From e7905a9395c1a002f50bab29e16a729e14d4ed6f Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Thu, 30 Jul 2015 16:15:43 -0700
Subject: [SPARK-9463] [ML] Expose model coefficients with names in SparkR
 RFormula

Preview:

```
> summary(m)
            features coefficients
1        (Intercept)    1.6765001
2       Sepal_Length    0.3498801
3 Species.versicolor   -0.9833885
4  Species.virginica   -1.0075104

```

Design doc from umbrella task: https://docs.google.com/document/d/10NZNSEurN2EdWM31uFYsgayIPfCFHiuIu3pCWrUmP_c/edit

cc mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #7771 from ericl/summary and squashes the following commits:

ccd54c3 [Eric Liang] second pass
a5ca93b [Eric Liang] comments
2772111 [Eric Liang] clean up
70483ef [Eric Liang] fix test
7c247d4 [Eric Liang] Merge branch 'master' into summary
3c55024 [Eric Liang] working
8c539aa [Eric Liang] first pass
---
 R/pkg/NAMESPACE               |  3 ++-
 R/pkg/R/mllib.R               | 26 ++++++++++++++++++++++++++
 R/pkg/inst/tests/test_mllib.R | 11 +++++++++++
 3 files changed, 39 insertions(+), 1 deletion(-)

(limited to 'R/pkg')

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 7f7a8a2e4d..a329e14f25 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -12,7 +12,8 @@ export("print.jobj")
 
 # MLlib integration
 exportMethods("glm",
-              "predict")
+              "predict",
+              "summary")
 
 # Job group lifecycle management methods
 export("setJobGroup",
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 6a8bacaa55..efddcc1d8d 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -71,3 +71,29 @@ setMethod("predict", signature(object = "PipelineModel"),
           function(object, newData) {
             return(dataFrame(callJMethod(object@model, "transform", newData@sdf)))
           })
+
+#' Get the summary of a model
+#'
+#' Returns the summary of a model produced by glm(), similarly to R's summary().
+#'
+#' @param model A fitted MLlib model
+#' @return a list with a 'coefficient' component, which is the matrix of coefficients. See
+#'         summary.glm for more information.
+#' @rdname glm
+#' @export
+#' @examples
+#'\dontrun{
+#' model <- glm(y ~ x, trainingData)
+#' summary(model)
+#'}
+setMethod("summary", signature(object = "PipelineModel"),
+          function(object) {
+            features <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
+                                   "getModelFeatures", object@model)
+            weights <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
+                                   "getModelWeights", object@model)
+            coefficients <- as.matrix(unlist(weights))
+            colnames(coefficients) <- c("Estimate")
+            rownames(coefficients) <- unlist(features)
+            return(list(coefficients = coefficients))
+          })
diff --git a/R/pkg/inst/tests/test_mllib.R b/R/pkg/inst/tests/test_mllib.R
index 3bef693247..f272de78ad 100644
--- a/R/pkg/inst/tests/test_mllib.R
+++ b/R/pkg/inst/tests/test_mllib.R
@@ -48,3 +48,14 @@ test_that("dot minus and intercept vs native glm", {
   rVals <- predict(glm(Sepal.Width ~ . - Species + 0, data = iris), iris)
   expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
 })
+
+test_that("summary coefficients match with native glm", {
+  training <- createDataFrame(sqlContext, iris)
+  stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training))
+  coefs <- as.vector(stats$coefficients)
+  rCoefs <- as.vector(coef(glm(Sepal.Width ~ Sepal.Length + Species, data = iris)))
+  expect_true(all(abs(rCoefs - coefs) < 1e-6))
+  expect_true(all(
+    as.character(stats$features) ==
+    c("(Intercept)", "Sepal_Length", "Species__versicolor", "Species__virginica")))
+})
-- 
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