nvtabular.ops.Normalize

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

Bases: nvtabular.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_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 :param root_schema: Base schema of the dataset before running any operators.
`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 :param input_schema: The schemas of the columns to apply this operator to :type input_schema: Schema :param col_selector: The column selector to apply to the input schema :type col_selector: ColumnSelector
`compute_selector`(input_schema, selector, …)
`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 :param columns: The columns to apply this operator to :type columns: list of str, or list of list of str
`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

Attributes

`dependencies`	Defines an optional list of column dependencies for this operator.
`dynamic_dtypes`
`label`
`output_dtype`
`output_properties`
`output_tags`
`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

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

property supports

clear()[source]

property output_tags

property output_dtype