Repository: spark Updated Branches: refs/heads/master e391abdf2 -> e222d7584
[SPARK-11684][R][ML][DOC] Update SparkR glm API doc, user guide and example codes This PR includes: * Update SparkR:::glm, SparkR:::summary API docs. * Update SparkR machine learning user guide and example codes to show: * supporting feature interaction in R formula. * summary for gaussian GLM model. * coefficients for binomial GLM model. mengxr Author: Yanbo Liang <yblia...@gmail.com> Closes #9727 from yanboliang/spark-11684. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/e222d758 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/e222d758 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/e222d758 Branch: refs/heads/master Commit: e222d758499ad2609046cc1a2cc8afb45c5bccbb Parents: e391abd Author: Yanbo Liang <yblia...@gmail.com> Authored: Wed Nov 18 13:30:29 2015 -0800 Committer: Xiangrui Meng <m...@databricks.com> Committed: Wed Nov 18 13:30:29 2015 -0800 ---------------------------------------------------------------------- R/pkg/R/mllib.R | 18 +++++-- docs/sparkr.md | 50 ++++++++++++++++---- .../spark/ml/regression/LinearRegression.scala | 3 ++ 3 files changed, 60 insertions(+), 11 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/e222d758/R/pkg/R/mllib.R ---------------------------------------------------------------------- diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index f23e1c7..8d3b438 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -32,6 +32,12 @@ setClass("PipelineModel", representation(model = "jobj")) #' @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) +#' @param standardize Whether to standardize features before training +#' @param solver The solver algorithm used for optimization, this can be "l-bfgs", "normal" and +#' "auto". "l-bfgs" denotes Limited-memory BFGS which is a limited-memory +#' quasi-Newton optimization method. "normal" denotes using Normal Equation as an +#' analytical solution to the linear regression problem. The default value is "auto" +#' which means that the solver algorithm is selected automatically. #' @return a fitted MLlib model #' @rdname glm #' @export @@ -79,9 +85,15 @@ setMethod("predict", signature(object = "PipelineModel"), #' #' Returns the summary of a model produced by glm(), similarly to R's summary(). #' -#' @param x A fitted MLlib model -#' @return a list with a 'coefficient' component, which is the matrix of coefficients. See -#' summary.glm for more information. +#' @param object A fitted MLlib model +#' @return a list with 'devianceResiduals' and 'coefficients' components for gaussian family +#' or a list with 'coefficients' component for binomial family. \cr +#' For gaussian family: the 'devianceResiduals' gives the min/max deviance residuals +#' of the estimation, the 'coefficients' gives the estimated coefficients and their +#' estimated standard errors, t values and p-values. (It only available when model +#' fitted by normal solver.) \cr +#' For binomial family: the 'coefficients' gives the estimated coefficients. +#' See summary.glm for more information. \cr #' @rdname summary #' @export #' @examples http://git-wip-us.apache.org/repos/asf/spark/blob/e222d758/docs/sparkr.md ---------------------------------------------------------------------- diff --git a/docs/sparkr.md b/docs/sparkr.md index 437bd47..a744b76 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -286,24 +286,37 @@ head(teenagers) # Machine Learning -SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', '+', and '-'. The example below shows the use of building a gaussian GLM model using SparkR. +SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', ':', '+', and '-'. + +The [summary()](api/R/summary.html) function gives the summary of a model produced by [glm()](api/R/glm.html). + +* For gaussian GLM model, it returns a list with 'devianceResiduals' and 'coefficients' components. The 'devianceResiduals' gives the min/max deviance residuals of the estimation; the 'coefficients' gives the estimated coefficients and their estimated standard errors, t values and p-values. (It only available when model fitted by normal solver.) +* For binomial GLM model, it returns a list with 'coefficients' component which gives the estimated coefficients. + +The examples below show the use of building gaussian GLM model and binomial GLM model using SparkR. + +## Gaussian GLM model <div data-lang="r" markdown="1"> {% highlight r %} # Create the DataFrame df <- createDataFrame(sqlContext, iris) -# Fit a linear model over the dataset. +# Fit a gaussian GLM model over the dataset. model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian") -# Model coefficients are returned in a similar format to R's native glm(). +# Model summary are returned in a similar format to R's native glm(). summary(model) +##$devianceResiduals +## Min Max +## -1.307112 1.412532 +## ##$coefficients -## Estimate -##(Intercept) 2.2513930 -##Sepal_Width 0.8035609 -##Species_versicolor 1.4587432 -##Species_virginica 1.9468169 +## Estimate Std. Error t value Pr(>|t|) +##(Intercept) 2.251393 0.3697543 6.08889 9.568102e-09 +##Sepal_Width 0.8035609 0.106339 7.556598 4.187317e-12 +##Species_versicolor 1.458743 0.1121079 13.01195 0 +##Species_virginica 1.946817 0.100015 19.46525 0 # Make predictions based on the model. predictions <- predict(model, newData = df) @@ -317,3 +330,24 @@ head(select(predictions, "Sepal_Length", "prediction")) ##6 5.4 5.385281 {% endhighlight %} </div> + +## Binomial GLM model + +<div data-lang="r" markdown="1"> +{% highlight r %} +# Create the DataFrame +df <- createDataFrame(sqlContext, iris) +training <- filter(df, df$Species != "setosa") + +# Fit a binomial GLM model over the dataset. +model <- glm(Species ~ Sepal_Length + Sepal_Width, data = training, family = "binomial") + +# Model coefficients are returned in a similar format to R's native glm(). +summary(model) +##$coefficients +## Estimate +##(Intercept) -13.046005 +##Sepal_Length 1.902373 +##Sepal_Width 0.404655 +{% endhighlight %} +</div> http://git-wip-us.apache.org/repos/asf/spark/blob/e222d758/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index ca55d59..f7c44f0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -145,6 +145,9 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String /** * Set the solver algorithm used for optimization. * In case of linear regression, this can be "l-bfgs", "normal" and "auto". + * "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton + * optimization method. "normal" denotes using Normal Equation as an analytical + * solution to the linear regression problem. * The default value is "auto" which means that the solver algorithm is * selected automatically. * @group setParam --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
