ValueError: Classification metrics can't handle a mix of multilabel-indicator and continuous-mu-CodePudding

I don't know what the problem is and why I'm getting this error:

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

Can anyone please help me with this code?

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import make_classification
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.model_selection import train_test_split
tf.random.set_seed(0)

# generate the data
X, y = make_classification(n_classes=6, n_samples=1000, n_features=10, n_informative=10, n_redundant=0, random_state=42)
print(y.shape)
# (1000, )

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# one-hot encode the target
enc = OneHotEncoder(sparse=False, handle_unknown='ignore')
enc.fit(y_train.reshape(-1, 1))
y_train = enc.transform(y_train.reshape(-1, 1))
y_test = enc.transform(y_test.reshape(-1, 1))
print(y_train.shape, y_test.shape)
# (750, 6) (250, 6)

# scale the features
scaler = MinMaxScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# define the model
model = Sequential()
model.add(Dense(units=30, activation='relu'))
model.add(Dense(units=15, activation='relu'))
model.add(Dense(6, activation='softmax'))

# fit the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=X_train, y=y_train, epochs=3, batch_size=10, validation_data=(X_test, y_test))

predictions = model.predict(X_test)

confusion_matrix(y_test,predictions)

print(classification_report(y_lab,predictions))

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

CodePudding user response：

The reason for the error is you're comparing a one-hot label y_test with a class probability label which is estimated by the model predictions. A quick fix would be converting both to simple categorical labels like below:

confusion_matrix(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))
classification_report(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))

CodePudding user response：

y_test_arg=np.argmax(y_test,axis=1)
Y_pred = np.argmax(predictions,axis=1)
print(confusion_matrix(y_test_arg, Y_pred))

It will solve the problem, Inshallah.