Feature Transformation -- ElementwiseProduct
Computes the element-wise product between two columns. Generally, this is intended as a scaling transformation, where an input vector is scaled by another vector, but this should apply for all element-wise product transformations.
ft_elementwise_product(x, input.col, output.col, scaling.col, ...)Arguments
| x | An object (usually a |
| input.col | The name of the input column(s). |
| output.col | The name of the output column. |
| scaling.col | The column used to scale |
| ... | Optional arguments; currently unused. |
See also
See http://spark.apache.org/docs/latest/ml-features for more information on the set of transformations available for DataFrame columns in Spark.
Other feature transformation routines: ft_binarizer,
ft_bucketizer,
ft_count_vectorizer,
ft_discrete_cosine_transform,
ft_index_to_string,
ft_one_hot_encoder,
ft_quantile_discretizer,
ft_regex_tokenizer,
ft_stop_words_remover,
ft_string_indexer,
ft_tokenizer,
ft_vector_assembler,
sdf_mutate