sci-kit學習了對某些數據量的崩潰

Question

我正在嘗試處理一個numpy數組，其中包含71,000行200列浮點數，當我超過5853行時，我正在嘗試的兩個sci-kit學習模型都會出現不同的錯誤。 我嘗試刪除有問題的行，但它仍然失敗。 sci-kit可以學習不處理這么多數據，還是其他什么？ X是列表列表的numpy數組。

KNN：

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

錯誤：

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：數據類型未被理解

K-方式：

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

錯誤：

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

請檢查dtype的矩陣X ，例如，通過鍵入X.dtype 。 如果是object或dtype('O') ，則將X行的長度寫入數組：

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

然后通過調用來查看是否所有行都具有相同的長度

np.unique(lengths)

如果輸出中有多個數字，那么您的線路長度是不同的，例如從線路5853開始，但可能不是所有時間。

Numpy數據數組僅在所有行具有相同長度時才有用（如果沒有，它們將繼續工作，但不會按預期執行）。 您應該檢查是什么導致了這一點，糾正它，然后返回knn 。

以下是行長不相同時會發生什么的示例：

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