5 examples of 'roc auc curve' in Python

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def auc(self):
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    if self.type != DatasetType.binary:
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        # raise ValueError("AUC metric is only supported for binary classification: {}.".format(self.classes))
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        log.warning("AUC metric is only supported for binary classification: %s.", self.classes)
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        return nan
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    return float(roc_auc_score(self.truth, self.probabilities[:, 1]))

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def calc_auc(x, y):
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    """ Given x and y values it calculates the approx. integral and normalizes it: area under curve"""
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    integral = np.trapz(y, x)
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    norm = np.trapz(np.ones_like(y), x)
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    return integral / norm

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def plot_roc(y_test, y_pred, label=''):
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    """Compute ROC curve and ROC area"""
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    fpr, tpr, _ = roc_curve(y_test, y_pred)
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    roc_auc = auc(fpr, tpr)
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    # Plot of a ROC curve for a specific class
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    plt.figure()
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    plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
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    plt.plot([0, 1], [0, 1], 'k--')
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    plt.xlim([0.0, 1.0])
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    plt.ylim([0.0, 1.05])
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    plt.xlabel('False Positive Rate')
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    plt.ylabel('True Positive Rate')
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    plt.title('Receiver operating characteristic' + label)
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    plt.legend(loc="lower right")
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    plt.show()

19
def auc_score(y_true, y_pred, positive_label=1):
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    if hasattr(sklearn.metrics, 'roc_auc_score'):
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        return sklearn.metrics.roc_auc_score(y_true, y_pred)
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    fp_rate, tp_rate, thresholds = sklearn.metrics.roc_curve(
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        y_true, y_pred, pos_label=positive_label)
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    return sklearn.metrics.auc(fp_rate, tp_rate)

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def plot_roc_auc_per_class(self):
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    """
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    Plot the ROC AUC per class as a barplot.
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    """
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    self.per_class_metrics_list[0] = sorted(self.per_class_metrics_list[0], key=lambda x: -float(x['ROC_auc']))
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    fig, ax = plt.subplots()
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    ax.bar(x=list(range(len(self.per_class_metrics_list[0]))),
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           height=[float(x['ROC_auc']) for x in self.per_class_metrics_list[0]],
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           width=1,
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           color=colors['blue'],
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           alpha=0.7
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           )
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    ax.set_ylabel('ROC AUC')
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    ax.set_xlabel('Class')
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    ax.set_title('ROC per Class')
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    plt.savefig(os.path.join(self.plot_path, 'roc_auc_per_class_{}{}.png'.format(self.step, self.early)))
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    plt.close()