Question

我正在尝试使用以下代码在SKlearn中训练逻辑回归模型：

preds = np.zeros((len(test), len(label_cols)))
for i, j in enumerate(label_cols):
    print('fit', j)
    model = linear_model.LogisticRegression(C=4, dual=True)
    model.fit(train_x[5:10], list(train[j])[5:10])
    preds[:,i] = m.predict_proba(test_x)[:,1]

我有一个多标签分类问题，train[j]一次代表一个类来训练多个标签的LR模型。

但是我收到以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-66-4c73d23d3af1> in <module>()
      3     model = linear_model.LogisticRegression(C=4, dual=True)
      4     model.fit(train_x[5:10], list(train[j])[5:10])
----> 5     preds[:,i] = m.predict_proba(test_x)[:,1]

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in predict_proba(self, X)
   1334         calculate_ovr = self.coef_.shape[0] == 1 or self.multi_class == "ovr"
   1335         if calculate_ovr:
-> 1336             return super(LogisticRegression, self)._predict_proba_lr(X)
   1337         else:
   1338             return softmax(self.decision_function(X), copy=False)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\base.py in _predict_proba_lr(self, X)
    336         multiclass is handled by normalizing that over all classes.
    337         """
--> 338         prob = self.decision_function(X)
    339         prob *= -1
    340         np.exp(prob, prob)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\base.py in decision_function(self, X)
    298                                  "yet" % {'name': type(self).__name__})
    299 
--> 300         X = check_array(X, accept_sparse='csr')
    301 
    302         n_features = self.coef_.shape[1]

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py 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)
    431                                       force_all_finite)
    432     else:
--> 433         array = np.array(array, dtype=dtype, order=order, copy=copy)
    434 
    435         if ensure_2d:

ValueError: setting an array element with a sequence.

我已经针对同一问题阅读了其他答案，但无法调试我的问题。 X的所有实例的尺寸都是300尺寸：

len(list(filter(lambda x:len(x)!=300, train_x)))
>> True

和len of train_x（feature）等于train（标签）：

len(train_x) == len(train)
>>True

我还测试了train_x中的所有值是否为float，也是True：

len(list(filter(lambda x: all(isinstance(n, float) for n in x), train_x))) == len(train_x)
>> True

Sklearn Logistic回归 - ValueError：使用序列设置数组元素：

0 个答案: