ValueError: setting an array element with a sequence with Decision Tree where all the rows have equal elements?

问题

I am trying to fit a decision tree to matrices of features and labels. Here is my code:

print FEATURES_DATA[0]
print ""
print TARGET[0]
print ""
print np.unique(list(map(len, FEATURES_DATA[0])))

which gives the following output:

[ array([[3, 3, 3, ..., 7, 7, 7],
       [3, 3, 3, ..., 7, 7, 7],
       [3, 3, 3, ..., 7, 7, 7],
       ..., 
       [2, 2, 2, ..., 6, 6, 6],
       [2, 2, 2, ..., 6, 6, 6],
       [2, 2, 2, ..., 6, 6, 6]], dtype=uint8)]

[ array([[31],
       [31],
       [31],
       ..., 
       [22],
       [22],
       [22]], dtype=uint8)]

[463511]

The matrix actually contains 463511 samples.

Thereafter, I run the following block:

from sklearn.tree import DecisionTreeClassifier
for i in xrange(5):
    Xtrain=FEATURES_DATA[i]
    Ytrain=TARGET[i]
    clf=DecisionTreeClassifier()
    clf.fit(Xtrain,Ytrain)

which gives me the following error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-3d8b2a7a3e5f> in <module>()
      4     Ytrain=TARGET[i]
      5     clf=DecisionTreeClassifier()
----> 6     clf.fit(Xtrain,Ytrain)

C:\Users\singhg2\AppData\Local\Enthought\Canopy\User\lib\site-packages\sklearn\tree\tree.pyc in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    152         random_state = check_random_state(self.random_state)
    153         if check_input:
--> 154             X = check_array(X, dtype=DTYPE, accept_sparse="csc")
    155             if issparse(X):
    156                 X.sort_indices()

C:\Users\singhg2\AppData\Local\Enthought\Canopy\User\lib\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)
    371                                       force_all_finite)
    372     else:
--> 373         array = np.array(array, dtype=dtype, order=order, copy=copy)
    374 
    375         if ensure_2d:

ValueError: setting an array element with a sequence.

I searched other posts on SO and found that most of the answers were that the matrices were not completely numbers, or the array is differing in the length across samples. But, this is not the case with my problem?

Any help?

回答1:

if print FEATURES_DATA[0] actually prints

[ array([[3, 3, 3, ..., 7, 7, 7],
       [3, 3, 3, ..., 7, 7, 7],
       [3, 3, 3, ..., 7, 7, 7],
       ..., 
       [2, 2, 2, ..., 6, 6, 6],
       [2, 2, 2, ..., 6, 6, 6],
       [2, 2, 2, ..., 6, 6, 6]], dtype=uint8)]

then the problem is that FEATURES_DATA[0] is a python list with a numpy array inside it. (You can understand that from the [ and ])

You can select the first (and only) element of of the list to fix it

from sklearn.tree import DecisionTreeClassifier
for i in xrange(5):
    Xtrain=FEATURES_DATA[i][0]
    Ytrain=TARGET[i][0]
    clf=DecisionTreeClassifier()
    clf.fit(Xtrain,Ytrain)

来源：https://stackoverflow.com/questions/37548189/valueerror-setting-an-array-element-with-a-sequence-with-decision-tree-where-al

标签

numpy

matrix

machine-learning

scikit-learn

decision-tree