nvtabular.ops.DropLowCardinality

class nvtabular.ops.DropLowCardinality(min_cardinality=2)[source]

Bases: nvtabular.ops.operator.Operator

DropLowCardinality drops low cardinality categorical columns. This requires the cardinality of these columns to be known in the schema - for instance by first encoding these columns using Categorify.

__init__(min_cardinality=2)[source]

Methods

`__init__`([min_cardinality])
`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, …)	Checks the cardinality of the input columns and drops any categorical columns with cardinality less than the specified minimum.
`create_node`(selector)
`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)	Selects all non-categorical columns and any categorical columns of at least the minimum cardinality from the dataframe.
`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

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

Selects all non-categorical columns and any categorical columns of at least the minimum cardinality from the dataframe.

Parameters

col_selector (ColumnSelector) – The columns to select.
df (DataFrameType) – The dataframe to transform

Returns

Dataframe with only the selected columns.

Return type

DataFrameType

compute_selector(input_schema: merlin.schema.schema.Schema, selector: merlin.dag.selector.ColumnSelector, parents_selector: merlin.dag.selector.ColumnSelector, dependencies_selector: merlin.dag.selector.ColumnSelector) → merlin.dag.selector.ColumnSelector [source]

Checks the cardinality of the input columns and drops any categorical columns with cardinality less than the specified minimum.

Parameters

input_schema (Schema) – The current node’s input schema
selector (ColumnSelector) – The current node’s selector
parents_selector (ColumnSelector) – A selector for the output columns of the current node’s parents
dependencies_selector (ColumnSelector) – A selector for the output columns of the current node’s dependencies

Returns

Selector that contains all non-categorical columns and any categorical columns of at least the minimum cardinality.

Return type

ColumnSelector