nvtabular.ops.Clip

class nvtabular.ops.Clip(min_value=None, max_value=None)[source]

Bases: nvtabular.ops.operator.Operator

This operation clips continuous values so that they are within a min/max bound. For instance by setting the min value to 0, you can replace all negative values with 0. This is helpful in cases where you want to log normalize values:

# clip all continuous columns to be positive only, and then take the log of the clipped
# columns
columns = ColumnSelector(CONT_NAMES) >> Clip(min_value=0) >> LogOp()

Parameters

min_value (float, default None) – The minimum value to clip values to: values less than this will be replaced with this value. Specifying None means don’t apply a minimum threshold.
max_value (float, default None) – The maximum value to clip values to: values greater than this will be replaced with this value. Specifying None means don’t apply a maximum threshold.

__init__(min_value=None, max_value=None)[source]

Methods

`__init__`([min_value, max_value])
`column_mapping`(col_selector)	Compute which output columns depend on which input columns
`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)
`inference_initialize`(col_selector, model_config)	Configures this operator for use in inference.
`output_column_names`(col_selector)	Given a set of columns names returns the names of the transformed columns this operator will produce
`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`	Defines an optional list of column dependencies for this operator.
`dynamic_dtypes`
`label`
`output_dtype`
`output_properties`
`output_tags`
`supported_formats`
`supports`	Returns what kind of data representation this operator supports

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

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