Args for Data¶

RecBole provides several arguments for describing:

See below for the details:

Atomic File Format¶

field_separator (str) : Separator of different columns in atomic files. Defaults to "\t".
seq_separator (str) : Separator inside the sequence features. Defaults to " ".

USER_ID_FIELD (str) : Field name of user ID feature. Defaults to user_id.
ITEM_ID_FIELD (str) : Field name of item ID feature. Defaults to item_id.
RATING_FIELD (str) : Field name of rating feature. Defaults to rating.
TIME_FIELD (str) : Field name of timestamp feature. Defaults to timestamp.
seq_len (dict) : Keys are field names of sequence features, values are maximum length of each sequence (which means sequences too long will be cut off). If not set, the sequences will not be cut off. Defaults to None.

LABEL_FIELD (str) : Expected field name of the generated labels. Defaults to label.
threshold (dict) : The format is {k (str): v (float)}. 0/1 labels will be generated according to the value of inter_feat[k] and v. The rows with inter_feat[k] >= v will be labeled as positive, otherwise the label is negative. Note that at most one pair of k and v can exist in threshold. Defaults to None.

NEG_PREFIX (str) : Prefix of field names which are generated as negative cases. E.g. if we have positive item ID named item_id, then those item ID in negative samples will be called NEG_PREFIX + item_id. Defaults to neg_.

ITEM_LIST_LENGTH_FIELD (str) : Field name of the feature representing item sequences’ length. Defaults to item_length.
LIST_SUFFIX (str) : Suffix of field names which are generated as sequences. E.g. if we have item ID named item_id, then those item ID sequences will be called item_id + LIST_SUFFIX. Defaults to _list.
MAX_ITEM_LIST_LENGTH (int): Maximum length of each generated sequence. Defaults to 50.
POSITION_FIELD (str) : Field name of the generated position sequence. For sequence of length k, its position sequence is range(k). Note that this field will only be generated if this arg is not None. Defaults to position_id.

HEAD_ENTITY_ID_FIELD (str) : Field name of the head entity ID feature. Defaults to head_id.
TAIL_ENTITY_ID_FIELD (str) : Field name of the tail entity ID feature. Defaults to tail_id.
RELATION_ID_FIELD (str) : Field name of the relation ID feature. Defaults to relation_id.
ENTITY_ID_FIELD (str) : Field name of the entity ID. Note that it’s only a symbol of entities, not real feature of one of the xxx_feat. Defaults to entity_id.

load_col (dict) : Keys are the suffix of loaded atomic files, values are the list of field names to be loaded. If a suffix doesn’t exist in load_col, the corresponding atomic file will not be loaded. Note that if load_col is None, then all the existed atomic files will be loaded. Defaults to {inter: [user_id, item_id]}.
unload_col (dict) : Keys are suffix of loaded atomic files, values are list of field names NOT to be loaded. Note that load_col and unload_col can not be set at the same time. Defaults to None.
additional_feat_suffix (list): Control loading additional atomic files. E.g. if you want to load features from ml-100k.hello, just set this arg as additional_feat_suffix: [hello]. Features of additional features will be stored in Dataset.feat_list. Defaults to None.

rm_dup_inter (str) : Whether to remove duplicated user-item interactions. If time_field exists, inter_feat will be sorted by time_field in ascending order. Otherwise it will remain unchanged. After that, if rm_dup_inter == first, we will keep the first user-item interaction in duplicates; if rm_dup_inter == last, we will keep the last user-item interaction in duplicates. Defaults to None.

max_user_inter_num (int) : Users whose number of interactions is more than max_user_inter_num will be filtered. Defaults to None.
min_user_inter_num (int) : Users whose number of interactions is less than min_user_inter_num will be filtered. Defaults to 0.
max_item_inter_num (int) : Items whose number of interactions is more than max_item_inter_num will be filtered. Defaults to None.
min_item_inter_num (int) : Items whose number of interactions is less than min_item_inter_num will be filtered. Defaults to 0.

lowest_val (dict) : Has the format {k (str): v (float)}, .... The rows whose feat[k] < v will be filtered. Defaults to None.
highest_val (dict) : Has the format {k (str): v (float)}, .... The rows whose feat[k] > v will be filtered. Defaults to None.
equal_val (dict) : Has the format {k (str): v (float)}, .... The rows whose feat[k] != v will be filtered. Defaults to None.
not_equal_val (dict) : Has the format {k (str): v (float)}, .... The rows whose feat[k] == v will be filtered. Defaults to None.
drop_filter_field (bool) : Fields that occured in lowest_val, highest_val, equal_val and not_equal_val will be dropped if drop_filter_field == True. Defaults to True.

fields_in_same_space (list) : List of spaces. Space is a list of string similar to the fields’ names. The fields in the same space will be remapped into the same index system. Note that if you want to make some fields remapped in the same space with entities, then just set fields_in_same_space = [entity_id, xxx, ...]. (if ENTITY_ID_FIELD != 'entity_id', then change the 'entity_id' in the above example.) Defaults to None.
fill_nan (bool) : Fill the NaN in the features if True. Token fileds will be filled as [PAD]. Float fields will be filled as the average value. Sequence fields will be filled as [0]. Defaults to True.
preload_weight (dict) : Has the format {k (str): v (float)}, .... k if a token field, representing the IDs of each row of preloaded weight matrix. v is a float like fields. Each pair of u and v should be from the same atomic file. This arg can be used to load pretrained vectors. Defaults to None.
drop_preload_weight (bool) : Drop the fields whose names are keys of preload_weight, if drop_preload_weight == True. Defaults to True.
normalize_field (list) : List of filed names to be normalized. Note that only float like fields can be normalized. Defaults to None.
normalize_all (bool) : Normalize all the float like fields if True. Defaults to True.