nvtabular.ops.LambdaOp

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

Bases: 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 >> LambdaOp(lambda col: col.str.slice(0, 5))
workflow = 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")
workflow = 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.

__init__(f, dependency=None, label=None, dtype=None, tags=None, properties=None)

Methods

__init__(f[, dependency, label, dtype, ...])
column_mapping(col_selector)
compute_column_schema(col_name, input_schema)
compute_input_schema(root_schema, ...)	Given the schemas coming from upstream sources and a column selector for the input columns, returns a set of schemas for the input columns this operator will use
compute_output_schema(input_schema, col_selector)	Given a set of schemas and a column selector for the input columns, returns a set of schemas for the transformed columns this operator will produce
compute_selector(input_schema, selector[, ...])	Provides a hook method for sub-classes to override to implement custom column selection logic.
create_node(selector)
export(path, input_schema, output_schema, ...)	Export the class object as a config and all related files to the user defined path.
inference_initialize(col_selector, model_config)	Configures this operator for use in inference.
load_artifacts([artifact_path])	Load artifacts from disk required for operator function.
output_column_names(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
save_artifacts([artifact_path])	Save artifacts required to be reload operator state from disk
transform(col_selector, df)	Transform the dataframe by applying this operator to the set of input columns
validate_schemas(parents_schema, ...[, ...])	Provides a hook method that sub-classes can override to implement schema validation logic.

Attributes

dependencies
dynamic_dtypes
export_name	Provides a clear common english identifier for this operator.
is_subgraph
label
output_dtype
output_properties
output_tags
supported_formats
supports	Returns what kind of data representation this operator supports

transform(col_selector: ColumnSelector, df: DataFrame) → 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

property dependencies

property label

column_mapping(col_selector)

property dynamic_dtypes

property output_dtype

property output_tags

property output_properties