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GPU availability:  True
Num of GPU:  1
NVIDIA A2
Current GPU index:  0

18 Feb 12:52    INFO  
General Hyper Parameters:
gpu_id = 0
use_gpu = True
seed = 2022
state = INFO
reproducibility = True
data_path = dataset/tripadvisor
checkpoint_dir = saved
show_progress = False
save_dataset = False
dataset_save_path = None
save_dataloaders = False
dataloaders_save_path = None
log_wandb = False

Training Hyper Parameters:
epochs = 50
train_batch_size = 500
learner = adam
learning_rate = 0.01
train_neg_sample_args = {'distribution': 'none', 'sample_num': 'none', 'alpha': 'none', 'dynamic': False, 'candidate_num': 0}
eval_step = 1
stopping_step = 10
clip_grad_norm = None
weight_decay = 0.0
loss_decimal_place = 4

Evaluation Hyper Parameters:
eval_args = {'split': {'CV': 5}, 'group_by': 'user', 'mode': 'labeled', 'order': 'RO'}
repeatable = False
metrics = ['MAE', 'RMSE', 'AUC']
topk = [10, 20, 30]
valid_metric = MAE
valid_metric_bigger = False
eval_batch_size = 409600
metric_decimal_place = 4

Dataset Hyper Parameters:
field_separator = ,
seq_separator =  
USER_ID_FIELD = user_id
ITEM_ID_FIELD = item_id
RATING_FIELD = rating
TIME_FIELD = timestamp
seq_len = None
LABEL_FIELD = label
threshold = {'rating': 0}
NEG_PREFIX = neg_
load_col = None
unload_col = None
unused_col = None
additional_feat_suffix = None
rm_dup_inter = None
val_interval = None
filter_inter_by_user_or_item = True
user_inter_num_interval = [0,inf)
item_inter_num_interval = [0,inf)
alias_of_user_id = None
alias_of_item_id = None
alias_of_entity_id = None
alias_of_relation_id = None
preload_weight = None
normalize_field = None
normalize_all = None
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
benchmark_filename = None

Other Hyper Parameters: 
worker = 0
wandb_project = recbole
shuffle = True
require_pow = False
enable_amp = False
enable_scaler = False
transform = None
numerical_features = []
discretization = None
kg_reverse_r = False
entity_kg_num_interval = [0,inf)
relation_kg_num_interval = [0,inf)
MODEL_TYPE = ModelType.CONTEXT
CONTEXT_SITUATION_FIELD = contexts
USER_CONTEXT_FIELD = uc_id
neg_sampling = None
mf_embedding_size = 64
mlp_embedding_size = 64
mlp_hidden_size = [128, 64, 32]
dropout_prob = 0.1
mf_train = True
mlp_train = True
embedding_size = 64
ranking = False
sigmoid = False
ranking_valid_metric = Recall@10
ranking_metrics = ['Precision', 'Recall', 'NDCG', 'MRR', 'MAP']
err_valid_metric = MAE
err_metrics = ['MAE', 'RMSE', 'AUC']
MODEL_INPUT_TYPE = InputType.POINTWISE
eval_type = EvaluatorType.VALUE
single_spec = True
local_rank = 0
device = cuda
eval_neg_sample_args = {'distribution': 'none', 'sample_num': 'none'}


18 Feb 12:52    INFO  tripadvisor
The number of users: 2372
Average actions of users: 5.978490088570224
The number of items: 2270
Average actions of items: 6.24724548259145
The number of inters: 14175
The sparsity of the dataset: 99.73674142529214%
Remain Fields: ['user_id', 'item_id', 'rating', 'trip', 'contexts', 'uc_id']
Context dimension - trip: 6 values: : ['BUSINESS' 'COUPLES' 'FAMILY' 'FRIENDS' 'SOLO' '[PAD]']
Traceback (most recent call last):
  File "/scratch/apeddi/DeepCARSKit/run.py", line 32, in <module>
    run(config_file_list=config_list)
  File "/scratch/apeddi/DeepCARSKit/deepcarskit/quick_start/quick_start.py", line 96, in run
    train_data, valid_data = data_preparation(config, dataset)
  File "/scratch/apeddi/DeepCARSKit/deepcarskit/data/utils.py", line 132, in data_preparation
    train_sampler, valid_sampler = create_samplers(config, dataset, built_datasets[fold])
  File "/scratch/apeddi/DeepCARSKit/deepcarskit/data/utils.py", line 301, in create_samplers
    if train_neg_sample_args['strategy'] != 'none':
KeyError: 'strategy'