# ft_count_vectorizer

0th

Percentile

##### Feature Transformation -- CountVectorizer (Estimator)

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,
dataset = NULL, uid = random_string("count_vectorizer_"), ...)ml_vocabulary(model)
##### Arguments
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.

dataset

(Optional) A tbl_spark. If provided, eagerly fit the (estimator) feature "transformer" against dataset. See details.

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

model

A ml_count_vectorizer_model.

##### Details

When dataset is provided for an estimator transformer, the function internally calls ml_fit() against dataset. Hence, the methods for spark_connection and ml_pipeline will then return a ml_transformer and a ml_pipeline with a ml_transformer appended, respectively. When x is a tbl_spark, the estimator will be fit against dataset before transforming x.

When dataset is not specified, the constructor returns a ml_estimator, and, in the case where x is a tbl_spark, the estimator fits against x then to obtain a transformer, which is then immediately used to transform x.

##### Value

The object returned depends on the class of x.

• spark_connection: When x is a spark_connection, the function returns a ml_transformer, a ml_estimator, or one of their subclasses. The object contains a pointer to a Spark Transformer or 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 transformer or estimator appended to the pipeline.

• tbl_spark: When x is a tbl_spark, a transformer is constructed then immediately applied to the input tbl_spark, returning a tbl_spark

ml_vocabulary() returns a vector of vocabulary built.

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, ft_pca, ft_polynomial_expansion, ft_quantile_discretizer, ft_r_formula, ft_regex_tokenizer, 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