ft_word2vec

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Feature Transformation -- Word2Vec (Estimator)

Word2Vec transforms a word into a code for further natural language processing or machine learning process.

Usage
ft_word2vec(x, input_col = NULL, output_col = NULL,
  vector_size = 100, min_count = 5, max_sentence_length = 1000,
  num_partitions = 1, step_size = 0.025, max_iter = 1, seed = NULL,
  dataset = NULL, uid = random_string("word2vec_"), ...)

ml_find_synonyms(model, word, num)

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.

vector_size

The dimension of the code that you want to transform from words. Default: 100

min_count

The minimum number of times a token must appear to be included in the word2vec model's vocabulary. Default: 5

max_sentence_length

(Spark 2.0.0+) Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to max_sentence_length size. Default: 1000

num_partitions

Number of partitions for sentences of words. Default: 1

step_size

Param for Step size to be used for each iteration of optimization (> 0).

max_iter

The maximum number of iterations to use.

seed

A random seed. Set this value if you need your results to be reproducible across repeated calls.

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 fitted Word2Vec model, returned by ft_word2vec().

word

A word, as a length-one character vector.

num

Number of words closest in similarity to the given word to find.

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_find_synonyms() returns a DataFrame of synonyms and cosine similarities

See Also

See http://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_count_vectorizer, 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

Aliases
  • ft_word2vec
  • ml_find_synonyms
Documentation reproduced from package sparklyr, version 0.9.1, License: Apache License 2.0 | file LICENSE

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