nvtabular.ops.ReduceDtypeSize

class nvtabular.ops.ReduceDtypeSize(float_dtype=<class 'numpy.float32'>)

Bases: nvtabular.ops.stat_operator.StatOperator

ReduceDtypeSize changes the dtypes of numeric columns. For integer columns this will choose a dtype such that the minimum and maximum values in the column will fit. For float columns this will cast to a float32.

__init__(float_dtype=<class 'numpy.float32'>)

Methods

__init__([float_dtype])
clear()	zero and reinitialize all relevant statistical properties
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, 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)	Calculate statistics for this operator, and return a dask future to these statistics, which will be computed by the workflow.
fit_finalize(dask_stats)	Finalize statistics calculation - the workflow calls this function with the computed statistics from the ‘fit’ object’
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)	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
supports	Returns what kind of data representation this operator supports

fit(col_selector: merlin.dag.selector.ColumnSelector, ddf: dask.dataframe.core.DataFrame)

Calculate statistics for this operator, and return a dask future to these statistics, which will be computed by the workflow.

fit_finalize(dask_stats)

Finalize statistics calculation - the workflow calls this function with the computed statistics from the ‘fit’ object’

clear()

zero and reinitialize all relevant statistical properties

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

compute_output_schema(input_schema, selector, prev_output_schema=None)

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

Parameters

• input_schema (Schema) – The schemas of the columns to apply this operator to

• col_selector (ColumnSelector) – The column selector to apply to the input schema

Returns

The schemas of the columns produced by this operator

Return type

Schema