IndexError: too many indices for array while plotting ROC curve with scikit-learn?

Question

我想绘制 scikit-lern 实现的 ROC 曲线，所以我尝试了以下操作：

from sklearn.metrics import roc_curve, auc
false_positive_rate, recall, thresholds = roc_curve(y_test, prediction[:, 1])
roc_auc = auc(false_positive_rate, recall)
plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, recall, 'b', label='AUC = %0.2f' % roc_auc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.ylabel('Recall')
plt.xlabel('Fall-out')
plt.show()

这是输出：

Traceback (most recent call last):
  File "/Users/user/script.py", line 62, in <module>
    false_positive_rate, recall, thresholds = roc_curve(y_test, prediction[:, 1])
IndexError: too many indices for array

然后从 previous question 我尝试了这个：

false_positive_rate, recall, thresholds = roc_curve(y_test, prediction)

并得到了这个回溯：

/usr/local/lib/python2.7/site-packages/sklearn/metrics/metrics.py:705: DeprecationWarning: elementwise comparison failed; this will raise the error in the future.
  not (np.all(classes == [0, 1]) or
/usr/local/lib/python2.7/site-packages/sklearn/metrics/metrics.py:706: DeprecationWarning: elementwise comparison failed; this will raise the error in the future.
  np.all(classes == [-1, 1]) or
Traceback (most recent call last):
  File "/Users/user/PycharmProjects/TESIS_CODE/clasificacion_simple_v1.py", line 62, in <module>
    false_positive_rate, recall, thresholds = roc_curve(y_test, prediction)
  File "/usr/local/lib/python2.7/site-packages/sklearn/metrics/metrics.py", line 890, in roc_curve
    y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)
  File "/usr/local/lib/python2.7/site-packages/sklearn/metrics/metrics.py", line 710, in _binary_clf_curve
    raise ValueError("Data is not binary and pos_label is not specified")
ValueError: Data is not binary and pos_label is not specified

然后我也试了这个：

false_positive_rate, recall, thresholds = roc_curve(y_test, prediction[0].values)

这是追溯：

AttributeError: 'numpy.int64' object has no attribute 'values'

知道如何正确绘制此指标吗？提前致谢！

这是预测变量的形状：

print prediction.shape
(650,)

这是testing_matrix: (650, 9596)

的形状

Answer 1

变量prediction需要是一个1d array（与y_test形状相同）。您可以通过检查形状属性来检查，例如y_test.shape。我觉得

prediction[0].values

returns

AttributeError: 'numpy.int64' object has no attribute 'values'

因为您正在尝试调用 .values 预测元素。

更新：

ValueError: Data is not binary and pos_label is not specified

我以前没有注意到这一点。如果您的 classes 不是二进制的，您必须在 roc_curve 中指定 pos_label 参数，以便绘制一个 class 与其余的。为此，您需要 class 标签为整数。您可以使用：

from sklearn.preprocessing import LabelEncoder
class_labels = LabelEncoder()
prediction_le = class_lables.fit_transform(prediction)

pediction_le returns classes重新编码一个int

更新二：

您的预测器仅返回一个 class，因此您无法绘制 ROC 曲线

IndexError: too many indices for array while plotting ROC curve with scikit-learn?

IndexError: too many indices for array while plotting ROC curve with scikit-learn?

python

numpy

matplotlib

scikit-learn