Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/19020#discussion_r147316970
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
---
@@ -998,6 +1047,198 @@ class LinearRegressionSuite
}
}
}
+
+ test("linear regression (huber loss) with intercept without
regularization") {
+ val trainer1 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(true).setStandardization(true)
+ val trainer2 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(true).setStandardization(false)
+
+ val model1 = trainer1.fit(datasetWithOutlier)
+ val model2 = trainer2.fit(datasetWithOutlier)
+
+ /*
+ Using the following Python code to load the data and train the model
using
+ scikit-learn package.
+
+ import pandas as pd
+ import numpy as np
+ from sklearn.linear_model import HuberRegressor
+ df = pd.read_csv("path", header = None)
+ X = df[df.columns[1:3]]
+ y = np.array(df[df.columns[0]])
+ huber = HuberRegressor(fit_intercept=True, alpha=0.0, max_iter=100,
epsilon=1.35)
+ huber.fit(X, y)
+
+ >>> huber.coef_
+ array([ 4.68998007, 7.19429011])
+ >>> huber.intercept_
+ 6.3002404351083037
+ >>> huber.scale_
+ 0.077810159205220747
+ */
+ val coefficientsPy = Vectors.dense(4.68998007, 7.19429011)
+ val interceptPy = 6.30024044
+ val scalePy = 0.07781016
+
+ assert(model1.coefficients ~= coefficientsPy relTol 1E-3)
+ assert(model1.intercept ~== interceptPy relTol 1E-3)
+ assert(model1.scale ~== scalePy relTol 1E-3)
+
+ // Without regularization, with or without standardization will
converge to the same solution.
+ assert(model2.coefficients ~= coefficientsPy relTol 1E-3)
+ assert(model2.intercept ~== interceptPy relTol 1E-3)
+ assert(model2.scale ~== scalePy relTol 1E-3)
+ }
+
+ test("linear regression (huber loss) without intercept without
regularization") {
+ val trainer1 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(false).setStandardization(true)
+ val trainer2 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(false).setStandardization(false)
+
+ val model1 = trainer1.fit(datasetWithOutlier)
+ val model2 = trainer2.fit(datasetWithOutlier)
+
+ /*
+ huber = HuberRegressor(fit_intercept=False, alpha=0.0, max_iter=100,
epsilon=1.35)
+ huber.fit(X, y)
+
+ >>> huber.coef_
+ array([ 6.71756703, 5.08873222])
+ >>> huber.intercept_
+ 0.0
+ >>> huber.scale_
+ 2.5560209922722317
+ */
+ val coefficientsPy = Vectors.dense(6.71756703, 5.08873222)
+ val interceptPy = 0.0
+ val scalePy = 2.55602099
+
+ assert(model1.coefficients ~= coefficientsPy relTol 1E-3)
+ assert(model1.intercept === interceptPy)
+ assert(model1.scale ~== scalePy relTol 1E-3)
+
+ // Without regularization, with or without standardization will
converge to the same solution.
+ assert(model2.coefficients ~= coefficientsPy relTol 1E-3)
+ assert(model2.intercept === interceptPy)
+ assert(model2.scale ~== scalePy relTol 1E-3)
+ }
+
+ test("linear regression (huber loss) with intercept with L2
regularization") {
+ val trainer1 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(true).setRegParam(0.21).setStandardization(true)
+ val trainer2 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(true).setRegParam(0.21).setStandardization(false)
+
+ val model1 = trainer1.fit(datasetWithOutlier)
+ val model2 = trainer2.fit(datasetWithOutlier)
+
+ /*
+ Since scikit-learn HuberRegressor does not support standardization,
+ we do it manually out of the estimator.
+
+ xStd = np.std(X, axis=0)
+ scaledX = X / xStd
+ huber = HuberRegressor(fit_intercept=True, alpha=210, max_iter=100,
epsilon=1.35)
+ huber.fit(scaledX, y)
+
+ >>> np.array(huber.coef_ / xStd)
+ array([ 1.97732633, 3.38816722])
+ >>> huber.intercept_
+ 3.7527581430531227
+ >>> huber.scale_
+ 3.787363673371801
+ */
+ val coefficientsPy1 = Vectors.dense(1.97732633, 3.38816722)
+ val interceptPy1 = 3.75275814
+ val scalePy1 = 3.78736367
+
+ assert(model1.coefficients ~= coefficientsPy1 relTol 1E-2)
+ assert(model1.intercept ~== interceptPy1 relTol 1E-2)
+ assert(model1.scale ~== scalePy1 relTol 1E-2)
+
+ /*
+ huber = HuberRegressor(fit_intercept=True, alpha=210, max_iter=100,
epsilon=1.35)
+ huber.fit(X, y)
+
+ >>> huber.coef_
+ array([ 1.73346444, 3.63746999])
+ >>> huber.intercept_
+ 4.3017134790781739
+ >>> huber.scale_
+ 3.6472742809286793
+ */
+ val coefficientsPy2 = Vectors.dense(1.73346444, 3.63746999)
+ val interceptPy2 = 4.30171347
+ val scalePy2 = 3.64727428
+
+ assert(model2.coefficients ~= coefficientsPy2 relTol 1E-3)
+ assert(model2.intercept ~== interceptPy2 relTol 1E-3)
+ assert(model2.scale ~== scalePy2 relTol 1E-3)
+ }
+
+ test("linear regression (huber loss) without intercept with L2
regularization") {
+ val trainer1 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(false).setRegParam(0.21).setStandardization(true)
+ val trainer2 = (new LinearRegression).setLoss("huber")
+ .setFitIntercept(false).setRegParam(0.21).setStandardization(false)
+
+ val model1 = trainer1.fit(datasetWithOutlier)
+ val model2 = trainer2.fit(datasetWithOutlier)
+
+ /*
+ Since scikit-learn HuberRegressor does not support standardization,
+ we do it manually out of the estimator.
+
+ xStd = np.std(X, axis=0)
+ scaledX = X / xStd
+ huber = HuberRegressor(fit_intercept=False, alpha=210, max_iter=100,
epsilon=1.35)
+ huber.fit(scaledX, y)
+
+ >>> np.array(huber.coef_ / xStd)
+ array([ 2.59679008, 2.26973102])
+ >>> huber.intercept_
+ 0.0
+ >>> huber.scale_
+ 4.5766311924091791
+ */
+ val coefficientsPy1 = Vectors.dense(2.59679008, 2.26973102)
+ val interceptPy1 = 0.0
+ val scalePy1 = 4.57663119
+
+ assert(model1.coefficients ~= coefficientsPy1 relTol 1E-2)
+ assert(model1.intercept === interceptPy1)
+ assert(model1.scale ~== scalePy1 relTol 1E-2)
+
+ /*
+ huber = HuberRegressor(fit_intercept=False, alpha=210, max_iter=100,
epsilon=1.35)
+ huber.fit(X, y)
+
+ >>> huber.coef_
+ array([ 2.28423908, 2.25196887])
+ >>> huber.intercept_
+ 0.0
+ >>> huber.scale_
+ 4.5979643506051753
+ */
+ val coefficientsPy2 = Vectors.dense(2.28423908, 2.25196887)
+ val interceptPy2 = 0.0
+ val scalePy2 = 4.59796435
+
+ assert(model2.coefficients ~= coefficientsPy2 relTol 1E-3)
+ assert(model2.intercept === interceptPy2)
+ assert(model2.scale ~== scalePy2 relTol 1E-3)
+ }
+
+ test("huber loss model match squared error for large m") {
--- End diff --
m -> epsilon
---
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