{"action":"created","comment":{"url":"https://api.github.com/repos/apache/spark/pulls/comments/74672274","id":74672274,"diff_hunk":"@@ -944,13 +955,140 @@ class BinaryLogisticRegressionSummary private[classification] (\n private class LogisticAggregator(\n     private val numFeatures: Int,\n     numClasses: Int,\n-    fitIntercept: Boolean) extends Serializable {\n+    fitIntercept: Boolean,\n+    multinomial: Boolean,\n+    standardize: Boolean) extends Serializable {\n \n   private var weightSum = 0.0\n   private var lossSum = 0.0\n \n+  private val totalCoefficientLength = {\n+    val cols = if (fitIntercept) numFeatures + 1 else numFeatures\n+    val rows = if (multinomial) numClasses else math.max(1, numClasses - 1)\n+    rows * cols\n+  }\n+\n   private val gradientSumArray =\n-    Array.ofDim[Double](if (fitIntercept) numFeatures + 1 else numFeatures)\n+    Array.ofDim[Double](totalCoefficientLength)\n+\n+  /** Update gradient and loss using binary loss function. */\n+  private def binaryUpdateInPlace(\n+      features: Vector,\n+      weight: Double,\n+      label: Double,\n+      coefficients: Array[Double],\n+      gradient: Array[Double],\n+      featuresStd: Array[Double],\n+      numFeaturesPlusIntercept: Int,\n+      standardize: Boolean): Unit = {\n+    val margin = - {\n+      var sum = 0.0\n+      features.foreachActive { (index, value) =>\n+        if (featuresStd(index) != 0.0 && value != 0.0) {\n+          val x = if (standardize) value / featuresStd(index) else value\n+          sum += coefficients(index) * x\n+        }\n+      }\n+      sum + {\n+        if (fitIntercept) coefficients(numFeaturesPlusIntercept - 1) else 0.0\n+      }\n+    }\n+\n+    val multiplier = weight * (1.0 / (1.0 + math.exp(margin)) - label)\n+\n+    features.foreachActive { (index, value) =>\n+      if (featuresStd(index) != 0.0 && value != 0.0) {\n+        val x = if (standardize) value / featuresStd(index) else value\n+        gradient(index) += multiplier * x\n+      }\n+    }\n+\n+    if (fitIntercept) {\n+      gradient(numFeaturesPlusIntercept - 1) += multiplier\n+    }\n+\n+    if (label > 0) {\n+      // The following is equivalent to log(1 + exp(margin)) but more numerically stable.\n+      lossSum += weight * MLUtils.log1pExp(margin)\n+    } else {\n+      lossSum += weight * (MLUtils.log1pExp(margin) - margin)\n+    }\n+  }\n+\n+  /** Update gradient and loss using multinomial loss function. */","path":"mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala","position":195,"original_position":199,"commit_id":"026e1f69fd6aa45cdb6e6d641e86b624df52839a","original_commit_id":"f4c817fb941bfd4176da145956ae1ee61a687c68","user":{"login":"dbtsai","id":1134574,"avatar_url":"https://avatars.githubusercontent.com/u/1134574?v=3","gravatar_id":"","url":"https://api.github.com/users/dbtsai","html_url":"https://github.com/dbtsai","followers_url":"https://api.github.com/users/dbtsai/followers","following_url":"https://api.github.com/users/dbtsai/following{/other_user}","gists_url":"https://api.github.com/users/dbtsai/gists{/gist_id}","starred_url":"https://api.github.com/users/dbtsai/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dbtsai/subscriptions","organizations_url":"https://api.github.com/users/dbtsai/orgs","repos_url":"https://api.github.com/users/dbtsai/repos","events_url":"https://api.github.com/users/dbtsai/events{/privacy}","received_events_url":"https://api.github.com/users/dbtsai/received_events","type":"User","site_admin":false},"body":"many people use softmax as more academic term. Maybe we should have both `multinomial (softmax) loss` in the documentation, and use `multinomial` for api or more user face place.","created_at":"2016-08-12T23:03:14Z","updated_at":"2016-08-12T23:03:14Z","html_url":"https://github.com/apache/spark/pull/13796#discussion_r74672274","pull_request_url":"https://api.github.com/repos/apache/spark/pulls/13796","_links":{"self":{"href":"https://api.github.com/repos/apache/spark/pulls/comments/74672274"},"html":{"href":"https://github.com/apache/spark/pull/13796#discussion_r74672274"},"pull_request":{"href":"https://api.github.com/repos/apache/spark/pulls/13796"}}},"pull_request":{"url":"https://api.github.com/repos/apache/spark/pulls/13796","id":74546117,"html_url":"https://github.com/apache/spark/pull/13796","diff_url":"https://github.com/apache/spark/pull/13796.diff","patch_url":"https://github.com/apache/spark/pull/13796.patch","issue_url":"https://api.github.com/repos/apache/spark/issues/13796","number":13796,"state":"open","locked":false,"title":"[SPARK-7159][ML] Add multiclass logistic regression to Spark ML","user":{"login":"sethah","id":7275795,"avatar_url":"https://avatars.githubusercontent.com/u/7275795?v=3","gravatar_id":"","url":"https://api.github.com/users/sethah","html_url":"https://github.com/sethah","followers_url":"https://api.github.com/users/sethah/followers","following_url":"https://api.github.com/users/sethah/following{/other_user}","gists_url":"https://api.github.com/users/sethah/gists{/gist_id}","starred_url":"https://api.github.com/users/sethah/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sethah/subscriptions","organizations_url":"https://api.github.com/users/sethah/orgs","repos_url":"https://api.github.com/users/sethah/repos","events_url":"https://api.github.com/users/sethah/events{/privacy}","received_events_url":"https://api.github.com/users/sethah/received_events","type":"User","site_admin":false},"body":"## What changes were proposed in this pull request?\r\n\r\nThis patch adds a new estimator/transformer `MultinomialLogisticRegression` to spark ML. \r\n\r\nJIRA: [SPARK-7159](https://issues.apache.org/jira/browse/SPARK-7159)\r\n\r\n## How was this patch tested?\r\n\r\nAdded new test suite `MultinomialLogisticRegressionSuite`.\r\n\r\n## Approach\r\n\r\n### Do not use a \"pivot\" class in the algorithm formulation\r\n\r\nMany implementations of multinomial logistic regression treat the problem as K - 1 independent binary logistic regression models where K is the number of possible outcomes in the output variable. In this case, one outcome is chosen as a \"pivot\" and the other K - 1 outcomes are regressed against the pivot. This is somewhat undesirable since the coefficients returned will be different for different choices of pivot variables. An alternative approach to the problem models class conditional probabilites using the softmax function and will return uniquely identifiable coefficients (assuming regularization is applied). This second approach is used in R's glmnet and was also recommended by @dbtsai. \r\n\r\n### Separate multinomial logistic regression and binary logistic regression\r\n\r\nThe initial design makes multinomial logistic regression a separate estimator/transformer than the existing LogisticRegression estimator/transformer. An alternative design would be to merge them into one. \r\n\r\n**Arguments for:**\r\n\r\n* The multinomial case without pivot is distinctly different than the current binary case since the binary case uses a pivot class. \r\n* The current logistic regression model in ML uses a vector of coefficients and a scalar intercept. In the multinomial case, we require a matrix of coefficients and a vector of intercepts. There are potential workarounds for this issue if we were to merge the two estimators, but none are particularly elegant. \r\n\r\n**Arguments against:**\r\n\r\n* It may be inconvenient for users to have to switch the estimator class when transitioning between binary and multiclass (although the new multinomial estimator can be used for two class outcomes).\r\n* Some portions of the code are repeated.\r\n\r\nThis is a major design point and warrants more discussion.\r\n\r\n### Mean centering\r\n\r\nWhen no regularization is applied, the coefficients will not be uniquely identifiable. This is not hard to show and is discussed in further detail [here](https://core.ac.uk/download/files/153/6287975.pdf). R's glmnet deals with this by choosing the minimum l2 regularized solution (i.e. mean centering). Additionally, the intercepts are never regularized so they are always mean centered. This is the approach taken in this PR as well.\r\n\r\n### Feature scaling\r\n\r\nIn current ML logistic regression, the features are always standardized when running the optimization algorithm. They are always returned to the user in the original feature space, however. This same approach is maintained in this patch as well, but the implementation details are different. In ML logistic regression, the unregularized feature values are divided by the column standard deviation in every gradient update iteration. In contrast, MLlib transforms the entire input dataset to the scaled space _before_ optimizaton. In ML, this means that `numFeatures * numClasses` extra scalar divisions are required in every iteration. Performance testing shows that this has significant (4x in some cases) slow downs in each iteration. This can be avoided by transforming the input to the scaled space ala MLlib once, before iteration begins. This does add some overhead initially, but can make significant time savings in some cases. \r\n\r\nOne issue with this approach is that if the input data is already cached, there may not be enough memory to cache the transformed data, which would make the algorithm _much_ slower. The tradeoffs here merit more discussion. \r\n\r\n### Specifying and inferring the number of outcome classes\r\n\r\nThe estimator checks the dataframe label column for metadata which specifies the number of values. If they are not specified, the length of the `histogram` variable is used, which is essentially the maximum value found in the column. The assumption then, is that the labels are zero-indexed when they are provided to the algorithm. \r\n\r\n## Performance\r\n\r\nBelow are some performance tests I have run so far. I am happy to add more cases or trials if we deem them necessary.\r\n\r\nTest cluster: 4 bare metal nodes, 128 GB RAM each, 48 cores each\r\n\r\nNotes:\r\n\r\n* Time in units of seconds\r\n* Metric is classification accuracy\r\n\r\n| algo   |   elasticNetParam | fitIntercept   |   metric |   maxIter |   numPoints |   numClasses |   numFeatures |    time | standardization   |   regParam |\r\n|--------|-------------------|----------------|----------|-----------|-------------|--------------|---------------|---------|-------------------|------------|\r\n| ml     |                 0 | true           | 0.746415 |        30 |      100000 |            3 |        100000 | 327.923 | true              |          0 |\r\n| mllib  |                 0 | true           | 0.743785 |        30 |      100000 |            3 |        100000 | 390.217 | true              |          0 |\r\n\r\n| algo   |   elasticNetParam | fitIntercept   |   metric |   maxIter |   numPoints |   numClasses |   numFeatures |    time | standardization   |   regParam |\r\n|--------|-------------------|----------------|----------|-----------|-------------|--------------|---------------|---------|-------------------|------------|\r\n| ml     |                 0 | true           | 0.973238 |        30 |     2000000 |            3 |         10000 | 385.476 | true              |          0 |\r\n| mllib  |                 0 | true           | 0.949828 |        30 |     2000000 |            3 |         10000 | 550.403 | true              |          0 |\r\n\r\n| algo   |   elasticNetParam | fitIntercept   |   metric |   maxIter |   numPoints |   numClasses |   numFeatures |    time | standardization   |   regParam |\r\n|--------|-------------------|----------------|----------|-----------|-------------|--------------|---------------|---------|-------------------|------------|\r\n| mllib  |                 0 | true           | 0.864358 |        30 |     2000000 |            3 |         10000 | 543.359 | true              |        0.1 |\r\n| ml     |                 0 | true           | 0.867418 |        30 |     2000000 |            3 |         10000 | 401.955 | true              |        0.1 |\r\n\r\n| algo   |   elasticNetParam | fitIntercept   |   metric |   maxIter |   numPoints |   numClasses |   numFeatures |    time | standardization   |   regParam |\r\n|--------|-------------------|----------------|----------|-----------|-------------|--------------|---------------|---------|-------------------|------------|\r\n| ml     |                 1 | true           | 0.807449 |        30 |     2000000 |            3 |         10000 | 334.892 | true              |       0.05 |\r\n\r\n| algo   |   elasticNetParam | fitIntercept   |   metric |   maxIter |   numPoints |   numClasses |   numFeatures |    time | standardization   |   regParam |\r\n|--------|-------------------|----------------|----------|-----------|-------------|--------------|---------------|---------|-------------------|------------|\r\n| ml     |                 0 | true           | 0.602006 |        30 |     2000000 |          500 |           100 | 112.319 | true              |          0 |\r\n| mllib  |                 0 | true           | 0.567226 |        30 |     2000000 |          500 |           100 | 263.768 | true              |          0 |e           | 0.567226 |        30 |     2000000 |          500 |           100 | 263.768 | true              |          0 |\r\n\r\n## References\r\n\r\nFriedman, et al. [\"Regularization Paths for Generalized Linear Models via Coordinate Descent\"](https://core.ac.uk/download/files/153/6287975.pdf)\r\n[http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html](http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html)\r\n","created_at":"2016-06-21T03:22:36Z","updated_at":"2016-08-12T23:03:14Z","closed_at":null,"merged_at":null,"merge_commit_sha":"3c6bd26a4f60242cbbb6a0645aaed1081d7cb76e","assignee":null,"assignees":[],"milestone":null,"commits_url":"https://api.github.com/repos/apache/spark/pulls/13796/commits","review_comments_url":"https://api.github.com/repos/apache/spark/pulls/13796/comments","review_comment_url":"https://api.github.com/repos/apache/spark/pulls/comments{/number}","comments_url":"https://api.github.com/repos/apache/spark/issues/13796/comments","statuses_url":"https://api.github.com/repos/apache/spark/statuses/026e1f69fd6aa45cdb6e6d641e86b624df52839a","head":{"label":"sethah:SPARK-7159_M","ref":"SPARK-7159_M","sha":"026e1f69fd6aa45cdb6e6d641e86b624df52839a","user":{"login":"sethah","id":7275795,"avatar_url":"https://avatars.githubusercontent.com/u/7275795?v=3","gravatar_id":"","url":"https://api.github.com/users/sethah","html_url":"https://github.com/sethah","followers_url":"https://api.github.com/users/sethah/followers","following_url":"https://api.github.com/users/sethah/following{/other_user}","gists_url":"https://api.github.com/users/sethah/gists{/gist_id}","starred_url":"https://api.github.com/users/sethah/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sethah/subscriptions","organizations_url":"https://api.github.com/users/sethah/orgs","repos_url":"https://api.github.com/users/sethah/repos","events_url":"https://api.github.com/users/sethah/events{/privacy}","received_events_url":"https://api.github.com/users/sethah/received_events","type":"User","site_admin":false},"repo":{"id":38016887,"name":"spark","full_name":"sethah/spark","owner":{"login":"sethah","id":7275795,"avatar_url":"https://avatars.githubusercontent.com/u/7275795?v=3","gravatar_id":"","url":"https://api.github.com/users/sethah","html_url":"https://github.com/sethah","followers_url":"https://api.github.com/users/sethah/followers","following_url":"https://api.github.com/users/sethah/following{/other_user}","gists_url":"https://api.github.com/users/sethah/gists{/gist_id}","starred_url":"https://api.github.com/users/sethah/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sethah/subscriptions","organizations_url":"https://api.github.com/users/sethah/orgs","repos_url":"https://api.github.com/users/sethah/repos","events_url":"https://api.github.com/users/sethah/events{/privacy}","received_events_url":"https://api.github.com/users/sethah/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/sethah/spark","description":"Mirror 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