nvtabular.ops.ValueCount

class nvtabular.ops.ValueCount

Bases: StatOperator

The operator calculates the min and max lengths of multihot columns.

__init__() → None

Methods

__init__()
clear()
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)
export(path, input_schema, output_schema, ...)	Export the class object as a config and all related files to the user defined path.
fit(col_selector, ddf)
fit_finalize(dask_stats)
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
set_storage_path(new_path[, copy])	Certain stat operators need external storage - for instance Categorify writes out parquet files containing the categorical mapping.
transform(col_selector, df)
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
export_name	Provides a clear common english identifier for this operator.
fitted
is_subgraph
label
output_dtype
output_properties
output_tags
supported_formats
supports	Returns what kind of data representation this operator supports

fit(col_selector: ColumnSelector, ddf: DataFrame) → Any

fit_finalize(dask_stats)

transform(col_selector: ColumnSelector, df: DataFrame) → DataFrame

clear()