ml_isotonic_regression
Spark ML -- Isotonic Regression
Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
Usage
ml_isotonic_regression(x, formula = NULL, feature_index = 0,
isotonic = TRUE, weight_col = NULL, features_col = "features",
label_col = "label", prediction_col = "prediction",
uid = random_string("isotonic_regression_"), ...)
Arguments
- x
A
spark_connection
,ml_pipeline
, or atbl_spark
.- formula
Used when
x
is atbl_spark
. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.- feature_index
Index of the feature if
features_col
is a vector column (default: 0), no effect otherwise.- isotonic
Whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false). Default: true
- weight_col
The name of the column to use as weights for the model fit.
- features_col
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by
ft_r_formula
.- label_col
Label column name. The column should be a numeric column. Usually this column is output by
ft_r_formula
.- prediction_col
Prediction column name.
- uid
A character string used to uniquely identify the ML estimator.
- ...
Optional arguments; see Details.
Details
When x
is a tbl_spark
and formula
(alternatively, response
and features
) is specified, the function returns a ml_model
object wrapping a ml_pipeline_model
which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col
(defaults to "predicted_label"
) can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model
, ml_model
objects also contain a ml_pipeline
object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save
with type = "pipeline"
to faciliate model refresh workflows.
Value
The object returned depends on the class of x
.
spark_connection
: Whenx
is aspark_connection
, the function returns an instance of aml_estimator
object. The object contains a pointer to a SparkPredictor
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the predictor appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a predictor is constructed then immediately fit with the inputtbl_spark
, returning a prediction model.tbl_spark
, withformula
: specified Whenformula
is specified, the inputtbl_spark
is first transformed using aRFormula
transformer before being fit by the predictor. The object returned in this case is aml_model
which is a wrapper of aml_pipeline_model
.
See Also
See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.
Other ml algorithms: ml_aft_survival_regression
,
ml_decision_tree_classifier
,
ml_gbt_classifier
,
ml_generalized_linear_regression
,
ml_linear_regression
,
ml_linear_svc
,
ml_logistic_regression
,
ml_multilayer_perceptron_classifier
,
ml_naive_bayes
,
ml_one_vs_rest
,
ml_random_forest_classifier
Examples
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
iris_training <- partitions$training
iris_test <- partitions$test
iso_res <- iris_tbl %>%
ml_isotonic_regression(Petal_Length ~ Petal_Width)
pred <- ml_predict(iso_res, iris_test)
pred
# }
# NOT RUN {
# }