yaochenzhu / deep-deconf Goto Github PK

I have item purchase data. How do I add user features like age, gender etc. Currently I see that user features are a PCA of randomly sampled data from a Normal distribution. Does it make sense to randomly assign user features?

Shouldn't the arguments be DCG_at_k(y_true,y_pred,k) instead of y_true,y_true

Traceback (most recent call last):
  File "simulate.py", line 270, in <module>
    simulate()
  File "simulate.py", line 201, in simulate
    rat_dist = get_rat_dist(rat_table)
  File "simulate.py", line 62, in get_rat_dist
    return adj_rat_dist(rat_dist_raw)
  File "simulate.py", line 55, in adj_rat_dist
    rat_dist_raw = rat_dist_raw*weights
ValueError: operands could not be broadcast together with shapes (138,) (5,)

Gettig this error in simulate.py, could you please suggest a fix?

Cannot run preprocess.py as it is not present

I have a very sparse dataset, where the average no. of ratings per user is 3, and under strong generalization we do a further split of val data, which further reduces the ratings per user in the val data. Hence does it even make sense to calculate recall@k,ndcg@k, where k>1?. Should i change the evaluation method to weak generalization?

Why do we split the val and test data into observed and unkown? I can understand the code but what is the reasoning behind this split?

I am getting NDCG as nan while simulating for ratings, But no issue while simulating for exposure. Could you please suggest a solution for this?