Spark ML – Isotonic Regression
Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
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_"),
...
)x |
A |
formula |
Used when |
feature_index |
Index of the feature if |
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 |
label_col |
Label column name. The column should be a numeric column. Usually this column is output by |
prediction_col |
Prediction column name. |
uid |
A character string used to uniquely identify the ML estimator. |
... |
Optional arguments; see 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.
The object returned depends on the class of x.
spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to
a Spark Predictor 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 predictor appended to the pipeline.
tbl_spark: When x is a tbl_spark, a predictor is constructed then
immediately fit with the input tbl_spark, returning a prediction model.
tbl_spark, with formula: specified When formula
is specified, the input tbl_spark is first transformed using a
RFormula transformer before being fit by
the predictor. The object returned in this case is a ml_model which is a
wrapper of a ml_pipeline_model.
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()
## 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 ## End(Not run)
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