Feature Transformation – CountVectorizer (Estimator)
R/ml_feature_count_vectorizer.R
ft_count_vectorizer
Description
Extracts a vocabulary from document collections.
Usage
ft_count_vectorizer(
x, input_col = NULL,
output_col = NULL,
binary = FALSE,
min_df = 1,
min_tf = 1,
vocab_size = 2^18,
uid = random_string("count_vectorizer_"),
...
)
ml_vocabulary(model)
Arguments
Arguments | Description |
---|---|
x | A spark_connection , ml_pipeline , or a tbl_spark . |
input_col | The name of the input column. |
output_col | The name of the output column. |
binary | Binary toggle to control the output vector values. If TRUE , all nonzero counts (after min_tf filter applied) are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default: FALSE |
min_df | Specifies the minimum number of different documents a term must appear in to be included in the vocabulary. If this is an integer greater than or equal to 1, this specifies the number of documents the term must appear in; if this is a double in [0,1), then this specifies the fraction of documents. Default: 1. |
min_tf | Filter to ignore rare words in a document. For each document, terms with frequency/count less than the given threshold are ignored. If this is an integer greater than or equal to 1, then this specifies a count (of times the term must appear in the document); if this is a double in [0,1), then this specifies a fraction (out of the document’s token count). Default: 1. |
vocab_size | Build a vocabulary that only considers the top vocab_size terms ordered by term frequency across the corpus. Default: 2^18 . |
uid | A character string used to uniquely identify the feature transformer. |
… | Optional arguments; currently unused. |
model | A ml_count_vectorizer_model . |
Details
In the case where x
is a tbl_spark
, the estimator fits against x
to obtain a transformer, which is then immediately used to transform x
, returning a tbl_spark
.
Value
The object returned depends on the class of x
.
spark_connection
: Whenx
is aspark_connection
, the function returns aml_transformer
, aml_estimator
, or one of their subclasses. The object contains a pointer to a SparkTransformer
orEstimator
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the transformer or estimator appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a transformer is constructed then immediately applied to the inputtbl_spark
, returning atbl_spark
ml_vocabulary()
returns a vector of vocabulary built.
See Also
See https://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in Spark.
Other feature transformers: ft_binarizer()
, ft_bucketizer()
, ft_chisq_selector()
, ft_dct()
, ft_elementwise_product()
, ft_feature_hasher()
, ft_hashing_tf()
, ft_idf()
, ft_imputer()
, ft_index_to_string()
, ft_interaction()
, ft_lsh
, ft_max_abs_scaler()
, ft_min_max_scaler()
, ft_ngram()
, ft_normalizer()
, ft_one_hot_encoder_estimator()
, ft_one_hot_encoder()
, ft_pca()
, ft_polynomial_expansion()
, ft_quantile_discretizer()
, ft_r_formula()
, ft_regex_tokenizer()
, ft_robust_scaler()
, ft_sql_transformer()
, ft_standard_scaler()
, ft_stop_words_remover()
, ft_string_indexer()
, ft_tokenizer()
, ft_vector_assembler()
, ft_vector_indexer()
, ft_vector_slicer()
, ft_word2vec()