ValueError: multilabel-indicator is not supported

First Code :

# Artificial Neural Net – keras MLP로 iris 데이터를 이용하여 분석
# -*- coding: utf-8 -*-
"""
Created on Wed May 13 20:24:49 2020

@author: user
"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from sklearn.metrics import confusion_matrix, accuracy_score

iris_data = load_iris()
print('Example data: ')
print(iris_data.data[:5])
print('Example labels: ')
print(iris_data.target[:5])

x = iris_data.data
y_ = iris_data.target.reshape(-1, 1) 

encoder = OneHotEncoder(sparse=False)
y = encoder.fit_transform(y_)

train_x, test_x, train_y, test_y= train_test_split(x, y, test_size=0.20)

model= Sequential()

model.add(Dense(10, input_shape=(4,), activation='relu', name='fc1'))
model.add(Dense(10, activation='relu', name='fc2'))
model.add(Dense(3, activation='softmax', name='output'))

optimizer= Adam(lr=0.001)
model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
print('Neural Network Model Summary: ')
print(model.summary())

model.fit(train_x, train_y, verbose=2, batch_size=5, epochs=200)

results= model.evaluate(test_x, test_y)
print('Final test set loss: {:4f}'.format(results[0]))
print('Final test set accuracy: {:4f}'.format(results[1]))

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# StandardScaler를 담은 변수에 X_train을 학습해 데이터를 정규화
scaler.fit(train_x)
# X_train과 X_test를 StandardScaler를 이용해 정규화
train_x = scaler.transform(train_x)
test_x = scaler.transform(test_x)

from sklearn.neural_network import MLPClassifier

# MLP 알고리즘의 hidden 레이어를 3계층(10,10,10)으로 할당
mlp = MLPClassifier(hidden_layer_sizes=(10,10,10))

# 위에서 분류한 X_train과 y_train을 MLP를 이용해 학습
mlp.fit(train_x, train_y)

predictions = mlp.predict(test_x)

print(confusion_matrix(test_y, predictions))
print(accuracy_score(test_y, predictions))

 

ErrorCode :

ValueError: multilabel-indicator is not supported

 

 

Solution :

confusion_matrix must be a list of predictions.

Call argmax on test_y and predictions.

print(confusion_matrix(test_y.argmax(axis=1), predictions.argmax(axis=1)))
print(accuracy_score(test_y.argmax(axis=1), predictions.argmax(axis=1)))