找到包含 0 个样本的数组(shape=(0， 40))，而至少需要 1 个样本

Question

我正在用Python 2.7，sklearn 0.17.1，numpy 1.11.0测试一个简单的预测程序。我从LDA模型中得到了具有可预测性的矩阵，现在我想创建RandomForestClassifier来预测可能性的结果。我的代码是：

maxlen = 40
props = []
for doc in corpus:
    topics = model.get_document_topics(doc) 
    tprops = [0] * maxlen
    for topic in topics:
        tprops[topics[0]] = topics[1]
    props.append(tprops)
ntheta = np.array(props)
ny = np.array(y)
clf = RandomForestClassifier(n_estimators=100)
accuracy = cross_val_score(clf, ntheta, ny, scoring = 'accuracy')
print accuracy

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ValueError                                Traceback (most recent call last)
<ipython-input-65-a7d276df43e9> in <module>()
      1 # clf.fit(nteta, ny)
      2 print nteta.shape, ny.shape
----> 3 accuracy = cross_val_score(clf, nteta, ny, scoring = 'accuracy')
      4 print accuracy
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.pyc in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
   1431                                               train, test, verbose, None,
   1432                                               fit_params)
-> 1433                       for train, test in cv)
   1434     return np.array(scores)[:, 0]
   1435 
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    798             # was dispatched. In particular this covers the edge
    799             # case of Parallel used with an exhausted iterator.
--> 800             while self.dispatch_one_batch(iterator):
    801                 self._iterating = True
    802             else:
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
    656                 return False
    657             else:
--> 658                 self._dispatch(tasks)
    659                 return True
    660 
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in _dispatch(self, batch)
    564 
    565         if self._pool is None:
--> 566             job = ImmediateComputeBatch(batch)
    567             self._jobs.append(job)
    568             self.n_dispatched_batches += 1
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, batch)
    178         # Don't delay the application, to avoid keeping the input
    179         # arguments in memory
--> 180         self.results = batch()
    181 
    182     def get(self):
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
     73 
     74     def __len__(self):
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)
   1529             estimator.fit(X_train, **fit_params)
   1530         else:
-> 1531             estimator.fit(X_train, y_train, **fit_params)
   1532 
   1533     except Exception as e:
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc in fit(self, X, y, sample_weight)
    210         """
    211         # Validate or convert input data
--> 212         X = check_array(X, dtype=DTYPE, accept_sparse="csc")
    213         if issparse(X):
    214             # Pre-sort indices to avoid that each individual tree of the
/home/egor/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.pyc in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    405                              " minimum of %d is required%s."
    406                              % (n_samples, shape_repr, ensure_min_samples,
--> 407                                 context))
    408 
    409     if ensure_min_features > 0 and array.ndim == 2:
ValueError: Found array with 0 sample(s) (shape=(0, 40)) while a minimum of 1 is required.

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UPD对于我得到的 2 减去什么？让批评者具有建设性。

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UPD

科蒂克发现Y被填充不正确（一定是其他类）。如果 y 填充正确，则问题不会发生。在我的案例中，类是错误的，它们的计数是 39774。但从理论上讲，这不是一个答案，为什么当我们有 39774 个类并且必须预测它们时会发生错误。

Answer 1

这是来自scikit-learn存储库（validation.py#L409）的原始代码：

if ensure_min_samples > 0:
   n_samples = _num_samples(array)
   if n_samples < ensure_min_samples:
      raise ValueError("Found array with %d sample(s) (shape=%s) while a"
                       " minimum of %d is required%s."
                        % (n_samples, shape_repr, ensure_min_samples,
                        context))

所以，n_samples = _num_samples(array).顺便说一句，array是input object to check / convert.

接下来，验证.py#L111：

def _num_samples(x):
    """Return number of samples in array-like x."""
    if hasattr(x, 'fit'):
        # stuff
    if not hasattr(x, '__len__') and not hasattr(x, 'shape'):
        # stuff
    if hasattr(x, 'shape'):
        if len(x.shape) == 0:
            # raise TypeError
        return x.shape[0]
    else:
        return len(x)

因此，样本数等于array第一维的长度，这是自array.shape = (0, 40)年以来0。

我不知道这一切意味着什么，但我希望它能让事情更清楚。

Answer 2

只是可能，您编写了错误的测试数据路径，请进行一些检查。