399277422dc4be4592df305b94d59b889a74e7ba,delft/textClassification/wrapper.py,Classifier,eval,#Classifier#Any#Any#,182

Before Change

        if len(self.model_config.list_classes) is 1:
            total_accuracy = accuracy_score(y_test, result_binary)
            total_f1 = f1_score(y_test, result_binary)
            total_loss = log_loss(y_test, result)
            total_roc_auc = roc_auc_score(y_test, result)
        else:
            for j in range(0, len(self.model_config.list_classes)):
                accuracy = accuracy_score(y_test[:, j], result_binary[:, j])

After Change

        if len(self.model_config.list_classes) is 1:
            total_accuracy = accuracy_score(y_test, result_binary)
            total_f1 = f1_score(y_test, result_binary)
            total_loss = log_loss(y_test, result, labels=[0,1])
            if len(np.unique(y_test)) == 1:
                // roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
                // a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
                total_roc_auc = r2_score(y_test, result)
                if total_roc_auc < 0:
                    total_roc_auc = 0 
            else:
                total_roc_auc = roc_auc_score(y_test, result)
        else:
            for j in range(0, len(self.model_config.list_classes)):
                accuracy = accuracy_score(y_test[:, j], result_binary[:, j])
                total_accuracy += accuracy
                f1 = f1_score(y_test[:, j], result_binary[:, j], average="micro")
                total_f1 += f1
                loss = log_loss(y_test[:, j], result[:, j], labels=[0,1])
                total_loss += loss
                if len(np.unique(y_test[:, j])) == 1:
                    // roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
                    // a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
                    roc_auc = r2_score(y_test[:, j], result[:, j])
                    if roc_auc < 0:
                        roc_auc = 0 
                else:
                    roc_auc = roc_auc_score(y_test[:, j], result[:, j])
                total_roc_auc += roc_auc
                print("\nClass:", self.model_config.list_classes[j])
                print("\taccuracy at 0.5 =", accuracy)
                print("\tf-1 at 0.5 =", f1)

Instances