nvtabular.ops.NormalizeMinMax

class nvtabular.ops.NormalizeMinMax(out_dtype=None)

Bases: StatOperator

This operator standardizes continuous features such that they are between 0 and 1.

Example usage:

# Use NormalizeMinMax to define a NVTabular workflow
cont_features = CONTINUOUS_COLUMNS >> ops.NormalizeMinMax()
processor = nvtabular.Workflow(cont_features)

Parameters:

out_dtype (str, default is float64) – dtype of output columns.

__init__(out_dtype=None)

Methods

__init__([out_dtype])
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)	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.
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)	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
export_name	Provides a clear common english identifier for this operator.
is_subgraph
label
output_dtype
output_properties
output_tags
supported_formats
supports

transform(col_selector: ColumnSelector, df: 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

fit(col_selector: ColumnSelector, 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’

clear()

property supports

property output_tags

property output_dtype