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@@ -29,7 +29,7 @@ Collate: | |
'client.R' | ||
'context.R' | ||
'deserialize.R' | ||
'mllib.R' | ||
'serialize.R' | ||
'sparkR.R' | ||
'utils.R' | ||
'zzz.R' |
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# | ||
# Licensed to the Apache Software Foundation (ASF) under one or more | ||
# contributor license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright ownership. | ||
# The ASF licenses this file to You under the Apache License, Version 2.0 | ||
# (the "License"); you may not use this file except in compliance with | ||
# the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
|
||
# mllib.R: Provides methods for MLlib integration | ||
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#' @title S4 class that represents a PipelineModel | ||
#' @param model A Java object reference to the backing Scala PipelineModel | ||
#' @export | ||
setClass("PipelineModel", representation(model = "jobj")) | ||
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#' Fits a generalized linear model | ||
#' | ||
#' Fits a generalized linear model, similarly to R's glm(). Also see the glmnet package. | ||
#' | ||
#' @param formula A symbolic description of the model to be fitted. Currently only a few formula | ||
#' operators are supported, including '~' and '+'. | ||
#' @param data DataFrame for training | ||
#' @param family Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg. | ||
#' @param lambda Regularization parameter | ||
#' @param alpha Elastic-net mixing parameter (see glmnet's documentation for details) | ||
#' @return a fitted MLlib model | ||
#' @rdname glm | ||
#' @export | ||
#' @examples | ||
#'\dontrun{ | ||
#' sc <- sparkR.init() | ||
#' sqlContext <- sparkRSQL.init(sc) | ||
#' data(iris) | ||
#' df <- createDataFrame(sqlContext, iris) | ||
#' model <- glm(Sepal_Length ~ Sepal_Width, df) | ||
#'} | ||
setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFrame"), | ||
function(formula, family = c("gaussian", "binomial"), data, lambda = 0, alpha = 0) { | ||
family <- match.arg(family) | ||
model <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers", | ||
"fitRModelFormula", deparse(formula), data@sdf, family, lambda, | ||
alpha) | ||
return(new("PipelineModel", model = model)) | ||
}) | ||
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#' Make predictions from a model | ||
#' | ||
#' Makes predictions from a model produced by glm(), similarly to R's predict(). | ||
#' | ||
#' @param model A fitted MLlib model | ||
#' @param newData DataFrame for testing | ||
#' @return DataFrame containing predicted values | ||
#' @rdname glm | ||
#' @export | ||
#' @examples | ||
#'\dontrun{ | ||
#' model <- glm(y ~ x, trainingData) | ||
#' predicted <- predict(model, testData) | ||
#' showDF(predicted) | ||
#'} | ||
setMethod("predict", signature(object = "PipelineModel"), | ||
function(object, newData) { | ||
return(dataFrame(callJMethod(object@model, "transform", newData@sdf))) | ||
}) |
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