[SPARK-19395][SPARKR] Convert coefficients in summary to matrix

## What changes were proposed in this pull request?
The `coefficients` component in model summary should be 'matrix' but the underlying structure is indeed list. This affects several models except for 'AFTSurvivalRegressionModel' which has the correct implementation. The fix is to first `unlist` the coefficients returned from the `callJMethod` before converting to matrix. An example illustrates the issues:

```
data(iris)
df <- createDataFrame(iris)
model <- spark.glm(df, Sepal_Length ~ Sepal_Width, family = "gaussian")
s <- summary(model)

> str(s$coefficients)
List of 8
 $ : num 6.53
 $ : num -0.223
 $ : num 0.479
 $ : num 0.155
 $ : num 13.6
 $ : num -1.44
 $ : num 0
 $ : num 0.152
 - attr(*, "dim")= int [1:2] 2 4
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:2] "(Intercept)" "Sepal_Width"
  ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
> s$coefficients[, 2]
$`(Intercept)`
[1] 0.4788963

$Sepal_Width
[1] 0.1550809
```

This  shows that the underlying structure of coefficients is still `list`.

felixcheung wangmiao1981

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16730 from actuaryzhang/sparkRCoef.

R/pkg/R/mllib_classification.R

@@ -145,7 +145,7 @@ setMethod("summary", signature(object = "LogisticRegressionModel"),
             labels <- callJMethod(jobj, "labels")
             coefficients <- callJMethod(jobj, "rCoefficients")
             nCol <- length(coefficients) / length(features)
-            coefficients <- matrix(coefficients, ncol = nCol)
+            coefficients <- matrix(unlist(coefficients), ncol = nCol)
             # If nCol == 1, means this is a binomial logistic regression model with pivoting.
             # Otherwise, it's a multinomial logistic regression model without pivoting.
             if (nCol == 1) {

R/pkg/R/mllib_clustering.R

@@ -390,7 +390,7 @@ setMethod("summary", signature(object = "KMeansModel"),
             coefficients <- callJMethod(jobj, "coefficients")
             k <- callJMethod(jobj, "k")
             size <- callJMethod(jobj, "size")
-            coefficients <- t(matrix(coefficients, ncol = k))
+            coefficients <- t(matrix(unlist(coefficients), ncol = k))
             colnames(coefficients) <- unlist(features)
             rownames(coefficients) <- 1:k
             cluster <- if (is.loaded) {

R/pkg/R/mllib_regression.R

@@ -182,11 +182,11 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
             # coefficients, standard error of coefficients, t value and p value. Otherwise,
             # it will be fitted by local "l-bfgs", we can only provide coefficients.
             if (length(features) == length(coefficients)) {
-              coefficients <- matrix(coefficients, ncol = 1)
+              coefficients <- matrix(unlist(coefficients), ncol = 1)
               colnames(coefficients) <- c("Estimate")
               rownames(coefficients) <- unlist(features)
             } else {
-              coefficients <- matrix(coefficients, ncol = 4)
+              coefficients <- matrix(unlist(coefficients), ncol = 4)
               colnames(coefficients) <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)")
               rownames(coefficients) <- unlist(features)
             }

R/pkg/inst/tests/testthat/test_mllib_classification.R

@@ -68,12 +68,17 @@ test_that("spark.logit", {
   df <- suppressWarnings(createDataFrame(iris))
   model <- spark.logit(df, Species ~ ., regParam = 0.5)
   summary <- summary(model)
+
+  # test summary coefficients return matrix type
+  expect_true(class(summary$coefficients) == "matrix")
+  expect_true(class(summary$coefficients[, 1]) == "numeric")
+
   versicolorCoefsR <- c(1.52, 0.03, -0.53, 0.04, 0.00)
   virginicaCoefsR <- c(-2.62, 0.27, -0.02, 0.16, 0.42)
   setosaCoefsR <- c(1.10, -0.29, 0.55, -0.19, -0.42)
-  versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
-  virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
-  setosaCoefs <- unlist(summary$coefficients[, "setosa"])
+  versicolorCoefs <- summary$coefficients[, "versicolor"]
+  virginicaCoefs <- summary$coefficients[, "virginica"]
+  setosaCoefs <- summary$coefficients[, "setosa"]
   expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
   expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
   expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1))
@@ -136,8 +141,8 @@ test_that("spark.logit", {
   summary <- summary(model)
   versicolorCoefsR <- c(3.94, -0.16, -0.02, -0.35, -0.78)
   virginicaCoefsR <- c(-3.94, 0.16, -0.02, 0.35, 0.78)
-  versicolorCoefs <- unlist(summary$coefficients[, "versicolor"])
-  virginicaCoefs <- unlist(summary$coefficients[, "virginica"])
+  versicolorCoefs <- summary$coefficients[, "versicolor"]
+  virginicaCoefs <- summary$coefficients[, "virginica"]
   expect_true(all(abs(versicolorCoefsR - versicolorCoefs) < 0.1))
   expect_true(all(abs(virginicaCoefsR - virginicaCoefs) < 0.1))
 
@@ -145,7 +150,7 @@ test_that("spark.logit", {
   model <- spark.logit(training, Species ~ ., regParam = 0.5)
   summary <- summary(model)
   coefsR <- c(-6.08, 0.25, 0.16, 0.48, 1.04)
-  coefs <- unlist(summary$coefficients[, "Estimate"])
+  coefs <- summary$coefficients[, "Estimate"]
   expect_true(all(abs(coefsR - coefs) < 0.1))
 
   # Test prediction with string label

R/pkg/inst/tests/testthat/test_mllib_clustering.R

@@ -166,6 +166,10 @@ test_that("spark.kmeans", {
   expect_equal(k, 2)
   expect_equal(sort(collect(distinct(select(cluster, "prediction")))$prediction), c(0, 1))
 
+  # test summary coefficients return matrix type
+  expect_true(class(summary.model$coefficients) == "matrix")
+  expect_true(class(summary.model$coefficients[1, ]) == "numeric")
+
   # Test model save/load
   modelPath <- tempfile(pattern = "spark-kmeans", fileext = ".tmp")
   write.ml(model, modelPath)

R/pkg/inst/tests/testthat/test_mllib_regression.R

@@ -87,11 +87,14 @@ test_that("spark.glm summary", {
   # gaussian family
   training <- suppressWarnings(createDataFrame(iris))
   stats <- summary(spark.glm(training, Sepal_Width ~ Sepal_Length + Species))
-
   rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
 
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
+  # test summary coefficients return matrix type
+  expect_true(class(stats$coefficients) == "matrix")
+  expect_true(class(stats$coefficients[, 1]) == "numeric")
+
+  coefs <- stats$coefficients
+  rCoefs <- rStats$coefficients
   expect_true(all(abs(rCoefs - coefs) < 1e-4))
   expect_true(all(
     rownames(stats$coefficients) ==
@@ -117,8 +120,8 @@ test_that("spark.glm summary", {
   rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
                         family = binomial(link = "logit")))
 
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
+  coefs <- stats$coefficients
+  rCoefs <- rStats$coefficients
   expect_true(all(abs(rCoefs - coefs) < 1e-4))
   expect_true(all(
     rownames(stats$coefficients) ==
@@ -141,8 +144,8 @@ test_that("spark.glm summary", {
   stats <- summary(spark.glm(df, b ~ a1 + a2, family = "binomial", weightCol = "w"))
   rStats <- summary(glm(b ~ a1 + a2, family = "binomial", data = data, weights = w))
 
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
+  coefs <- stats$coefficients
+  rCoefs <- rStats$coefficients
   expect_true(all(abs(rCoefs - coefs) < 1e-3))
   expect_true(all(rownames(stats$coefficients) == c("(Intercept)", "a1", "a2")))
   expect_equal(stats$dispersion, rStats$dispersion)
@@ -169,7 +172,7 @@ test_that("spark.glm summary", {
   data <- as.data.frame(cbind(A, b))
   df <- createDataFrame(data)
   stats <- summary(spark.glm(df, b ~ . - 1))
-  coefs <- unlist(stats$coefficients)
+  coefs <- stats$coefficients
   expect_true(all(abs(c(0.5, 0.25) - coefs) < 1e-4))
 })
 
@@ -259,8 +262,8 @@ test_that("glm summary", {
 
   rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris))
 
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
+  coefs <- stats$coefficients
+  rCoefs <- rStats$coefficients
   expect_true(all(abs(rCoefs - coefs) < 1e-4))
   expect_true(all(
     rownames(stats$coefficients) ==
@@ -282,8 +285,8 @@ test_that("glm summary", {
   rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
                         family = binomial(link = "logit")))
 
-  coefs <- unlist(stats$coefficients)
-  rCoefs <- unlist(rStats$coefficients)
+  coefs <- stats$coefficients
+  rCoefs <- rStats$coefficients
   expect_true(all(abs(rCoefs - coefs) < 1e-4))
   expect_true(all(
     rownames(stats$coefficients) ==