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
xis 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_colis 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: Whenxis aspark_connection, the function returns an instance of aml_estimatorobject. The object contains a pointer to a SparkPredictorobject and can be used to composePipelineobjects.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the predictor appended to the pipeline.tbl_spark: Whenxis atbl_spark, a predictor is constructed then immediately fit with the inputtbl_spark, returning a prediction model.tbl_spark, withformula: specified Whenformulais specified, the inputtbl_sparkis first transformed using aRFormulatransformer before being fit by the predictor. The object returned in this case is aml_modelwhich 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 {
# }