[ML] Deemphasizing X-Pack (#312)

ML is now a feature of the Elastic Stack

Author: droberts195

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-# Contributing to X-Pack Machine Learning Core
+# Contributing to Elasticsearch Machine Learning Core
 
 We love to receive contributions from our community — you! There are many ways to contribute, from writing tutorials or blog posts, improving the documentation, submitting bug reports and feature requests or writing code which can be incorporated into the Elastic Stack itself.
 

README.md

@@ -1,16 +1,16 @@
 # Machine Learning for the Elastic Stack
 
-<https://www.elastic.co/products/x-pack>
+<https://www.elastic.co/products/stack/machine-learning>
 
-The ml-cpp repo is a part of Machine Learning for X-Pack and is available with
-either a trial or platinum license for the
+The ml-cpp repo is a part of Machine Learning for the Elastic Stack, which is
+available with either a trial or platinum license for the
 [Elastic Stack](https://www.elastic.co/products).
 
 This repo only contains the the C++ code that implements the core analytics for
 machine learning.
 
-Code for integrating into the Elastic Stack and source for its documentation can
-be found in the main
+Code for integrating into Elasticsearch and source for its documentation can be
+found in the main
 [elasticsearch repo](https://github.com/elastic/elasticsearch).
 
 ## Elastic License Functionality
@@ -23,24 +23,25 @@ subject to the Elastic License are in the [3rd_party](3rd_party) and
 
 ## Getting Started
 
-Before starting with Machine Learning for X-Pack, it's a good idea to get some
-experience with the
+Before starting with Machine Learning, it's a good idea to get some experience
+with the rest of the
 [Elastic Stack](https://www.elastic.co/guide/en/elastic-stack/current/index.html)
 first.
 
 To get started with Machine Learning please have a look at
-<https://www.elastic.co/guide/en/x-pack/current/ml-getting-started.html>.
+<https://www.elastic.co/guide/en/elastic-stack-overview/current/ml-getting-started.html>.
 
 Full documentation of Machine Learning can be found at
-<https://www.elastic.co/guide/en/x-pack/current/xpack-ml.html>.
+<https://www.elastic.co/guide/en/elastic-stack-overview/current/xpack-ml.html>.
 
 ## Questions/Bug Reports/Help
 
 We are happy to help and to make sure your questions can be answered by the
 right people, please follow the guidelines below:
 
 * If you have a general question about functionality please use our
-  [discuss](https://discuss.elastic.co/c/x-pack) forums.
+  [discuss](https://discuss.elastic.co/tags/c/elasticsearch/machine-learning)
+  forums.
 * If you have a support contract please use your dedicated support channel.
 * For questions regarding subscriptions please
   [contact](https://www.elastic.co/subscriptions#request-info) us.

bin/autodetect/Main.cc

@@ -166,7 +166,7 @@ int main(int argc, char** argv) {
     const TDataSearcherUPtr restoreSearcher{[isRestoreFileNamedPipe, &ioMgr]() -> TDataSearcherUPtr {
         if (ioMgr.restoreStream()) {
             // Check whether state is restored from a file, if so we assume that this is a debugging case
-            // and therefore does not originate from X-Pack.
+            // and therefore does not originate from the ML Java code.
             if (!isRestoreFileNamedPipe) {
                 // apply a filter to overcome differences in the way persistence vs. restore works
                 auto strm = std::make_shared<boost::iostreams::filtering_istream>();

bin/categorize/Main.cc

@@ -121,7 +121,7 @@ int main(int argc, char** argv) {
     const TDataSearcherUPtr restoreSearcher{[isRestoreFileNamedPipe, &ioMgr]() -> TDataSearcherUPtr {
         if (ioMgr.restoreStream()) {
             // Check whether state is restored from a file, if so we assume that this is a debugging case
-            // and therefore does not originate from X-Pack.
+            // and therefore does not originate from the ML Java code.
             if (!isRestoreFileNamedPipe) {
                 // apply a filter to overcome differences in the way persistence vs. restore works
                 auto strm = std::make_shared<boost::iostreams::filtering_istream>();

include/api/CForecastRunner.h

@@ -76,10 +76,10 @@ class API_EXPORT CForecastRunner final : private core::CNonCopyable {
     //! max memory allowed to use for forecast models persisting to disk
     static const size_t MAX_FORECAST_MODEL_PERSISTANCE_MEMORY = 524288000ull; // 500MB
 
-    //! Note: This value is lower than on X-pack side to prevent side-effects,
-    //! if you change this value also change the limit on X-pack side.
-    //! The purpose of this value is to guard the rest of the system regarding
-    //! an out of disk space
+    //! Note: This value is lower than in the ML Java code to prevent side-effects.
+    //! If you change this value also change the limit in the ML Java code.
+    //! The purpose of this value is to guard the rest of the system against
+    //! running out of disk space.
     //! minimum disk space required for disk persistence
     static const size_t MIN_FORECAST_AVAILABLE_DISK_SPACE = 4294967296ull; // 4GB
 

lib/api/CForecastRunner.cc

@@ -228,7 +228,7 @@ void CForecastRunner::forecastWorker() {
                 boost::system::error_code errorCode;
                 boost::filesystem::remove_all(temporaryFolder, errorCode);
                 if (errorCode) {
-                    // not an error: there is also cleanup code on X-pack side
+                    // not an error: there is also cleanup code on the Java side
                     LOG_WARN(<< "Failed to cleanup temporary data from: "
                              << forecastJob.s_TemporaryFolder << " error "
                              << errorCode.message());
@@ -429,7 +429,7 @@ bool CForecastRunner::parseAndValidateForecastRequest(const std::string& control
         // use -1 as default to allow 0 as 'never expires'
         expiresIn = properties.get<core_t::TTime>("expires_in", -1l);
 
-        // note: this is not exposed on x-pack side
+        // note: this is not exposed on the Java side
         forecastJob.s_BoundsPercentile = properties.get<double>("boundspercentile", 95.0);
     } catch (const std::exception& e) {
         LOG_ERROR(<< ERROR_FORECAST_REQUEST_FAILED_TO_PARSE << e.what());

mk/make_rc_defines.sh

@@ -16,7 +16,7 @@ if git -c core.fileMode=false diff-index --quiet HEAD -- ; then
     ML_FILEFLAGS=0
 else
     ML_FILEFLAGS=VS_FF_PRIVATEBUILD
-fi    
+fi
 echo $1 | grep '\.dll$' > /dev/null
 if [ $? -eq 0 ] ; then
     ML_FILETYPE=VFT_DLL
@@ -39,6 +39,6 @@ echo -DML_USER=\'\"$ML_USER\"\' \
      -DML_NAME=\'\"$ML_NAME\"\' \
      -DML_YEAR=\'\"$ML_YEAR\"\' \
      -DML_ICON=\'\"$ML_ICON\"\' \
-     -DRIGHT_CLICK_PRODUCT_NAME=\'\"Elastic X-Pack\"\' \
+     -DRIGHT_CLICK_PRODUCT_NAME=\'\"Elasticsearch machine learning\"\' \
      -N