config.yaml

field_separator: ","
seq_separator: " "

gpu_id: 0
use_gpu: True
show_progress: False
save_dataset: False
save_dataloaders: False

############### data setting ###############
seed: 2022
dataset: depaulmovie
# define data_path as the parent directory of your data folder
# data_path: d:\dataset\

USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
RATING_FIELD: rating
CONTEXT_SITUATION_FIELD: contexts
USER_CONTEXT_FIELD: uc_id

# note: you can use either load or unload, cannot use them both
# load_col is used to load specific columns; unload_col is used to ignore selected columns
# set "load_col: ~", if you want to load all cols
# load_col: {'inter': ['user_id','item_id','rating','contexts','uc_id']}
# unload_col: {'inter': ['contexts']}
# by default, we load all cols, unless there are some special requirements
load_col: ~

# used for topN ranking only
LABEL_FIELD: label
threshold:
    rating: 0
# the current library does not support negative sampling
neg_sampling: ~

############### model setting ###############
model: NeuCMFii

# General model
epochs: 50
train_batch_size: 500
eval_batch_size: 409600
learner: adam
# learner: adam, RMSprop

stopping_step: 10
clip_grad_norm: ~
# clip_grad_norm:  {'max_norm': 5, 'norm_type': 2}
weight_decay: 0.0

# NeuCF models
mf_embedding_size: 64
mlp_embedding_size: 64
mlp_hidden_size: [128,64,32]
learning_rate: 0.01
dropout_prob: 0.1

#tf_train: True
mf_train: True
mlp_train: True

# FM models
embedding_size: 64
#mlp_hidden_size: [128,64,32]
#learning_rate: 0.01
#dropout_prob: 0.3

############### Evaluation setting ###############
eval_args:
  # split: {'RS': [0.8, 0.2]} # hold-out evaluation
  split: {'CV': 5, 'num_processes': 4} # N-fold cross validation by multiprocessing
  group_by: user
  mode: labeled # do not change it, DeepCARSKit only support this mode
  order: RO

# indicate the task is ranking or rating prediction
# evaluation metrics automatically selected based on True/False setting here
ranking: False
# indicate activation function for ranking task
# LeakyReLu is the default activation function for both ranking or rating prediction
sigmoid: False

# define metrics for ranking and rating prediction tasks
ranking_valid_metric: Recall@10
ranking_metrics: ['Precision','Recall','NDCG','MRR','MAP']
topk: [10,20,30]

err_valid_metric: MAE
err_metrics: ['MAE','RMSE','AUC']

############### Output setting ###############
loss_decimal_place: 4
metric_decimal_place: 4