Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/9756#discussion_r46605232
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -592,21 +594,47 @@ class LinearRegressionSuite
}
/*
- Use the following R code to generate model training results.
-
- predictions <- predict(fit, newx=features)
- residuals <- label - predictions
- > mean(residuals^2) # MSE
- [1] 0.009720325
- > mean(abs(residuals)) # MAD
- [1] 0.07863206
- > cor(predictions, label)^2# r^2
- [,1]
- s0 0.9998749
+ # Use the following R code to generate model training results.
+
+ # path/part-00000 is the file generated by running
LinearDataGenerator.generateLinearInput
+ # as described before the beforeAll() method.
+ d1 <- read.csv("path/part-00000", header=FALSE,
stringsAsFactors=FALSE)
+ fit <- glm(V1 ~ V2 + V3, data = d1, family = "gaussian")
+ names(f1)[1] = c("V2")
+ names(f1)[2] = c("V3")
+ f1 <- data.frame(as.numeric(d1$V2), as.numeric(d1$V3))
+ predictions <- predict(fit, newdata=f1)
+ l1 <- as.numeric(d1$V1)
+
+ residuals <- l1 - predictions
+ > mean(residuals^2) # MSE
+ [1] 0.00985449
+ > mean(abs(residuals)) # MAD
+ [1] 0.07961668
+ > cor(predictions, l1)^2 # r^2
+ [1] 0.9998737
+
+ > summary(fit)
+
+ Call:
+ glm(formula = V1 ~ V2 + V3, family = "gaussian", data = d1)
+
+ Deviance Residuals:
+ Min 1Q Median 3Q Max
+ -0.47082 -0.06797 0.00002 0.06725 0.34635
+
+ Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+ (Intercept) 6.3022157 0.0018600 3388 <2e-16 ***
+ V2 4.6982442 0.0011805 3980 <2e-16 ***
+ V3 7.1994344 0.0009044 7961 <2e-16 ***
+ ---
+
+ ....
*/
- assert(model.summary.meanSquaredError ~== 0.00972035 relTol 1E-5)
- assert(model.summary.meanAbsoluteError ~== 0.07863206 relTol 1E-5)
- assert(model.summary.r2 ~== 0.9998749 relTol 1E-5)
+ assert(model.summary.meanSquaredError ~== 0.00985449 relTol 1E-5)
+ assert(model.summary.meanAbsoluteError ~== 0.07961668 relTol 1E-5)
+ assert(model.summary.r2 ~== 0.9998737 relTol 1E-5)
--- End diff --
Yes, "or more" -- I'm just saying nobody expects an exact tolerance based
on some principled analysis.
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