Python 分类指标无法处理连续多输出和多标签指标目标的混合
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classification metrics can't handle a mix of continuous-multioutput and multi-label-indicator targets
提问by user8512104
I have created an ANN with numerical inputs and a single categorical output which is one hot encoded to be 1 of 19 categories. I set my output layer to have 19 units. I don't know how to perform the confusion matrix now nor how to classifier.predict() in light of this rather than a single binary output. I keep getting an error saying classification metrics can't handle a mix of continuous-multioutput and multi-label-indicator targets. Not sure how to proceed.
我创建了一个带有数字输入和单个分类输出的 ANN，它是一个热编码为 19 个类别中的一个。我将输出层设置为 19 个单元。我不知道现在如何执行混淆矩阵，也不知道如何根据这个而不是单个二进制输出来执行分类器.预测（）。我不断收到错误消息，指出分类指标无法处理连续多输出和多标签指标目标的混合。不知道如何继续。
#Importing Datasets
dataset=pd.read_csv('Data.csv')
x = dataset.iloc[:,1:36].values # lower bound independent variable to upper bound in a matrix (in this case only 1 column 'NC')
y = dataset.iloc[:,36:].values # dependent variable vector
print(x.shape)
print(y.shape)
#One Hot Encoding fuel rail column
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_y= LabelEncoder()
y[:,0]=labelencoder_y.fit_transform(y[:,0])
onehotencoder= OneHotEncoder(categorical_features=[0])
y = onehotencoder.fit_transform(y).toarray()
print(y[:,0:])
print(x.shape)
print (y.shape)
#splitting data into Training and Test Data
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.1,random_state=0)
#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
#x_train = sc.fit_transform(x_train)
#x_test=sc.transform(x_test)
y_train = sc.fit_transform(y_train)
y_test=sc.transform(y_test)
# PART2 - Making ANN, deep neural network
#Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
#Initialising ANN
classifier = Sequential()
#Adding the input layer and first hidden layer
classifier.add(Dense(activation= 'relu', input_dim =35, units=2, kernel_initializer="uniform"))#rectifier activation function, include all input with one hot encoding
#Adding second hidden layer
classifier.add(Dense(activation= 'relu', units=2, kernel_initializer="uniform")) #rectifier activation function
#Adding the Output Layer
classifier.add(Dense(activation='softmax', units=19, kernel_initializer="uniform"))
#Compiling ANN - stochastic gradient descent
classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])#stochastic gradient descent
#Fit ANN to training set
#PART 3 - Making predictions and evaluating the model
#Fitting classifier to the training set
classifier.fit(x_train, y_train, batch_size=10, epochs=100)#original batch is 10 and epoch is 100
#Predicting the Test set rules
y_pred = classifier.predict(x_test)
y_pred = (y_pred > 0.5) #greater than 0.50 on scale 0 to 1
print(y_pred)
#Making confusion matrix that checks accuracy of the model
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
回答by Jakub Bartczuk
y_pred = (y_pred > 0.5)
Outputs a boolean matrix. The problem is that it has the same shape as it had before, but when you evaluate accuracy you need a vector of labels.
输出一个布尔矩阵。问题在于它的形状与以前相同，但是当您评估准确性时，您需要一个标签向量。
To do this take np.argmax(y_pred, axis=1)instead to output correct labels.
为此，请np.argmax(y_pred, axis=1)改为输出正确的标签。
