ft_imputer

From sparklyr v0.8.2 by Javier Luraschi

Feature Transformation -- Imputer (Estimator)

Imputation estimator for completing missing values, either using the mean or the median of the columns in which the missing values are located. The input columns should be of numeric type. This function requires Spark 2.2.0+.

Usage

ft_imputer(x, input_cols, output_cols, missing_value = NULL,
  strategy = "mean", dataset = NULL, uid = random_string("imputer_"), ...)

Arguments

x: A spark_connection, ml_pipeline, or a tbl_spark.
input_cols: The names of the input columns
output_cols: The names of the output columns.
missing_value: The placeholder for the missing values. All occurrences of missing_value will be imputed. Note that null values are always treated as missing.
strategy: The imputation strategy. Currently only "mean" and "median" are supported. If "mean", then replace missing values using the mean value of the feature. If "median", then replace missing values using the approximate median value of the feature. Default: mean
dataset: (Optional) A tbl_spark. If provided, eagerly fit the (estimator) feature "transformer" against dataset. See details.
uid: A character string used to uniquely identify the feature transformer.
...: Optional arguments; currently unused.

Details

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.

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

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_index_to_string, ft_interaction, ft_lsh, 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

Documentation reproduced from package sparklyr, version 0.8.2, License: Apache License 2.0 | file LICENSE

Community examples

Looks like there are no examples yet.