LambdaOp

class nvtabular.ops.LambdaOp(f, dependency=None, label=None)

Bases: nvtabular.ops.operator.Operator

LambdaOp allows you to apply row level functions to an NVTabular workflow.

Example usage 1:

# Define a ColumnSelector that LamdaOp will apply to
# then define a custom function, e.g. extract first 5 character from a string
lambda_feature = ColumnSelector(["col1"])
new_lambda_feature = lambda_feature >> (lambda col: col.str.slice(0, 5))
processor = nvtabular.Workflow(new_lambda_feature + 'label')

Example usage 2:

# define a custom function e.g. calculate probability for different events.
# Rename the each new feature column name.
lambda_features = ColumnSelector(['event1', 'event2', 'event3']), # columns, f is applied to
def cond_prob(col, gdf):
    col = col.astype(np.float32)
    col = col / gdf['total_events']
    return col
new_lambda_features = lambda_features >> LambdaOp(cond_prob, dependency=["total_events"]) >> Rename(postfix="_cond")
processor = nvtabular.Workflow(new_lambda_features + 'label')

Parameters

• f (callable) – Defines a function that takes a Series and an optional DataFrame as input, and returns a new Series as the output.

• dependency (list, default None) – Whether to provide a dependency column or not.

transform(col_selector: nvtabular.columns.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

dependencies()

property label