Question

我已经通过以下方式创建了一个名为category_predicted的列表：

def prediction(optimal_alpha, metric):
    category_predicted = []
    multinomial_naive_bayes_optimal = MultinomialNB(alpha=optimal_alpha)

    # fitting the model
    multinomial_naive_bayes_optimal.fit(x_train_counts, y_train)

    # predict the response
    pred_cat = multinomial_naive_bayes_optimal.predict(x_test_counts)
    category_predicted.append(pred_cat)
    pred = multinomial_naive_bayes_optimal.predict_proba(x_test_counts)

    log_loss_acc = log_loss(y_test, pred) 
    print('\nThe accuracy of the Multinomial Naive Bayes classifier for alpha = %f and metric = %s is %f' % (optimal_alpha, metric, log_loss_acc))
    return category_predicted

category_predicted = prediction(optimal_alpha, 'neg_log_loss')

接下来，我试图创建一个包含两列y_test和category_predicted的数据框，并尝试用y_test和预测的category的值填充数据框：

df = pd.DataFrame()
df['Y_test']  = y_test
df['category_predicted'] = category_predicted
print(df)

它给出以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-88-a34fff6f93f7> in <module>()
      3 #category_predicted = np.array(category_predicted)
      4 #categor_trans = category_predicted.transpose()
----> 5 df['category_predicted'] = category_predicted
      6 print(df)
      7 print(len(category_predicted))

~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/frame.py in __setitem__(self, key, value)
   2517         else:
   2518             # set column
-> 2519             self._set_item(key, value)
   2520 
   2521     def _setitem_slice(self, key, value):

~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/frame.py in _set_item(self, key, value)
   2583 
   2584         self._ensure_valid_index(value)
-> 2585         value = self._sanitize_column(key, value)
   2586         NDFrame._set_item(self, key, value)
   2587 

~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/frame.py in _sanitize_column(self, key, value, broadcast)
   2758 
   2759             # turn me into an ndarray
-> 2760             value = _sanitize_index(value, self.index, copy=False)
   2761             if not isinstance(value, (np.ndarray, Index)):
   2762                 if isinstance(value, list) and len(value) > 0:

~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/series.py in _sanitize_index(data, index, copy)
   3119 
   3120     if len(data) != len(index):
-> 3121         raise ValueError('Length of values does not match length of ' 'index')
   3122 
   3123     if isinstance(data, PeriodIndex):

ValueError: Length of values does not match length of index

y_test和category_predicted的大小不明确匹配：

print(len(category_predicted))
print(len(y_test))

输出：

1
289

编辑：

df['category_predicted'] = category_predicted
df['Y_test']  = y_test
print(df)

输出：

              category_predicted  Y_test
0  [11, 19, 19, 33, 12, 1, 22, 30, 11, 19, 22, 11...      31

如果我写： df ['category_predicted'] = category_predicted df ['Y_test'] = y_test 打印（df）输出：

                                  category_predicted
0  [11, 19, 19, 33, 12,enter code here 1, 22, 30, 11, 19, 22, 11...

如果我写：

df['Y_test']  = y_test
print(df)

输出：

      Y_test
1208      16
1013      19
1016      19
1153       5
1434      12
65         1
943       17
425       23
1104       4
1052      19
342       22
523       11
487       11
458       11
1243      10
771        6
1355       7
692        9
981       32
1159       5
924       17
880       33
273       22
360       23
295       22
1101       4
391       23
1025      19
1047      19
1238      10
...      ...
1240      10
168        2
174        2
484       11
194       30
1184       5
967       32
1250      10
185        2
772        6
750        6
633       29
230       30
1309       8
279       22
542       35
119        2
439       23
392       23
1152       5
769        6
1129      21
858       33
615       29
661        9
244       30
1295      27
1100       4
345       22
960       32

[289 rows x 1 columns]

我希望y_test和category_predicted都打印为两列并排显示。

并且，为什么category_predicted的长度是一个？应该是列表中元素的数量。

Answer 1

我认为您的问题是这些行：

pred_cat = multinomial_naive_bayes_optimal.predict(x_test_counts)
category_predicted.append(pred_cat)

.predict（）已经返回一个列表。然后，您将列表添加到列表中。所以category_predicted现在看起来像这样：

category_predicted = [[1,2,3,3,4,3,4,54]]

我认为您只需要将其更改为：

df['category_predicted'] = category_predicted[0]
df['Y_test']  = y_test

或者只返回pred_cat而不是您的预测函数中的category_predicted

Python + Pandas：使用列表填充新的数据框

1 个答案: