ft_quantile_discretizer {sparklyr} | R Documentation

## Feature Transformation – QuantileDiscretizer (Estimator)

### Description

`ft_quantile_discretizer` takes a column with continuous features and outputs a column with binned categorical features. The number of bins can be set using the `num_buckets` parameter. It is possible that the number of buckets used will be smaller than this value, for example, if there are too few distinct values of the input to create enough distinct quantiles.

### Usage

    ft_quantile_discretizer(x, input_col = NULL, output_col = NULL,
      num_buckets = 2L, input_cols = NULL, output_cols = NULL,
      num_buckets_array = NULL, handle_invalid = "error",
      relative_error = 0.001, dataset = NULL,
      uid = random_string("quantile_discretizer_"), ...)

### 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.
`num_buckets` | Number of buckets (quantiles, or categories) into which data points are grouped. Must be greater than or equal to 2.
`input_cols` | Names of input columns.
`output_cols` | Names of output columns.
`num_buckets_array` | Array of number of buckets (quantiles, or categories) into which data points are grouped. Each value must be greater than or equal to 2.
`handle_invalid` | (Spark 2.1.0+) Param for how to handle invalid entries. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Default: "error"
`relative_error` | (Spark 2.0.0+) Relative error (see documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile here for description). Must be in the range [0, 1]. default: 0.001
`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

NaN handling: null and NaN values will be ignored from the column during `QuantileDiscretizer` fitting. This will produce a `Bucketizer` model for making predictions. During the transformation, `Bucketizer` will raise an error when it finds NaN values in the dataset, but the user can also choose to either keep or remove NaN values within the dataset by setting `handle_invalid` If the user chooses to keep NaN values, they will be handled specially and placed into their own bucket, for example, if 4 buckets are used, then non-NaN data will be put into buckets[0-3], but NaNs will be counted in a special bucket[4].

Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for org.apache.spark.sql.DataFrameStatFunctions.approxQuantile here for a detailed description). The precision of the approximation can be controlled with the `relative_error` parameter. The lower and upper bin bounds will be -Infinity and +Infinity, covering all real values.

Note that the result may be different every time you run it, since the sample strategy behind it is non-deterministic.

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`

### 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.

`ft_bucketizer`

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_lsh`, `ft_max_abs_scaler`, `ft_min_max_scaler`, `ft_ngram`, `ft_normalizer`, `ft_one_hot_encoder`, `ft_pca`, `ft_polynomial_expansion`, `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`

[Package sparklyr version 0.8.4 Index]