You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This question references following the demo which uses synthetic data: synthetic data demo
I'm trying to understand what is going on with this model and how it works. I got it installed and I'm following the demo where the synthetic data is generated. The demo uses a NextItemPrediction head which I follow and train.
I'm able to serve the model on Triton but when I generate more data to now predict on I can quite understand what's going on. I generated 1k new rows with 966 unique session_ids but the prediction have be back an array of size (34, 500). Why am I only getting back 34 rows of predictions??
I followed the demo exactly so feel free to reference that if you'd like. The demo itself shows in it's example that passing in 1k examples returned 28 predictions:
NUM_ROWS =1000
long_tailed_item_distribution = np.clip(np.random.lognormal(3., 1., int(NUM_ROWS)).astype(np.int32), 1, 50000)
# generate random item interaction features
df = pd.DataFrame(np.random.randint(70000, 90000, int(NUM_ROWS)), columns=['session_id'])
df['item_id'] = long_tailed_item_distribution
# generate category mapping for each item-id
df['category'] = pd.cut(df['item_id'], bins=334, labels=np.arange(1, 335)).astype(np.int32)
df['age_days'] = np.random.uniform(0, 1, int(NUM_ROWS)).astype(np.float32)
df['weekday_sin']= np.random.uniform(0, 1, int(NUM_ROWS)).astype(np.float32)
# generate day mapping for each session
map_day = dict(zip(df.session_id.unique(), np.random.randint(1, 10, size=(df.session_id.nunique()))))
df['day'] = df.session_id.map(map_day)
print(df.head(2))
Answered my own question. There is a minimum sequence length of 2 set when creating the schema so any sessions that do now have more then 1 event will be discarded
❓ Questions & Help
Details
This question references following the demo which uses synthetic data: synthetic data demo
I'm trying to understand what is going on with this model and how it works. I got it installed and I'm following the demo where the synthetic data is generated. The demo uses a NextItemPrediction head which I follow and train.
I'm able to serve the model on Triton but when I generate more data to now predict on I can quite understand what's going on. I generated 1k new rows with 966 unique session_ids but the prediction have be back an array of size (34, 500). Why am I only getting back 34 rows of predictions??
I followed the demo exactly so feel free to reference that if you'd like. The demo itself shows in it's example that passing in 1k examples returned 28 predictions:
{'next-item': array([[-3.9399953, -2.632081 , -4.2211075, ..., -3.6699016, -3.673493 ,
-3.1244578],
[-3.940445 , -2.6335964, -4.2203593, ..., -3.671566 , -3.6745713,
-3.1240335],
[-3.9393594, -2.6300201, -4.222065 , ..., -3.6674871, -3.672068 ,
-3.1251097],
...,
[-3.9396427, -2.6304667, -4.2218847, ..., -3.6677885, -3.6724825,
-3.1250875],
[-3.939829 , -2.6316376, -4.221267 , ..., -3.6693997, -3.6732295,
-3.1245873],
[-3.9399223, -2.631995 , -4.2210817, ..., -3.669589 , -3.6734715,
-3.1244512]], dtype=float32)}
response['next-item'].shape
(28, 495)
The text was updated successfully, but these errors were encountered: