nvtabular.ops.Normalize

class nvtabular.ops.Normalize(out_dtype=None)[source]

Bases: merlin.dag.ops.stat_operator.StatOperator

Standardizing the features around 0 with a standard deviation of 1 is a common technique to compare measurements that have different units. This operation can be added to the workflow to standardize the features.

It performs Normalization using the mean std method.

Example usage:

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

Parameters: out_dtype (str) – Specifies the data type for the output columns. The default value is numpy.float64 if not set here

__init__(out_dtype=None)[source]

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’
`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.
`fitted`
`is_subgraph`
`label`
`output_dtype`
`output_properties`
`output_tags`
`supported_formats`
`supports`

fit(col_selector: merlin.dag.selector.ColumnSelector, ddf: dask.dataframe.core.DataFrame)[source]: Calculate statistics for this operator, and return a dask future to these statistics, which will be computed by the workflow.

fit_finalize(dask_stats)[source]: Finalize statistics calculation - the workflow calls this function with the computed statistics from the ‘fit’ object’

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

Transform the dataframe by applying this operator to the set of input columns

Parameters

col_selector (ColumnSelector) – The columns to apply this operator to
transformable (Transformable) – A pandas or cudf dataframe that this operator will work on

Returns

Returns a transformed dataframe or dictarray for this operator

Return type

Transformable

property supports

property supported_formats

clear()[source]

property output_tags

property output_dtype