Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15721#discussion_r93171343
--- 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 --
I am a bit worried whether we should provide this general noisy data
generation function:
* It's better we can generate data following the rule of specific
algorithms, for example, users provide coefficients, the mean and variance of
generated features for ```LogisticRegression```.
* Actually, some generators such as
[```LinearDataGenerator.generateLinearInput```](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala#L97)
has already considered the noise level.
Just like ```LinearDataGenerator.generateLinearInput```, I think we should
add argument ```eps``` for other generators such as
```LogisticRegressionSuite.generateLogisticInput,
LogisticRegressionSuite.generateMultinomialLogisticInput,
NaiveBayesSuite.generateNaiveBayesInput```, to make them output noisy label
natively.
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