http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.gbt.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.gbt.html b/site/docs/2.1.3/api/R/spark.gbt.html new file mode 100644 index 0000000..de7f2d1 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.gbt.html @@ -0,0 +1,244 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Gradient Boosted Tree Model for Regression and Classification</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.gbt {SparkR}"><tr><td>spark.gbt {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Gradient Boosted Tree Model for Regression and Classification</h2> + +<h3>Description</h3> + +<p><code>spark.gbt</code> fits a Gradient Boosted Tree Regression model or Classification model on a +SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted +Gradient Boosted Tree model, <code>predict</code> to make predictions on new data, and +<code>write.ml</code>/<code>read.ml</code> to save/load fitted models. +For more details, see +<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression"> +GBT Regression</a> and +<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier"> +GBT Classification</a> +</p> + + +<h3>Usage</h3> + +<pre> +spark.gbt(data, formula, ...) + +## S4 method for signature 'SparkDataFrame,formula' +spark.gbt(data, formula, + type = c("regression", "classification"), maxDepth = 5, maxBins = 32, + maxIter = 20, stepSize = 0.1, lossType = NULL, seed = NULL, + subsamplingRate = 1, minInstancesPerNode = 1, minInfoGain = 0, + checkpointInterval = 10, maxMemoryInMB = 256, cacheNodeIds = FALSE) + +## S4 method for signature 'GBTRegressionModel' +predict(object, newData) + +## S4 method for signature 'GBTClassificationModel' +predict(object, newData) + +## S4 method for signature 'GBTRegressionModel,character' +write.ml(object, path, + overwrite = FALSE) + +## S4 method for signature 'GBTClassificationModel,character' +write.ml(object, path, + overwrite = FALSE) + +## S4 method for signature 'GBTRegressionModel' +summary(object) + +## S4 method for signature 'GBTClassificationModel' +summary(object) + +## S3 method for class 'summary.GBTRegressionModel' +print(x, ...) + +## S3 method for class 'summary.GBTClassificationModel' +print(x, ...) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional arguments passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>type</code></td> +<td> +<p>type of model, one of "regression" or "classification", to fit</p> +</td></tr> +<tr valign="top"><td><code>maxDepth</code></td> +<td> +<p>Maximum depth of the tree (>= 0).</p> +</td></tr> +<tr valign="top"><td><code>maxBins</code></td> +<td> +<p>Maximum number of bins used for discretizing continuous features and for choosing +how to split on features at each node. More bins give higher granularity. Must be +>= 2 and >= number of categories in any categorical feature.</p> +</td></tr> +<tr valign="top"><td><code>maxIter</code></td> +<td> +<p>Param for maximum number of iterations (>= 0).</p> +</td></tr> +<tr valign="top"><td><code>stepSize</code></td> +<td> +<p>Param for Step size to be used for each iteration of optimization.</p> +</td></tr> +<tr valign="top"><td><code>lossType</code></td> +<td> +<p>Loss function which GBT tries to minimize. +For classification, must be "logistic". For regression, must be one of +"squared" (L2) and "absolute" (L1), default is "squared".</p> +</td></tr> +<tr valign="top"><td><code>seed</code></td> +<td> +<p>integer seed for random number generation.</p> +</td></tr> +<tr valign="top"><td><code>subsamplingRate</code></td> +<td> +<p>Fraction of the training data used for learning each decision tree, in +range (0, 1].</p> +</td></tr> +<tr valign="top"><td><code>minInstancesPerNode</code></td> +<td> +<p>Minimum number of instances each child must have after split. If a +split causes the left or right child to have fewer than +minInstancesPerNode, the split will be discarded as invalid. Should be +>= 1.</p> +</td></tr> +<tr valign="top"><td><code>minInfoGain</code></td> +<td> +<p>Minimum information gain for a split to be considered at a tree node.</p> +</td></tr> +<tr valign="top"><td><code>checkpointInterval</code></td> +<td> +<p>Param for set checkpoint interval (>= 1) or disable checkpoint (-1).</p> +</td></tr> +<tr valign="top"><td><code>maxMemoryInMB</code></td> +<td> +<p>Maximum memory in MB allocated to histogram aggregation.</p> +</td></tr> +<tr valign="top"><td><code>cacheNodeIds</code></td> +<td> +<p>If FALSE, the algorithm will pass trees to executors to match instances with +nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching +can speed up training of deeper trees. Users can set how often should the +cache be checkpointed or disable it by setting checkpointInterval.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>A fitted Gradient Boosted Tree regression model or classification model.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>The directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>Overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +<tr valign="top"><td><code>x</code></td> +<td> +<p>summary object of Gradient Boosted Tree regression model or classification model +returned by <code>summary</code>.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.gbt</code> returns a fitted Gradient Boosted Tree model. +</p> +<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named +"prediction". +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list of components includes <code>formula</code> (formula), +<code>numFeatures</code> (number of features), <code>features</code> (list of features), +<code>featureImportances</code> (feature importances), <code>numTrees</code> (number of trees), +and <code>treeWeights</code> (tree weights). +</p> + + +<h3>Note</h3> + +<p>spark.gbt since 2.1.0 +</p> +<p>predict(GBTRegressionModel) since 2.1.0 +</p> +<p>predict(GBTClassificationModel) since 2.1.0 +</p> +<p>write.ml(GBTRegressionModel, character) since 2.1.0 +</p> +<p>write.ml(GBTClassificationModel, character) since 2.1.0 +</p> +<p>summary(GBTRegressionModel) since 2.1.0 +</p> +<p>summary(GBTClassificationModel) since 2.1.0 +</p> +<p>print.summary.GBTRegressionModel since 2.1.0 +</p> +<p>print.summary.GBTClassificationModel since 2.1.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D # fit a Gradient Boosted Tree Regression Model +##D df <- createDataFrame(longley) +##D model <- spark.gbt(df, Employed ~ ., type = "regression", maxDepth = 5, maxBins = 16) +##D +##D # get the summary of the model +##D summary(model) +##D +##D # make predictions +##D predictions <- predict(model, df) +##D +##D # save and load the model +##D path <- "path/to/model" +##D write.ml(model, path) +##D savedModel <- read.ml(path) +##D summary(savedModel) +##D +##D # fit a Gradient Boosted Tree Classification Model +##D # label must be binary - Only binary classification is supported for GBT. +##D df <- createDataFrame(iris[iris$Species != "virginica", ]) +##D model <- spark.gbt(df, Species ~ Petal_Length + Petal_Width, "classification") +##D +##D # numeric label is also supported +##D iris2 <- iris[iris$Species != "virginica", ] +##D iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1) +##D df <- createDataFrame(iris2) +##D model <- spark.gbt(df, NumericSpecies ~ ., type = "classification") +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html>
http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.getSparkFiles.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.getSparkFiles.html b/site/docs/2.1.3/api/R/spark.getSparkFiles.html new file mode 100644 index 0000000..ea11b54 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.getSparkFiles.html @@ -0,0 +1,59 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Get the absolute path of a file added through spark.addFile.</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.getSparkFiles {SparkR}"><tr><td>spark.getSparkFiles {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Get the absolute path of a file added through spark.addFile.</h2> + +<h3>Description</h3> + +<p>Get the absolute path of a file added through spark.addFile. +</p> + + +<h3>Usage</h3> + +<pre> +spark.getSparkFiles(fileName) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>fileName</code></td> +<td> +<p>The name of the file added through spark.addFile</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p>the absolute path of a file added through spark.addFile. +</p> + + +<h3>Note</h3> + +<p>spark.getSparkFiles since 2.1.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D spark.getSparkFiles("myfile") +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.getSparkFilesRootDirectory.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.getSparkFilesRootDirectory.html b/site/docs/2.1.3/api/R/spark.getSparkFilesRootDirectory.html new file mode 100644 index 0000000..da07604 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.getSparkFilesRootDirectory.html @@ -0,0 +1,49 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Get the root directory that contains files added through...</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.getSparkFilesRootDirectory {SparkR}"><tr><td>spark.getSparkFilesRootDirectory {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Get the root directory that contains files added through spark.addFile.</h2> + +<h3>Description</h3> + +<p>Get the root directory that contains files added through spark.addFile. +</p> + + +<h3>Usage</h3> + +<pre> +spark.getSparkFilesRootDirectory() +</pre> + + +<h3>Value</h3> + +<p>the root directory that contains files added through spark.addFile +</p> + + +<h3>Note</h3> + +<p>spark.getSparkFilesRootDirectory since 2.1.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D spark.getSparkFilesRootDirectory() +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.glm.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.glm.html b/site/docs/2.1.3/api/R/spark.glm.html new file mode 100644 index 0000000..c56c567 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.glm.html @@ -0,0 +1,174 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Generalized Linear Models</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.glm {SparkR}"><tr><td>spark.glm {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Generalized Linear Models</h2> + +<h3>Description</h3> + +<p>Fits generalized linear model against a SparkDataFrame. +Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make +predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models. +</p> + + +<h3>Usage</h3> + +<pre> +spark.glm(data, formula, ...) + +## S4 method for signature 'SparkDataFrame,formula' +spark.glm(data, formula, family = gaussian, + tol = 1e-06, maxIter = 25, weightCol = NULL, regParam = 0) + +## S4 method for signature 'GeneralizedLinearRegressionModel' +summary(object) + +## S3 method for class 'summary.GeneralizedLinearRegressionModel' +print(x, ...) + +## S4 method for signature 'GeneralizedLinearRegressionModel' +predict(object, newData) + +## S4 method for signature 'GeneralizedLinearRegressionModel,character' +write.ml(object, path, + overwrite = FALSE) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', '.', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional arguments passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>family</code></td> +<td> +<p>a description of the error distribution and link function to be used in the model. +This can be a character string naming a family function, a family function or +the result of a call to a family function. Refer R family at +<a href="https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html">https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html</a>. +Currently these families are supported: <code>binomial</code>, <code>gaussian</code>, +<code>Gamma</code>, and <code>poisson</code>.</p> +</td></tr> +<tr valign="top"><td><code>tol</code></td> +<td> +<p>positive convergence tolerance of iterations.</p> +</td></tr> +<tr valign="top"><td><code>maxIter</code></td> +<td> +<p>integer giving the maximal number of IRLS iterations.</p> +</td></tr> +<tr valign="top"><td><code>weightCol</code></td> +<td> +<p>the weight column name. If this is not set or <code>NULL</code>, we treat all instance +weights as 1.0.</p> +</td></tr> +<tr valign="top"><td><code>regParam</code></td> +<td> +<p>regularization parameter for L2 regularization.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>a fitted generalized linear model.</p> +</td></tr> +<tr valign="top"><td><code>x</code></td> +<td> +<p>summary object of fitted generalized linear model returned by <code>summary</code> function.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>the directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.glm</code> returns a fitted generalized linear model. +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list of components includes at least the <code>coefficients</code> (coefficients matrix, which includes +coefficients, standard error of coefficients, t value and p value), +<code>null.deviance</code> (null/residual degrees of freedom), <code>aic</code> (AIC) +and <code>iter</code> (number of iterations IRLS takes). If there are collinear columns in the data, +the coefficients matrix only provides coefficients. +</p> +<p><code>predict</code> returns a SparkDataFrame containing predicted labels in a column named +"prediction". +</p> + + +<h3>Note</h3> + +<p>spark.glm since 2.0.0 +</p> +<p>summary(GeneralizedLinearRegressionModel) since 2.0.0 +</p> +<p>print.summary.GeneralizedLinearRegressionModel since 2.0.0 +</p> +<p>predict(GeneralizedLinearRegressionModel) since 1.5.0 +</p> +<p>write.ml(GeneralizedLinearRegressionModel, character) since 2.0.0 +</p> + + +<h3>See Also</h3> + +<p><a href="glm.html">glm</a>, <a href="read.ml.html">read.ml</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D sparkR.session() +##D data(iris) +##D df <- createDataFrame(iris) +##D model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian") +##D summary(model) +##D +##D # fitted values on training data +##D fitted <- predict(model, df) +##D head(select(fitted, "Sepal_Length", "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) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.isoreg.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.isoreg.html b/site/docs/2.1.3/api/R/spark.isoreg.html new file mode 100644 index 0000000..5822430 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.isoreg.html @@ -0,0 +1,146 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Isotonic Regression Model</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.isoreg {SparkR}"><tr><td>spark.isoreg {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Isotonic Regression Model</h2> + +<h3>Description</h3> + +<p>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. +</p> + + +<h3>Usage</h3> + +<pre> +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' +predict(object, newData) + +## S4 method for signature 'IsotonicRegressionModel' +summary(object) + +## S4 method for signature 'IsotonicRegressionModel,character' +write.ml(object, path, + overwrite = FALSE) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>A symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', '.', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional arguments passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>isotonic</code></td> +<td> +<p>Whether the output sequence should be isotonic/increasing (TRUE) or +antitonic/decreasing (FALSE).</p> +</td></tr> +<tr valign="top"><td><code>featureIndex</code></td> +<td> +<p>The index of the feature if <code>featuresCol</code> is a vector column +(default: 0), no effect otherwise.</p> +</td></tr> +<tr valign="top"><td><code>weightCol</code></td> +<td> +<p>The weight column name.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>a fitted IsotonicRegressionModel.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>The directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>Overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.isoreg</code> returns a fitted Isotonic Regression model. +</p> +<p><code>predict</code> returns a SparkDataFrame containing predicted values. +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes model's <code>boundaries</code> (boundaries in increasing order) +and <code>predictions</code> (predictions associated with the boundaries at the same index). +</p> + + +<h3>Note</h3> + +<p>spark.isoreg since 2.1.0 +</p> +<p>predict(IsotonicRegressionModel) since 2.1.0 +</p> +<p>summary(IsotonicRegressionModel) since 2.1.0 +</p> +<p>write.ml(IsotonicRegression, character) since 2.1.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## 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) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.kmeans.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.kmeans.html b/site/docs/2.1.3/api/R/spark.kmeans.html new file mode 100644 index 0000000..970b85e --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.kmeans.html @@ -0,0 +1,166 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: K-Means Clustering Model</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.kmeans {SparkR}"><tr><td>spark.kmeans {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>K-Means Clustering Model</h2> + +<h3>Description</h3> + +<p>Fits a k-means clustering model against a SparkDataFrame, similarly to R's kmeans(). +Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make +predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models. +</p> + + +<h3>Usage</h3> + +<pre> +spark.kmeans(data, formula, ...) + +## S4 method for signature 'SparkDataFrame,formula' +spark.kmeans(data, formula, k = 2, + maxIter = 20, initMode = c("k-means||", "random"), seed = NULL, + initSteps = 2, tol = 1e-04) + +## S4 method for signature 'KMeansModel' +summary(object) + +## S4 method for signature 'KMeansModel' +predict(object, newData) + +## S4 method for signature 'KMeansModel,character' +write.ml(object, path, overwrite = FALSE) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', '.', ':', '+', and '-'. +Note that the response variable of formula is empty in spark.kmeans.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional argument(s) passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>k</code></td> +<td> +<p>number of centers.</p> +</td></tr> +<tr valign="top"><td><code>maxIter</code></td> +<td> +<p>maximum iteration number.</p> +</td></tr> +<tr valign="top"><td><code>initMode</code></td> +<td> +<p>the initialization algorithm choosen to fit the model.</p> +</td></tr> +<tr valign="top"><td><code>seed</code></td> +<td> +<p>the random seed for cluster initialization</p> +</td></tr> +<tr valign="top"><td><code>initSteps</code></td> +<td> +<p>the number of steps for the k-means|| initialization mode. +This is an advanced setting, the default of 2 is almost always enough. Must be > 0.</p> +</td></tr> +<tr valign="top"><td><code>tol</code></td> +<td> +<p>convergence tolerance of iterations.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>a fitted k-means model.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>the directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.kmeans</code> returns a fitted k-means model. +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes the model's <code>k</code> (the configured number of cluster centers), +<code>coefficients</code> (model cluster centers), +<code>size</code> (number of data points in each cluster), <code>cluster</code> +(cluster centers of the transformed data), is.loaded (whether the model is loaded +from a saved file), and <code>clusterSize</code> +(the actual number of cluster centers. When using initMode = "random", +<code>clusterSize</code> may not equal to <code>k</code>). +</p> +<p><code>predict</code> returns the predicted values based on a k-means model. +</p> + + +<h3>Note</h3> + +<p>spark.kmeans since 2.0.0 +</p> +<p>summary(KMeansModel) since 2.0.0 +</p> +<p>predict(KMeansModel) since 2.0.0 +</p> +<p>write.ml(KMeansModel, character) since 2.0.0 +</p> + + +<h3>See Also</h3> + +<p><a href="predict.html">predict</a>, <a href="read.ml.html">read.ml</a>, <a href="write.ml.html">write.ml</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D sparkR.session() +##D data(iris) +##D df <- createDataFrame(iris) +##D model <- spark.kmeans(df, Sepal_Length ~ Sepal_Width, k = 4, initMode = "random") +##D summary(model) +##D +##D # fitted values on training data +##D fitted <- predict(model, df) +##D head(select(fitted, "Sepal_Length", "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) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.kstest.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.kstest.html b/site/docs/2.1.3/api/R/spark.kstest.html new file mode 100644 index 0000000..9e75200 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.kstest.html @@ -0,0 +1,130 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: (One-Sample) Kolmogorov-Smirnov Test</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.kstest {SparkR}"><tr><td>spark.kstest {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>(One-Sample) Kolmogorov-Smirnov Test</h2> + +<h3>Description</h3> + +<p><code>spark.kstest</code> Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a +continuous distribution. +</p> +<p>By comparing the largest difference between the empirical cumulative +distribution of the sample data and the theoretical distribution we can provide a test for the +the null hypothesis that the sample data comes from that theoretical distribution. +</p> +<p>Users can call <code>summary</code> to obtain a summary of the test, and <code>print.summary.KSTest</code> +to print out a summary result. +</p> + + +<h3>Usage</h3> + +<pre> +spark.kstest(data, ...) + +## S4 method for signature 'SparkDataFrame' +spark.kstest(data, testCol = "test", + nullHypothesis = c("norm"), distParams = c(0, 1)) + +## S4 method for signature 'KSTest' +summary(object) + +## S3 method for class 'summary.KSTest' +print(x, ...) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a SparkDataFrame of user data.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional argument(s) passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>testCol</code></td> +<td> +<p>column name where the test data is from. It should be a column of double type.</p> +</td></tr> +<tr valign="top"><td><code>nullHypothesis</code></td> +<td> +<p>name of the theoretical distribution tested against. Currently only +<code>"norm"</code> for normal distribution is supported.</p> +</td></tr> +<tr valign="top"><td><code>distParams</code></td> +<td> +<p>parameters(s) of the distribution. For <code>nullHypothesis = "norm"</code>, +we can provide as a vector the mean and standard deviation of +the distribution. If none is provided, then standard normal will be used. +If only one is provided, then the standard deviation will be set to be one.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>test result object of KSTest by <code>spark.kstest</code>.</p> +</td></tr> +<tr valign="top"><td><code>x</code></td> +<td> +<p>summary object of KSTest returned by <code>summary</code>.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.kstest</code> returns a test result object. +</p> +<p><code>summary</code> returns summary information of KSTest object, which is a list. +The list includes the <code>p.value</code> (p-value), <code>statistic</code> (test statistic +computed for the test), <code>nullHypothesis</code> (the null hypothesis with its +parameters tested against) and <code>degreesOfFreedom</code> (degrees of freedom of the test). +</p> + + +<h3>Note</h3> + +<p>spark.kstest since 2.1.0 +</p> +<p>summary(KSTest) since 2.1.0 +</p> +<p>print.summary.KSTest since 2.1.0 +</p> + + +<h3>See Also</h3> + +<p><a href="http://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing"> +MLlib: Hypothesis Testing</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D data <- data.frame(test = c(0.1, 0.15, 0.2, 0.3, 0.25)) +##D df <- createDataFrame(data) +##D test <- spark.kstest(df, "test", "norm", c(0, 1)) +##D +##D # get a summary of the test result +##D testSummary <- summary(test) +##D testSummary +##D +##D # print out the summary in an organized way +##D print.summary.KSTest(testSummary) +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.lapply.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.lapply.html b/site/docs/2.1.3/api/R/spark.lapply.html new file mode 100644 index 0000000..8ec0472 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.lapply.html @@ -0,0 +1,95 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Run a function over a list of elements, distributing the...</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.lapply {SparkR}"><tr><td>spark.lapply {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Run a function over a list of elements, distributing the computations with Spark</h2> + +<h3>Description</h3> + +<p>Run a function over a list of elements, distributing the computations with Spark. Applies a +function in a manner that is similar to doParallel or lapply to elements of a list. +The computations are distributed using Spark. It is conceptually the same as the following code: +lapply(list, func) +</p> + + +<h3>Usage</h3> + +<pre> +spark.lapply(list, func) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>list</code></td> +<td> +<p>the list of elements</p> +</td></tr> +<tr valign="top"><td><code>func</code></td> +<td> +<p>a function that takes one argument.</p> +</td></tr> +</table> + + +<h3>Details</h3> + +<p>Known limitations: +</p> + +<ul> +<li><p> variable scoping and capture: compared to R's rich support for variable resolutions, +the distributed nature of SparkR limits how variables are resolved at runtime. All the +variables that are available through lexical scoping are embedded in the closure of the +function and available as read-only variables within the function. The environment variables +should be stored into temporary variables outside the function, and not directly accessed +within the function. +</p> +</li> +<li><p> loading external packages: In order to use a package, you need to load it inside the +closure. For example, if you rely on the MASS module, here is how you would use it: +</p> +<pre> + train <- function(hyperparam) { + library(MASS) + lm.ridge("y ~ x+z", data, lambda=hyperparam) + model + } + </pre> +</li></ul> + + + +<h3>Value</h3> + +<p>a list of results (the exact type being determined by the function) +</p> + + +<h3>Note</h3> + +<p>spark.lapply since 2.0.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D sparkR.session() +##D doubled <- spark.lapply(1:10, function(x){2 * x}) +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.lda.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.lda.html b/site/docs/2.1.3/api/R/spark.lda.html new file mode 100644 index 0000000..b1884c3 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.lda.html @@ -0,0 +1,237 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Latent Dirichlet Allocation</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.lda {SparkR}"><tr><td>spark.lda {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Latent Dirichlet Allocation</h2> + +<h3>Description</h3> + +<p><code>spark.lda</code> fits a Latent Dirichlet Allocation model on a SparkDataFrame. Users can call +<code>summary</code> to get a summary of the fitted LDA model, <code>spark.posterior</code> to compute +posterior probabilities on new data, <code>spark.perplexity</code> to compute log perplexity on new +data and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models. +</p> + + +<h3>Usage</h3> + +<pre> +spark.lda(data, ...) + +spark.posterior(object, newData) + +spark.perplexity(object, data) + +## S4 method for signature 'LDAModel,SparkDataFrame' +spark.posterior(object, newData) + +## S4 method for signature 'LDAModel' +summary(object, maxTermsPerTopic) + +## S4 method for signature 'LDAModel,SparkDataFrame' +spark.perplexity(object, data) + +## S4 method for signature 'LDAModel,character' +write.ml(object, path, overwrite = FALSE) + +## S4 method for signature 'SparkDataFrame' +spark.lda(data, features = "features", k = 10, + maxIter = 20, optimizer = c("online", "em"), subsamplingRate = 0.05, + topicConcentration = -1, docConcentration = -1, + customizedStopWords = "", maxVocabSize = bitwShiftL(1, 18)) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>A SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional argument(s) passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>A Latent Dirichlet Allocation model fitted by <code>spark.lda</code>.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>A SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>maxTermsPerTopic</code></td> +<td> +<p>Maximum number of terms to collect for each topic. Default value of 10.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>The directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>Overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +<tr valign="top"><td><code>features</code></td> +<td> +<p>Features column name. Either libSVM-format column or character-format column is +valid.</p> +</td></tr> +<tr valign="top"><td><code>k</code></td> +<td> +<p>Number of topics.</p> +</td></tr> +<tr valign="top"><td><code>maxIter</code></td> +<td> +<p>Maximum iterations.</p> +</td></tr> +<tr valign="top"><td><code>optimizer</code></td> +<td> +<p>Optimizer to train an LDA model, "online" or "em", default is "online".</p> +</td></tr> +<tr valign="top"><td><code>subsamplingRate</code></td> +<td> +<p>(For online optimizer) Fraction of the corpus to be sampled and used in +each iteration of mini-batch gradient descent, in range (0, 1].</p> +</td></tr> +<tr valign="top"><td><code>topicConcentration</code></td> +<td> +<p>concentration parameter (commonly named <code>beta</code> or <code>eta</code>) for +the prior placed on topic distributions over terms, default -1 to set automatically on the +Spark side. Use <code>summary</code> to retrieve the effective topicConcentration. Only 1-size +numeric is accepted.</p> +</td></tr> +<tr valign="top"><td><code>docConcentration</code></td> +<td> +<p>concentration parameter (commonly named <code>alpha</code>) for the +prior placed on documents distributions over topics (<code>theta</code>), default -1 to set +automatically on the Spark side. Use <code>summary</code> to retrieve the effective +docConcentration. Only 1-size or <code>k</code>-size numeric is accepted.</p> +</td></tr> +<tr valign="top"><td><code>customizedStopWords</code></td> +<td> +<p>stopwords that need to be removed from the given corpus. Ignore the +parameter if libSVM-format column is used as the features column.</p> +</td></tr> +<tr valign="top"><td><code>maxVocabSize</code></td> +<td> +<p>maximum vocabulary size, default 1 << 18</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.posterior</code> returns a SparkDataFrame containing posterior probabilities +vectors named "topicDistribution". +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes +</p> +<table summary="R valueblock"> +<tr valign="top"><td><code><code>docConcentration</code></code></td> +<td> +<p>concentration parameter commonly named <code>alpha</code> for +the prior placed on documents distributions over topics <code>theta</code></p> +</td></tr> +<tr valign="top"><td><code><code>topicConcentration</code></code></td> +<td> +<p>concentration parameter commonly named <code>beta</code> or +<code>eta</code> for the prior placed on topic distributions over terms</p> +</td></tr> +<tr valign="top"><td><code><code>logLikelihood</code></code></td> +<td> +<p>log likelihood of the entire corpus</p> +</td></tr> +<tr valign="top"><td><code><code>logPerplexity</code></code></td> +<td> +<p>log perplexity</p> +</td></tr> +<tr valign="top"><td><code><code>isDistributed</code></code></td> +<td> +<p>TRUE for distributed model while FALSE for local model</p> +</td></tr> +<tr valign="top"><td><code><code>vocabSize</code></code></td> +<td> +<p>number of terms in the corpus</p> +</td></tr> +<tr valign="top"><td><code><code>topics</code></code></td> +<td> +<p>top 10 terms and their weights of all topics</p> +</td></tr> +<tr valign="top"><td><code><code>vocabulary</code></code></td> +<td> +<p>whole terms of the training corpus, NULL if libsvm format file +used as training set</p> +</td></tr> +</table> +<p><code>spark.perplexity</code> returns the log perplexity of given SparkDataFrame, or the log +perplexity of the training data if missing argument "data". +</p> +<p><code>spark.lda</code> returns a fitted Latent Dirichlet Allocation model. +</p> + + +<h3>Note</h3> + +<p>spark.posterior(LDAModel) since 2.1.0 +</p> +<p>summary(LDAModel) since 2.1.0 +</p> +<p>spark.perplexity(LDAModel) since 2.1.0 +</p> +<p>write.ml(LDAModel, character) since 2.1.0 +</p> +<p>spark.lda since 2.1.0 +</p> + + +<h3>See Also</h3> + +<p><a href="read.ml.html">read.ml</a> +</p> +<p>topicmodels: <a href="https://cran.r-project.org/package=topicmodels">https://cran.r-project.org/package=topicmodels</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D # nolint start +##D # An example "path/to/file" can be +##D # paste0(Sys.getenv("SPARK_HOME"), "/data/mllib/sample_lda_libsvm_data.txt") +##D # nolint end +##D text <- read.df("path/to/file", source = "libsvm") +##D model <- spark.lda(data = text, optimizer = "em") +##D +##D # get a summary of the model +##D summary(model) +##D +##D # compute posterior probabilities +##D posterior <- spark.posterior(model, text) +##D showDF(posterior) +##D +##D # compute perplexity +##D perplexity <- spark.perplexity(model, text) +##D +##D # save and load the model +##D path <- "path/to/model" +##D write.ml(model, path) +##D savedModel <- read.ml(path) +##D summary(savedModel) +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.logit.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.logit.html b/site/docs/2.1.3/api/R/spark.logit.html new file mode 100644 index 0000000..dc1d13b --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.logit.html @@ -0,0 +1,195 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Logistic Regression Model</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.logit {SparkR}"><tr><td>spark.logit {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Logistic Regression Model</h2> + +<h3>Description</h3> + +<p>Fits an logistic regression model against a SparkDataFrame. It supports "binomial": Binary logistic regression +with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting, similar to glmnet. +Users can print, make predictions on the produced model and save the model to the input path. +</p> + + +<h3>Usage</h3> + +<pre> +spark.logit(data, formula, ...) + +## S4 method for signature 'SparkDataFrame,formula' +spark.logit(data, formula, regParam = 0, + elasticNetParam = 0, maxIter = 100, tol = 1e-06, family = "auto", + standardization = TRUE, thresholds = 0.5, weightCol = NULL) + +## S4 method for signature 'LogisticRegressionModel' +predict(object, newData) + +## S4 method for signature 'LogisticRegressionModel' +summary(object) + +## S4 method for signature 'LogisticRegressionModel,character' +write.ml(object, path, + overwrite = FALSE) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>A symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', '.', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional arguments passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>regParam</code></td> +<td> +<p>the regularization parameter.</p> +</td></tr> +<tr valign="top"><td><code>elasticNetParam</code></td> +<td> +<p>the ElasticNet mixing parameter. For alpha = 0.0, the penalty is an L2 penalty. +For alpha = 1.0, it is an L1 penalty. For 0.0 < alpha < 1.0, the penalty is a combination +of L1 and L2. Default is 0.0 which is an L2 penalty.</p> +</td></tr> +<tr valign="top"><td><code>maxIter</code></td> +<td> +<p>maximum iteration number.</p> +</td></tr> +<tr valign="top"><td><code>tol</code></td> +<td> +<p>convergence tolerance of iterations.</p> +</td></tr> +<tr valign="top"><td><code>family</code></td> +<td> +<p>the name of family which is a description of the label distribution to be used in the model. +Supported options: +</p> + +<ul> +<li><p>"auto": Automatically select the family based on the number of classes: +If number of classes == 1 || number of classes == 2, set to "binomial". +Else, set to "multinomial". +</p> +</li> +<li><p>"binomial": Binary logistic regression with pivoting. +</p> +</li> +<li><p>"multinomial": Multinomial logistic (softmax) regression without pivoting. +</p> +</li></ul> +</td></tr> +<tr valign="top"><td><code>standardization</code></td> +<td> +<p>whether to standardize the training features before fitting the model. The coefficients +of models will be always returned on the original scale, so it will be transparent for +users. Note that with/without standardization, the models should be always converged +to the same solution when no regularization is applied. Default is TRUE, same as glmnet.</p> +</td></tr> +<tr valign="top"><td><code>thresholds</code></td> +<td> +<p>in binary classification, in range [0, 1]. If the estimated probability of class label 1 +is > threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 +more often; a low threshold encourages the model to predict 1 more often. Note: Setting this with +threshold p is equivalent to setting thresholds c(1-p, p). In multiclass (or binary) classification to adjust the probability of +predicting each class. Array must have length equal to the number of classes, with values > 0, +excepting that at most one value may be 0. The class with largest value p/t is predicted, where p +is the original probability of that class and t is the class's threshold.</p> +</td></tr> +<tr valign="top"><td><code>weightCol</code></td> +<td> +<p>The weight column name.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>an LogisticRegressionModel fitted by <code>spark.logit</code>.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>The directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>Overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.logit</code> returns a fitted logistic regression model. +</p> +<p><code>predict</code> returns the predicted values based on an LogisticRegressionModel. +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes <code>coefficients</code> (coefficients matrix of the fitted model). +</p> + + +<h3>Note</h3> + +<p>spark.logit since 2.1.0 +</p> +<p>predict(LogisticRegressionModel) since 2.1.0 +</p> +<p>summary(LogisticRegressionModel) since 2.1.0 +</p> +<p>write.ml(LogisticRegression, character) since 2.1.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D sparkR.session() +##D # binary logistic regression +##D df <- createDataFrame(iris) +##D training <- df[df$Species %in% c("versicolor", "virginica"), ] +##D model <- spark.logit(training, Species ~ ., regParam = 0.5) +##D summary <- summary(model) +##D +##D # fitted values on training data +##D fitted <- predict(model, training) +##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 predict +##D # Note that summary deos not work on loaded model +##D savedModel <- read.ml(path) +##D summary(savedModel) +##D +##D # multinomial logistic regression +##D +##D df <- createDataFrame(iris) +##D model <- spark.logit(df, Species ~ ., regParam = 0.5) +##D summary <- summary(model) +##D +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.mlp.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.mlp.html b/site/docs/2.1.3/api/R/spark.mlp.html new file mode 100644 index 0000000..478dfcf --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.mlp.html @@ -0,0 +1,180 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Multilayer Perceptron Classification Model</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.mlp {SparkR}"><tr><td>spark.mlp {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Multilayer Perceptron Classification Model</h2> + +<h3>Description</h3> + +<p><code>spark.mlp</code> fits a multi-layer perceptron neural network model against a SparkDataFrame. +Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make +predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models. +Only categorical data is supported. +For more details, see +<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html"> +Multilayer Perceptron</a> +</p> + + +<h3>Usage</h3> + +<pre> +spark.mlp(data, formula, ...) + +## S4 method for signature 'SparkDataFrame,formula' +spark.mlp(data, formula, layers, + blockSize = 128, solver = "l-bfgs", maxIter = 100, tol = 1e-06, + stepSize = 0.03, seed = NULL, initialWeights = NULL) + +## S4 method for signature 'MultilayerPerceptronClassificationModel' +predict(object, newData) + +## S4 method for signature 'MultilayerPerceptronClassificationModel' +summary(object) + +## S4 method for signature 'MultilayerPerceptronClassificationModel,character' +write.ml(object, + path, overwrite = FALSE) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a <code>SparkDataFrame</code> of observations and labels for model fitting.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', '.', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional arguments passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>layers</code></td> +<td> +<p>integer vector containing the number of nodes for each layer.</p> +</td></tr> +<tr valign="top"><td><code>blockSize</code></td> +<td> +<p>blockSize parameter.</p> +</td></tr> +<tr valign="top"><td><code>solver</code></td> +<td> +<p>solver parameter, supported options: "gd" (minibatch gradient descent) or "l-bfgs".</p> +</td></tr> +<tr valign="top"><td><code>maxIter</code></td> +<td> +<p>maximum iteration number.</p> +</td></tr> +<tr valign="top"><td><code>tol</code></td> +<td> +<p>convergence tolerance of iterations.</p> +</td></tr> +<tr valign="top"><td><code>stepSize</code></td> +<td> +<p>stepSize parameter.</p> +</td></tr> +<tr valign="top"><td><code>seed</code></td> +<td> +<p>seed parameter for weights initialization.</p> +</td></tr> +<tr valign="top"><td><code>initialWeights</code></td> +<td> +<p>initialWeights parameter for weights initialization, it should be a +numeric vector.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>a Multilayer Perceptron Classification Model fitted by <code>spark.mlp</code></p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>the directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.mlp</code> returns a fitted Multilayer Perceptron Classification Model. +</p> +<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named +"prediction". +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes <code>numOfInputs</code> (number of inputs), <code>numOfOutputs</code> +(number of outputs), <code>layers</code> (array of layer sizes including input +and output layers), and <code>weights</code> (the weights of layers). +For <code>weights</code>, it is a numeric vector with length equal to the expected +given the architecture (i.e., for 8-10-2 network, 112 connection weights). +</p> + + +<h3>Note</h3> + +<p>spark.mlp since 2.1.0 +</p> +<p>predict(MultilayerPerceptronClassificationModel) since 2.1.0 +</p> +<p>summary(MultilayerPerceptronClassificationModel) since 2.1.0 +</p> +<p>write.ml(MultilayerPerceptronClassificationModel, character) since 2.1.0 +</p> + + +<h3>See Also</h3> + +<p><a href="read.ml.html">read.ml</a> +</p> +<p><a href="write.ml.html">write.ml</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm") +##D +##D # fit a Multilayer Perceptron Classification Model +##D model <- spark.mlp(df, label ~ features, blockSize = 128, layers = c(4, 3), solver = "l-bfgs", +##D maxIter = 100, tol = 0.5, stepSize = 1, seed = 1, +##D initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9)) +##D +##D # get the summary of the model +##D summary(model) +##D +##D # make predictions +##D predictions <- predict(model, df) +##D +##D # save and load the model +##D path <- "path/to/model" +##D write.ml(model, path) +##D savedModel <- read.ml(path) +##D summary(savedModel) +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.naiveBayes.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.naiveBayes.html b/site/docs/2.1.3/api/R/spark.naiveBayes.html new file mode 100644 index 0000000..21cb9ce --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.naiveBayes.html @@ -0,0 +1,143 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Naive Bayes Models</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.naiveBayes {SparkR}"><tr><td>spark.naiveBayes {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Naive Bayes Models</h2> + +<h3>Description</h3> + +<p><code>spark.naiveBayes</code> fits a Bernoulli naive Bayes model against a SparkDataFrame. +Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make +predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models. +Only categorical data is supported. +</p> + + +<h3>Usage</h3> + +<pre> +spark.naiveBayes(data, formula, ...) + +## S4 method for signature 'NaiveBayesModel' +predict(object, newData) + +## S4 method for signature 'NaiveBayesModel' +summary(object) + +## S4 method for signature 'SparkDataFrame,formula' +spark.naiveBayes(data, formula, + smoothing = 1) + +## S4 method for signature 'NaiveBayesModel,character' +write.ml(object, path, + overwrite = FALSE) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a <code>SparkDataFrame</code> of observations and labels for model fitting.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', '.', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional argument(s) passed to the method. Currently only <code>smoothing</code>.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>a naive Bayes model fitted by <code>spark.naiveBayes</code>.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>smoothing</code></td> +<td> +<p>smoothing parameter.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>the directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named +"prediction". +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes <code>apriori</code> (the label distribution) and +<code>tables</code> (conditional probabilities given the target label). +</p> +<p><code>spark.naiveBayes</code> returns a fitted naive Bayes model. +</p> + + +<h3>Note</h3> + +<p>predict(NaiveBayesModel) since 2.0.0 +</p> +<p>summary(NaiveBayesModel) since 2.0.0 +</p> +<p>spark.naiveBayes since 2.0.0 +</p> +<p>write.ml(NaiveBayesModel, character) since 2.0.0 +</p> + + +<h3>See Also</h3> + +<p>e1071: <a href="https://cran.r-project.org/package=e1071">https://cran.r-project.org/package=e1071</a> +</p> +<p><a href="write.ml.html">write.ml</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D data <- as.data.frame(UCBAdmissions) +##D df <- createDataFrame(data) +##D +##D # fit a Bernoulli naive Bayes model +##D model <- spark.naiveBayes(df, Admit ~ Gender + Dept, smoothing = 0) +##D +##D # get the summary of the model +##D summary(model) +##D +##D # make predictions +##D predictions <- predict(model, df) +##D +##D # save and load the model +##D path <- "path/to/model" +##D write.ml(model, path) +##D savedModel <- read.ml(path) +##D summary(savedModel) +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.randomForest.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.randomForest.html b/site/docs/2.1.3/api/R/spark.randomForest.html new file mode 100644 index 0000000..2479fcd --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.randomForest.html @@ -0,0 +1,236 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Random Forest Model for Regression and Classification</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.randomForest {SparkR}"><tr><td>spark.randomForest {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Random Forest Model for Regression and Classification</h2> + +<h3>Description</h3> + +<p><code>spark.randomForest</code> fits a Random Forest Regression model or Classification model on +a SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted Random Forest +model, <code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to +save/load fitted models. +For more details, see +<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-regression"> +Random Forest Regression</a> and +<a href="http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier"> +Random Forest Classification</a> +</p> + + +<h3>Usage</h3> + +<pre> +spark.randomForest(data, formula, ...) + +## S4 method for signature 'SparkDataFrame,formula' +spark.randomForest(data, formula, + type = c("regression", "classification"), maxDepth = 5, maxBins = 32, + numTrees = 20, impurity = NULL, featureSubsetStrategy = "auto", + seed = NULL, subsamplingRate = 1, minInstancesPerNode = 1, + minInfoGain = 0, checkpointInterval = 10, maxMemoryInMB = 256, + cacheNodeIds = FALSE) + +## S4 method for signature 'RandomForestRegressionModel' +predict(object, newData) + +## S4 method for signature 'RandomForestClassificationModel' +predict(object, newData) + +## S4 method for signature 'RandomForestRegressionModel,character' +write.ml(object, path, + overwrite = FALSE) + +## S4 method for signature 'RandomForestClassificationModel,character' +write.ml(object, path, + overwrite = FALSE) + +## S4 method for signature 'RandomForestRegressionModel' +summary(object) + +## S4 method for signature 'RandomForestClassificationModel' +summary(object) + +## S3 method for class 'summary.RandomForestRegressionModel' +print(x, ...) + +## S3 method for class 'summary.RandomForestClassificationModel' +print(x, ...) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', ':', '+', and '-'.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>additional arguments passed to the method.</p> +</td></tr> +<tr valign="top"><td><code>type</code></td> +<td> +<p>type of model, one of "regression" or "classification", to fit</p> +</td></tr> +<tr valign="top"><td><code>maxDepth</code></td> +<td> +<p>Maximum depth of the tree (>= 0).</p> +</td></tr> +<tr valign="top"><td><code>maxBins</code></td> +<td> +<p>Maximum number of bins used for discretizing continuous features and for choosing +how to split on features at each node. More bins give higher granularity. Must be +>= 2 and >= number of categories in any categorical feature.</p> +</td></tr> +<tr valign="top"><td><code>numTrees</code></td> +<td> +<p>Number of trees to train (>= 1).</p> +</td></tr> +<tr valign="top"><td><code>impurity</code></td> +<td> +<p>Criterion used for information gain calculation. +For regression, must be "variance". For classification, must be one of +"entropy" and "gini", default is "gini".</p> +</td></tr> +<tr valign="top"><td><code>featureSubsetStrategy</code></td> +<td> +<p>The number of features to consider for splits at each tree node. +Supported options: "auto", "all", "onethird", "sqrt", "log2", (0.0-1.0], [1-n].</p> +</td></tr> +<tr valign="top"><td><code>seed</code></td> +<td> +<p>integer seed for random number generation.</p> +</td></tr> +<tr valign="top"><td><code>subsamplingRate</code></td> +<td> +<p>Fraction of the training data used for learning each decision tree, in +range (0, 1].</p> +</td></tr> +<tr valign="top"><td><code>minInstancesPerNode</code></td> +<td> +<p>Minimum number of instances each child must have after split.</p> +</td></tr> +<tr valign="top"><td><code>minInfoGain</code></td> +<td> +<p>Minimum information gain for a split to be considered at a tree node.</p> +</td></tr> +<tr valign="top"><td><code>checkpointInterval</code></td> +<td> +<p>Param for set checkpoint interval (>= 1) or disable checkpoint (-1).</p> +</td></tr> +<tr valign="top"><td><code>maxMemoryInMB</code></td> +<td> +<p>Maximum memory in MB allocated to histogram aggregation.</p> +</td></tr> +<tr valign="top"><td><code>cacheNodeIds</code></td> +<td> +<p>If FALSE, the algorithm will pass trees to executors to match instances with +nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching +can speed up training of deeper trees. Users can set how often should the +cache be checkpointed or disable it by setting checkpointInterval.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>A fitted Random Forest regression model or classification model.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>The directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>Overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +<tr valign="top"><td><code>x</code></td> +<td> +<p>summary object of Random Forest regression model or classification model +returned by <code>summary</code>.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.randomForest</code> returns a fitted Random Forest model. +</p> +<p><code>predict</code> returns a SparkDataFrame containing predicted labeled in a column named +"prediction". +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list of components includes <code>formula</code> (formula), +<code>numFeatures</code> (number of features), <code>features</code> (list of features), +<code>featureImportances</code> (feature importances), <code>numTrees</code> (number of trees), +and <code>treeWeights</code> (tree weights). +</p> + + +<h3>Note</h3> + +<p>spark.randomForest since 2.1.0 +</p> +<p>predict(RandomForestRegressionModel) since 2.1.0 +</p> +<p>predict(RandomForestClassificationModel) since 2.1.0 +</p> +<p>write.ml(RandomForestRegressionModel, character) since 2.1.0 +</p> +<p>write.ml(RandomForestClassificationModel, character) since 2.1.0 +</p> +<p>summary(RandomForestRegressionModel) since 2.1.0 +</p> +<p>summary(RandomForestClassificationModel) since 2.1.0 +</p> +<p>print.summary.RandomForestRegressionModel since 2.1.0 +</p> +<p>print.summary.RandomForestClassificationModel since 2.1.0 +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D # fit a Random Forest Regression Model +##D df <- createDataFrame(longley) +##D model <- spark.randomForest(df, Employed ~ ., type = "regression", maxDepth = 5, maxBins = 16) +##D +##D # get the summary of the model +##D summary(model) +##D +##D # make predictions +##D predictions <- predict(model, df) +##D +##D # save and load the model +##D path <- "path/to/model" +##D write.ml(model, path) +##D savedModel <- read.ml(path) +##D summary(savedModel) +##D +##D # fit a Random Forest Classification Model +##D df <- createDataFrame(iris) +##D model <- spark.randomForest(df, Species ~ Petal_Length + Petal_Width, "classification") +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/spark.survreg.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/spark.survreg.html b/site/docs/2.1.3/api/R/spark.survreg.html new file mode 100644 index 0000000..7b2cce1 --- /dev/null +++ b/site/docs/2.1.3/api/R/spark.survreg.html @@ -0,0 +1,133 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Accelerated Failure Time (AFT) Survival Regression Model</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for spark.survreg {SparkR}"><tr><td>spark.survreg {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Accelerated Failure Time (AFT) Survival Regression Model</h2> + +<h3>Description</h3> + +<p><code>spark.survreg</code> fits an accelerated failure time (AFT) survival regression model on +a SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted AFT model, +<code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to +save/load fitted models. +</p> + + +<h3>Usage</h3> + +<pre> +spark.survreg(data, formula) + +## S4 method for signature 'AFTSurvivalRegressionModel,character' +write.ml(object, path, + overwrite = FALSE) + +## S4 method for signature 'SparkDataFrame,formula' +spark.survreg(data, formula) + +## S4 method for signature 'AFTSurvivalRegressionModel' +summary(object) + +## S4 method for signature 'AFTSurvivalRegressionModel' +predict(object, newData) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>data</code></td> +<td> +<p>a SparkDataFrame for training.</p> +</td></tr> +<tr valign="top"><td><code>formula</code></td> +<td> +<p>a symbolic description of the model to be fitted. Currently only a few formula +operators are supported, including '~', ':', '+', and '-'. +Note that operator '.' is not supported currently.</p> +</td></tr> +<tr valign="top"><td><code>object</code></td> +<td> +<p>a fitted AFT survival regression model.</p> +</td></tr> +<tr valign="top"><td><code>path</code></td> +<td> +<p>the directory where the model is saved.</p> +</td></tr> +<tr valign="top"><td><code>overwrite</code></td> +<td> +<p>overwrites or not if the output path already exists. Default is FALSE +which means throw exception if the output path exists.</p> +</td></tr> +<tr valign="top"><td><code>newData</code></td> +<td> +<p>a SparkDataFrame for testing.</p> +</td></tr> +</table> + + +<h3>Value</h3> + +<p><code>spark.survreg</code> returns a fitted AFT survival regression model. +</p> +<p><code>summary</code> returns summary information of the fitted model, which is a list. +The list includes the model's <code>coefficients</code> (features, coefficients, +intercept and log(scale)). +</p> +<p><code>predict</code> returns a SparkDataFrame containing predicted values +on the original scale of the data (mean predicted value at scale = 1.0). +</p> + + +<h3>Note</h3> + +<p>write.ml(AFTSurvivalRegressionModel, character) since 2.0.0 +</p> +<p>spark.survreg since 2.0.0 +</p> +<p>summary(AFTSurvivalRegressionModel) since 2.0.0 +</p> +<p>predict(AFTSurvivalRegressionModel) since 2.0.0 +</p> + + +<h3>See Also</h3> + +<p><a href="write.ml.html">write.ml</a> +</p> +<p>survival: <a href="https://cran.r-project.org/package=survival">https://cran.r-project.org/package=survival</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D df <- createDataFrame(ovarian) +##D model <- spark.survreg(df, Surv(futime, fustat) ~ ecog_ps + rx) +##D +##D # get a summary of the model +##D summary(model) +##D +##D # make predictions +##D predicted <- predict(model, df) +##D showDF(predicted) +##D +##D # save and load the model +##D path <- "path/to/model" +##D write.ml(model, path) +##D savedModel <- read.ml(path) +##D summary(savedModel) +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/sparkR.callJMethod.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/sparkR.callJMethod.html b/site/docs/2.1.3/api/R/sparkR.callJMethod.html new file mode 100644 index 0000000..453886f --- /dev/null +++ b/site/docs/2.1.3/api/R/sparkR.callJMethod.html @@ -0,0 +1,90 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Call Java Methods</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for sparkR.callJMethod {SparkR}"><tr><td>sparkR.callJMethod {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Call Java Methods</h2> + +<h3>Description</h3> + +<p>Call a Java method in the JVM running the Spark driver. The return +values are automatically converted to R objects for simple objects. Other +values are returned as "jobj" which are references to objects on JVM. +</p> + + +<h3>Usage</h3> + +<pre> +sparkR.callJMethod(x, methodName, ...) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>x</code></td> +<td> +<p>object to invoke the method on. Should be a "jobj" created by newJObject.</p> +</td></tr> +<tr valign="top"><td><code>methodName</code></td> +<td> +<p>method name to call.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>parameters to pass to the Java method.</p> +</td></tr> +</table> + + +<h3>Details</h3> + +<p>This is a low level function to access the JVM directly and should only be used +for advanced use cases. The arguments and return values that are primitive R +types (like integer, numeric, character, lists) are automatically translated to/from +Java types (like Integer, Double, String, Array). A full list can be found in +serialize.R and deserialize.R in the Apache Spark code base. +</p> + + +<h3>Value</h3> + +<p>the return value of the Java method. Either returned as a R object +if it can be deserialized or returned as a "jobj". See details section for more. +</p> + + +<h3>Note</h3> + +<p>sparkR.callJMethod since 2.0.1 +</p> + + +<h3>See Also</h3> + +<p><a href="sparkR.callJStatic.html">sparkR.callJStatic</a>, <a href="sparkR.newJObject.html">sparkR.newJObject</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D sparkR.session() # Need to have a Spark JVM running before calling newJObject +##D # Create a Java ArrayList and populate it +##D jarray <- sparkR.newJObject("java.util.ArrayList") +##D sparkR.callJMethod(jarray, "add", 42L) +##D sparkR.callJMethod(jarray, "get", 0L) # Will print 42 +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> http://git-wip-us.apache.org/repos/asf/spark-website/blob/3a634959/site/docs/2.1.3/api/R/sparkR.callJStatic.html ---------------------------------------------------------------------- diff --git a/site/docs/2.1.3/api/R/sparkR.callJStatic.html b/site/docs/2.1.3/api/R/sparkR.callJStatic.html new file mode 100644 index 0000000..cbca349 --- /dev/null +++ b/site/docs/2.1.3/api/R/sparkR.callJStatic.html @@ -0,0 +1,88 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Call Static Java Methods</title> +<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> +<link rel="stylesheet" type="text/css" href="R.css" /> + +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js"></script> +<script>hljs.initHighlightingOnLoad();</script> +</head><body> + +<table width="100%" summary="page for sparkR.callJStatic {SparkR}"><tr><td>sparkR.callJStatic {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> + +<h2>Call Static Java Methods</h2> + +<h3>Description</h3> + +<p>Call a static method in the JVM running the Spark driver. The return +value is automatically converted to R objects for simple objects. Other +values are returned as "jobj" which are references to objects on JVM. +</p> + + +<h3>Usage</h3> + +<pre> +sparkR.callJStatic(x, methodName, ...) +</pre> + + +<h3>Arguments</h3> + +<table summary="R argblock"> +<tr valign="top"><td><code>x</code></td> +<td> +<p>fully qualified Java class name that contains the static method to invoke.</p> +</td></tr> +<tr valign="top"><td><code>methodName</code></td> +<td> +<p>name of static method to invoke.</p> +</td></tr> +<tr valign="top"><td><code>...</code></td> +<td> +<p>parameters to pass to the Java method.</p> +</td></tr> +</table> + + +<h3>Details</h3> + +<p>This is a low level function to access the JVM directly and should only be used +for advanced use cases. The arguments and return values that are primitive R +types (like integer, numeric, character, lists) are automatically translated to/from +Java types (like Integer, Double, String, Array). A full list can be found in +serialize.R and deserialize.R in the Apache Spark code base. +</p> + + +<h3>Value</h3> + +<p>the return value of the Java method. Either returned as a R object +if it can be deserialized or returned as a "jobj". See details section for more. +</p> + + +<h3>Note</h3> + +<p>sparkR.callJStatic since 2.0.1 +</p> + + +<h3>See Also</h3> + +<p><a href="sparkR.callJMethod.html">sparkR.callJMethod</a>, <a href="sparkR.newJObject.html">sparkR.newJObject</a> +</p> + + +<h3>Examples</h3> + +<pre><code class="r">## Not run: +##D sparkR.session() # Need to have a Spark JVM running before calling callJStatic +##D sparkR.callJStatic("java.lang.System", "currentTimeMillis") +##D sparkR.callJStatic("java.lang.System", "getProperty", "java.home") +## End(Not run) +</code></pre> + + +<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.1.3 <a href="00Index.html">Index</a>]</div> +</body></html> --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org