nvtabular.ops.ValueCount

class nvtabular.ops.ValueCount[source]

Bases: nvtabular.ops.stat_operator.StatOperator

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

__init__()None[source]

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)

fit(col_selector, ddf)

fit_finalize(dask_stats)

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

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

is_subgraph

label

output_dtype

output_properties

output_tags

supported_formats

supports

Returns what kind of data representation this operator supports

fit(col_selector: merlin.dag.selector.ColumnSelector, ddf: dask.dataframe.core.DataFrame)Any[source]
fit_finalize(dask_stats)[source]
transform(col_selector: merlin.dag.selector.ColumnSelector, df: pandas.core.frame.DataFrame)pandas.core.frame.DataFrame[source]
clear()[source]