Feature Transformation – Word2Vec (Estimator)
Word2Vec transforms a word into a code for further natural language processing or machine learning process.
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)x |
A |
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 |
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 |
word |
A word, as a length-one character vector. |
num |
Number of words closest in similarity to the given word to find. |
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.
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 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()
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