scikit learn 在一定数量的数据上崩溃

Question

我正在尝试处理一个包含 71,000 行 200 列浮点数的 numpy 数组，当我超过 5853 行时，我尝试的两个 sci-kit 学习模型都会给出不同的错误。我尝试删除有问题的行，但它仍然失败。 sci-kit learn 不能处理这么多数据，还是别的什么？ X 是列表列表的 numpy 数组。

KNN：

nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)

错误：

File "knn.py", line 48, in <module>
  nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X)
File "/usr/local/lib/python2.7/dist-packages/sklearn/neighbors/base.py", line 642, in fit
  return self._fit(X)
File "/usr/local/lib/python2.7/dist-packages/sklearn/neighbors/base.py", line 180, in _fit
  raise ValueError("data type not understood")

ValueError: 数据类型不理解

K 均值：

kmeans_model = KMeans(n_clusters=2, random_state=1).fit(X)

错误：

Traceback (most recent call last):
File "knn.py", line 48, in <module>
kmeans_model = KMeans(n_clusters=2, random_state=1).fit(X)
File "/usr/local/lib/python2.7/dist-packages/sklearn/cluster/k_means_.py", line 702, in fit
X = self._check_fit_data(X)
File "/usr/local/lib/python2.7/dist-packages/sklearn/cluster/k_means_.py", line 668, in _check_fit_data
X = atleast2d_or_csr(X, dtype=np.float64)
File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 134, in atleast2d_or_csr
"tocsr", force_all_finite)
File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 111, in _atleast2d_or_sparse
force_all_finite=force_all_finite)
File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 91, in array2d
X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
File "/usr/local/lib/python2.7/dist-packages/numpy/core/numeric.py", line 235, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: setting an array element with a sequence.

Answer 1

请检查您的矩阵X 的dtype，例如通过输入X.dtype。如果是object或dtype('O')，则将X的行长度写入数组：

lengths = [len(line) for line in X]

然后通过调用查看所有行的长度是否相同

np.unique(lengths)

如果输出中有多个数字，则您的行长不同，例如从第 5853 行开始，但可能并非一直如此。

Numpy 数据数组仅在所有行的长度相同时才有用（如果不是，它们会继续工作，但不会按照您的预期进行。）。您应该检查一下是什么原因造成的，纠正它，然后返回knn。

以下是行长不同时会发生什么情况的示例：

import numpy as np
rng = np.random.RandomState(42)
X = rng.randn(100, 20)
# now remove one element from the 56th line
X = list(X)
X[55] = X[55][:-1]
# turn it back into an ndarray
X = np.array(X)
# check the dtype
print X.dtype  # returns dtype('O')

from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors()
nbrs.fit(X)  # raises your first error

from sklearn.cluster import KMeans
kmeans = KMeans()
kmeans.fit(X)  # raises your second error