ft_word2vec
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,
uid = random_string("word2vec_"),
...
)ml_find_synonyms(model, word, num)
Arguments
- x
A
spark_connection
,ml_pipeline
, or atbl_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.
- uid
A character string used to uniquely identify the feature transformer.
- ...
Optional arguments; currently unused.
- model
A fitted
Word2Vec
model, returned byft_word2vec()
.- word
A word, as a length-one character vector.
- num
Number of words closest in similarity to the given word to find.
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_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_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()