{"action":"created","comment":{"url":"https://api.github.com/repos/apache/spark/pulls/comments/54521794","id":54521794,"diff_hunk":"@@ -0,0 +1,499 @@\n+/*\n+ * Licensed to the Apache Software Foundation (ASF) under one or more\n+ * contributor license agreements.  See the NOTICE file distributed with\n+ * this work for additional information regarding copyright ownership.\n+ * The ASF licenses this file to You under the Apache License, Version 2.0\n+ * (the \"License\"); you may not use this file except in compliance with\n+ * the License.  You may obtain a copy of the License at\n+ *\n+ *    http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+package org.apache.spark.ml.regression\n+\n+import scala.util.Random\n+\n+import org.apache.spark.SparkFunSuite\n+import org.apache.spark.ml.param.ParamsSuite\n+import org.apache.spark.ml.util.MLTestingUtils\n+import org.apache.spark.mllib.classification.LogisticRegressionSuite._\n+import org.apache.spark.mllib.linalg.{BLAS, DenseVector, Vectors}\n+import org.apache.spark.mllib.random._\n+import org.apache.spark.mllib.regression.LabeledPoint\n+import org.apache.spark.mllib.util.MLlibTestSparkContext\n+import org.apache.spark.mllib.util.TestingUtils._\n+import org.apache.spark.sql.{DataFrame, Row}\n+\n+class GeneralizedLinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {\n+\n+  private val seed: Int = 42\n+  @transient var datasetGaussianIdentity: DataFrame = _\n+  @transient var datasetGaussianLog: DataFrame = _\n+  @transient var datasetGaussianInverse: DataFrame = _\n+  @transient var datasetBinomial: DataFrame = _\n+  @transient var datasetPoissonLog: DataFrame = _\n+  @transient var datasetPoissonIdentity: DataFrame = _\n+  @transient var datasetPoissonSqrt: DataFrame = _\n+  @transient var datasetGammaInverse: DataFrame = _\n+  @transient var datasetGammaIdentity: DataFrame = _\n+  @transient var datasetGammaLog: DataFrame = _\n+\n+  override def beforeAll(): Unit = {\n+    super.beforeAll()\n+\n+    import GeneralizedLinearRegressionSuite._\n+\n+    datasetGaussianIdentity = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 2.5, coefficients = Array(2.2, 0.6), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"gaussian\", link = \"identity\"), 2))\n+\n+    datasetGaussianLog = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 0.25, coefficients = Array(0.22, 0.06), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"gaussian\", link = \"log\"), 2))\n+\n+    datasetGaussianInverse = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 2.5, coefficients = Array(2.2, 0.6), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"gaussian\", link = \"inverse\"), 2))\n+\n+    datasetBinomial = {\n+      val nPoints = 10000\n+      val coefficients = Array(-0.57997, 0.912083, -0.371077, -0.819866, 2.688191)\n+      val xMean = Array(5.843, 3.057, 3.758, 1.199)\n+      val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)\n+\n+      val testData =\n+        generateMultinomialLogisticInput(coefficients, xMean, xVariance, true, nPoints, seed)\n+\n+      sqlContext.createDataFrame(sc.parallelize(testData, 4))\n+    }\n+\n+    datasetPoissonLog = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 0.25, coefficients = Array(0.22, 0.06), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"poisson\", link = \"log\"), 2))\n+\n+    datasetPoissonIdentity = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 2.5, coefficients = Array(2.2, 0.6), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"poisson\", link = \"identity\"), 2))\n+\n+    datasetPoissonSqrt = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 2.5, coefficients = Array(2.2, 0.6), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"poisson\", link = \"sqrt\"), 2))\n+\n+    datasetGammaInverse = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 2.5, coefficients = Array(2.2, 0.6), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"gamma\", link = \"inverse\"), 2))\n+\n+    datasetGammaIdentity = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 2.5, coefficients = Array(2.2, 0.6), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"gamma\", link = \"identity\"), 2))\n+\n+    datasetGammaLog = sqlContext.createDataFrame(\n+      sc.parallelize(generateGeneralizedLinearRegressionInput(\n+        intercept = 0.25, coefficients = Array(0.22, 0.06), xMean = Array(2.9, 10.5),\n+        xVariance = Array(0.7, 1.2), nPoints = 10000, seed, eps = 0.01,\n+        family = \"gamma\", link = \"log\"), 2))\n+  }\n+\n+  test(\"params\") {\n+    ParamsSuite.checkParams(new GeneralizedLinearRegression)\n+    val model = new GeneralizedLinearRegressionModel(\"genLinReg\", Vectors.dense(0.0), 0.0)\n+    ParamsSuite.checkParams(model)\n+  }\n+\n+  test(\"generalized linear regression: default params\") {\n+    val glr = new GeneralizedLinearRegression\n+    assert(glr.getLabelCol === \"label\")\n+    assert(glr.getFeaturesCol === \"features\")\n+    assert(glr.getPredictionCol === \"prediction\")\n+    assert(glr.getFitIntercept)\n+    assert(glr.getTol === 1E-6)\n+    assert(glr.getWeightCol === \"\")\n+    assert(glr.getRegParam === 0.0)\n+    assert(glr.getSolver == \"irls\")\n+    val model = glr.setFamily(\"gaussian\").setLink(\"identity\")\n+      .fit(datasetGaussianIdentity)\n+\n+    // copied model must have the same parent.\n+    MLTestingUtils.checkCopy(model)\n+    model.transform(datasetGaussianIdentity)\n+      .select(\"label\", \"prediction\")\n+      .collect()\n+\n+    assert(model.getFeaturesCol === \"features\")\n+    assert(model.getPredictionCol === \"prediction\")\n+    assert(model.intercept !== 0.0)\n+    assert(model.hasParent)\n+    assert(model.getFamily === \"gaussian\")\n+    assert(model.getLink === \"identity\")\n+  }\n+\n+  test(\"generalized linear regression: gaussian family against glm\") {\n+    /*\n+       R code:\n+       f1 <- data$V1 ~ data$V2 + data$V3 - 1\n+       f2 <- data$V1 ~ data$V2 + data$V3\n+\n+       data <- read.csv(\"path\", header=FALSE)\n+       for (formula in c(f1, f2)) {\n+         model <- glm(formula, family=\"gaussian\", data=data)\n+         print(as.vector(coef(model)))\n+       }\n+\n+       [1] 2.2960999 0.8087933\n+       [1] 2.5002642 2.2000403 0.5999485\n+\n+       data <- read.csv(\"path\", header=FALSE)\n+       model1 <- glm(f1, family=gaussian(link=log), data=data, start=c(0,0))\n+       model2 <- glm(f2, family=gaussian(link=log), data=data, start=c(0,0,0))\n+       print(as.vector(coef(model1)))\n+       print(as.vector(coef(model2)))\n+\n+       [1] 0.23069326 0.07993778\n+       [1] 0.25001858 0.22002452 0.05998789\n+\n+       data <- read.csv(\"path\", header=FALSE)\n+       for (formula in c(f1, f2)) {\n+         model <- glm(formula, family=gaussian(link=inverse), data=data)\n+         print(as.vector(coef(model)))\n+       }\n+\n+       [1] 2.3010179 0.8198976\n+       [1] 2.4108902 2.2130248 0.6086152\n+     */\n+\n+    val expected = Seq(\n+      Vectors.dense(0.0, 2.2960999, 0.8087933),\n+      Vectors.dense(2.5002642, 2.2000403, 0.5999485),\n+      Vectors.dense(0.0, 0.23069326, 0.07993778),\n+      Vectors.dense(0.25001858, 0.22002452, 0.05998789),\n+      Vectors.dense(0.0, 2.3010179, 0.8198976),\n+      Vectors.dense(2.4108902, 2.2130248, 0.6086152))\n+\n+    import GeneralizedLinearRegression._\n+\n+    var idx = 0\n+    for ((link, dataset) <- Seq((\"identity\", datasetGaussianIdentity), (\"log\", datasetGaussianLog),\n+      (\"inverse\", datasetGaussianInverse))) {\n+      for (fitIntercept <- Seq(false, true)) {\n+        val trainer = new GeneralizedLinearRegression().setFamily(\"gaussian\").setLink(link)\n+          .setFitIntercept(fitIntercept)\n+        val model = trainer.fit(dataset)\n+        val actual = Vectors.dense(model.intercept, model.coefficients(0), model.coefficients(1))\n+        assert(actual ~== expected(idx) absTol 1e-4)\n+\n+        val familyLink = new FamilyAndLink(Gaussian, Link.fromName(link))\n+        model.transform(dataset).select(\"features\", \"prediction\").collect().foreach {\n+          case Row(features: DenseVector, prediction1: Double) =>\n+            val eta = BLAS.dot(features, model.coefficients) + model.intercept\n+            val prediction2 = familyLink.fitted(eta)\n+            assert(prediction1 ~== prediction2 relTol 1E-5)","path":"mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala","position":null,"original_position":211,"commit_id":"31a912cd74cf3dffbf8cc0af8c57b777d49579eb","original_commit_id":"c05a94899c39bfa9ede9071bd6db6a937b33cf83","user":{"login":"yanboliang","id":1962026,"avatar_url":"https://avatars.githubusercontent.com/u/1962026?v=3","gravatar_id":"","url":"https://api.github.com/users/yanboliang","html_url":"https://github.com/yanboliang","followers_url":"https://api.github.com/users/yanboliang/followers","following_url":"https://api.github.com/users/yanboliang/following{/other_user}","gists_url":"https://api.github.com/users/yanboliang/gists{/gist_id}","starred_url":"https://api.github.com/users/yanboliang/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yanboliang/subscriptions","organizations_url":"https://api.github.com/users/yanboliang/orgs","repos_url":"https://api.github.com/users/yanboliang/repos","events_url":"https://api.github.com/users/yanboliang/events{/privacy}","received_events_url":"https://api.github.com/users/yanboliang/received_events","type":"User","site_admin":false},"body":"Fixed.","created_at":"2016-03-01T04:30:03Z","updated_at":"2016-03-01T04:30:03Z","html_url":"https://github.com/apache/spark/pull/11136#discussion_r54521794","pull_request_url":"https://api.github.com/repos/apache/spark/pulls/11136","_links":{"self":{"href":"https://api.github.com/repos/apache/spark/pulls/comments/54521794"},"html":{"href":"https://github.com/apache/spark/pull/11136#discussion_r54521794"},"pull_request":{"href":"https://api.github.com/repos/apache/spark/pulls/11136"}}},"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/11136","id":58764607,"html_url":"https://github.com/apache/spark/pull/11136","diff_url":"https://github.com/apache/spark/pull/11136.diff","patch_url":"https://github.com/apache/spark/pull/11136.patch","issue_url":"https://api.github.com/repos/apache/spark/issues/11136","number":11136,"state":"open","locked":false,"title":"[SPARK-12811] [ML] Estimator for Generalized Linear Models(GLMs)","user":{"login":"yanboliang","id":1962026,"avatar_url":"https://avatars.githubusercontent.com/u/1962026?v=3","gravatar_id":"","url":"https://api.github.com/users/yanboliang","html_url":"https://github.com/yanboliang","followers_url":"https://api.github.com/users/yanboliang/followers","following_url":"https://api.github.com/users/yanboliang/following{/other_user}","gists_url":"https://api.github.com/users/yanboliang/gists{/gist_id}","starred_url":"https://api.github.com/users/yanboliang/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yanboliang/subscriptions","organizations_url":"https://api.github.com/users/yanboliang/orgs","repos_url":"https://api.github.com/users/yanboliang/repos","events_url":"https://api.github.com/users/yanboliang/events{/privacy}","received_events_url":"https://api.github.com/users/yanboliang/received_events","type":"User","site_admin":false},"body":"Estimator for Generalized Linear Models(GLMs) which will be solved by IRLS.\r\n\r\ncc @mengxr 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