I am using the MlKnn method and I am able to fit the classifier and to make predictions through the command classifier.predict(Test).
The result is a scipy.sparse.lil.lil_matrix which has only the predictions itself.
I don't understand how to assign these predictions to the original data set Test which is scipy.sparse.csr.csr_matrix format.
Could somebody help me please? Thanks.
Need to know how your original dataset Test looks like. You can convert lil_matrix to csr_matrix or the full numpy.ndarray format like here:
from skmultilearn.adapt import MLkNN
from scipy import sparse
from skmultilearn.dataset import load_dataset
import sklearn.metrics as metrics
X_train, y_train, feature_names, label_names = load_dataset('emotions', 'train')
X_test, y_test, _, _ =load_dataset('emotions', 'test')
clf = MLkNN(k=5)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(type(y_pred)) # <class 'scipy.sparse.lil.lil_matrix'>
print(type(y_pred.toarray())) # <class 'numpy.ndarray'>
y_pred_csr = sparse.csr_matrix(y_pred)
print(type(y_pred_csr)) # <class 'scipy.sparse.csr.csr_matrix'>
accuracy = metrics.accuracy_score(y_test, y_pred)
print(accuracy) # 0.148
