Finding the precision and auc scores.

I am building a recommendation model for user-article dataset where each interaction is represented by 1.

model = LightFM(loss='warp', item_alpha=ITEM_ALPHA, user_alpha=USER_ALPHA, no_components=NUM_COMPONENTS, learning_rate=LEARNING_RATE, learning_schedule=LEARNING_SCHEDULE)

model = model.fit(train, item_features=itemf, user_features=uf, epochs=NUM_EPOCHS, num_threads=NUM_THREADS)

print("train shape: ",train.shape) print("test shape: ",test.shape)

train shape: (25900, 790)
test shape: (25900, 790)

My predict model looks like this:

predictions = model.predict( user_id, pid_array, user_features=uf, item_features=itemf, num_threads=4)

where pid_array are indexes of number of items

train_precision = precision_at_k(model, train, k=10).mean()

I am trying to predict the precision and subsequently want auc score also. But I get this error.

Traceback (most recent call last):
File "new_light_fm.py", line 366, in
train_precision = precision_at_k(model, train, k=10).mean()
File "/home/nt/anaconda3/lib/python3.6/site-packages/lightfm/evaluation.py", line 69, in precision_at_k
check_intersections=check_intersections,
File "/home/nt/anaconda3/lib/python3.6/site-packages/lightfm/lightfm.py", line 807, in predict_rank
raise ValueError('Incorrect number of features in item_features')
ValueError: Incorrect number of features in item_features

Start in lightfm/evaluation.py at precision_at_k and follow its call into lightfm/lightfm.py predict_rank, using the reported train, itemf, and user-feature shapes as the reproduction case. Determine why the feature count check fails and document a verified way for precision_at_k and AUC evaluation to run successfully.