Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15721#discussion_r93481030
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala ---
@@ -224,4 +208,139 @@ object MLTestingUtils extends SparkFunSuite {
}.toDF()
(overSampledData, weightedData)
}
+
+ /**
+ * Generates a linear prediction function where the coefficients are
generated randomly.
+ * The function produces a continuous (numClasses = 0) or categorical
(numClasses > 0) label.
+ */
+ def getRandomLinearPredictionFunction(
+ numFeatures: Int,
+ numClasses: Int,
+ seed: Long): (Vector => Double) = {
+ val rng = new scala.util.Random(seed)
+ val trueNumClasses = if (numClasses == 0) 1 else numClasses
+ val coefArray = Array.fill(numFeatures *
trueNumClasses)(rng.nextDouble - 0.5)
+ (features: Vector) => {
+ if (numClasses == 0) {
+ BLAS.dot(features, new DenseVector(coefArray))
+ } else {
+ val margins = new DenseVector(new Array[Double](numClasses))
+ val coefMat = new DenseMatrix(numClasses, numFeatures, coefArray)
+ BLAS.gemv(1.0, coefMat, features, 1.0, margins)
+ margins.argmax.toDouble
+ }
+ }
+ }
+
+ /**
+ * A helper function to generate synthetic data. Generates random
feature values,
+ * both categorical and continuous, according to
`categoricalFeaturesInfo`. The label is generated
+ * from a random prediction function, and noise is added to the true
label.
+ *
+ * @param numPoints The number of data points to generate.
+ * @param numClasses The number of classes the outcome can take on. 0
for continuous labels.
+ * @param numFeatures The number of features in the data.
+ * @param categoricalFeaturesInfo Map of (featureIndex -> numCategories)
for categorical features.
+ * @param seed Random seed.
+ * @param noiseLevel A number in [0.0, 1.0] indicating how much noise to
add to the label.
+ * @return Generated sequence of noisy instances.
+ */
+ def generateNoisyData(
--- End diff --
Fair point. Actually, the noise is not strictly necessary for this patch in
the other cases. I can use the existing datasets (for the most part). I removed
this generator and passed the test data to the testing util methods.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
