, groups)))
639
640 # if one choose to see train score, "out" will contain train score info
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
777 # was dispatched. In particular this covers the edge
778 # case of Parallel used with an exhausted iterator.
--> 779 while self.dispatch_one_batch(iterator):
780 self._iterating = True
781 else:
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
623 return False
624 else:
--> 625 self._dispatch(tasks)
626 return True
627
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
586 dispatch_timestamp = time.time()
587 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588 job = self._backend.apply_async(batch, callback=cb)
589 self._jobs.append(job)
590
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
109 def apply_async(self, func, callback=None):
110 """Schedule a func to be run"""
--> 111 result = ImmediateResult(func)
112 if callback:
113 callback(result)
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
330 # Don't delay the application, to avoid keeping the input
331 # arguments in memory
--> 332 self.results = batch()
333
334 def get(self):
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
425 start_time = time.time()
426
--> 427 X_train, y_train = _safe_split(estimator, X, y, train)
428 X_test, y_test = _safe_split(estimator, X, y, test, train)
429
~\Anaconda3\lib\site-packages\sklearn\utils\metaestimators.py in _safe_split(estimator, X, y, indices, train_indices)
198 X_subset = X[np.ix_(indices, train_indices)]
199 else:
--> 200 X_subset = safe_indexing(X, indices)
201
202 if y is not None:
~\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in safe_indexing(X, indices)
160 return X.take(indices, axis=0)
161 else:
--> 162 return X[indices]
163 else:
164 return [X[idx] for idx in indices]
~\Anaconda3\lib\site-packages\scipy\sparse\csr.py in __getitem__(self, key)
315 if isintlike(col) or isinstance(col,slice):
316 P = extractor(row, self.shape[0]) # [[1,2],j] or [[1,2],1:2]
--> 317 extracted = P * self
318 if col == slice(None, None, None):
319 return extracted
~\Anaconda3\lib\site-packages\scipy\sparse\base.py in __mul__(self, other)
367 if self.shape[1] != other.shape[0]:
368 raise ValueError('dimension mismatch')
--> 369 return self._mul_sparse_matrix(other)
370
371 # If it's a list or whatever, treat it like a matrix
~\Anaconda3\lib\site-packages\scipy\sparse\compressed.py in _mul_sparse_matrix(self, other)
539 indptr = np.asarray(indptr, dtype=idx_dtype)
540 indices = np.empty(nnz, dtype=idx_dtype)
--> 541 data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
542
543 fn = getattr(_sparsetools, self.format + '_matmat_pass2')
~\Anaconda3\lib\site-packages\scipy\sparse\sputils.py in upcast(*args)
49 return t
50
---> 51 raise TypeError('no supported conversion for types: %r' % (args,))
52
53
TypeError: no supported conversion for types: (dtype('O'), dtype('O'))
答案 0 :(得分：0)
这是因为GridSearchCV在fit（）方法中不支持稀疏矩阵。请查看fit method here的签名：
参数：
X : array-like, shape = [n_samples, n_features]
正如您所看到的那样，只支持类似数组的输入。
至于为什么它在没有网格搜索的情况下正常工作，那是因为XGBRegressor支持稀疏矩阵。
在交叉验证期间出现实际错误，X被分成列和测试，这对于稀疏矩阵不像普通数组那样。
另外，请确保对于XGBRegressor，稀疏矩阵的类型为CSC而不是CSR，因为它会给你错误的结果。其描述如下：https://github.com/dmlc/xgboost/issues/1238