ft_interaction
Feature Transformation -- Interaction (Transformer)
Implements the feature interaction transform. This transformer takes in Double and Vector type columns and outputs a flattened vector of their feature interactions. To handle interaction, we first one-hot encode any nominal features. Then, a vector of the feature cross-products is produced.
Usage
ft_interaction(x, input_cols = NULL, output_col = NULL,
uid = random_string("interaction_"), ...)Arguments
- x
A
spark_connection,ml_pipeline, or atbl_spark.- input_cols
The names of the input columns
- output_col
The name of the output column.
- 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: Whenxis aspark_connection, the function returns aml_transformer, aml_estimator, or one of their subclasses. The object contains a pointer to a SparkTransformerorEstimatorobject and can be used to composePipelineobjects.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the transformer or estimator appended to the pipeline.tbl_spark: Whenxis atbl_spark, a transformer is constructed then immediately applied to the inputtbl_spark, returning atbl_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_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_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_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