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 nan368    return float(roc_auc_score(self.truth, self.probabilities[:, 1]))``
``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)658659    return integral / norm``
``109def plot_roc(y_test, y_pred, label=''):110    """Compute ROC curve and ROC area"""111112    fpr, tpr, _ = roc_curve(y_test, y_pred)113    roc_auc = auc(fpr, tpr)114115    # Plot of a ROC curve for a specific class116    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)2223    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()197198    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=0.7203           )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()``