Feature Transformation -- ElementwiseProduct (Transformer)

Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector. In other words, it scales each column of the dataset by a scalar multiplier.

ft_elementwise_product(x, input_col, output_col, scaling_vec,
  uid = random_string("elementwise_product_"), ...)

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.

scaling_vec

the vector to multiply with input vectors

uid

A character string used to uniquely identify the feature transformer.

...

Optional arguments; currently unused.

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

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_count_vectorizer, ft_dct, ft_hashing_tf, ft_idf, ft_index_to_string, ft_ngram, ft_one_hot_encoder, ft_pca, ft_quantile_discretizer, ft_r_formula, ft_regex_tokenizer, ft_sql_transformer, ft_stop_words_remover, ft_string_indexer, ft_tokenizer, ft_vector_assembler, ft_word2vec