[🐛BUG] [field_name] == FeatureType.TOKEN: KeyError: 'class'

Hi I have been trying to run the GRU4recF model, but encounter an error. Let me know what additional info I can give to understand the issue and resolve it. Thanks!

(base) C:\Users\Administrator\RecBole>python run_recbole.py --model=GRU4RecF --dataset=OfekMetaData --config_files=OfekMetaData.yaml
20 Jan 20:30    INFO General Hyper Parameters:
gpu_id=0
use_gpu=True
seed=2020
state=INFO
reproducibility=True
data_path=dataset/OfekMetaData\OfekMetaData

Training Hyper Parameters:
checkpoint_dir=saved
epochs=300
train_batch_size=2048
learner=adam
learning_rate=0.001
training_neg_sample_num=1
eval_step=1
stopping_step=10

Evaluation Hyper Parameters:
eval_setting=TO_LS,full
group_by_user=True
split_ratio=[0.8, 0.1, 0.1]
leave_one_num=2
real_time_process=True
metrics=['Recall', 'MRR', 'NDCG', 'Hit', 'Precision']
topk=[5]
valid_metric=MRR@5
eval_batch_size=4096

Dataset Hyper Parameters:
field_separator=
seq_separator=
USER_ID_FIELD=session_id
ITEM_ID_FIELD=item_id
RATING_FIELD=rating
LABEL_FIELD=label
threshold=None
NEG_PREFIX=neg_
load_col={'inter': ['session_id', 'item_id', 'timestamp'], 'user': ['session_id', 'PatientLocationID', 'GenderID', 'AgeGroup', 'JobGroup']}
unload_col=None
additional_feat_suffix=None
max_user_inter_num=None
min_user_inter_num=0
max_item_inter_num=None
min_item_inter_num=0
lowest_val=None
highest_val=None
equal_val=None
not_equal_val=None
drop_filter_field=True
fields_in_same_space=None
fill_nan=True
preload_weight=None
drop_preload_weight=True
normalize_field=None
normalize_all=True
ITEM_LIST_LENGTH_FIELD=item_length
LIST_SUFFIX=_list
MAX_ITEM_LIST_LENGTH=50
POSITION_FIELD=position_id
HEAD_ENTITY_ID_FIELD=head_id
TAIL_ENTITY_ID_FIELD=tail_id
RELATION_ID_FIELD=relation_id
ENTITY_ID_FIELD=entity_id


20 Jan 20:31    INFO OfekMetaData
The number of users: 2145929
Average actions of users: 3.7118747693305645
The number of items: 46
Average actions of items: 177009.24444444446
The number of inters: 7965416
The sparsity of the dataset: 91.93071078347398%
Remain Fields: ['item_id', 'timestamp', 'session_id', 'JobGroup', 'PatientLocationID', 'GenderID', 'AgeGroup']
20 Jan 20:31    INFO Build [ModelType.SEQUENTIAL] DataLoader for [train] with format [InputType.POINTWISE]
20 Jan 20:31    INFO Evaluation Setting:
        Group by session_id
        Ordering: {'strategy': 'by', 'field': ['timestamp'], 'ascending': True}
        Splitting: {'strategy': 'loo', 'leave_one_num': 2}
        Negative Sampling: {'strategy': 'by', 'distribution': 'uniform', 'by': 1}
20 Jan 20:31    INFO batch_size = [[2048]], shuffle = [True]

20 Jan 20:31    INFO Build [ModelType.SEQUENTIAL] DataLoader for [evaluation] with format [InputType.POINTWISE]
20 Jan 20:31    INFO Evaluation Setting:
        Group by session_id
        Ordering: {'strategy': 'by', 'field': ['timestamp'], 'ascending': True}
        Splitting: {'strategy': 'loo', 'leave_one_num': 2}
        Negative Sampling: {'strategy': 'full', 'distribution': 'uniform'}
20 Jan 20:31    INFO batch_size = [[4096, 4096]], shuffle = [False]

Traceback (most recent call last):
  File "run_recbole.py", line 25, in <module>
    run_recbole(model=args.model, dataset=args.dataset, config_file_list=config_file_list)
  File "C:\Users\Administrator\RecBole\recbole\quick_start\quick_start.py", line 45, in run_recbole
    model = get_model(config['model'])(config, train_data).to(config['device'])
  File "C:\Users\Administrator\RecBole\recbole\model\sequential_recommender\gru4recf.py", line 60, in __init__
    self.pooling_mode, self.device)
  File "C:\Users\Administrator\RecBole\recbole\model\layers.py", line 810, in __init__
    self.get_fields_name_dim()
  File "C:\Users\Administrator\RecBole\recbole\model\layers.py", line 569, in get_fields_name_dim
    if self.dataset.field2type[field_name] == FeatureType.TOKEN:
KeyError: 'class'

(base) C:\Users\Administrator\RecBole>

[batmanfly]

@mayaKaplansky I supposed you would like to implement session-based recommendation with user features, right?

If so, there is no such model that utilized user features for session-based recommendation in RecBole.

I think I have replied on the issue “Get a prediction” with one possibility: you first learned the session representation (just like GRU4RecF with no user features), and then combine it (the embedding encoding the sequence of the items in a session) with user representations (e.g., sum, concatenation or others. If you have multiple features, you can also design a MLP or more complicated architecture). If you would like to find some models with user features for reference, please refer to context-aware models, e.g., deep & wide (however, it seemed to be not explicitly with user features: it accepted general features, including user features).

For this purpose, you should make two attempts:

1. Try to load user features via .user file and can explicitly use these features in programs. (this is what you ask in a previous issue)
2. If this step is successful, you can design your own architecture for using user features (this can be implemented in full_sort and associated functions, which are discussed in a previous issue, too).

[mayaKaplansky]

Thanks! How can I figure out from documentation which model supports user features?