def calc_score(y_true: csr_matrix, y_pred: csr_matrix):
"""Score predictions in csr format"""
# Hits are when we guessed a track that appeared
# This is an efficient operation when both entries are csr_matrix instances
hits = y_true.multiply(y_pred)
# Sum the hits along the rows; results will be an nx1 array of scores, one for each training point
scores = np.sum(hits, axis=1)
# Return the scores as a flattened numpy array
return scores.A.flatten()
def transform(self, X: csr_matrix) -> coo_matrix:
return X.multiply(self.r)
