How to fix the error "Singleton array cannot be considered a valid collection"

Question

I'm starting my first machine learning code with python. But, I encountered an error while developing the confusion matrix for my multiclass model.

#Defining the model 

model = Sequential()

model.add(Dense(32,input_shape=(22,),activation='tanh'))
model.add(Dense(16,activation='tanh'))
model.add(Dense(6,activation='tanh'))
model.add(Dense(5,activation='softmax'))

model.compile(Adam(lr=0.004),'sparse_categorical_crossentropy',metrics=['accuracy'])

#fitting the model and predicting 

model.fit(X_train,Y_train,epochs=1)

Y_pred = model.predict(X_test)

Y_pred = Y_pred.astype(int)

Y_test_class = np.argmax(Y_test, axis=0)
Y_pred_class = np.argmax(Y_pred, axis=0)

#Accuracy of the predicted values

print(metrics.classification_report(Y_test_class,Y_pred_class))
print(metrics.confusion_matrix(Y_test_class,Y_pred_class))

I'm getting this error:

TypeError: Singleton array 3045 cannot be considered a valid collection.

Test data details X_test[:5]

[['0' '1' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0'
  '0' '1' '0' '0']
 ['1' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0'
  '0' '0' '0' '0']
 ['1' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0'
  '0' '0' '0' '0']
 ['0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0'
  '0' '0' '0' '0']
 ['0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0' '0'
  '0' '1' '1' '0']]

Y_test[:5]

['1' '2' '2' '2' '2']

The shape of

Y_test_class ==> ()
Y_pred_class ==> (5,)

scottbaker · Accepted Answer · 2020-03-11 03:07:17Z

1

Have you looked at:

Multilabel-indicator is not supported for confusion matrix

Depending on if you're using OHE or a vector of labels, this might help.

answered Mar 11, 2020 at 3:07

scottbaker

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3 Comments

Asma Mekki Over a year ago

Thx for the link! It helps. I think the problem is that Y_test_class is empty while the shape of Y_test is (32863,). Have you any idea how can I solve that PLS?

scottbaker Over a year ago

I'm not familiar with your data, but check that astype(int) isn't messing it up. Otherwise, I'd assume that np.argmax will output the same result potentially without the astype(int) part

Asma Mekki Over a year ago

That's true! but even when I remove that line the error persists. I added some details about my test data. I hope that can help!

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