nlp_date_normalizer: Spark NLP DateNormalizer

In r-spark/sparknlp: R Interface to John Snow Labs Spark NLP

Spark NLP DateNormalizer

Description

Spark ML transformer that tries to normalize dates in chunks annotations. The expected format for the date will be YYYY/MM/DD. If the date is normalized then field normalized in metadata will be true else will be false. See https://nlp.johnsnowlabs.com/licensed/api/com/johnsnowlabs/nlp/annotators/normalizer/DateNormalizer.html

Usage

nlp_date_normalizer(
  x,
  input_cols,
  output_col,
  anchor_date_day = NULL,
  anchor_date_month = NULL,
  anchor_date_year = NULL,
  uid = random_string("date_normalizer_")
)

Arguments

x	A spark_connection, ml_pipeline, or a tbl_spark.
input_cols	Input columns. String array.
output_col	Output column. String.
anchor_date_day	Add an anchor day for the relative dates such as a day after tomorrow (Default: -1). By default it will use the current day. The first day of the month has value 1
anchor_date_month	Add an anchor month for the relative dates such as a day after tomorrow (Default: -1). By default it will use the current month. Month values start from 1, so 1 stands for January.
anchor_date_year	Add an anchor year for the relative dates such as a day after tomorrow (Default: -1). If it is not set, the by default it will use the current year. Example: 2021
uid	A character string used to uniquely identify the ML estimator.

Value

The object returned depends on the class of x.

• spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Estimator object and can be used to compose Pipeline objects.

• ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the NLP estimator appended to the pipeline.

• tbl_spark: When x is a tbl_spark, an estimator is constructed then immediately fit with the input tbl_spark, returning an NLP model.

r-spark/sparknlp documentation built on Oct. 15, 2022, 10:50 a.m.