在 XGBoost 中反转 one-hot 编码标签？

Question

I'm trying to print the accuracy score for an XGBoost multilabel classifier. However, I'm stuck on this error:

ValueError: Classification metrics can't handle a mix of multilabel-indicator and binary targets

I think y_test needs to not be one-hot encoded when passed to accuracy_score() ? But everything I've tried creates more errors. Any idea how I get this to work?

Code:

        X = X.reshape(X.shape[0], -1)
        print(X.shape)


        # Split the dataset
        x_train, x_test, y_train, y_test = train_test_split(X, yy, test_size=0.2, random_state=42, stratify=y)

        dtrain = xgb.DMatrix(data=x_train, label=y_train)
        dtest = xgb.DMatrix(data=x_test, label=y_test)
        eval_list = [(dtest, 'eval')]

        # Train the model
        params = {
            'max_depth': 3,
            'objective': 'multi:softmax', 
            'num_class': 3,
            'tree_method':'gpu_hist'
        }

        # Train the model
        model = xgb.train(params, dtrain, evals=eval_list, early_stopping_rounds=20, verbose_eval=True)

        # Evaluate predictions
        y_pred = model.predict(dtest)
        predictions = [round(value) for value in y_pred]
        accuracy = accuracy_score(y_test, predictions)
        print("Accuracy: %.2f%%" % (accuracy * 100.0))

Answer 1

Adding argmax to y_test seemed to work:

accuracy = accuracy_score(y_test.argmax(axis=1), predictions)