spark.isoreg {SparkR} | R Documentation |
Fits an Isotonic Regression model against a SparkDataFrame, similarly to R's isoreg(). Users can print, make predictions on the produced model and save the model to the input path.
spark.isoreg(data, formula, ...) ## S4 method for signature 'SparkDataFrame,formula' spark.isoreg( data, formula, isotonic = TRUE, featureIndex = 0, weightCol = NULL ) ## S4 method for signature 'IsotonicRegressionModel' summary(object) ## S4 method for signature 'IsotonicRegressionModel' predict(object, newData) ## S4 method for signature 'IsotonicRegressionModel,character' write.ml(object, path, overwrite = FALSE)
data |
SparkDataFrame for training. |
formula |
A symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'. |
... |
additional arguments passed to the method. |
isotonic |
Whether the output sequence should be isotonic/increasing (TRUE) or antitonic/decreasing (FALSE). |
featureIndex |
The index of the feature if |
weightCol |
The weight column name. |
object |
a fitted IsotonicRegressionModel. |
newData |
SparkDataFrame for testing. |
path |
The directory where the model is saved. |
overwrite |
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists. |
spark.isoreg
returns a fitted Isotonic Regression model.
summary
returns summary information of the fitted model, which is a list.
The list includes model's boundaries
(boundaries in increasing order)
and predictions
(predictions associated with the boundaries at the same index).
predict
returns a SparkDataFrame containing predicted values.
spark.isoreg since 2.1.0
summary(IsotonicRegressionModel) since 2.1.0
predict(IsotonicRegressionModel) since 2.1.0
write.ml(IsotonicRegression, character) since 2.1.0
## Not run:
##D sparkR.session()
##D data <- list(list(7.0, 0.0), list(5.0, 1.0), list(3.0, 2.0),
##D list(5.0, 3.0), list(1.0, 4.0))
##D df <- createDataFrame(data, c("label", "feature"))
##D model <- spark.isoreg(df, label ~ feature, isotonic = FALSE)
##D # return model boundaries and prediction as lists
##D result <- summary(model, df)
##D # prediction based on fitted model
##D predict_data <- list(list(-2.0), list(-1.0), list(0.5),
##D list(0.75), list(1.0), list(2.0), list(9.0))
##D predict_df <- createDataFrame(predict_data, c("feature"))
##D # get prediction column
##D predict_result <- collect(select(predict(model, predict_df), "prediction"))
##D
##D # save fitted model to input path
##D path <- "path/to/model"
##D write.ml(model, path)
##D
##D # can also read back the saved model and print
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)