5 examples of 'roc auc curve' in Python

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363def auc(self):
364 if self.type != DatasetType.binary:
365 # raise ValueError("AUC metric is only supported for binary classification: {}.".format(self.classes))
366 log.warning("AUC metric is only supported for binary classification: %s.", self.classes)
367 return nan
368 return float(roc_auc_score(self.truth, self.probabilities[:, 1]))
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654def calc_auc(x, y):
655 """ Given x and y values it calculates the approx. integral and normalizes it: area under curve"""
656 integral = np.trapz(y, x)
657 norm = np.trapz(np.ones_like(y), x)
658
659 return integral / norm
109def plot_roc(y_test, y_pred, label=''):
110 """Compute ROC curve and ROC area"""
111
112 fpr, tpr, _ = roc_curve(y_test, y_pred)
113 roc_auc = auc(fpr, tpr)
114
115 # Plot of a ROC curve for a specific class
116 plt.figure()
117 plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
118 plt.plot([0, 1], [0, 1], 'k--')
119 plt.xlim([0.0, 1.0])
120 plt.ylim([0.0, 1.05])
121 plt.xlabel('False Positive Rate')
122 plt.ylabel('True Positive Rate')
123 plt.title('Receiver operating characteristic' + label)
124 plt.legend(loc="lower right")
125 plt.show()
19def auc_score(y_true, y_pred, positive_label=1):
20 if hasattr(sklearn.metrics, 'roc_auc_score'):
21 return sklearn.metrics.roc_auc_score(y_true, y_pred)
22
23 fp_rate, tp_rate, thresholds = sklearn.metrics.roc_curve(
24 y_true, y_pred, pos_label=positive_label)
25 return sklearn.metrics.auc(fp_rate, tp_rate)
191def plot_roc_auc_per_class(self):
192 """
193 Plot the ROC AUC per class as a barplot.
194 """
195 self.per_class_metrics_list[0] = sorted(self.per_class_metrics_list[0], key=lambda x: -float(x['ROC_auc']))
196 fig, ax = plt.subplots()
197
198 ax.bar(x=list(range(len(self.per_class_metrics_list[0]))),
199 height=[float(x['ROC_auc']) for x in self.per_class_metrics_list[0]],
200 width=1,
201 color=colors['blue'],
202 alpha=0.7
203 )
204 ax.set_ylabel('ROC AUC')
205 ax.set_xlabel('Class')
206 ax.set_title('ROC per Class')
207 plt.savefig(os.path.join(self.plot_path, 'roc_auc_per_class_{}{}.png'.format(self.step, self.early)))
208 plt.close()

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