Question

我想修改scikit-lern实现的ROC曲线，所以我尝试了以下内容：

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)

得到了这个追溯：

/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

然后我也尝试了这个：

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

这是追溯：

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

有关如何正确绘制此指标的想法吗？提前谢谢！

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必须是1d array（形状与y_test相同）。您可以通过检查形状属性来检查，例如y_test.shape。我想

prediction[0].values

返回

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

因为你试图在预测元素上调用.values。

<强>更新

ValueError: Data is not binary and pos_label is not specified

我之前没有注意到这一点。如果您的类不是二进制文件，则必须在pos_label中指定roc_curve参数，以便绘制一个类与其余类。为此，您需要将类标签设为整数。您可以使用：

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

pediction_le返回类重新编码int

更新2：

您的预测器只返回一个类，因此您无法绘制ROC曲线

IndexError：使用scikit-learn绘制ROC曲线时，数组的索引太多了？

1 个答案: