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author | Yanbo Liang <ybliang8@gmail.com> | 2016-04-15 08:23:51 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-04-15 08:23:51 -0700 |
commit | 83af297ac42546580983f91079f74e3a4cf25050 (patch) | |
tree | abaa00d9f381bcd4fa4adae7a2bc79b54ad325b4 /R | |
parent | 06b9d623e8f58d7bd450a50d938f83b4b3472a32 (diff) | |
download | spark-83af297ac42546580983f91079f74e3a4cf25050.tar.gz spark-83af297ac42546580983f91079f74e3a4cf25050.tar.bz2 spark-83af297ac42546580983f91079f74e3a4cf25050.zip |
[SPARK-13925][ML][SPARKR] Expose R-like summary statistics in SparkR::glm for more family and link functions
## What changes were proposed in this pull request?
Expose R-like summary statistics in SparkR::glm for more family and link functions.
Note: Not all values in R [summary.glm](http://stat.ethz.ch/R-manual/R-patched/library/stats/html/summary.glm.html) are exposed, we only provide the most commonly used statistics in this PR. More statistics can be added in the followup work.
## How was this patch tested?
Unit tests.
SparkR Output:
```
Deviance Residuals:
(Note: These are approximate quantiles with relative error <= 0.01)
Min 1Q Median 3Q Max
-0.95096 -0.16585 -0.00232 0.17410 0.72918
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6765 0.23536 7.1231 4.4561e-11
Sepal_Length 0.34988 0.046301 7.5566 4.1873e-12
Species_versicolor -0.98339 0.072075 -13.644 0
Species_virginica -1.0075 0.093306 -10.798 0
(Dispersion parameter for gaussian family taken to be 0.08351462)
Null deviance: 28.307 on 149 degrees of freedom
Residual deviance: 12.193 on 146 degrees of freedom
AIC: 59.22
Number of Fisher Scoring iterations: 1
```
R output:
```
Deviance Residuals:
Min 1Q Median 3Q Max
-0.95096 -0.16522 0.00171 0.18416 0.72918
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.67650 0.23536 7.123 4.46e-11 ***
Sepal.Length 0.34988 0.04630 7.557 4.19e-12 ***
Speciesversicolor -0.98339 0.07207 -13.644 < 2e-16 ***
Speciesvirginica -1.00751 0.09331 -10.798 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.08351462)
Null deviance: 28.307 on 149 degrees of freedom
Residual deviance: 12.193 on 146 degrees of freedom
AIC: 59.217
Number of Fisher Scoring iterations: 2
```
cc mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #12393 from yanboliang/spark-13925.
Diffstat (limited to 'R')
-rw-r--r-- | R/pkg/NAMESPACE | 3 | ||||
-rw-r--r-- | R/pkg/R/mllib.R | 49 | ||||
-rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 49 |
3 files changed, 97 insertions, 4 deletions
diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE index f48c61c1d5..94ac7e7df7 100644 --- a/R/pkg/NAMESPACE +++ b/R/pkg/NAMESPACE @@ -292,7 +292,8 @@ export("as.DataFrame", "tableToDF", "tableNames", "tables", - "uncacheTable") + "uncacheTable", + "print.summary.GeneralizedLinearRegressionModel") export("structField", "structField.jobj", diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 31bca16580..922a9b13db 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -101,12 +101,55 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"), jobj <- object@jobj features <- callJMethod(jobj, "rFeatures") coefficients <- callJMethod(jobj, "rCoefficients") - coefficients <- as.matrix(unlist(coefficients)) - colnames(coefficients) <- c("Estimate") + deviance.resid <- callJMethod(jobj, "rDevianceResiduals") + dispersion <- callJMethod(jobj, "rDispersion") + null.deviance <- callJMethod(jobj, "rNullDeviance") + deviance <- callJMethod(jobj, "rDeviance") + df.null <- callJMethod(jobj, "rResidualDegreeOfFreedomNull") + df.residual <- callJMethod(jobj, "rResidualDegreeOfFreedom") + aic <- callJMethod(jobj, "rAic") + iter <- callJMethod(jobj, "rNumIterations") + family <- callJMethod(jobj, "rFamily") + + deviance.resid <- dataFrame(deviance.resid) + coefficients <- matrix(coefficients, ncol = 4) + colnames(coefficients) <- c("Estimate", "Std. Error", "t value", "Pr(>|t|)") rownames(coefficients) <- unlist(features) - return(list(coefficients = coefficients)) + ans <- list(deviance.resid = deviance.resid, coefficients = coefficients, + dispersion = dispersion, null.deviance = null.deviance, + deviance = deviance, df.null = df.null, df.residual = df.residual, + aic = aic, iter = iter, family = family) + class(ans) <- "summary.GeneralizedLinearRegressionModel" + return(ans) }) +#' Print the summary of GeneralizedLinearRegressionModel +#' +#' @rdname print +#' @name print.summary.GeneralizedLinearRegressionModel +#' @export +print.summary.GeneralizedLinearRegressionModel <- function(x, ...) { + x$deviance.resid <- setNames(unlist(approxQuantile(x$deviance.resid, "devianceResiduals", + c(0.0, 0.25, 0.5, 0.75, 1.0), 0.01)), c("Min", "1Q", "Median", "3Q", "Max")) + x$deviance.resid <- zapsmall(x$deviance.resid, 5L) + cat("\nDeviance Residuals: \n") + cat("(Note: These are approximate quantiles with relative error <= 0.01)\n") + print.default(x$deviance.resid, digits = 5L, na.print = "", print.gap = 2L) + + cat("\nCoefficients:\n") + print.default(x$coefficients, digits = 5L, na.print = "", print.gap = 2L) + + cat("\n(Dispersion parameter for ", x$family, " family taken to be ", format(x$dispersion), + ")\n\n", apply(cbind(paste(format(c("Null", "Residual"), justify = "right"), "deviance:"), + format(unlist(x[c("null.deviance", "deviance")]), digits = 5L), + " on", format(unlist(x[c("df.null", "df.residual")])), " degrees of freedom\n"), + 1L, paste, collapse = " "), sep = "") + cat("AIC: ", format(x$aic, digits = 4L), "\n\n", + "Number of Fisher Scoring iterations: ", x$iter, "\n", sep = "") + cat("\n") + invisible(x) + } + #' Make predictions from a generalized linear model #' #' Makes predictions from a generalized linear model produced by glm(), similarly to R's predict(). diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R index a9dbd2bdc4..47bbf7e5bd 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -77,6 +77,55 @@ test_that("glm and predict", { expect_equal(length(predict(lm(y ~ x))), 15) }) +test_that("glm summary", { + # gaussian family + training <- suppressWarnings(createDataFrame(sqlContext, iris)) + stats <- summary(glm(Sepal_Width ~ Sepal_Length + Species, data = training)) + + rStats <- summary(glm(Sepal.Width ~ Sepal.Length + Species, data = iris)) + + coefs <- unlist(stats$coefficients) + rCoefs <- unlist(rStats$coefficients) + expect_true(all(abs(rCoefs - coefs) < 1e-4)) + expect_true(all( + rownames(stats$coefficients) == + c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica"))) + expect_equal(stats$dispersion, rStats$dispersion) + expect_equal(stats$null.deviance, rStats$null.deviance) + expect_equal(stats$deviance, rStats$deviance) + expect_equal(stats$df.null, rStats$df.null) + expect_equal(stats$df.residual, rStats$df.residual) + expect_equal(stats$aic, rStats$aic) + + # binomial family + df <- suppressWarnings(createDataFrame(sqlContext, iris)) + training <- df[df$Species %in% c("versicolor", "virginica"), ] + stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training, + family = binomial(link = "logit"))) + + rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ] + rStats <- summary(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining, + family = binomial(link = "logit"))) + + coefs <- unlist(stats$coefficients) + rCoefs <- unlist(rStats$coefficients) + expect_true(all(abs(rCoefs - coefs) < 1e-4)) + expect_true(all( + rownames(stats$coefficients) == + c("(Intercept)", "Sepal_Length", "Sepal_Width"))) + expect_equal(stats$dispersion, rStats$dispersion) + expect_equal(stats$null.deviance, rStats$null.deviance) + expect_equal(stats$deviance, rStats$deviance) + expect_equal(stats$df.null, rStats$df.null) + expect_equal(stats$df.residual, rStats$df.residual) + expect_equal(stats$aic, rStats$aic) + + # Test summary works on base GLM models + baseModel <- stats::glm(Sepal.Width ~ Sepal.Length + Species, data = iris) + baseSummary <- summary(baseModel) + expect_true(abs(baseSummary$deviance - 12.19313) < 1e-4) +}) + test_that("kmeans", { newIris <- iris newIris$Species <- NULL |