Multilabel-indicator is not supported for confusion matrix
Question:
multilabel-indicator is not supported is the error message I get, when trying to run:
confusion_matrix(y_test, predictions)
y_test is a DataFrame which is of shape:
Horse | Dog | Cat
1 0 0
0 1 0
0 1 0
... ... ...
predictions is a numpy array:
[[1, 0, 0],
[0, 1, 0],
[0, 1, 0]]
I’ve searched a bit for the error message, but haven’t really found something I could apply. Any hints?
Answers:
No, your input to confusion_matrix must be a list of predictions, not OHEs (one hot encodings). Call argmax on your y_test and y_pred, and you should get what you expect.
confusion_matrix(
y_test.values.argmax(axis=1), predictions.argmax(axis=1))
array([[1, 0],
[0, 2]])
The confusion matrix takes a vector of labels (not the one-hot encoding). You should run
confusion_matrix(y_test.values.argmax(axis=1), predictions.argmax(axis=1))
from sklearn.metrics import confusion_matrix
predictions_one_hot = model.predict(test_data)
cm = confusion_matrix(labels_one_hot.argmax(axis=1), predictions_one_hot.argmax(axis=1))
print(cm)
Output would be something like this:
[[298 2 47 15 77 3 49]
[ 14 31 2 0 5 1 2]
[ 64 5 262 22 94 38 43]
[ 16 1 20 779 15 14 34]
[ 49 0 71 33 316 7 118]
[ 14 0 42 23 5 323 9]
[ 20 1 27 32 97 13 436]]
If you have numpy.ndarray you can try the following
import seaborn as sns
T5_lables = ['4TCM','WCM','WSCCM','IWCM','CCM']
ax= plt.subplot()
cm = confusion_matrix(np.asarray(Y_Test).argmax(axis=1), np.asarray(Y_Pred).argmax(axis=1))
sns.heatmap(cm, annot=True, fmt='g', ax=ax); #annot=True to annotate cells, ftm='g' to disable scientific notation
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');
ax.set_title('Confusion Matrix');
ax.xaxis.set_ticklabels(T5_lables); ax.yaxis.set_ticklabels(T5_lables);
multilabel-indicator is not supported is the error message I get, when trying to run:
confusion_matrix(y_test, predictions)
y_test is a DataFrame which is of shape:
Horse | Dog | Cat
1 0 0
0 1 0
0 1 0
... ... ...
predictions is a numpy array:
[[1, 0, 0],
[0, 1, 0],
[0, 1, 0]]
I’ve searched a bit for the error message, but haven’t really found something I could apply. Any hints?
No, your input to confusion_matrix must be a list of predictions, not OHEs (one hot encodings). Call argmax on your y_test and y_pred, and you should get what you expect.
confusion_matrix(
y_test.values.argmax(axis=1), predictions.argmax(axis=1))
array([[1, 0],
[0, 2]])
The confusion matrix takes a vector of labels (not the one-hot encoding). You should run
confusion_matrix(y_test.values.argmax(axis=1), predictions.argmax(axis=1))
from sklearn.metrics import confusion_matrix
predictions_one_hot = model.predict(test_data)
cm = confusion_matrix(labels_one_hot.argmax(axis=1), predictions_one_hot.argmax(axis=1))
print(cm)
Output would be something like this:
[[298 2 47 15 77 3 49]
[ 14 31 2 0 5 1 2]
[ 64 5 262 22 94 38 43]
[ 16 1 20 779 15 14 34]
[ 49 0 71 33 316 7 118]
[ 14 0 42 23 5 323 9]
[ 20 1 27 32 97 13 436]]
If you have numpy.ndarray you can try the following
import seaborn as sns
T5_lables = ['4TCM','WCM','WSCCM','IWCM','CCM']
ax= plt.subplot()
cm = confusion_matrix(np.asarray(Y_Test).argmax(axis=1), np.asarray(Y_Pred).argmax(axis=1))
sns.heatmap(cm, annot=True, fmt='g', ax=ax); #annot=True to annotate cells, ftm='g' to disable scientific notation
# labels, title and ticks
ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');
ax.set_title('Confusion Matrix');
ax.xaxis.set_ticklabels(T5_lables); ax.yaxis.set_ticklabels(T5_lables);