HashBucket

class nvtabular.ops.HashBucket(num_buckets: Union[int, Dict[str, int]])

Bases: nvtabular.ops.operator.Operator

This op maps categorical columns to a contiguous integer range by first hashing the column, then reducing modulo the number of buckets.

Example usage:

cat_names = ["feature_a", "feature_b"]

# this will hash both features a and b to 100 buckets
hash_features = cat_names >> ops.HashBucket({"feature_a": 100, "feature_b": 50})
processor = nvtabular.Workflow(hash_features)

The output of this op would be:

   feature_a  feature_b
0         90         11
1         70         40
2         52          9

If you would like to do frequency capping or frequency hashing, you should use Categorify op instead. See Categorify op for example usage.

Parameters

num_buckets (int or dictionary:{column: num_hash_buckets}) – Column-wise modulo to apply after hash function. Note that this means that the corresponding value will be the categorical cardinality of the transformed categorical feature. If given as an int, that value will be used as the number of “hash buckets” for every feature. If a dictionary is passed, it will be used to specify explicit mappings from a column name to a number of buckets. In this case, only the columns specified in the keys of num_buckets will be transformed.

transform(col_selector: merlin.dag.selector.ColumnSelector, df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Transform the dataframe by applying this operator to the set of input columns

Parameters

• columns (list of str or list of list of str) – The columns to apply this operator to

• df (Dataframe) – A pandas or cudf dataframe that this operator will work on

Returns

Returns a transformed dataframe for this operator

Return type

DataFrame

get_embedding_sizes(columns)

property output_tags

property output_dtype