Locality Sensitive Hashing functions for Euclidean distance (Bucketed Random Projection) and Jaccard distance (MinHash).
ft_bucketed_random_projection_lsh(x, input_col, output_col, bucket_length,
num_hash_tables = 1L, seed = NULL, dataset = NULL,
uid = random_string("bucketed_random_projection_lsh_"), ...)ft_minhash_lsh(x, input_col, output_col, num_hash_tables = 1L, seed = NULL,
dataset = NULL, uid = random_string("minhash_lsh_"), ...)
A spark_connection, ml_pipeline, or a tbl_spark.
The name of the input column.
The name of the output column.
The length of each hash bucket, a larger bucket lowers the false negative rate. The number of buckets will be (max L2 norm of input vectors) / bucketLength.
Number of hash tables used in LSH OR-amplification. LSH OR-amplification can be used to reduce the false negative rate. Higher values for this param lead to a reduced false negative rate, at the expense of added computational complexity.
A random seed. Set this value if you need your results to be reproducible across repeated calls.
(Optional) A tbl_spark. If provided, eagerly fit the (estimator)
feature "transformer" against dataset. See details.
A character string used to uniquely identify the feature transformer.
Optional arguments; currently unused.
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
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.
See http://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in Spark.
ft_lsh_utils
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_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